Optimal time-domain combination of the two calibrated output quadratures of GEO 600

International Nuclear Information System (INIS)

Hewitson, M; Grote, H; Hild, S; Lueck, H; Ajith, P; Smith, J R; Strain, K A; Willke, B; Woan, G

2005-01-01

GEO 600 is an interferometric gravitational wave detector with a 600 m arm-length and which uses a dual-recycled optical configuration to give enhanced sensitivity over certain frequencies in the detection band. Due to the dual-recycling, GEO 600 has two main output signals, both of which potentially contain gravitational wave signals. These two outputs are calibrated to strain using a time-domain method. In order to simplify the analysis of the GEO 600 data set, it is desirable to combine these two calibrated outputs to form a single strain signal that has optimal signal-to-noise ratio across the detection band. This paper describes a time-domain method for doing this combination. The method presented is similar to one developed for optimally combining the outputs of two colocated gravitational wave detectors. In the scheme presented in this paper, some simplifications are made to allow its implementation using time-domain methods

Concrete Plant Operations Optimization Using Combined Simulation and Genetic Algorithms

NARCIS (Netherlands)

Cao, Ming; Lu, Ming; Zhang, Jian-Ping

2004-01-01

This work presents a new approach for concrete plant operations optimization by combining a ready mixed concrete (RMC) production simulation tool (called HKCONSIM) with a genetic algorithm (GA) based optimization procedure. A revamped HKCONSIM computer system can be used to automate the simulation

Parametric analysis and optimization for a combined power and refrigeration cycle

International Nuclear Information System (INIS)

Wang Jiangfeng; Dai Yiping; Gao Lin

2008-01-01

A combined power and refrigeration cycle is proposed, which combines the Rankine cycle and the absorption refrigeration cycle. This combined cycle uses a binary ammonia-water mixture as the working fluid and produces both power output and refrigeration output simultaneously with only one heat source. A parametric analysis is conducted to evaluate the effects of thermodynamic parameters on the performance of the combined cycle. It is shown that heat source temperature, environment temperature, refrigeration temperature, turbine inlet pressure, turbine inlet temperature, and basic solution ammonia concentration have significant effects on the net power output, refrigeration output and exergy efficiency of the combined cycle. A parameter optimization is achieved by means of genetic algorithm to reach the maximum exergy efficiency. The optimized exergy efficiency is 43.06% under the given condition

Development of optimized segmentation map in dual energy computed tomography

Science.gov (United States)

Yamakawa, Keisuke; Ueki, Hironori

2012-03-01

Dual energy computed tomography (DECT) has been widely used in clinical practice and has been particularly effective for tissue diagnosis. In DECT the difference of two attenuation coefficients acquired by two kinds of X-ray energy enables tissue segmentation. One problem in conventional DECT is that the segmentation deteriorates in some cases, such as bone removal. This is due to two reasons. Firstly, the segmentation map is optimized without considering the Xray condition (tube voltage and current). If we consider the tube voltage, it is possible to create an optimized map, but unfortunately we cannot consider the tube current. Secondly, the X-ray condition is not optimized. The condition can be set empirically, but this means that the optimized condition is not used correctly. To solve these problems, we have developed methods for optimizing the map (Method-1) and the condition (Method-2). In Method-1, the map is optimized to minimize segmentation errors. The distribution of the attenuation coefficient is modeled by considering the tube current. In Method-2, the optimized condition is decided to minimize segmentation errors depending on tube voltagecurrent combinations while keeping the total exposure constant. We evaluated the effectiveness of Method-1 by performing a phantom experiment under the fixed condition and of Method-2 by performing a phantom experiment under different combinations calculated from the total exposure constant. When Method-1 was followed with Method-2, the segmentation error was reduced from 37.8 to 13.5 %. These results demonstrate that our developed methods can achieve highly accurate segmentation while keeping the total exposure constant.

Improved particle swarm optimization combined with chaos

International Nuclear Information System (INIS)

Liu Bo; Wang Ling; Jin Yihui; Tang Fang; Huang Dexian

2005-01-01

As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of particle swarm optimization (PSO), which is an evolutionary computation technique through individual improvement plus population cooperation and competition, hybrid particle swarm optimization algorithm is proposed by incorporating chaos. Firstly, adaptive inertia weight factor (AIWF) is introduced in PSO to efficiently balance the exploration and exploitation abilities. Secondly, PSO with AIWF and chaos are hybridized to form a chaotic PSO (CPSO), which reasonably combines the population-based evolutionary searching ability of PSO and chaotic searching behavior. Simulation results and comparisons with the standard PSO and several meta-heuristics show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality

Promoter library-based module combination (PLMC) technology for optimization of threonine biosynthesis in Corynebacterium glutamicum.

Science.gov (United States)

Wei, Liang; Xu, Ning; Wang, Yiran; Zhou, Wei; Han, Guoqiang; Ma, Yanhe; Liu, Jun

2018-05-01

Due to the lack of efficient control elements and tools, the fine-tuning of gene expression in the multi-gene metabolic pathways is still a great challenge for engineering microbial cell factories, especially for the important industrial microorganism Corynebacterium glutamicum. In this study, the promoter library-based module combination (PLMC) technology was developed to efficiently optimize the expression of genes in C. glutamicum. A random promoter library was designed to contain the putative - 10 (NNTANANT) and - 35 (NNGNCN) consensus motifs, and refined through a three-step screening procedure to achieve numerous genetic control elements with different strength levels, including fluorescence-activated cell sorting (FACS) screening, agar plate screening, and 96-well plate screening. Multiple conventional strategies were employed for further precise characterizations of the promoter library, such as real-time quantitative PCR, sodium dodecyl sulfate polyacrylamide gel electrophoresis, FACS analysis, and the lacZ reporter system. These results suggested that the established promoter elements effectively regulated gene expression and showed varying strengths over a wide range. Subsequently, a multi-module combination technology was created based on the efficient promoter elements for combination and optimization of modules in the multi-gene pathways. Using this technology, the threonine biosynthesis pathway was reconstructed and optimized by predictable tuning expression of five modules in C. glutamicum. The threonine titer of the optimized strain was significantly improved to 12.8 g/L, an approximate 6.1-fold higher than that of the control strain. Overall, the PLMC technology presented in this study provides a rapid and effective method for combination and optimization of multi-gene pathways in C. glutamicum.

An optimization method for gas refrigeration cycle based on the combination of both thermodynamics and entransy theory

International Nuclear Information System (INIS)

Chen, Qun; Xu, Yun-Chao; Hao, Jun-Hong

2014-01-01

Highlights: • An optimization method for practical thermodynamic cycle is developed. • The entransy-based heat transfer analysis and thermodynamic analysis are combined. • Theoretical relation between system requirements and design parameters is derived. • The optimization problem can be converted into conditional extremum problem. • The proposed method provides several useful optimization criteria. - Abstract: A thermodynamic cycle usually consists of heat transfer processes in heat exchangers and heat-work conversion processes in compressors, expanders and/or turbines. This paper presents a new optimization method for effective improvement of thermodynamic cycle performance with the combination of entransy theory and thermodynamics. The heat transfer processes in a gas refrigeration cycle are analyzed by entransy theory and the heat-work conversion processes are analyzed by thermodynamics. The combination of these two analysis yields a mathematical relation directly connecting system requirements, e.g. cooling capacity rate and power consumption rate, with design parameters, e.g. heat transfer area of each heat exchanger and heat capacity rate of each working fluid, without introducing any intermediate variable. Based on this relation together with the conditional extremum method, we theoretically derive an optimization equation group. Simultaneously solving this equation group offers the optimal structural and operating parameters for every single gas refrigeration cycle and furthermore provides several useful optimization criteria for all the cycles. Finally, a practical gas refrigeration cycle is taken as an example to show the application and validity of the newly proposed optimization method

Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity.

Science.gov (United States)

Penrod, Nadia M; Greene, Casey S; Moore, Jason H

2014-01-01

Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18). We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment. To derive the greatest benefit from molecularly targeted drugs it is critical to design combination

Optimal Partner Combination for Joint Distribution Alliance using Integrated Fuzzy EW-AHP and TOPSIS for Online Shopping

Directory of Open Access Journals (Sweden)

Yandong He

2016-04-01

Full Text Available With the globalization of online shopping, deterioration of the ecological environment and the increasing pressure of urban transportation, a novel logistics service mode—joint distribution (JD—was developed. Selecting the optimal partner combination is important to ensure the joint distribution alliance (JDA is sustainable and stable, taking into consideration conflicting criteria. In this paper, we present an integrated fuzzy entropy weight, fuzzy analytic hierarchy process (fuzzy EW-AHP and fuzzy technique for order preference by similarity to an ideal solution (TOPSIS approach to select the optimal partner combination of JDA. A three-phase approach is proposed. In the first phase, we identify partner combination evaluation criteria using an economy-society-environment-flexibility (ESEF framework from a perspective that considers sustainability. In the second phase, the criteria weights and criteria combination performance of different partner combinations were calculated by using an integrated fuzzy EW-AHP approach considering the objective and subjective factors of experts. In the third phase, the JDA partner combinations are ranked by employing fuzzy TOPSIS approach. The sensitivity analysis is considered for the optimal partner combination. Taking JDA in Chongqing for example, the results indicate the alternative partner combination 3 (PC3 is always ranked first no matter how the criteria weights change. It is effective and robust to apply the integrated fuzzy EW-AHP and TOPSIS approach to the partner selection of JDA.

Solving Optimization Problems via Vortex Optimization Algorithm and Cognitive Development Optimization Algorithm

Directory of Open Access Journals (Sweden)

Ahmet Demir

2017-01-01

Full Text Available In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature. Today, intelligent optimization techniques based on Artificial Intelligence are widely used for optimization problems. The objective of this paper is to provide a comparative study on the employment of classical optimization solutions and Artificial Intelligence solutions for enabling readers to have idea about the potential of intelligent optimization techniques. At this point, two recently developed intelligent optimization algorithms, Vortex Optimization Algorithm (VOA and Cognitive Development Optimization Algorithm (CoDOA, have been used to solve some multidisciplinary optimization problems provided in the source book Thomas' Calculus 11th Edition and the obtained results have compared with classical optimization solutions. 

Optimal combinations of control strategies and cost-effective analysis for visceral leishmaniasis disease transmission.

Directory of Open Access Journals (Sweden)

Santanu Biswas

Full Text Available Visceral leishmaniasis (VL is a deadly neglected tropical disease that poses a serious problem in various countries all over the world. Implementation of various intervention strategies fail in controlling the spread of this disease due to issues of parasite drug resistance and resistance of sandfly vectors to insecticide sprays. Due to this, policy makers need to develop novel strategies or resort to a combination of multiple intervention strategies to control the spread of the disease. To address this issue, we propose an extensive SIR-type model for anthroponotic visceral leishmaniasis transmission with seasonal fluctuations modeled in the form of periodic sandfly biting rate. Fitting the model for real data reported in South Sudan, we estimate the model parameters and compare the model predictions with known VL cases. Using optimal control theory, we study the effects of popular control strategies namely, drug-based treatment of symptomatic and PKDL-infected individuals, insecticide treated bednets and spray of insecticides on the dynamics of infected human and vector populations. We propose that the strategies remain ineffective in curbing the disease individually, as opposed to the use of optimal combinations of the mentioned strategies. Testing the model for different optimal combinations while considering periodic seasonal fluctuations, we find that the optimal combination of treatment of individuals and insecticide sprays perform well in controlling the disease for the time period of intervention introduced. Performing a cost-effective analysis we identify that the same strategy also proves to be efficacious and cost-effective. Finally, we suggest that our model would be helpful for policy makers to predict the best intervention strategies for specific time periods and their appropriate implementation for elimination of visceral leishmaniasis.

A combined stochastic programming and optimal control approach to personal finance and pensions

DEFF Research Database (Denmark)

Konicz, Agnieszka Karolina; Pisinger, David; Rasmussen, Kourosh Marjani

2015-01-01

The paper presents a model that combines a dynamic programming (stochastic optimal control) approach and a multi-stage stochastic linear programming approach (SLP), integrated into one SLP formulation. Stochastic optimal control produces an optimal policy that is easy to understand and implement....

Optimal Operation of Network-Connected Combined Heat and Powers for Customer Profit Maximization

Directory of Open Access Journals (Sweden)

Da Xie

2016-06-01

Full Text Available Network-connected combined heat and powers (CHPs, owned by a community, can export surplus heat and electricity to corresponding heat and electric networks after community loads are satisfied. This paper proposes a new optimization model for network-connected CHP operation. Both CHPs’ overall efficiency and heat to electricity ratio (HTER are assumed to vary with loading levels. Based on different energy flow scenarios where heat and electricity are exported to the network from the community or imported, four profit models are established accordingly. They reflect the different relationships between CHP energy supply and community load demand across time. A discrete optimization model is then developed to maximize the profit for the community. The models are derived from the intervals determined by the daily operation modes of CHP and real-time buying and selling prices of heat, electricity and natural gas. By demonstrating the proposed models on a 1 MW network-connected CHP, results show that the community profits are maximized in energy markets. Thus, the proposed optimization approach can help customers to devise optimal CHP operating strategies for maximizing benefits.

A new method for the determination of peak distribution across a two-dimensional separation space for the identification of optimal column combinations.

Science.gov (United States)

Leonhardt, Juri; Teutenberg, Thorsten; Buschmann, Greta; Gassner, Oliver; Schmidt, Torsten C

2016-11-01

For the identification of the optimal column combinations, a comparative orthogonality study of single columns and columns coupled in series for the first dimension of a microscale two-dimensional liquid chromatographic approach was performed. In total, eight columns or column combinations were chosen. For the assessment of the optimal column combination, the orthogonality value as well as the peak distributions across the first and second dimension was used. In total, three different methods of orthogonality calculation, namely the Convex Hull, Bin Counting, and Asterisk methods, were compared. Unfortunately, the first two methods do not provide any information of peak distribution. The third method provides this important information, but is not optimal when only a limited number of components are used for method development. Therefore, a new concept for peak distribution assessment across the separation space of two-dimensional chromatographic systems and clustering detection was developed. It could be shown that the Bin Counting method in combination with additionally calculated histograms for the respective dimensions is well suited for the evaluation of orthogonality and peak clustering. The newly developed method could be used generally in the assessment of 2D separations. Graphical Abstract ᅟ.

A method to combine hydrodynamics and constructive design in the optimization of the runner blades of Kaplan turbines

International Nuclear Information System (INIS)

Miclosina, C O; Balint, D I; Campian, C V; Frunzaverde, D; Ion, I

2012-01-01

This paper deals with the optimization of the axial hydraulic turbines of Kaplan type. The optimization of the runner blade is presented systematically from two points of view: hydrodynamic and constructive. Combining these aspects in order to gain a safer operation when unsteady effects occur in the runner of the turbine is attempted. The design and optimization of the runner blade is performed with QTurbo3D software developed at the Center for Research in Hydraulics, Automation and Thermal Processes (CCHAPT) from 'Eftimie Murgu' University of Resita, Romania. QTurbo3D software offers possibilities to design the meridian channel of hydraulic turbines design the blades and optimize the runner blade. 3D modeling and motion analysis of the runner blade operating mechanism are accomplished using SolidWorks software. The purpose of motion study is to obtain forces, torques or stresses in the runner blade operating mechanism, necessary to estimate its lifetime. This paper clearly states the importance of combining the hydrodynamics with the structural design in the optimization procedure of the runner of hydraulic turbines.

A method to combine hydrodynamics and constructive design in the optimization of the runner blades of Kaplan turbines

Science.gov (United States)

Miclosina, C. O.; Balint, D. I.; Campian, C. V.; Frunzaverde, D.; Ion, I.

2012-11-01

This paper deals with the optimization of the axial hydraulic turbines of Kaplan type. The optimization of the runner blade is presented systematically from two points of view: hydrodynamic and constructive. Combining these aspects in order to gain a safer operation when unsteady effects occur in the runner of the turbine is attempted. The design and optimization of the runner blade is performed with QTurbo3D software developed at the Center for Research in Hydraulics, Automation and Thermal Processes (CCHAPT) from "Eftimie Murgu" University of Resita, Romania. QTurbo3D software offers possibilities to design the meridian channel of hydraulic turbines design the blades and optimize the runner blade. 3D modeling and motion analysis of the runner blade operating mechanism are accomplished using SolidWorks software. The purpose of motion study is to obtain forces, torques or stresses in the runner blade operating mechanism, necessary to estimate its lifetime. This paper clearly states the importance of combining the hydrodynamics with the structural design in the optimization procedure of the runner of hydraulic turbines.

K-maps: a vehicle to an optimal solution in combinational logic ...

African Journals Online (AJOL)

K-maps: a vehicle to an optimal solution in combinational logic design problems using digital multiplexers. ... Abstract. Application of Karnaugh maps (K-Maps) for the design of combinational logic circuits and sequential logic circuits is a subject that has been widely discussed. However, the use of K-Maps in the design of ...

WE-AB-303-06: Combining DAO with MV + KV Optimization to Improve Skin Dose Sparing with Real-Time Fluoroscopy

Energy Technology Data Exchange (ETDEWEB)

Grelewicz, Z; Wiersma, R [The University of Chicago, Chicago, IL (United States)

2015-06-15

Purpose: Real-time fluoroscopy may allow for improved patient positioning and tumor tracking, particularly in the treatment of lung tumors. In order to mitigate the effects of the imaging dose, previous studies have demonstrated the effect of including both imaging dose and imaging constraints into the inverse treatment planning object function. That method of combined MV+kV optimization may Result in plans with treatment beams chosen to allow for more gentle imaging beam-on times. Direct-aperture optimization (DAO) is also known to produce treatment plans with fluence maps more conducive to lower beam-on times. Therefore, in this work we demonstrate the feasibility of a combination of DAO and MV+kV optimization for further optimized real-time kV imaging. Methods: Therapeutic and imaging beams were modeled in the EGSnrc Monte Carlo environment, and applied to a patient model for a previously treated lung patient to provide dose influence matrices from DOSXYZnrc. An MV + kV IMRT DAO treatment planning system was developed to compare DAO treatment plans with and without MV+kV optimization. The objective function was optimized using simulated annealing. In order to allow for comparisons between different cases of the stochastically optimized plans, the optimization was repeated twenty times. Results: Across twenty optimizations, combined MV+kV IMRT resulted in an average of 12.8% reduction in peak skin dose. Both non-optimized and MV+kV optimized imaging beams delivered, on average, mean dose of approximately 1 cGy per fraction to the target, with peak doses to target of approximately 6 cGy per fraction. Conclusion: When using DAO, MV+kV optimization is shown to Result in improvements to plan quality in terms of skin dose, when compared to the case of MV optimization with non-optimized kV imaging. The combination of DAO and MV+kV optimization may allow for real-time imaging without excessive imaging dose. Financial support for the work has been provided in part by NIH

Combined genetic algorithm and multiple linear regression (GA-MLR) optimizer: Application to multi-exponential fluorescence decay surface.

Science.gov (United States)

Fisz, Jacek J

2006-12-07

The optimization approach based on the genetic algorithm (GA) combined with multiple linear regression (MLR) method, is discussed. The GA-MLR optimizer is designed for the nonlinear least-squares problems in which the model functions are linear combinations of nonlinear functions. GA optimizes the nonlinear parameters, and the linear parameters are calculated from MLR. GA-MLR is an intuitive optimization approach and it exploits all advantages of the genetic algorithm technique. This optimization method results from an appropriate combination of two well-known optimization methods. The MLR method is embedded in the GA optimizer and linear and nonlinear model parameters are optimized in parallel. The MLR method is the only one strictly mathematical "tool" involved in GA-MLR. The GA-MLR approach simplifies and accelerates considerably the optimization process because the linear parameters are not the fitted ones. Its properties are exemplified by the analysis of the kinetic biexponential fluorescence decay surface corresponding to a two-excited-state interconversion process. A short discussion of the variable projection (VP) algorithm, designed for the same class of the optimization problems, is presented. VP is a very advanced mathematical formalism that involves the methods of nonlinear functionals, algebra of linear projectors, and the formalism of Fréchet derivatives and pseudo-inverses. Additional explanatory comments are added on the application of recently introduced the GA-NR optimizer to simultaneous recovery of linear and weakly nonlinear parameters occurring in the same optimization problem together with nonlinear parameters. The GA-NR optimizer combines the GA method with the NR method, in which the minimum-value condition for the quadratic approximation to chi(2), obtained from the Taylor series expansion of chi(2), is recovered by means of the Newton-Raphson algorithm. The application of the GA-NR optimizer to model functions which are multi

Optimal steel thickness combined with computed radiography for portal imaging of nasopharyngeal cancer patients

International Nuclear Information System (INIS)

Wu Shixiu; Jin Xiance; Xie Congying; Cao Guoquan

2005-01-01

The poor image quality of conventional metal screen-film portal imaging system has long been of concern, and various methods have been investigated in an attempt to enhance the quality of portal images. Computed radiography (CR) used in combination with a steel plate displays image enhancement. The optimal thickness of the steel plate had been studied by measuring the modulation transfer function (MTF) characteristics. Portal images of nasopharyngeal carcinoma patients were taken by both a conventional metal screen-film system and this optimal steel and CR plate combination system. Compared with a conventional metal screen-film system, the CR-metal screen system achieves a much higher image contrast. The measured modulation transfer function (MTF) of the CR combination is greater than conventional film-screen portal imaging systems and also results in superior image performance, as demonstrated by receiver operator characteristic (ROC) analysis. This optimal combination steel CR plate portal imaging system is capable of producing high contrast portal images conveniently

Economic optimization of the combined cycle integrated with multi-product gasification system

International Nuclear Information System (INIS)

Liszka, M.; Ziebik, A.

2009-01-01

The system taken into consideration consists of the Corex unit, combined cycle power plant and air separation unit (ASU). The Corex process (trademark of Siemens-VAI) is one of technologies for cokeless hot metal production. Coal is gasified by oxygen in the hot metal environment. The excess gas can be used out of installation. It has been assumed that the Corex export gas is fired in combined cycle. The gas turbine (GT) structure was assumed as a fixed simple cycle while the heat recovery steam generator (HRSG) and steam turbine arrangements are free for optimization. The examples of independent variables selected for optimization are number of HRSG pressure levels, GT pressure ratio, minimal temperature differences in HRSG, flow rate of compressed air form GT compressor to ASU. Finally, 16 independent variables have been qualified for optimization. The synthesis optimization is based on the superstructure method. The economic net present value (NPV) has been chosen as the objective function. All power plant facilities have been modeled on the GateCycle software. The off-design models include, among others, the GT blade cooling and HRSG heat transfer coefficient analyses. Two optimization methods - genetic algorithm and Powells conjugate directions have been coupled in one hybrid procedure. The whole optimization analysis has been repeated several times for different price scenarios on the coal, iron and electricity markets

Rapid optimization of enzyme mixtures for deconstruction of diverse pretreatment/biomass feedstock combinations

Directory of Open Access Journals (Sweden)

Walton Jonathan D

2010-10-01

Full Text Available Abstract Background Enzymes for plant cell wall deconstruction are a major cost in the production of ethanol from lignocellulosic biomass. The goal of this research was to develop optimized synthetic mixtures of enzymes for multiple pretreatment/substrate combinations using our high-throughput biomass digestion platform, GENPLAT, which combines robotic liquid handling, statistical experimental design and automated Glc and Xyl assays. Proportions of six core fungal enzymes (CBH1, CBH2, EG1, β-glucosidase, a GH10 endo-β1,4-xylanase, and β-xylosidase were optimized at a fixed enzyme loading of 15 mg/g glucan for release of Glc and Xyl from all combinations of five biomass feedstocks (corn stover, switchgrass, Miscanthus, dried distillers' grains plus solubles [DDGS] and poplar subjected to three alkaline pretreatments (AFEX, dilute base [0.25% NaOH] and alkaline peroxide [AP]. A 16-component mixture comprising the core set plus 10 accessory enzymes was optimized for three pretreatment/substrate combinations. Results were compared to the performance of two commercial enzymes (Accellerase 1000 and Spezyme CP at the same protein loadings. Results When analyzed with GENPLAT, corn stover gave the highest yields of Glc with commercial enzymes and with the core set with all pretreatments, whereas corn stover, switchgrass and Miscanthus gave comparable Xyl yields. With commercial enzymes and with the core set, yields of Glc and Xyl were highest for grass stovers pretreated by AP compared to AFEX or dilute base. Corn stover, switchgrass and DDGS pretreated with AFEX and digested with the core set required a higher proportion of endo-β1,4-xylanase (EX3 and a lower proportion of endo-β1,4-glucanase (EG1 compared to the same materials pretreated with dilute base or AP. An optimized enzyme mixture containing 16 components (by addition of α-glucuronidase, a GH11 endoxylanase [EX2], Cel5A, Cel61A, Cip1, Cip2, β-mannanase, amyloglucosidase, �

Combined shape and topology optimization for minimization of maximal von Mises stress

DEFF Research Database (Denmark)

Lian, Haojie; Christiansen, Asger Nyman; Tortorelli, Daniel A.

2017-01-01

This work shows that a combined shape and topology optimization method can produce optimal 2D designs with minimal stress subject to a volume constraint. The method represents the surface explicitly and discretizes the domain into a simplicial complex which adapts both structural shape and topology....... By performing repeated topology and shape optimizations and adaptive mesh updates, we can minimize the maximum von Mises stress using the p-norm stress measure with p-values as high as 30, provided that the stress is calculated with sufficient accuracy....

Simulation and OR (operations research) in combination for practical optimization

NARCIS (Netherlands)

van Dijk, N.; van der Sluis, E.; Haijema, R.; Al-Ibrahim, A.; van der Wal, J.; Kuhl, M.E.; Steiger, N.M.; Armstrong, F.B.; Joines, J.A.

2005-01-01

Should we pool capacities or not? This is a question that one can regularly be confronted with in operations and service management. It is a question that necessarily requires a combination of queueing (as OR discipline) and simulation (as evaluative tool) and further steps for optimization. It will

Development and optimization of a self-microemulsifying drug delivery system for atorvastatin calcium by using D-optimal mixture design.

Science.gov (United States)

Yeom, Dong Woo; Song, Ye Seul; Kim, Sung Rae; Lee, Sang Gon; Kang, Min Hyung; Lee, Sangkil; Choi, Young Wook

2015-01-01

In this study, we developed and optimized a self-microemulsifying drug delivery system (SMEDDS) formulation for improving the dissolution and oral absorption of atorvastatin calcium (ATV), a poorly water-soluble drug. Solubility and emulsification tests were performed to select a suitable combination of oil, surfactant, and cosurfactant. A D-optimal mixture design was used to optimize the concentration of components used in the SMEDDS formulation for achieving excellent physicochemical characteristics, such as small droplet size and high dissolution. The optimized ATV-loaded SMEDDS formulation containing 7.16% Capmul MCM (oil), 48.25% Tween 20 (surfactant), and 44.59% Tetraglycol (cosurfactant) significantly enhanced the dissolution rate of ATV in different types of medium, including simulated intestinal fluid, simulated gastric fluid, and distilled water, compared with ATV suspension. Good agreement was observed between predicted and experimental values for mean droplet size and percentage of the drug released in 15 minutes. Further, pharmacokinetic studies in rats showed that the optimized SMEDDS formulation considerably enhanced the oral absorption of ATV, with 3.4-fold and 4.3-fold increases in the area under the concentration-time curve and time taken to reach peak plasma concentration, respectively, when compared with the ATV suspension. Thus, we successfully developed an optimized ATV-loaded SMEDDS formulation by using the D-optimal mixture design, that could potentially be used for improving the oral absorption of poorly water-soluble drugs.

Combined shape and topology optimization for minimization of maximal von Mises stress

International Nuclear Information System (INIS)

Lian, Haojie; Christiansen, Asger N.; Tortorelli, Daniel A.; Sigmund, Ole; Aage, Niels

2017-01-01

Here, this work shows that a combined shape and topology optimization method can produce optimal 2D designs with minimal stress subject to a volume constraint. The method represents the surface explicitly and discretizes the domain into a simplicial complex which adapts both structural shape and topology. By performing repeated topology and shape optimizations and adaptive mesh updates, we can minimize the maximum von Mises stress using the p-norm stress measure with p-values as high as 30, provided that the stress is calculated with sufficient accuracy.

Optimized combination model and algorithm of parking guidance information configuration

Directory of Open Access Journals (Sweden)

Tian Ye

2011-01-01

Full Text Available Abstract Operators of parking guidance and information (PGI systems often have difficulty in providing the best car park availability information to drivers in periods of high demand. A new PGI configuration model based on the optimized combination method was proposed by analyzing of parking choice behavior. This article first describes a parking choice behavioral model incorporating drivers perceptions of waiting times at car parks based on PGI signs. This model was used to predict the influence of PGI signs on the overall performance of the traffic system. Then relationships were developed for estimating the arrival rates at car parks based on driver characteristics, car park attributes as well as the car park availability information displayed on PGI signs. A mathematical program was formulated to determine the optimal display PGI sign configuration to minimize total travel time. A genetic algorithm was used to identify solutions that significantly reduced queue lengths and total travel time compared with existing practices. These procedures were applied to an existing PGI system operating in Deqing Town and Xiuning City. Significant reductions in total travel time of parking vehicles with PGI being configured. This would reduce traffic congestion and lead to various environmental benefits.

Application of Feedback System Control Optimization Technique in Combined Use of Dual Antiplatelet Therapy and Herbal Medicines

Directory of Open Access Journals (Sweden)

Wang Liu

2018-05-01

Full Text Available Aim: Combined use of herbal medicines in patients underwent dual antiplatelet therapy (DAPT might cause bleeding or thrombosis because herbal medicines with anti-platelet activities may exhibit interactions with DAPT. In this study, we tried to use a feedback system control (FSC optimization technique to optimize dose strategy and clarify possible interactions in combined use of DAPT and herbal medicines.Methods: Herbal medicines with reported anti-platelet activities were selected by searching related references in Pubmed. Experimental anti-platelet activities of representative compounds originated from these herbal medicines were investigated using in vitro assay, namely ADP-induced aggregation of rat platelet-rich-plasma. FSC scheme hybridized artificial intelligence calculation and bench experiments to iteratively optimize 4-drug combination and 2-drug combination from these drug candidates.Results: Totally 68 herbal medicines were reported to have anti-platelet activities. In the present study, 7 representative compounds from these herbal medicines were selected to study combinatorial drug optimization together with DAPT, i.e., aspirin and ticagrelor. FSC technique first down-selected 9 drug candidates to the most significant 5 drugs. Then, FSC further secured 4 drugs in the optimal combination, including aspirin, ticagrelor, ferulic acid from DangGui, and forskolin from MaoHouQiaoRuiHua. Finally, FSC quantitatively estimated the possible interactions between aspirin:ticagrelor, aspirin:ferulic acid, ticagrelor:forskolin, and ferulic acid:forskolin. The estimation was further verified by experimentally determined Combination Index (CI values.Conclusion: Results of the present study suggested that FSC optimization technique could be used in optimization of anti-platelet drug combinations and might be helpful in designing personal anti-platelet therapy strategy. Furthermore, FSC analysis could also identify interactions between different

Multiobjective Optimization Combining BMP Technology and Land Preservation for Watershed-based Stormwater Management

Science.gov (United States)

McGarity, A. E.

2009-12-01

Recent progress has been made developing decision-support models for optimal deployment of best management practices (BMP’s) in an urban watershed to achieve water quality goals. One example is the high-level screening model StormWISE, developed by the author (McGarity, 2006) that uses linear and nonlinear programming to narrow the search for optimal solutions to certain land use categories and drainage zones. Another example is the model SUSTAIN developed by USEPA and Tetra Tech (Lai, et al., 2006), which builds on the work of Yu, et al., 2002), that uses a detailed, computationally intensive simulation model driven by a genetic solver to select optimal BMP sites. However, a model that deals only with best management practice (BMP) site selections may fail to consider solutions that avoid future nonpoint pollutant loadings by preserving undeveloped land. This paper presents results of a recently completed research project in which water resource engineers partnered with experienced professionals at a land conservation trust to develop a multiobjective model for watershed management. The result is a revised version of StormWISE that can be used to identify optimal, cost-effective combinations of easements and similar land preservation tools for undeveloped sites along with low impact development (LID) and BMP technologies for developed sites. The goal is to achieve the watershed-wide limits on runoff volume and pollutant loads that are necessary to meet water quality goals as well as ecological benefits associated with habitat preservation and enhancement. A nonlinear programming formulation is presented for the extended StormWISE model that achieves desired levels of environmental benefits at minimum cost. Tradeoffs between different environmental benefits are generated by multiple runs of the model while varying the levels of each environmental benefit obtained. The model is solved using piecewise linearization of environmental benefit functions where each

Optimization of Serial Combined System of Ground-Coupled Heat Pump and Solar Collector

Institute of Scientific and Technical Information of China (English)

ZHAO Jun; CHEN Yan; LU Suzhen; CUI Junkui

2009-01-01

A mathematical optimization model was set up for a ground-solar combined system based on in-situ experimental results,in which the solar collector was combined serially with a ground-coupled heat pump(GCHP).The universal optimal equations were solved by the constrained variable metric method considering both the performance and economics.Then the model was applied to a specific case concerning an actual solar assisted GCHP system for space heating.The results indicated a system coefficient of performance(COP)of 3.9 for the optimal method under the seriaI heating mode,and 3.2 for the conventional one.In addition,the optimum solution also showed advantages in energy and cost saving.1eading to a 16.7%improvement in the heat pump performance at 17.2%less energy consumption and 11.8%lower annual cost,respectively.

Development of New Lipid-Based Paclitaxel Nanoparticles Using Sequential Simplex Optimization

Science.gov (United States)

Dong, Xiaowei; Mattingly, Cynthia A.; Tseng, Michael; Cho, Moo; Adams, Val R.; Mumper, Russell J.

2008-01-01

The objective of these studies was to develop Cremophor-free lipid-based paclitaxel (PX) nanoparticle formulations prepared from warm microemulsion precursors. To identify and optimize new nanoparticles, experimental design was performed combining Taguchi array and sequential simplex optimization. The combination of Taguchi array and sequential simplex optimization efficiently directed the design of paclitaxel nanoparticles. Two optimized paclitaxel nanoparticles (NPs) were obtained: G78 NPs composed of glyceryl tridodecanoate (GT) and polyoxyethylene 20-stearyl ether (Brij 78), and BTM NPs composed of Miglyol 812, Brij 78 and D-alpha-tocopheryl polyethylene glycol 1000 succinate (TPGS). Both nanoparticles successfully entrapped paclitaxel at a final concentration of 150 μg/ml (over 6% drug loading) with particle sizes less than 200 nm and over 85% of entrapment efficiency. These novel paclitaxel nanoparticles were stable at 4°C over three months and in PBS at 37°C over 102 hours as measured by physical stability. Release of paclitaxel was slow and sustained without initial burst release. Cytotoxicity studies in MDA-MB-231 cancer cells showed that both nanoparticles have similar anticancer activities compared to Taxol®. Interestingly, PX BTM nanocapsules could be lyophilized without cryoprotectants. The lyophilized powder comprised only of PX BTM NPs in water could be rapidly rehydrated with complete retention of original physicochemical properties, in-vitro release properties, and cytotoxicity profile. Sequential Simplex Optimization has been utilized to identify promising new lipid-based paclitaxel nanoparticles having useful attributes. PMID:19111929

Optimization of urban water supply portfolios combining infrastructure capacity expansion and water use decisions

Science.gov (United States)

Medellin-Azuara, J.; Fraga, C. C. S.; Marques, G.; Mendes, C. A.

2015-12-01

The expansion and operation of urban water supply systems under rapidly growing demands, hydrologic uncertainty, and scarce water supplies requires a strategic combination of various supply sources for added reliability, reduced costs and improved operational flexibility. The design and operation of such portfolio of water supply sources merits decisions of what and when to expand, and how much to use of each available sources accounting for interest rates, economies of scale and hydrologic variability. The present research provides a framework and an integrated methodology that optimizes the expansion of various water supply alternatives using dynamic programming and combining both short term and long term optimization of water use and simulation of water allocation. A case study in Bahia Do Rio Dos Sinos in Southern Brazil is presented. The framework couples an optimization model with quadratic programming model in GAMS with WEAP, a rain runoff simulation models that hosts the water supply infrastructure features and hydrologic conditions. Results allow (a) identification of trade offs between cost and reliability of different expansion paths and water use decisions and (b) evaluation of potential gains by reducing water system losses as a portfolio component. The latter is critical in several developing countries where water supply system losses are high and often neglected in favor of more system expansion. Results also highlight the potential of various water supply alternatives including, conservation, groundwater, and infrastructural enhancements over time. The framework proves its usefulness for planning its transferability to similarly urbanized systems.

Quantifying antiviral activity optimizes drug combinations against hepatitis C virus infection

Energy Technology Data Exchange (ETDEWEB)

Koizumi, Yoshiki [School of Medicine, College of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Ishikawa, Japan; Nakajim, Syo [Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan; Department of Applied Biological Sciences, Faculty of Science and Technology, Tokyo University of Sciences, Chiba, J; Ohash, Hirofumi [Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan: Department of Applied Biological Sciences, Faculty of Science and Technology, Tokyo University of Sciences, Chiba, J; Tanaka, Yasuhito [Department of Virology and Liver Unit, Nagoya City University Graduate School of Medicinal Sciences, Nagoya, Japan; Wakita, Takaji [Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan; Perelson, Alan S. [Los Alamos National Laboratory; Iwami, Shingo [Department of Biology, Faculty of Sciences, Kyushu University, Fukuoka, Japan: PRESTO, JST, Saitama, Japan: CREST, JST, Saitama, Japan; Watashi, Koichi [Department of Virology II, National Institute of Infectious Diseases, Tokyo, Japan: Department of Applied Biological Sciences, Faculty of Science and Technology, Tokyo University of Sciences, Chiba, J

2016-03-21

Cell culture study combing a mathematical model and computer simulation quantifies the anti-hepatitis C virus drug efficacy at any concentrations and any combinations in preclinical settings, and can obtain rich basic evidences for selecting optimal treatments prior to costly clinical trials.

Industrial Application of Topology Optimization for Combined Conductive and Convective Heat Transfer Problems

DEFF Research Database (Denmark)

Zhou, Mingdong; Alexandersen, Joe; Sigmund, Ole

2016-01-01

This paper presents an industrial application of topology optimization for combined conductive and convective heat transfer problems. The solution is based on a synergy of computer aided design and engineering software tools from Dassault Systemes. The considered physical problem of steady......-state heat transfer under convection is simulated using SIMULIA-Abaqus. A corresponding topology optimization feature is provided by SIMULIA-Tosca. By following a standard workflow of design optimization, the proposed solution is able to accommodate practical design scenarios and results in efficient...

Optimizing maintenance and repair policies via a combination of genetic algorithms and Monte Carlo simulation

International Nuclear Information System (INIS)

Marseguerra, M.; Zio, E.

2000-01-01

In this paper we present an optimization approach based on the combination of a Genetic Algorithms maximization procedure with a Monte Carlo simulation. The approach is applied within the context of plant logistic management for what concerns the choice of maintenance and repair strategies. A stochastic model of plant operation is developed from the standpoint of its reliability/availability behavior, i.e. of the failure/repair/maintenance processes of its components. The model is evaluated by Monte Carlo simulation in terms of economic costs and revenues of operation. The flexibility of the Monte Carlo method allows us to include several practical aspects such as stand-by operation modes, deteriorating repairs, aging, sequences of periodic maintenances, number of repair teams available for different kinds of repair interventions (mechanical, electronic, hydraulic, etc.), components priority rankings. A genetic algorithm is then utilized to optimize the components maintenance periods and number of repair teams. The fitness function object of the optimization is a profit function which inherently accounts for the safety and economic performance of the plant and whose value is computed by the above Monte Carlo simulation model. For an efficient combination of Genetic Algorithms and Monte Carlo simulation, only few hundreds Monte Carlo histories are performed for each potential solution proposed by the genetic algorithm. Statistical significance of the results of the solutions of interest (i.e. the best ones) is then attained exploiting the fact that during the population evolution the fit chromosomes appear repeatedly many times. The proposed optimization approach is applied on two case studies of increasing complexity

Combined optimization model for sustainable energization strategy

Science.gov (United States)

Abtew, Mohammed Seid

Access to energy is a foundation to establish a positive impact on multiple aspects of human development. Both developed and developing countries have a common concern of achieving a sustainable energy supply to fuel economic growth and improve the quality of life with minimal environmental impacts. The Least Developing Countries (LDCs), however, have different economic, social, and energy systems. Prevalence of power outage, lack of access to electricity, structural dissimilarity between rural and urban regions, and traditional fuel dominance for cooking and the resultant health and environmental hazards are some of the distinguishing characteristics of these nations. Most energy planning models have been designed for developed countries' socio-economic demographics and have missed the opportunity to address special features of the poor countries. An improved mixed-integer programming energy-source optimization model is developed to address limitations associated with using current energy optimization models for LDCs, tackle development of the sustainable energization strategies, and ensure diversification and risk management provisions in the selected energy mix. The Model predicted a shift from traditional fuels reliant and weather vulnerable energy source mix to a least cost and reliable modern clean energy sources portfolio, a climb on the energy ladder, and scored multifaceted economic, social, and environmental benefits. At the same time, it represented a transition strategy that evolves to increasingly cleaner energy technologies with growth as opposed to an expensive solution that leapfrogs immediately to the cleanest possible, overreaching technologies.

Development of an improved genetic algorithm and its application in the optimal design of ship nuclear power system

International Nuclear Information System (INIS)

Jia Baoshan; Yu Jiyang; You Songbo

2005-01-01

This article focuses on the development of an improved genetic algorithm and its application in the optimal design of the ship nuclear reactor system, whose goal is to find a combination of system parameter values that minimize the mass or volume of the system given the power capacity requirement and safety criteria. An improved genetic algorithm (IGA) was developed using an 'average fitness value' grouping + 'specified survival probability' rank selection method and a 'separate-recombine' duplication operator. Combining with a simulated annealing algorithm (SAA) that continues the local search after the IGA reaches a satisfactory point, the algorithm gave satisfactory optimization results from both search efficiency and accuracy perspectives. This IGA-SAA algorithm successfully solved the design optimization problem of ship nuclear power system. It is an advanced and efficient methodology that can be applied to the similar optimization problems in other areas. (authors)

Optimal operating rules definition in complex water resource systems combining fuzzy logic, expert criteria and stochastic programming

Science.gov (United States)

Macian-Sorribes, Hector; Pulido-Velazquez, Manuel

2016-04-01

This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to

Minimization of the LCA impact of thermodynamic cycles using a combined simulation-optimization approach

International Nuclear Information System (INIS)

Brunet, Robert; Cortés, Daniel; Guillén-Gosálbez, Gonzalo; Jiménez, Laureano; Boer, Dieter

2012-01-01

This work presents a computational approach for the simultaneous minimization of the total cost and environmental impact of thermodynamic cycles. Our method combines process simulation, multi-objective optimization and life cycle assessment (LCA) within a unified framework that identifies in a systematic manner optimal design and operating conditions according to several economic and LCA impacts. Our approach takes advantages of the complementary strengths of process simulation (in which mass, energy balances and thermodynamic calculations are implemented in an easy manner) and rigorous deterministic optimization tools. We demonstrate the capabilities of this strategy by means of two case studies in which we address the design of a 10 MW Rankine cycle modeled in Aspen Hysys, and a 90 kW ammonia-water absorption cooling cycle implemented in Aspen Plus. Numerical results show that it is possible to achieve environmental and cost savings using our rigorous approach. - Highlights: ► Novel framework for the optimal design of thermdoynamic cycles. ► Combined use of simulation and optimization tools. ► Optimal design and operating conditions according to several economic and LCA impacts. ► Design of a 10MW Rankine cycle in Aspen Hysys, and a 90kW absorption cycle in Aspen Plus.

Minimization of the LCA impact of thermodynamic cycles using a combined simulation-optimization approach

Energy Technology Data Exchange (ETDEWEB)

Brunet, Robert; Cortes, Daniel [Departament d' Enginyeria Quimica, Escola Tecnica Superior d' Enginyeria Quimica, Universitat Rovira i Virgili, Campus Sescelades, Avinguda Paisos Catalans 26, 43007 Tarragona (Spain); Guillen-Gosalbez, Gonzalo [Departament d' Enginyeria Quimica, Escola Tecnica Superior d' Enginyeria Quimica, Universitat Rovira i Virgili, Campus Sescelades, Avinguda Paisos Catalans 26, 43007 Tarragona (Spain); Jimenez, Laureano [Departament d' Enginyeria Quimica, Escola Tecnica Superior d' Enginyeria Quimica, Universitat Rovira i Virgili, Campus Sescelades, Avinguda Paisos Catalans 26, 43007 Tarragona (Spain); Boer, Dieter [Departament d' Enginyeria Mecanica, Escola Tecnica Superior d' Enginyeria, Universitat Rovira i Virgili, Campus Sescelades, Avinguda Paisos Catalans 26, 43007, Tarragona (Spain)

2012-12-15

This work presents a computational approach for the simultaneous minimization of the total cost and environmental impact of thermodynamic cycles. Our method combines process simulation, multi-objective optimization and life cycle assessment (LCA) within a unified framework that identifies in a systematic manner optimal design and operating conditions according to several economic and LCA impacts. Our approach takes advantages of the complementary strengths of process simulation (in which mass, energy balances and thermodynamic calculations are implemented in an easy manner) and rigorous deterministic optimization tools. We demonstrate the capabilities of this strategy by means of two case studies in which we address the design of a 10 MW Rankine cycle modeled in Aspen Hysys, and a 90 kW ammonia-water absorption cooling cycle implemented in Aspen Plus. Numerical results show that it is possible to achieve environmental and cost savings using our rigorous approach. - Highlights: Black-Right-Pointing-Pointer Novel framework for the optimal design of thermdoynamic cycles. Black-Right-Pointing-Pointer Combined use of simulation and optimization tools. Black-Right-Pointing-Pointer Optimal design and operating conditions according to several economic and LCA impacts. Black-Right-Pointing-Pointer Design of a 10MW Rankine cycle in Aspen Hysys, and a 90kW absorption cycle in Aspen Plus.

Development of a new formulation combining calcipotriol and betamethasone dipropionate in an ointment vehicle

DEFF Research Database (Denmark)

Simonsen, Lene; Høy, Gert; Didriksen, Erik

2004-01-01

was to develop a formulation which combines calcipotriol and betamethasone dipropionate in a single vehicle hereby achieving optimal delivery of both substances into the skin. As the two substances are incompatible in aqueous and alcoholic medias, different non-aqueous formulations were prepared. Skin permeation...

Optimization of fog inlet air cooling system for combined cycle power plants using genetic algorithm

International Nuclear Information System (INIS)

Ehyaei, Mehdi A.; Tahani, Mojtaba; Ahmadi, Pouria; Esfandiari, Mohammad

2015-01-01

In this research paper, a comprehensive thermodynamic modeling of a combined cycle power plant is first conducted and the effects of gas turbine inlet fogging system on the first and second law efficiencies and net power outputs of combined cycle power plants are investigated. The combined cycle power plant (CCPP) considered for this study consist of a double pressure heat recovery steam generator (HRSG) to utilize the energy of exhaust leaving the gas turbine and produce superheated steam to generate electricity in the Rankine cycle. In order to enhance understanding of this research and come up with optimum performance assessment of the plant, a complete optimization is using a genetic algorithm conducted. In order to achieve this goal, a new objective function is defined for the system optimization including social cost of air pollution for the power generation systems. The objective function is based on the first law efficiency, energy cost and the external social cost of air pollution for an operational system. It is concluded that using inlet air cooling system for the CCPP system and its optimization results in an increase in the average output power, first and second law efficiencies by 17.24%, 3.6% and 3.5%, respectively, for three warm months of year. - Highlights: • To model the combined cycle power plant equipped with fog inlet air cooling method. • To conduct both exergy and economic analyses for better understanding. • To conduct a complete optimization using a genetic algorithm to determine the optimal design parameters of the system

SAGD production optimization : combination of ESP and multiphase metering

Energy Technology Data Exchange (ETDEWEB)

Pinguet, B.G.; Guerra, E.; Drever, C. [Schlumberger Canada Ltd., Edmonton, AB (Canada)

2008-07-01

Many commercial oil reservoirs in Canada are completed using electric submersible pumps (ESP) due to low reservoir pressures and extra heavy oils and bitumens. This paper presented details of an optimization process for steam-assisted gravity drainage (SAGD) wells. The process used ESP and a multiphase flow meter (MFM) based on Vx technology. The MFM was based on a Venturi and nuclear fraction meter combination that was engineered to measure the steam phases during SAGD processes. The technology was designed to measure total mass or total volumetric flow rates as well as oil, water and gas in producing wells. Length fractions of oil, water, and gas were calculated based on the attenuation of Gamma-rays as they passed through the Venturi section. Production was optimized in real time using the frequency control of the pump to improve oil flow rates. The results of field tests showed that the optimization process resulted in longer life cycles for the ESP. It was concluded that use of the meter results in changes to lift system operating parameters at the well site as well as improved monitoring during the workflow process. 3 refs., 1 tab., 11 figs.

Combination of Compensations and Multi-Parameter Coil for Efficiency Optimization of Inductive Power Transfer System

Directory of Open Access Journals (Sweden)

Guozhen Hu

2017-12-01

Full Text Available A loosely coupled inductive power transfer (IPT system for industrial track applications has been researched in this paper. The IPT converter using primary Inductor-Capacitor-Inductor (LCL network and secondary parallel-compensations is analyzed combined coil design for optimal operating efficiency. Accurate mathematical analytical model and expressions of self-inductance and mutual inductance are proposed to achieve coil parameters. Furthermore, the optimization process is performed combined with the proposed resonant compensations and coil parameters. The results are evaluated and discussed using finite element analysis (FEA. Finally, an experimental prototype is constructed to verify the proposed approach and the experimental results show that the optimization can be better applied to industrial track distributed IPT system.

Optimal combined purchasing strategies for a risk-averse manufacturer under price uncertainty

Directory of Open Access Journals (Sweden)

Qiao Wu

2015-09-01

Full Text Available Purpose: The purpose of our paper is to analyze optimal purchasing strategies when a manufacturer can buy raw materials from a long-term contract supplier and a spot market under spot price uncertainty. Design/methodology/approach: This procurement model can be solved by using dynamic programming. First, we maximize the DM’s utility of the second period, obtaining the optimal contract quantity and spot quantity for the second period. Then, maximize the DM’s utility of both periods, obtaining the optimal purchasing strategy for the first period. We use a numerical method to compare the performance level of a pure spot sourcing strategy with that of a mixed strategy. Findings: Our results show that optimal purchasing strategies vary with the trend of contract prices. If the contract price falls, the total quantity purchased in period 1 will decrease in the degree of risk aversion. If the contract price increases, the total quantity purchased in period 1 will increase in the degree of risk aversion. The relationship between the optimal contract quantity and the degree of risk aversion depends on whether the expected spot price or the contract price is larger in period 2. Finally, we compare the performance levels between a combined strategy and a spot sourcing strategy. It shows that a combined strategy is optimal for a risk-averse buyer. Originality/value: It’s challenging to deal with a two-period procurement problem with risk consideration. We have obtained results of a two-period procurement problem with two sourcing options, namely contract procurement and spot purchases. Our model incorporates the buyer’s risk aversion factor and the change of contract prices, which are not addressed in early studies.

Internal combustion engine report: Spark ignited ICE GenSet optimization and novel concept development

Energy Technology Data Exchange (ETDEWEB)

Keller, J.; Blarigan, P. Van [Sandia National Labs., Livermore, CA (United States)

1998-08-01

In this manuscript the authors report on two projects each of which the goal is to produce cost effective hydrogen utilization technologies. These projects are: (1) the development of an electrical generation system using a conventional four-stroke spark-ignited internal combustion engine generator combination (SI-GenSet) optimized for maximum efficiency and minimum emissions, and (2) the development of a novel internal combustion engine concept. The SI-GenSet will be optimized to run on either hydrogen or hydrogen-blends. The novel concept seeks to develop an engine that optimizes the Otto cycle in a free piston configuration while minimizing all emissions. To this end the authors are developing a rapid combustion homogeneous charge compression ignition (HCCI) engine using a linear alternator for both power take-off and engine control. Targeted applications include stationary electrical power generation, stationary shaft power generation, hybrid vehicles, and nearly any other application now being accomplished with internal combustion engines.

Model for optimization of plant investments in combined power and heat production systems

Energy Technology Data Exchange (ETDEWEB)

Jantunen, E.; Sinisalo, A.; Koskelainen, L.

1980-01-01

A mathematical model is developed for optimal dimensioning and timing the investments of power and heat production system in a community. The required electric power may be purchased by different production systems, such as: thermal power plants, gas turbines, diesel plants, etc. or by delivering all or part of it from a national power company. Also the required heat may be produced in many different ways in single-purpose or combined plants. The model assumes the extent of the heating system fixed, and it is not optimized. It is assumed that the same company is responsible for supplying both the power and heat for the community. It's aim is to allocate the existing capital in an optimal way, and the model may be used for facilitating the decision in such questions as: what kind of production capacity should be purchased in future; how high should the heat and power capacities be; and when should this additional capacity be available. The report also reviews the methods for forecasting the demand of power and heat and their fluctuation during the planning period. The solution of this large-scale non-linear optimization problem is searched via successive linearizations by using the Method of Approximate Programming (MAP). It was found that the solution method is very suitable for this kind of multivariable problems. The computing times with the Functional Mathematical Programmin System (FMPS) in Univac 1108 computer were quite reasonable.

Combining spatial modeling and choice experiments for the optimal spatial allocation of wind turbines

International Nuclear Information System (INIS)

Drechsler, Martin; Ohl, Cornelia; Meyerhoff, Juergen; Eichhorn, Marcus; Monsees, Jan

2011-01-01

Although wind power is currently the most efficient source of renewable energy, the installation of wind turbines (WT) in landscapes often leads to conflicts in the affected communities. We propose that such conflicts can be mitigated by a welfare-optimal spatial allocation of WT in the landscape so that a given energy target is reached at minimum social costs. The energy target is motivated by the fact that wind power production is associated with relatively low CO 2 emissions. Social costs comprise energy production costs as well as external costs caused by harmful impacts on humans and biodiversity. We present a modeling approach that combines spatially explicit ecological-economic modeling and choice experiments to determine the welfare-optimal spatial allocation of WT in West Saxony, Germany. The welfare-optimal sites balance production and external costs. Results indicate that in the welfare-optimal allocation the external costs represent about 14% of the total costs (production costs plus external costs). Optimizing wind power production without consideration of the external costs would lead to a very different allocation of WT that would marginally reduce the production costs but strongly increase the external costs and thus lead to substantial welfare losses. - Highlights: → We combine modeling and economic valuation to optimally allocate wind turbines. → Welfare-optimal allocation balances energy production costs and external costs. → External costs (impacts on the environment) can be substantial. → Ignoring external costs leads to suboptimal allocations and welfare losses.

Two Examples of Exergy Optimization Regarding the “Thermo-Frigopump” and Combined Heat and Power Systems

Directory of Open Access Journals (Sweden)

Michel Feidt

2013-02-01

Full Text Available In a recent review an optimal thermodynamics and associated new upper bounds have been proposed, but it was only relative to power delivered by engines. In fact, it appears that for systems and processes with more than one utility (mainly mechanical or electrical power, energy conservation (First Law is limited for representing their efficiency. Consequently, exergy analysis combining the First and Second Law seems essential for optimization of systems or processes situated in their environment. For thermomechanical systems recent papers report on comparisons between energy and exergy analysis and corresponding optimization, but the proposed models mainly use heat transfer conductance modelling, except for internal combustion engine. Here we propose to reconsider direct and inverse configurations of Carnot machines, with two examples. The first example is concerned with “thermofrigo-pump” where the two utilities are hot and cold thermal exergies due to the difference in the temperature level compared to the ambient one. The second one is relative to a “combined heat and power” (CHP system. In the two cases, the model is developed based on the Carnot approach, and use of the efficiency-NTU method to characterize the heat exchangers. Obtained results are original thermodynamics optima, that represent exergy upper bounds for these two cases. Extension of the proposed method to other systems and processes is examined, with added technical constraints or not.

Parametric Investigation and Thermoeconomic Optimization of a Combined Cycle for Recovering the Waste Heat from Nuclear Closed Brayton Cycle

Directory of Open Access Journals (Sweden)

Lihuang Luo

2016-01-01

Full Text Available A combined cycle that combines AWM cycle with a nuclear closed Brayton cycle is proposed to recover the waste heat rejected from the precooler of a nuclear closed Brayton cycle in this paper. The detailed thermodynamic and economic analyses are carried out for the combined cycle. The effects of several important parameters, such as the absorber pressure, the turbine inlet pressure, the turbine inlet temperature, the ammonia mass fraction, and the ambient temperature, are investigated. The combined cycle performance is also optimized based on a multiobjective function. Compared with the closed Brayton cycle, the optimized power output and overall efficiency of the combined cycle are higher by 2.41% and 2.43%, respectively. The optimized LEC of the combined cycle is 0.73% lower than that of the closed Brayton cycle.

SU-E-T-256: Optimizing the Combination of Targeted Radionuclide Therapy Agents Using a Multi-Scale Patient-Specific Monte Carlo Dosimetry Platform

International Nuclear Information System (INIS)

Besemer, A; Bednarz, B; Titz, B; Grudzinski, J; Weichert, J; Hall, L

2014-01-01

Purpose: Combination targeted radionuclide therapy (TRT) is appealing because it can potentially exploit different mechanisms of action from multiple radionuclides as well as the variable dose rates due to the different radionuclide half-lives. The work describes the development of a multiobjective optimization algorithm to calculate the optimal ratio of radionuclide injection activities for delivery of combination TRT. Methods: The ‘diapeutic’ (diagnostic and therapeutic) agent, CLR1404, was used as a proof-of-principle compound in this work. Isosteric iodine substitution in CLR1404 creates a molecular imaging agent when labeled with I-124 or a targeted radiotherapeutic agent when labeled with I-125 or I-131. PET/CT images of high grade glioma patients were acquired at 4.5, 24, and 48 hours post injection of 124I-CLR1404. The therapeutic 131I-CLR1404 and 125ICLR1404 absorbed dose (AD) and biological effective dose (BED) were calculated for each patient using a patient-specific Monte Carlo dosimetry platform. The optimal ratio of injection activities for each radionuclide was calculated with a multi-objective optimization algorithm using the weighted sum method. Objective functions such as the tumor dose heterogeneity and the ratio of the normal tissue to tumor doses were minimized and the relative importance weights of each optimization function were varied. Results: For each optimization function, the program outputs a Pareto surface map representing all possible combinations of radionuclide injection activities so that values that minimize the objective function can be visualized. A Pareto surface map of the weighted sum given a set of user-specified importance weights is also displayed. Additionally, the ratio of optimal injection activities as a function of the all possible importance weights is generated so that the user can select the optimal ratio based on the desired weights. Conclusion: Multi-objective optimization of radionuclide injection activities

Dynamic optimization of combined harvesting of a two-species fishery

International Nuclear Information System (INIS)

Chaudhuri, K.

1986-06-01

In the present paper, the author considers the problem of dynamic optimization of the exploitation policy connected with the combined harvesting of two competing fish species, each of which obeys the logistic growth law. The singular extremal trajectory in the phase plane is derived by taking the harvesting effort as a dynamic variable. Biological or bioeconomic interpretations of the constraints required for this singular extremal are also given. (author)

Energy-Dissipation Performance of Combined Low Yield Point Steel Plate Damper Based on Topology Optimization and Its Application in Structural Control

Directory of Open Access Journals (Sweden)

Haoxiang He

2016-01-01

Full Text Available In view of the disadvantages such as higher yield stress and inadequate adjustability, a combined low yield point steel plate damper involving low yield point steel plates and common steel plates is proposed. Three types of combined plate dampers with new hollow shapes are proposed, and the specific forms include interior hollow, boundary hollow, and ellipse hollow. The “maximum stiffness” and “full stress state” are used as the optimization objectives, and the topology optimization of different hollow forms by alternating optimization method is to obtain the optimal shape. Various combined steel plate dampers are calculated by finite element simulation, the results indicate that the initial stiffness of the boundary optimized damper and interior optimized damper is lager, the hysteresis curves are full, and there is no stress concentration. These two types of optimization models made in different materials rations are studied by numerical simulation, and the adjustability of yield stress of these combined dampers is verified. The nonlinear dynamic responses, seismic capacity, and damping effect of steel frame structures with different combined dampers are analyzed. The results show that the boundary optimized damper has better energy-dissipation capacity and is suitable for engineering application.

Development of Pangasius steaks by improved sous-vide technology and its process optimization.

Science.gov (United States)

Kumari, Namita; Singh, Chongtham Baru; Kumar, Raushan; Martin Xavier, K A; Lekshmi, Manjusha; Venkateshwarlu, Gudipati; Balange, Amjad K

2016-11-01

The present study embarked on the objective of optimizing improved sous - vide processing condition for development of ready-to-cook Pangasius steaks with extended shelf-life using response surface methodology. For the development of improved sous - vide cooked product, Pangasius steaks were treated with additional hurdles in various combinations for optimization. Based on the study, suitable combination of chitosan and spices was selected which enhanced antimicrobial and oxidative stability of the product. The Box-Behnken experimental design with 15 trials per model was adopted for designing the experiment to know the effect of independent variables, namely chitosan concentration (X 1 ), cooking time (X 2 ) and cooking temperature (X 3 ) on dependent variable i.e. TBARS value (Y 1 ). From RSM generated model, the optimum condition for sous - vide processing of Pangasius steaks were 1.08% chitosan concentration, 70.93 °C of cooking temperature and 16.48 min for cooking time and predicted minimum value of multiple response optimal condition was Y = 0.855 mg MDA/Kg of fish. The high correlation coefficient (R 2  = 0.975) between the model and the experimental data showed that the model was able to efficiently predict processing condition for development of sous - vide processed Pangasius steaks. This research may help the processing industries and Pangasius fish farmer as it provides an alternative low cost technology for the proper utilization of Pangasius .

Modelling and optimization of combined cycle power plant based on exergoeconomic and environmental analyses

International Nuclear Information System (INIS)

Ganjehkaviri, A.; Mohd Jaafar, M.N.; Ahmadi, P.; Barzegaravval, H.

2014-01-01

This research paper presents a study on a comprehensive thermodynamic modelling of a combined cycle power plant (CCPP). The effects of economic strategies and design parameters on the plant optimization are also studied. Exergoeconomic analysis is conducted in order to determine the cost of electricity and cost of exergy destruction. In addition, a comprehensive optimization study is performed to determine the optimal design parameters of the power plant. Next, the effects of economic parameters variations on the sustainability, carbon dioxide emission and fuel consumption of the plant are investigated and are presented for a typical combined cycle power plant. Therefore, the changes in economic parameters caused the balance between cash flows and fix costs of the plant changes at optimum point. Moreover, economic strategies greatly limited the maximum reasonable carbon emission and fuel consumption reduction. The results showed that by using the optimum values, the exergy efficiency increases for about 6%, while CO 2 emission decreases by 5.63%. However, the variation in the cost was less than 1% due to the fact that a cost constraint was implemented. In addition, the sensitivity analysis for the optimization study was curtailed to be carried out; therefore, the optimization process and results to two important parameters are presented and discussed.

A hybrid optimization model of biomass trigeneration system combined with pit thermal energy storage

International Nuclear Information System (INIS)

Dominković, D.F.; Ćosić, B.; Bačelić Medić, Z.; Duić, N.

2015-01-01

Highlights: • Hybrid optimization model of biomass trigeneration system with PTES is developed. • Influence of premium feed-in tariffs on trigeneration systems is assessed. • Influence of total system efficiency on biomass trigeneration system with PTES is assessed. • Influence of energy savings on project economy is assessed. - Abstract: This paper provides a solution for managing excess heat production in trigeneration and thus, increases the power plant yearly efficiency. An optimization model for combining biomass trigeneration energy system and pit thermal energy storage has been developed. Furthermore, double piping district heating and cooling network in the residential area without industry consumers was assumed, thus allowing simultaneous flow of the heating and cooling energy. As a consequence, the model is easy to adopt in different regions. Degree-hour method was used for calculation of hourly heating and cooling energy demand. The system covers all the yearly heating and cooling energy needs, while it is assumed that all the electricity can be transferred to the grid due to its renewable origin. The system was modeled in Matlab© on hourly basis and hybrid optimization model was used to maximize the net present value (NPV), which was the objective function of the optimization. Economic figures become favorable if the economy-of-scale of both power plant and pit thermal energy storage can be utilized. The results show that the pit thermal energy storage was an excellent option for storing energy and shaving peaks in energy demand. Finally, possible switch from feed-in tariffs to feed-in premiums was assessed and possible subsidy savings have been calculated. The savings are potentially large and can be used for supporting other renewable energy projects

A combined geostatistical-optimization model for the optimal design of a groundwater quality monitoring network

Science.gov (United States)

Kolosionis, Konstantinos; Papadopoulou, Maria P.

2017-04-01

Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.

Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

International Nuclear Information System (INIS)

Zhang, Xiaoshun; Yu, Tao; Yang, Bo; Zheng, Limin; Huang, Linni

2015-01-01

Highlights: • A novel optimal carbon-energy combined-flow (OCECF) model is firstly established. • A novel approximate ideal multi-objective solution Q(λ) learning is designed. • The proposed algorithm has a high convergence stability and reliability. • The proposed algorithm can be applied for OCECF in a large-scale power grid. - Abstract: This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China.

Robust Optimization in Simulation : Taguchi and Krige Combined

NARCIS (Netherlands)

Dellino, G.; Kleijnen, Jack P.C.; Meloni, C.

2009-01-01

Optimization of simulated systems is the goal of many methods, but most methods as- sume known environments. We, however, develop a `robust' methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by

Robust optimization in simulation : Taguchi and Krige combined

NARCIS (Netherlands)

Dellino, G.; Kleijnen, Jack P.C.; Meloni, C.

2012-01-01

Optimization of simulated systems is the goal of many methods, but most methods assume known environments. We, however, develop a “robust” methodology that accounts for uncertain environments. Our methodology uses Taguchi's view of the uncertain world but replaces his statistical techniques by

Combining a 2-D multiphase CFD model with a Response Surface Methodology to optimize the gasification of Portuguese biomasses

International Nuclear Information System (INIS)

Silva, Valter; Rouboa, Abel

2015-01-01

Highlights: • A multiphase CFD model was combined with RSM. • Gasification optimal operating conditions were found in a pilot scale reactor. • Syngas quality indices were optimized in a biomass gasification process. • Propagation of error methodology was combined with a CFD model and RSM. - Abstract: This paper presents a study to evaluate the potential of Portuguese biomasses (coffee husks, forest residues and vine pruning residues) to produce syngas for different applications. By using a 2-D Eulerian–Eulerian approach within the CFD framework, a design of several computer experiments was developed and were used as analysis tools the response surface method (RSM) and the propagation of error (POE) approach. The CFD model was validated under experimental results collected at a semi-industrial reactor. For design purposes, temperature, steam to biomass ratio (SBR) and the type of biomass were selected as input factors. The responses were the H 2 generation, the H 2 /CO ratio, the CH 4 /H 2 ratio, the carbon conversion and the cold gas efficiency. It was concluded that after an optimization procedure to determine the operating conditions, vine pruning residues could show very promising results considering some of the typical syngas indice standards for commercial purposes. From the optimization procedure, it was also concluded that forest residues are preferable for domestic natural gas applications and vine pruning residues for fuel cells and integrated gasification systems application. By using the RSM combined with POE, it was verified that the operating conditions to get higher performances do not always coincide with those necessary to obtain a stable syngas composition

Development and Application of a Tool for Optimizing Composite Matrix Viscoplastic Material Parameters

Science.gov (United States)

Murthy, Pappu L. N.; Naghipour Ghezeljeh, Paria; Bednarcyk, Brett A.

2018-01-01

This document describes a recently developed analysis tool that enhances the resident capabilities of the Micromechanics Analysis Code with the Generalized Method of Cells (MAC/GMC) and its application. MAC/GMC is a composite material and laminate analysis software package developed at NASA Glenn Research Center. The primary focus of the current effort is to provide a graphical user interface (GUI) capability that helps users optimize highly nonlinear viscoplastic constitutive law parameters by fitting experimentally observed/measured stress-strain responses under various thermo-mechanical conditions for braided composites. The tool has been developed utilizing the MATrix LABoratory (MATLAB) (The Mathworks, Inc., Natick, MA) programming language. Illustrative examples shown are for a specific braided composite system wherein the matrix viscoplastic behavior is represented by a constitutive law described by seven parameters. The tool is general enough to fit any number of experimentally observed stress-strain responses of the material. The number of parameters to be optimized, as well as the importance given to each stress-strain response, are user choice. Three different optimization algorithms are included: (1) Optimization based on gradient method, (2) Genetic algorithm (GA) based optimization and (3) Particle Swarm Optimization (PSO). The user can mix and match the three algorithms. For example, one can start optimization with either 2 or 3 and then use the optimized solution to further fine tune with approach 1. The secondary focus of this paper is to demonstrate the application of this tool to optimize/calibrate parameters for a nonlinear viscoplastic matrix to predict stress-strain curves (for constituent and composite levels) at different rates, temperatures and/or loading conditions utilizing the Generalized Method of Cells. After preliminary validation of the tool through comparison with experimental results, a detailed virtual parametric study is

Approaches to modernize the combination drug development paradigm

Directory of Open Access Journals (Sweden)

Daphne Day

2016-10-01

Full Text Available Abstract Recent advances in genomic sequencing and omics-based capabilities are uncovering tremendous therapeutic opportunities and rapidly transforming the field of cancer medicine. Molecularly targeted agents aim to exploit key tumor-specific vulnerabilities such as oncogenic or non-oncogenic addiction and synthetic lethality. Additionally, immunotherapies targeting the host immune system are proving to be another promising and complementary approach. Owing to substantial tumor genomic and immunologic complexities, combination strategies are likely to be required to adequately disrupt intricate molecular interactions and provide meaningful long-term benefit to patients. To optimize the therapeutic success and application of combination therapies, systematic scientific discovery will need to be coupled with novel and efficient clinical trial approaches. Indeed, a paradigm shift is required to drive precision medicine forward, from the traditional “drug-centric” model of clinical development in pursuit of small incremental benefits in large heterogeneous groups of patients, to a “strategy-centric” model to provide customized transformative treatments in molecularly stratified subsets of patients or even in individual patients. Crucially, to combat the numerous challenges facing combination drug development—including our growing but incomplete understanding of tumor biology, technical and informatics limitations, and escalating financial costs—aligned goals and multidisciplinary collaboration are imperative to collectively harness knowledge and fuel continual innovation.

Gain optimization in fiber optical parametric amplifiers by combining standard and high-SBS threshold highly nonlinear fibers

DEFF Research Database (Denmark)

Da Ros, Francesco; Rottwitt, Karsten; Peucheret, Christophe

2012-01-01

Combining Al-doped and Ge-doped HNLFs as gain media in FOPAs is proposed and optimized, resulting in efficient SBS mitigation while circumventing the additional loss of the high SBS threshold Al-doped fiber.......Combining Al-doped and Ge-doped HNLFs as gain media in FOPAs is proposed and optimized, resulting in efficient SBS mitigation while circumventing the additional loss of the high SBS threshold Al-doped fiber....

LOW-CALORIES RAISINS OBTAINED BY COMBINED DEHYDRATION: PROCESS OPTIMIZATION AND EVALUATION OF THE ANTIOXIDANT EFFICIENCY

Directory of Open Access Journals (Sweden)

Mariana B. Laborde

2015-03-01

Full Text Available A healthy dehydrated food of high nutritional-quality and added-value was developed: low-calories raisin obtained by an ultrasonic assisted combined-dehydration with two-stage osmotic treatment (D3S complemented by drying. Pink Red Globe grape produced at Mendoza (Argentina, experienced a substitution of sugar by natural sweetener Stevia in two osmotic stages under different conditions (treatment with/without ultrasound; sweetener concentration 18, 20, 22% w/w; time 35, 75, 115 minutes, evaluating soluble solids (SS, moisture (M, total polyphenols (PF, antioxidant efficiency (AE and sugar profile. The multiple optimization of the process by response surface methodology and desirability analysis, allowed to minimize M, maximize SS (Stevia incorporation, and preserve the maximum amount of PF. After the first stage, the optimal treatment reduced the majority sugars of the grape in 32% (sucrose, glucose, and the 57% at the end of the dehydration process.

On optimal development and becoming an optimiser

NARCIS (Netherlands)

de Ruyter, D.J.

2012-01-01

The article aims to provide a justification for the claim that optimal development and becoming an optimiser are educational ideals that parents should pursue in raising their children. Optimal development is conceptualised as enabling children to grow into flourishing persons, that is persons who

Exergy analysis, parametric analysis and optimization for a novel combined power and ejector refrigeration cycle

International Nuclear Information System (INIS)

Dai Yiping; Wang Jiangfeng; Gao Lin

2009-01-01

A new combined power and refrigeration cycle is proposed, which combines the Rankine cycle and the ejector refrigeration cycle. This combined cycle produces both power output and refrigeration output simultaneously. It can be driven by the flue gas of gas turbine or engine, solar energy, geothermal energy and industrial waste heats. An exergy analysis is performed to guide the thermodynamic improvement for this cycle. And a parametric analysis is conducted to evaluate the effects of the key thermodynamic parameters on the performance of the combined cycle. In addition, a parameter optimization is achieved by means of genetic algorithm to reach the maximum exergy efficiency. The results show that the biggest exergy loss due to the irreversibility occurs in heat addition processes, and the ejector causes the next largest exergy loss. It is also shown that the turbine inlet pressure, the turbine back pressure, the condenser temperature and the evaporator temperature have significant effects on the turbine power output, refrigeration output and exergy efficiency of the combined cycle. The optimized exergy efficiency is 27.10% under the given condition.

A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

Directory of Open Access Journals (Sweden)

Weitian Lin

2014-01-01

Full Text Available Particle swarm optimization algorithm (PSOA is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA, and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA. Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

Ship speed optimization: Concepts, models and combined speed-routing scenarios

DEFF Research Database (Denmark)

Psaraftis, Harilaos N.; Kontovas, Christos A.

2014-01-01

The purpose of this paper is to clarify some important issues as regards ship speed optimization at the operational level and develop models that optimize ship speed for a spectrum of routing scenarios in a single ship setting. The paper's main contribution is the incorporation of those fundament...... parameters and other considerations that weigh heavily in a ship owner's or charterer's speed decision and in his routing decision, wherever relevant. Various examples are given so as to illustrate the properties of the optimal solution and the various trade-offs that are involved....

Spectral Quantitative Analysis Model with Combining Wavelength Selection and Topology Structure Optimization

Directory of Open Access Journals (Sweden)

Qian Wang

2016-01-01

Full Text Available Spectroscopy is an efficient and widely used quantitative analysis method. In this paper, a spectral quantitative analysis model with combining wavelength selection and topology structure optimization is proposed. For the proposed method, backpropagation neural network is adopted for building the component prediction model, and the simultaneousness optimization of the wavelength selection and the topology structure of neural network is realized by nonlinear adaptive evolutionary programming (NAEP. The hybrid chromosome in binary scheme of NAEP has three parts. The first part represents the topology structure of neural network, the second part represents the selection of wavelengths in the spectral data, and the third part represents the parameters of mutation of NAEP. Two real flue gas datasets are used in the experiments. In order to present the effectiveness of the methods, the partial least squares with full spectrum, the partial least squares combined with genetic algorithm, the uninformative variable elimination method, the backpropagation neural network with full spectrum, the backpropagation neural network combined with genetic algorithm, and the proposed method are performed for building the component prediction model. Experimental results verify that the proposed method has the ability to predict more accurately and robustly as a practical spectral analysis tool.

Development of radiation oncology learning system combined with multi-institutional radiotherapy database (ROGAD)

International Nuclear Information System (INIS)

Takemura, Akihiro; Iinuma, Masahiro; Kou, Hiroko; Harauchi, Hajime; Inamura, Kiyonari

1999-01-01

We have constructed and are operating a multi-institutional radiotherapy database ROGAD (Radiation Oncology Greater Area Database) since 1992. One of it's purpose is 'to optimize individual radiotherapy plans'. We developed Radiation oncology learning system combined with ROGAD' which conforms to that purpose. Several medical doctors evaluated our system. According to those evaluations, we are now confident that our system is able to contribute to improvement of radiotherapy results. Our final target is to generate a good cyclic relationship among three components: radiotherapy results according to ''Radiation oncology learning system combined with ROGAD.'; The growth of ROGAD; and radiation oncology learning system. (author)

Thermo-economic analysis and optimization of a combined cooling and power (CCP) system for engine waste heat recovery

International Nuclear Information System (INIS)

Xia, Jiaxi; Wang, Jiangfeng; Lou, Juwei; Zhao, Pan; Dai, Yiping

2016-01-01

Highlights: • A combined cooling and power system was proposed for engine waste heat recovery. • Effects of key parameters on thermodynamic performance of the system were studied. • Exergoeconomic parameter analysis was performed for the system. • A single-objective optimization by means of genetic algorithm was carried out. - Abstract: A combined cooling and power (CCP) system is developed, which comprises a CO 2 Brayton cycle (BC), an organic Rankine cycle (ORC) and an ejector refrigeration cycle for the cascade utilization of waste heat from an internal combustion engine. By establishing mathematical model to simulate the overall system, thermodynamic analysis and exergoeconomic analysis are conducted to examine the effects of five key parameters including the compressor pressure ratio, the compressor inlet temperature, the BC turbine inlet temperature, the ORC turbine inlet pressure and the ejector primary flow pressure on system performance. What’s more, a single-objective optimization by means of genetic algorithm (GA) is carried out to search the optimal system performance from viewpoint of exergoeconomic. Results show that the increases of the BC turbine inlet temperature, the ORC turbine inlet pressure and the ejector primary flow pressure are benefit to both thermodynamic and exergoeconimic performances of the CCP system. However, the rises in compressor pressure ratio and compressor inlet temperature will lead to worse system performances. By the single-objective optimization, the lowest average cost per unit of exergy product for the overall system is obtained.

Optimal database combinations for literature searches in systematic reviews : a prospective exploratory study

NARCIS (Netherlands)

Bramer, W. M.; Rethlefsen, Melissa L.; Kleijnen, Jos; Franco, Oscar H.

2017-01-01

Background: Within systematic reviews, when searching for relevant references, it is advisable to use multiple databases. However, searching databases is laborious and time-consuming, as syntax of search strategies are database specific. We aimed to determine the optimal combination of databases

A simplified counter diffusion method combined with a 1D simulation program for optimizing crystallization conditions.

Science.gov (United States)

Tanaka, Hiroaki; Inaka, Koji; Sugiyama, Shigeru; Takahashi, Sachiko; Sano, Satoshi; Sato, Masaru; Yoshitomi, Susumu

2004-01-01

We developed a new protein crystallization method has been developed using a simplified counter-diffusion method for optimizing crystallization condition. It is composed of only a single capillary, the gel in the silicon tube and the screw-top test tube, which are readily available in the laboratory. The one capillary can continuously scan a wide range of crystallization conditions (combination of the concentrations of the precipitant and the protein) unless crystallization occurs, which means that it corresponds to many drops in the vapor-diffusion method. The amount of the precipitant and the protein solutions can be much less than in conventional methods. In this study, lysozyme and alpha-amylase were used as model proteins for demonstrating the efficiency of this method. In addition, one-dimensional (1-D) simulations of the crystal growth were performed based on the 1-D diffusion model. The optimized conditions can be applied to the initial crystallization conditions for both other counter-diffusion methods with the Granada Crystallization Box (GCB) and for the vapor-diffusion method after some modification.

Optimal combination of marketing instruments as a basis for tourist destination strategic management

OpenAIRE

Zupanovic, Ivo

2007-01-01

The marketing mix concept is understood as a certain combination of the instruments that form a tourist offer. Marketing mix enables integration of marketing activities, in order to satisfy the needs of tourist clients,but also in order to achieve the goals of tourist subjects business. Marketing instruments optimising is necessary during the adaptation to certain tourist segments.A suitable marketing mix is not just a simple sum of different instruments but an optimal combination of certain ...

Combined methodology of optimization and life cycle inventory for a biomass gasification based BCHP system

International Nuclear Information System (INIS)

Wang, Jiang-Jiang; Yang, Kun; Xu, Zi-Long; Fu, Chao; Li, Li; Zhou, Zun-Kai

2014-01-01

Biomass gasification based building cooling, heating, and power (BCHP) system is an effective distributed energy system to improve the utilization of biomass resources. This paper proposes a combined methodology of optimization method and life cycle inventory (LCI) for the biomass gasification based BCHP system. The life cycle models including biomass planting, biomass collection-storage-transportation, BCHP plant construction and operation, and BCHP plant demolition and recycle, are constructed to obtain economic cost, energy consumption and CO 2 emission in the whole service-life. Then, the optimization model for the biomass BCHP system including variables, objective function and solution method are presented. Finally, a biomass BCHP case in Harbin, China, is optimized under different optimization objectives, the life-cycle performances including cost, energy and CO 2 emission are obtained and the grey incidence approach is employed to evaluate their comprehensive performances of the biomass BCHP schemes. The results indicate that the life-cycle cost, energy efficiency and CO 2 emission of the biomass BCHP system are about 41.9 $ MWh −1 , 41% and 59.60 kg MWh −1 respectively. The optimized biomass BCHP configuration to minimize the life-cycle cost is the best scheme to achieve comprehensive benefit including cost, energy consumption, renewable energy ratio, steel consumption, and CO 2 emission. - Highlights: • Propose the combined method of optimization and LCI for biomass BCHP system. • Optimize the biomass BCHP system to minimize the life-cycle cost, energy and emission. • Obtain the optimized life-cycle cost, energy efficiency and CO 2 emission. • Select the best biomass BCHP scheme using grey incidence approach

Formulation development and optimization of sustained release matrix tablet of Itopride HCl by response surface methodology and its evaluation of release kinetics.

Science.gov (United States)

Bose, Anirbandeep; Wong, Tin Wui; Singh, Navjot

2013-04-01

The objective of this present investigation was to develop and formulate sustained release (SR) matrix tablets of Itopride HCl, by using different polymer combinations and fillers, to optimize by Central Composite Design response surface methodology for different drug release variables and to evaluate drug release pattern of the optimized product. Sustained release matrix tablets of various combinations were prepared with cellulose-based polymers: hydroxy propyl methyl cellulose (HPMC) and polyvinyl pyrolidine (pvp) and lactose as fillers. Study of pre-compression and post-compression parameters facilitated the screening of a formulation with best characteristics that underwent here optimization study by response surface methodology (Central Composite Design). The optimized tablet was further subjected to scanning electron microscopy to reveal its release pattern. The in vitro study revealed that combining of HPMC K100M (24.65 MG) with pvp(20 mg)and use of LACTOSE as filler sustained the action more than 12 h. The developed sustained release matrix tablet of improved efficacy can perform therapeutically better than a conventional tablet.

Optimization of Combine Heat and Power Plants in the Russian Wholesale Power Market Conditions

Directory of Open Access Journals (Sweden)

I. A. Chuchueva

2015-01-01

Full Text Available The paper concerns the relevant problem to optimize the combine heat and power (CHP plants in the Russian wholesale power market conditions. Since 1975 the CHP plants specialists faced the problem of fuel rate or fuel cost reduction while ensuring the fixed level of heat and power production. The optimality criterion was the fuel rate or fuel cost which has to be minimized. Produced heat and power was paid by known tariff. Since the power market started in 2006 the power payment scheme has essentially changed: produced power is paid by market price. In such condition a new optimality criterion the paper offers is a profit which has to be maximized for the given time horizon. Depending on the optimization horizon the paper suggests four types of the problem urgency, namely: long-term, mid-term, short-term, and operative optimization. It clearly shows that the previous problem of fuel cost minimization is a special case of profit maximization problem. To bring the problem to the mixed-integer linear programming problem a new linear characteristic curves of steam and gas turbine are introduced. Error of linearization is 0.6%. The formal statement of the problem of short-term CHP plants optimization in the market conditions is offered. The problem was solved with IRM software (OpenLinkInternational for seven power plants of JSC “Quadra”: Dyagilevskaya CHP, Kurskaya CHP-1, Lipetskaya CHP-2, Orlovskaya CHP, Kurskaya CHP NWR, Tambovskaya CHP, and Smolenskaya CHP-2.The conducted computational experiment showed that a potential profit is between 1.7% and 4.7% of the fuel cost of different CHP plants and depends on the power plant operation conditions. The potential profit value is 2–3 times higher than analogous estimations, which were obtained solving fuel cost minimization problem. The perspectives of the work are formalization of mid-term and long-term CHP plants optimization problem and development of domestic software for the new problem

Optimal design and operating strategies for a biomass-fueled combined heat and power system with energy storage

DEFF Research Database (Denmark)

Zheng, Yingying; Jenkins, Bryan M.; Kornbluth, Kurt

2018-01-01

An economic linear programming model with a sliding time window was developed to assess designing and scheduling a biomass-fueled combined heat and power system consisting of biomass gasifier, internal combustion engine, heat recovery set, heat-only boiler, producer gas storage and thermal energy......, utility tariff structure and technical and finical performance of the system components. Engine partial load performance was taken into consideration. Sensitivity analyses demonstrate how the optimal BCHP configuration changes with varying demands and utility tariff rates....

Hybrid Modeling and Optimization of Manufacturing Combining Artificial Intelligence and Finite Element Method

CERN Document Server

Quiza, Ramón; Davim, J Paulo

2012-01-01

Artificial intelligence (AI) techniques and the finite element method (FEM) are both powerful computing tools, which are extensively used for modeling and optimizing manufacturing processes. The combination of these tools has resulted in a new flexible and robust approach as several recent studies have shown. This book aims to review the work already done in this field as well as to expose the new possibilities and foreseen trends. The book is expected to be useful for postgraduate students and researchers, working in the area of modeling and optimization of manufacturing processes.

QbD for pediatric oral lyophilisates development: risk assessment followed by screening and optimization.

Science.gov (United States)

Casian, Tibor; Iurian, Sonia; Bogdan, Catalina; Rus, Lucia; Moldovan, Mirela; Tomuta, Ioan

2017-12-01

This study proposed the development of oral lyophilisates with respect to pediatric medicine development guidelines, by applying risk management strategies and DoE as an integrated QbD approach. Product critical quality attributes were overviewed by generating Ishikawa diagrams for risk assessment purposes, considering process, formulation and methodology related parameters. Failure Mode Effect Analysis was applied to highlight critical formulation and process parameters with an increased probability of occurrence and with a high impact on the product performance. To investigate the effect of qualitative and quantitative formulation variables D-optimal designs were used for screening and optimization purposes. Process parameters related to suspension preparation and lyophilization were classified as significant factors, and were controlled by implementing risk mitigation strategies. Both quantitative and qualitative formulation variables introduced in the experimental design influenced the product's disintegration time, mechanical resistance and dissolution properties selected as CQAs. The optimum formulation selected through Design Space presented ultra-fast disintegration time (5 seconds), a good dissolution rate (above 90%) combined with a high mechanical resistance (above 600 g load). Combining FMEA and DoE allowed the science based development of a product with respect to the defined quality target profile by providing better insights on the relevant parameters throughout development process. The utility of risk management tools in pharmaceutical development was demonstrated.

Combined Structural Optimization and Aeroelastic Analysis of a Vertical Axis Wind Turbine

DEFF Research Database (Denmark)

Roscher, Björn; Ferreira, Carlos Simao; Bernhammer, Lars O.

2015-01-01

Floating offshore wind energy poses challenges on the turbine design. A possible solution is vertical axis wind turbines, which are possibly easier to scale-up and require less components (lower maintenance) and a smaller floating structure than horizontal axis wind turbines. This paper presents...... a structural optimization and aeroelastic analysis of an optimized Troposkein vertical axis wind turbine to minimize the relation between the rotor mass and the swept area. The aeroelastic behavior of the different designs has been analyzed using a modified version of the HAWC2 code with the Actuator Cylinder...... model to compute the aerodynamics of the vertical axis wind turbine. The combined shape and topology optimization of a vertical axis wind turbine show a minimum mass to area ratio of 1.82 kg/m2 for blades with varying blade sections from a NACA 0040 at the attachment points to a NACA 0015...

Thermoeconomic Optimization of a Combined Heating and Humidification Coil for HVAC Systems

Science.gov (United States)

Teodoros, Liliana; Andresen, Bjarne

2016-07-01

The total cost of ownership is calculated for a combined heating and humidification coil of an air-handling unit taking into account investment and operation costs simultaneously. This total cost represents the optimization function for which the minimum is sought. The parameters for the cost dependencies are the physical dimensions of the coil: length, width and height. The term "coil" is used generically since in this setup it generates heating as well as humidification in a single unit. The first part of the paper deals with the constructive optimization and finds the relationship between the dimensions for a minimum cost. The second part of the paper takes the results of the constructive optimization further and, based on the data derived in our previous papers, analyzes the minimum total cost for the humidification coil while balancing the amount of water used to humidify the air and modify its temperature.

Development of radiation oncology learning system combined with multi-institutional radiotherapy database (ROGAD)

Energy Technology Data Exchange (ETDEWEB)

Takemura, Akihiro; Iinuma, Masahiro; Kou, Hiroko [Kanazawa Univ. (Japan). School of Medicine; Harauchi, Hajime; Inamura, Kiyonari

1999-09-01

We have constructed and are operating a multi-institutional radiotherapy database ROGAD (Radiation Oncology Greater Area Database) since 1992. One of it's purpose is 'to optimize individual radiotherapy plans'. We developed Radiation oncology learning system combined with ROGAD' which conforms to that purpose. Several medical doctors evaluated our system. According to those evaluations, we are now confident that our system is able to contribute to improvement of radiotherapy results. Our final target is to generate a good cyclic relationship among three components: radiotherapy results according to ''Radiation oncology learning system combined with ROGAD.'; The growth of ROGAD; and radiation oncology learning system. (author)

Optimization of Biomass-Fuelled Combined Cooling, Heating and Power (CCHP Systems Integrated with Subcritical or Transcritical Organic Rankine Cycles (ORCs

Directory of Open Access Journals (Sweden)

Daniel Maraver

2014-04-01

Full Text Available This work is focused on the thermodynamic optimization of Organic Rankine Cycles (ORCs, coupled with absorption or adsorption cooling units, for combined cooling heating and power (CCHP generation from biomass combustion. Results were obtained by modelling with the main aim of providing optimization guidelines for the operating conditions of these types of systems, specifically the subcritical or transcritical ORC, when integrated in a CCHP system to supply typical heating and cooling demands in the tertiary sector. The thermodynamic approach was complemented, to avoid its possible limitations, by the technological constraints of the expander, the heat exchangers and the pump of the ORC. The working fluids considered are: n-pentane, n-heptane, octamethyltrisiloxane, toluene and dodecamethylcyclohexasiloxane. In addition, the energy and environmental performance of the different optimal CCHP plants was investigated. The optimal plant from the energy and environmental point of view is the one integrated by a toluene recuperative ORC, although it is limited to a development with a turbine type expander. Also, the trigeneration plant could be developed in an energy and environmental efficient way with an n-pentane recuperative ORC and a volumetric type expander.

Optimization of the rocket mode trajectory in a rocket based combined cycle (RBCC) engine powered SSTO vehicle

Science.gov (United States)

Foster, Richard W.

1989-07-01

The application of rocket-based combined cycle (RBCC) engines to booster-stage propulsion, in combination with all-rocket second stages in orbital-ascent missions, has been studied since the mid-1960s; attention is presently given to the case of the 'ejector scramjet' RBCC configuration's application to SSTO vehicles. While total mass delivered to initial orbit is optimized at Mach 20, payload delivery capability to initial orbit optimizes at Mach 17, primarily due to the reduction of hydrogen fuel tankage structure, insulation, and thermal protection system weights.

Optimization of advenced liquid natural gas-fuelled combined cycle machinery systems for a high-speed ferry

DEFF Research Database (Denmark)

Tveitaskog, Kari Anne; Haglind, Fredrik

2012-01-01

. Furthermore, practical and operational aspects of using these three machinery systems for a high-speed ferry are discussed. Two scenarios are evaluated. The first scenario evaluates the combined cycles with a given power requirement, optimizing the combined cycle while operating the gas turbine at part load...

Combining On-Line Characterization Tools with Modern Software Environments for Optimal Operation of Polymerization Processes

Directory of Open Access Journals (Sweden)

Navid Ghadipasha

2016-02-01

Full Text Available This paper discusses the initial steps towards the formulation and implementation of a generic and flexible model centric framework for integrated simulation, estimation, optimization and feedback control of polymerization processes. For the first time it combines the powerful capabilities of the automatic continuous on-line monitoring of polymerization system (ACOMP, with a modern simulation, estimation and optimization software environment towards an integrated scheme for the optimal operation of polymeric processes. An initial validation of the framework was performed for modelling and optimization using literature data, illustrating the flexibility of the method to apply under different systems and conditions. Subsequently, off-line capabilities of the system were fully tested experimentally for model validations, parameter estimation and process optimization using ACOMP data. Experimental results are provided for free radical solution polymerization of methyl methacrylate.

Pro Android Apps Performance Optimization

CERN Document Server

Guihot, Hervé

2012-01-01

Today's Android apps developers are often running into the need to refine, improve and optimize their apps performances. As more complex apps can be created, it is even more important for developers to deal with this critical issue. Android allows developers to write apps using Java, C or a combination of both with the Android SDK and the Android NDK. Pro Android Apps Performance Optimization reveals how to fine-tune your Android apps, making them more stable and faster. In this book, you'll learn the following: * How to optimize your Java code with the SDK, but also how to write and optimize

Optimal Scheduling of Integrated Energy Systems with Combined Heat and Power Generation, Photovoltaic and Energy Storage Considering Battery Lifetime Loss

Directory of Open Access Journals (Sweden)

Yongli Wang

2018-06-01

Full Text Available Integrated energy systems (IESs are considered a trending solution for the energy crisis and environmental problems. However, the diversity of energy sources and the complexity of the IES have brought challenges to the economic operation of IESs. Aiming at achieving optimal scheduling of components, an IES operation optimization model including photovoltaic, combined heat and power generation system (CHP and battery energy storage is developed in this paper. The goal of the optimization model is to minimize the operation cost under the system constraints. For the optimization process, an optimization principle is conducted, which achieves maximized utilization of photovoltaic by adjusting the controllable units such as energy storage and gas turbine, as well as taking into account the battery lifetime loss. In addition, an integrated energy system project is taken as a research case to validate the effectiveness of the model via the improved differential evolution algorithm (IDEA. The comparison between IDEA and a traditional differential evolution algorithm shows that IDEA could find the optimal solution faster, owing to the double variation differential strategy. The simulation results in three different battery states which show that the battery lifetime loss is an inevitable factor in the optimization model, and the optimized operation cost in 2016 drastically decreased compared with actual operation data.

A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation

Directory of Open Access Journals (Sweden)

J.-M. Beckers

2014-10-01

Full Text Available We present a method in which the optimal interpolation of multi-scale processes can be expanded into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of two processes can be obtained by different mathematical formulations involving iterations and analysis focusing on a single process. From the different mathematical equivalent formulations, we then select the most efficient ones by analyzing the behavior of the different possibilities in a simple and well-controlled test case. The clear guidelines deduced from this experiment are then applied to a real situation in which we combine large-scale analysis of hourly Spinning Enhanced Visible and Infrared Imager (SEVIRI satellite images using data interpolating empirical orthogonal functions (DINEOF with a local optimal interpolation using a Gaussian covariance. It is shown that the optimal combination indeed provides the best reconstruction and can therefore be exploited to extract the maximum amount of useful information from the original data.

Multi-objective thermodynamic optimization of combined Brayton and inverse Brayton cycles using genetic algorithms

International Nuclear Information System (INIS)

Besarati, S.M.; Atashkari, K.; Jamali, A.; Hajiloo, A.; Nariman-zadeh, N.

2010-01-01

This paper presents a simultaneous optimization study of two outputs performance of a previously proposed combined Brayton and inverse Brayton cycles. It has been carried out by varying the upper cycle pressure ratio, the expansion pressure of the bottom cycle and using variable, above atmospheric, bottom cycle inlet pressure. Multi-objective genetic algorithms are used for Pareto approach optimization of the cycle outputs. The two important conflicting thermodynamic objectives that have been considered in this work are net specific work (w s ) and thermal efficiency (η th ). It is shown that some interesting features among optimal objective functions and decision variables involved in the Baryton and inverse Brayton cycles can be discovered consequently.

Optimization of formulation of soy-cakes baked in infrared-microwave combination oven by response surface methodology.

Science.gov (United States)

Şakıyan, Özge

2015-05-01

The aim of present work is to optimize the formulation of a functional cake (soy-cake) to be baked in infrared-microwave combination oven. For this optimization process response surface methodology was utilized. It was also aimed to optimize the processing conditions of the combination baking. The independent variables were the baking time (8, 9, 10 min), the soy flour concentration (30, 40, 50 %) and the DATEM (diacetyltartaric acid esters of monoglycerides) concentration (0.4, 0.6 and 0.8 %). The quality parameters that were examined in the study were specific volume, weight loss, total color change and firmness of the cake samples. The results were analyzed by multiple regression; and the significant linear, quadratic, and interaction terms were used in the second order mathematical model. The optimum baking time, soy-flour concentration and DATEM concentration were found as 9.5 min, 30 and 0.72 %, respectively. The corresponding responses of the optimum points were almost comparable with those of conventionally baked soy-cakes. So it may be declared that it is possible to produce high quality soy cakes in a very short time by using infrared-microwave combination oven.

Development of GPT-based optimization algorithm

International Nuclear Information System (INIS)

White, J.R.; Chapman, D.M.; Biswas, D.

1985-01-01

The University of Lowell and Westinghouse Electric Corporation are involved in a joint effort to evaluate the potential benefits of generalized/depletion perturbation theory (GPT/DTP) methods for a variety of light water reactor (LWR) physics applications. One part of that work has focused on the development of a GPT-based optimization algorithm for the overall design, analysis, and optimization of LWR reload cores. The use of GPT sensitivity data in formulating the fuel management optimization problem is conceptually straightforward; it is the actual execution of the concept that is challenging. Thus, the purpose of this paper is to address some of the major difficulties, to outline our approach to these problems, and to present some illustrative examples of an efficient GTP-based optimization scheme

Combined Use of Mathematical Optimization and Design of Experiments for the Maximization of Profit in a Four-Echelon Supply Chain

Directory of Open Access Journals (Sweden)

Daniel Arturo Olivares Vera

2018-01-01

Full Text Available This paper develops a location-allocation model to optimize a four-echelon supply chain network, addressing manufacturing and distribution centers location, supplier selection and flow allocation for raw materials from suppliers to manufacturers, and finished products for end customers, while searching for system profit maximization. A fractional-factorial design of experiments is performed to analyze the effects of capacity, quality, delivery time, and interest rate on profit and system performance. The model is formulated as a mixed-integer linear programming problem and solved by using well-known commercial software. The usage of factorial experiments combined with mathematical optimization is a novel approach to address supply chain network design problems. The application of the proposed model to a case study shows that this combination of techniques yields satisfying results in terms of both its behavior and the obtained managerial insights. An ANOVA analysis is executed to quantify the effects of each factor and their interactions. In the analyzed case study, the transportation cost is the most relevant cost component, and the most relevant opportunity for profit improvement is found in the factor of quality. The proposed combination of methods can be adapted to different problems and industries.

Combined holographic-mechanical optical tweezers: Construction, optimization, and calibration

Science.gov (United States)

Hanes, Richard D. L.; Jenkins, Matthew C.; Egelhaaf, Stefan U.

2009-08-01

A spatial light modulator (SLM) and a pair of galvanometer-mounted mirrors (GMM) were combined into an optical tweezers setup. This provides great flexibility as the SLM creates an array of traps, which can be moved smoothly and quickly with the GMM. To optimize performance, the effect of the incidence angle on the SLM with respect to phase and intensity response was investigated. Although it is common to use the SLM at an incidence angle of 45°, smaller angles give a full 2π phase shift and an output intensity which is less dependent on the magnitude of the phase shift. The traps were calibrated using an active oscillatory technique and a passive probability distribution method.

Combined holographic-mechanical optical tweezers: Construction, optimization, and calibration

International Nuclear Information System (INIS)

Hanes, Richard D. L.; Jenkins, Matthew C.; Egelhaaf, Stefan U.

2009-01-01

A spatial light modulator (SLM) and a pair of galvanometer-mounted mirrors (GMM) were combined into an optical tweezers setup. This provides great flexibility as the SLM creates an array of traps, which can be moved smoothly and quickly with the GMM. To optimize performance, the effect of the incidence angle on the SLM with respect to phase and intensity response was investigated. Although it is common to use the SLM at an incidence angle of 45 deg., smaller angles give a full 2π phase shift and an output intensity which is less dependent on the magnitude of the phase shift. The traps were calibrated using an active oscillatory technique and a passive probability distribution method.

Combined adaptive multiple subtraction based on optimized event tracing and extended wiener filtering

Science.gov (United States)

Tan, Jun; Song, Peng; Li, Jinshan; Wang, Lei; Zhong, Mengxuan; Zhang, Xiaobo

2017-06-01

The surface-related multiple elimination (SRME) method is based on feedback formulation and has become one of the most preferred multiple suppression methods used. However, some differences are apparent between the predicted multiples and those in the source seismic records, which may result in conventional adaptive multiple subtraction methods being barely able to effectively suppress multiples in actual production. This paper introduces a combined adaptive multiple attenuation method based on the optimized event tracing technique and extended Wiener filtering. The method firstly uses multiple records predicted by SRME to generate a multiple velocity spectrum, then separates the original record to an approximate primary record and an approximate multiple record by applying the optimized event tracing method and short-time window FK filtering method. After applying the extended Wiener filtering method, residual multiples in the approximate primary record can then be eliminated and the damaged primary can be restored from the approximate multiple record. This method combines the advantages of multiple elimination based on the optimized event tracing method and the extended Wiener filtering technique. It is an ideal method for suppressing typical hyperbolic and other types of multiples, with the advantage of minimizing damage of the primary. Synthetic and field data tests show that this method produces better multiple elimination results than the traditional multi-channel Wiener filter method and is more suitable for multiple elimination in complicated geological areas.

Thermodynamic performance analysis and optimization of a solar-assisted combined cooling, heating and power system

International Nuclear Information System (INIS)

Wang, Jiangjiang; Lu, Yanchao; Yang, Ying; Mao, Tianzhi

2016-01-01

This study aims to present a thermodynamic performance analysis and to optimize the configurations of a hybrid combined cooling, heating and power (CCHP) system incorporating solar energy and natural gas. A basic natural gas CCHP system containing a power generation unit, a heat recovery system, an absorption cooling system and a storage tank is integrated with solar photovoltaic (PV) panels and/or a heat collector. Based on thermodynamic modeling, the thermodynamic performance, including energy and exergy efficiencies, under variable work conditions, such as electric load factor, solar irradiance and installation ratio, of the solar PV panels and heat collector is investigated and analyzed. The results of the energy supply side analysis indicate that the integration of solar PV into the CCHP system more efficiently improves the exergy efficiency, whereas the integration of a solar heat collector improves the energy efficiency. To match the building loads, the optimization method combined with the operation strategy is employed to optimize the system configurations to maximize the integrated benefits of energy and economic costs. The optimization results of demand–supply matching demonstrate that the integration of a solar heat collector achieves a better integrated performance than the solar PV integration in the specific case study. - Highlights: • Design a CCHP system integrated with solar PV and heat collector. • Present the energy and exergy analyses under variable work conditions. • Propose an optimization method of CCHP system for demand-supply matching.

Combined Data with Particle Swarm Optimization for Structural Damage Detection

Directory of Open Access Journals (Sweden)

Fei Kang

2013-01-01

Full Text Available This paper proposes a damage detection method based on combined data of static and modal tests using particle swarm optimization (PSO. To improve the performance of PSO, some immune properties such as selection, receptor editing, and vaccination are introduced into the basic PSO and an improved PSO algorithm is formed. Simulations on three benchmark functions show that the new algorithm performs better than PSO. The efficiency of the proposed damage detection method is tested on a clamped beam, and the results demonstrate that it is more efficient than PSO, differential evolution, and an adaptive real-parameter simulated annealing genetic algorithm.

Optimal Control Development System for Electrical Drives

Directory of Open Access Journals (Sweden)

Marian GAICEANU

2008-08-01

Full Text Available In this paper the optimal electrical drive development system is presented. It consists of both electrical drive types: DC and AC. In order to implement the optimal control for AC drive system an Altivar 71 inverter, a Frato magnetic particle brake (as load, three-phase induction machine, and dSpace 1104 controller have been used. The on-line solution of the matrix Riccati differential equation (MRDE is computed by dSpace 1104 controller, based on the corresponding feedback signals, generating the optimal speed reference for the AC drive system. The optimal speed reference is tracked by Altivar 71 inverter, conducting to energy reduction in AC drive. The classical control (consisting of rotor field oriented control with PI controllers and the optimal one have been implemented by designing an adequate ControlDesk interface. The three-phase induction machine (IM is controlled at constant flux. Therefore, the linear dynamic mathematical model of the IM has been obtained. The optimal control law provides transient regimes with minimal energy consumption. The obtained solution by integration of the MRDE is orientated towards the numerical implementation-by using a zero order hold. The development system is very useful for researchers, doctoral students or experts training in electrical drive. The experimental results are shown.

Adaptive prostate IGRT combining online re-optimization and re-positioning: a feasibility study

International Nuclear Information System (INIS)

Li Taoran; Zhu Xiaofeng; Lee, W Robert; Vujaskovic, Zeljko; Yin Fangfang; Wu, Q Jackie; Thongphiew, Danthai

2011-01-01

In prostate radiation therapy, inter-fractional organ motion/deformation has posed significant challenges on reliable daily dose delivery. To correct for this issue, off-line re-optimization and online re-positioning have been used clinically. In this paper, we propose an adaptive images guided radiation therapy (AIGRT) scheme that combines these two correction methods in an anatomy-driven fashion. The AIGRT process first tries to find a best plan for the daily target from a plan pool, which consists of the original CT plan and all previous re-optimized plans. If successful, the selected plan is used for daily treatment with translational shifts. Otherwise, the AIGRT invokes the re-optimization process of the CT plan for the anatomy of the day, which is afterward added to the plan pool as a candidate for future fractions. The AIGRT scheme is evaluated by comparisons with daily re-optimization and online re-positioning techniques based on daily target coverage, organs at risk (OAR) sparing and implementation efficiency. Simulated treatment courses for 18 patients with re-optimization alone, re-positioning alone and AIGRT shows that AIGRT offers reliable daily target coverage that is highly comparable to daily re-optimization and significantly improves from re-positioning. AIGRT is also seen to provide improved OAR sparing compared to re-positioning. Apart from dosimetric benefits, AIGRT in addition offers an efficient scheme to integrate re-optimization to current re-positioning-based IGRT workflow.

Optimizing Combinations of Flavonoids Deriving from Astragali Radix in Activating the Regulatory Element of Erythropoietin by a Feedback System Control Scheme

Directory of Open Access Journals (Sweden)

Hui Yu

2013-01-01

Full Text Available Identifying potent drug combination from a herbal mixture is usually quite challenging, due to a large number of possible trials. Using an engineering approach of the feedback system control (FSC scheme, we identified the potential best combinations of four flavonoids, including formononetin, ononin, calycosin, and calycosin-7-O-β-D-glucoside deriving from Astragali Radix (AR; Huangqi, which provided the best biological action at minimal doses. Out of more than one thousand possible combinations, only tens of trials were required to optimize the flavonoid combinations that stimulated a maximal transcriptional activity of hypoxia response element (HRE, a critical regulator for erythropoietin (EPO transcription, in cultured human embryonic kidney fibroblast (HEK293T. By using FSC scheme, 90% of the work and time can be saved, and the optimized flavonoid combinations increased the HRE mediated transcriptional activity by ~3-fold as compared with individual flavonoid, while the amount of flavonoids was reduced by ~10-fold. Our study suggests that the optimized combination of flavonoids may have strong effect in activating the regulatory element of erythropoietin at very low dosage, which may be used as new source of natural hematopoietic agent. The present work also indicates that the FSC scheme is able to serve as an efficient and model-free approach to optimize the drug combination of different ingredients within a herbal decoction.

Optimization of dilute sulfuric acid pretreatment to maximize combined sugar yield from sugarcane bagasse for ethanol production.

Science.gov (United States)

Benjamin, Y; Cheng, H; Görgens, J F

2014-01-01

Increasing fermentable sugar yields per gram of biomass depends strongly on optimal selection of varieties and optimization of pretreatment conditions. In this study, dilute acid pretreatment of bagasse from six varieties of sugarcane was investigated in connection with enzymatic hydrolysis for maximum combined sugar yield (CSY). The CSY from the varieties were also compared with the results from industrial bagasse. The results revealed considerable differences in CSY between the varieties. Up to 22.7 % differences in CSY at the optimal conditions was observed. The combined sugar yield difference between the best performing variety and the industrial bagasse was 34.1 %. High ratio of carbohydrates to lignin and low ash content favored the release of sugar from the substrates. At mild pretreatment conditions, the differences in bioconversion efficiency between varieties were greater than at severe condition. This observation suggests that under less severe conditions the glucose recovery was largely determined by chemical composition of biomass. The results from this study support the possibility of increasing sugar yields or improving the conversion efficiency when pretreatment optimization is performed on varieties with improved properties.

Green Infrastructure Simulation and Optimization to Achieve Combined Sewer Overflow Reductions in Philadelphia's Mill Creek Sewershed

Science.gov (United States)

Cohen, J. S.; McGarity, A. E.

2017-12-01

The ability for mass deployment of green stormwater infrastructure (GSI) to intercept significant amounts of urban runoff has the potential to reduce the frequency of a city's combined sewer overflows (CSOs). This study was performed to aid in the Overbrook Environmental Education Center's vision of applying this concept to create a Green Commercial Corridor in Philadelphia's Overbrook Neighborhood, which lies in the Mill Creek Sewershed. In an attempt to further implement physical and social reality into previous work using simulation-optimization techniques to produce GSI deployment strategies (McGarity, et al., 2016), this study's models incorporated land use types and a specific neighborhood in the sewershed. The low impact development (LID) feature in EPA's Storm Water Management Model (SWMM) was used to simulate various geographic configurations of GSI in Overbrook. The results from these simulations were used to obtain formulas describing the annual CSO reduction in the sewershed based on the deployed GSI practices. These non-linear hydrologic response formulas were then implemented into the Storm Water Investment Strategy Evaluation (StormWISE) model (McGarity, 2012), a constrained optimization model used to develop optimal stormwater management practices on the watershed scale. By saturating the avenue with GSI, not only will CSOs from the sewershed into the Schuylkill River be reduced, but ancillary social and economic benefits of GSI will also be achieved. The effectiveness of these ancillary benefits changes based on the type of GSI practice and the type of land use in which the GSI is implemented. Thus, the simulation and optimization processes were repeated while delimiting GSI deployment by land use (residential, commercial, industrial, and transportation). The results give a GSI deployment strategy that achieves desired annual CSO reductions at a minimum cost based on the locations of tree trenches, rain gardens, and rain barrels in specified land

Flow ripple reduction of an axial piston pump by a combination of cross-angle and pressure relief grooves: Analysis and optimization

International Nuclear Information System (INIS)

Xu, Bing; Ye, Shaogan; Zhang, Junhui; Zhang, Chunfeng

2016-01-01

This paper investigates the potential of flow ripple reduction of an axial piston pump by a combination of cross-angle and pressure relief grooves. A dynamic model is developed to analyze the pumping dynamics of the pump and validated by experimental results. The effects of cross-angle on the flow ripples in the outlet and inlet ports, and the piston chamber pressure are investigated. The effects of pressure relief grooves on the optimal solutions obtained by a multi-objective optimization method are identified. A sensitivity analysis is performed to investigate the sensitivity of cross-angle to different working conditions. The results reveal that the flow ripples from the optimal solutions are smaller using the cross-angle and pressure relief grooves than those using the cross-angle and ordinary precompression and decompression angles and the cross-angle can be smaller. In addition, when the optimal design is used, the outlet flow ripples sensitivity can be reduced significantly.

Optimization Settings in the Fuzzy Combined Mamdani PID Controller

Science.gov (United States)

Kudinov, Y. I.; Pashchenko, F. F.; Pashchenko, A. F.; Kelina, A. Y.; Kolesnikov, V. A.

2017-11-01

In the present work the actual problem of determining the optimal settings of fuzzy parallel proportional-integral-derivative (PID) controller is considered to control nonlinear plants that is not always possible to perform with classical linear PID controllers. In contrast to the linear fuzzy PID controllers there are no analytical methods of settings calculation. In this paper, we develop a numerical optimization approach to determining the coefficients of a fuzzy PID controller. Decomposition method of optimization is proposed, the essence of which was as follows. All homogeneous coefficients were distributed to the relevant groups, for example, three error coefficients, the three coefficients of the changes of errors and the three coefficients of the outputs P, I and D components. Consistently in each of such groups the search algorithm was selected that has determined the coefficients under which we receive the schedule of the transition process satisfying all the applicable constraints. Thus, with the help of Matlab and Simulink in a reasonable time were found the factors of a fuzzy PID controller, which meet the accepted limitations on the transition process.

Optimization an optimal artificial diet for the predatory bug Orius sauteri (hemiptera: anthocoridae.

Directory of Open Access Journals (Sweden)

Xiao-Ling Tan

Full Text Available BACKGROUND: The flower bug Orius sauteri is an important polyphagous predator that is widely used for the biological control of mites and aphids. However, the optimal conditions for mass rearing of this insect are still unclear, thus limiting its application. METHODOLOGY: In this study, we investigated the optimal ingredients of an artificial diet for raising O. sauteri using a microencapsulation technique. The ingredients included egg yolk (vitellus, whole-pupa homogenate of the Tussah silk moth (Antheraea paphia, honey, sucrose, rapeseed (Brassica napus pollen and sinkaline. We tested 25 combinations of the above ingredients using an orthogonal experimental design. Using statistical analysis, we confirmed the main effect factors amongst the components, and selected five optimal combinations based on different biological and physiological characters. PRINCIPAL FINDINGS: The results showed that, although different artificial diet formats significantly influenced the development and reproductive ability of O. sauteri, the complete development of O. sauteri to sexual maturity could only be achieved by optimizing the artificial diet according to specific biological characters. In general, pupae of A. paphia had more influence on O sauteri development than did artificial components. The results of a follow-up test of locomotory and respiratory capacity indicated that respiratory quotient, metabolic rate and average creeping speed were all influenced by different diets. Furthermore, the field evaluations of mating preference, predatory consumption and population dispersion also demonstrated the benefits that could be provided by optimal artificial diets. CONCLUSIONS: A microencapsulated artificial diet overcame many of the difficulties highlighted by previous studies on the mass rearing of O. sauteri. Optimization of the microencapsulated artificial diet directly increased the biological and physiological characters investigated. Successive

Modeling plant interspecific interactions from experiments with perennial crop mixtures to predict optimal combinations.

Science.gov (United States)

Halty, Virginia; Valdés, Matías; Tejera, Mauricio; Picasso, Valentín; Fort, Hugo

2017-12-01

The contribution of plant species richness to productivity and ecosystem functioning is a longstanding issue in ecology, with relevant implications for both conservation and agriculture. Both experiments and quantitative modeling are fundamental to the design of sustainable agroecosystems and the optimization of crop production. We modeled communities of perennial crop mixtures by using a generalized Lotka-Volterra model, i.e., a model such that the interspecific interactions are more general than purely competitive. We estimated model parameters -carrying capacities and interaction coefficients- from, respectively, the observed biomass of monocultures and bicultures measured in a large diversity experiment of seven perennial forage species in Iowa, United States. The sign and absolute value of the interaction coefficients showed that the biological interactions between species pairs included amensalism, competition, and parasitism (asymmetric positive-negative interaction), with various degrees of intensity. We tested the model fit by simulating the combinations of more than two species and comparing them with the polycultures experimental data. Overall, theoretical predictions are in good agreement with the experiments. Using this model, we also simulated species combinations that were not sown. From all possible mixtures (sown and not sown) we identified which are the most productive species combinations. Our results demonstrate that a combination of experiments and modeling can contribute to the design of sustainable agricultural systems in general and to the optimization of crop production in particular. © 2017 by the Ecological Society of America.

Integrated design optimization research and development in an industrial environment

Science.gov (United States)

Kumar, V.; German, Marjorie D.; Lee, S.-J.

1989-01-01

An overview is given of a design optimization project that is in progress at the GE Research and Development Center for the past few years. The objective of this project is to develop a methodology and a software system for design automation and optimization of structural/mechanical components and systems. The effort focuses on research and development issues and also on optimization applications that can be related to real-life industrial design problems. The overall technical approach is based on integration of numerical optimization techniques, finite element methods, CAE and software engineering, and artificial intelligence/expert systems (AI/ES) concepts. The role of each of these engineering technologies in the development of a unified design methodology is illustrated. A software system DESIGN-OPT has been developed for both size and shape optimization of structural components subjected to static as well as dynamic loadings. By integrating this software with an automatic mesh generator, a geometric modeler and an attribute specification computer code, a software module SHAPE-OPT has been developed for shape optimization. Details of these software packages together with their applications to some 2- and 3-dimensional design problems are described.

Developing optimized prioritizing road maintenance

Directory of Open Access Journals (Sweden)

Ewadh Hussein Ali

2018-01-01

Full Text Available Increased demand for efficient maintenance of the existing roadway system needs optimal usage of the allocated funds. The paper demonstrates optimized methods for prioritizing maintenance implementation projects. A selected zone of roadway system in Kerbala city represents the study area to demonstrate the application of the developed prioritization process. Paver system PAVER integrated with GIS is used to estimate and display the pavement condition index PCI, thereby to establish a priority of maintenance. In addition to simple ranking method by PCI produced by the output of PAVER, the paper introduces PCI measure for each section of roadway. The paper introduces ranking by multiple measures investigated through expert knowledge about measures that affect prioritization and their irrespective weights due to a predesigned questionnaire. The maintenance priority index (MPI is related to cost of suitable proposed maintenance, easiness of proposed maintenance, average daily traffic and functional classification of the roadway in addition to PCI. Further, incremental benefit-cost analysis ranking provide an optimized process due to benefit and cost of maintenance. The paper introduces efficient display of layout and ranking for the selected zone of roadway system based on MPI index and incremental BCR method. Although the two developed methods introduce different layout display for priority, statistical test shows that no significant difference between ranking of all methods of prioritization.

A two-stage optimal planning and design method for combined cooling, heat and power microgrid system

International Nuclear Information System (INIS)

Guo, Li; Liu, Wenjian; Cai, Jiejin; Hong, Bowen; Wang, Chengshan

2013-01-01

Highlights: • A two-stage optimal method is presented for CCHP microgrid system. • Economic and environmental performance are considered as assessment indicators. • Application case demonstrates its good economic and environmental performance. - Abstract: In this paper, a two-stage optimal planning and design method for combined cooling, heat and power (CCHP) microgrid system was presented. The optimal objective was to simultaneously minimize the total net present cost and carbon dioxide emission in life circle. On the first stage, multi-objective genetic algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) was applied to solve the optimal design problem including the optimization of equipment type and capacity. On the second stage, mixed-integer linear programming (MILP) algorithm was used to solve the optimal dispatch problem. The approach was applied to a typical CCHP microgrid system in a hospital as a case study, and the effectiveness of the proposed method was verified

Development of a structural optimization capability for the aeroelastic tailoring of composite rotor blades with straight and swept tips

Science.gov (United States)

Friedmann, P. P.; Venkatesan, C.; Yuan, K.

1992-01-01

This paper describes the development of a new structural optimization capability aimed at the aeroelastic tailoring of composite rotor blades with straight and swept tips. The primary objective is to reduce vibration levels in forward flight without diminishing the aeroelastic stability margins of the blade. In the course of this research activity a number of complicated tasks have been addressed: (1) development of a new, aeroelastic stability and response analysis; (2) formulation of a new comprehensive sensitive analysis, which facilitates the generation of the appropriate approximations for the objective and the constraints; (3) physical understanding of the new model and, in particular, determination of its potential for aeroelastic tailoring, and (4) combination of the newly developed analysis capability, the sensitivity derivatives and the optimizer into a comprehensive optimization capability. The first three tasks have been completed and the fourth task is in progress.

Combining gait optimization with passive system to increase the energy efficiency of a humanoid robot walking movement

International Nuclear Information System (INIS)

Pereira, Ana I.; Lima, José; Costa, Paulo

2015-01-01

There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The energy consumption can also be reduced with elastic elements coupled to each joint. The presented paper addresses an optimization method, the Stretched Simulated Annealing, that runs in an accurate and stable simulation model to find the optimal gait combined with elastic elements. Final results demonstrate that optimization is a valid gait planning technique

Combining gait optimization with passive system to increase the energy efficiency of a humanoid robot walking movement

Energy Technology Data Exchange (ETDEWEB)

Pereira, Ana I. [Polytechnic Institute of Bragança (Portugal); ALGORITMI,University of Minho (Portugal); Lima, José [Polytechnic Institute of Bragança (Portugal); INESC TEC (formerly INESC Porto) Porto (Portugal); Costa, Paulo [Faculty of Engineering, University of Porto (Portugal); INESC TEC (formerly INESC Porto) Porto (Portugal)

2015-03-10

There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The energy consumption can also be reduced with elastic elements coupled to each joint. The presented paper addresses an optimization method, the Stretched Simulated Annealing, that runs in an accurate and stable simulation model to find the optimal gait combined with elastic elements. Final results demonstrate that optimization is a valid gait planning technique.

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

Energy Technology Data Exchange (ETDEWEB)

Ettehadtavakkol, Amin, E-mail: amin.ettehadtavakkol@ttu.edu [Texas Tech University (United States); Jablonowski, Christopher [Shell Exploration and Production Company (United States); Lake, Larry [University of Texas at Austin (United States)

2017-04-15

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.

Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs

International Nuclear Information System (INIS)

Ettehadtavakkol, Amin; Jablonowski, Christopher; Lake, Larry

2017-01-01

Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.

Should a small combined heat and power plant (CHP) open to its regional power and heat networks? Integrated economic, energy, and emergy evaluation of optimization plans for Jiufa CHP

International Nuclear Information System (INIS)

Peng, T.; Lu, H.F.; Wu, W.L.; Campbell, D.E.; Zhao, G.S.; Zou, J.H.; Chen, J.

2008-01-01

The development of industrial ecology has led company managers to increasingly consider their company's niche in the regional system, and to develop optimization plans. We used emergy-based, ecological-economic synthesis to evaluate two optimization plans for the Jiufa Combined Heat and Power (CHP) Plant, Shandong China. In addition, we performed economic input-output analysis and energy analysis on the system. The results showed that appropriately incorporating a firm with temporary extra productivity into its regional system will help maximize the total productivity and improve ecological-economic efficiency and benefits to society, even without technical optimization of the firm itself. In addition, developing a closer relationship between a company and its regional system will facilitate the development of new optimization opportunities. Small coal-based CHP plants have lower-energy efficiency, higher environmental loading, and lower sustainability than large fossil fuel and renewable energy-based systems. The emergy exchange ratio (EER) proved to be an important index for evaluating the vitality of highly developed ecological-economic systems

METHOD FOR OPTIMAL RESOLUTION OF MULTI-AIRCRAFT CONFLICTS IN THREE-DIMENSIONAL SPACE

Directory of Open Access Journals (Sweden)

Denys Vasyliev

2017-03-01

Full Text Available Purpose: The risk of critical proximities of several aircraft and appearance of multi-aircraft conflicts increases under current conditions of high dynamics and density of air traffic. The actual problem is a development of methods for optimal multi-aircraft conflicts resolution that should provide the synthesis of conflict-free trajectories in three-dimensional space. Methods: The method for optimal resolution of multi-aircraft conflicts using heading, speed and altitude change maneuvers has been developed. Optimality criteria are flight regularity, flight economy and the complexity of maneuvering. Method provides the sequential synthesis of the Pareto-optimal set of combinations of conflict-free flight trajectories using multi-objective dynamic programming and selection of optimal combination using the convolution of optimality criteria. Within described method the following are defined: the procedure for determination of combinations of aircraft conflict-free states that define the combinations of Pareto-optimal trajectories; the limitations on discretization of conflict resolution process for ensuring the absence of unobservable separation violations. Results: The analysis of the proposed method is performed using computer simulation which results show that synthesized combination of conflict-free trajectories ensures the multi-aircraft conflict avoidance and complies with defined optimality criteria. Discussion: Proposed method can be used for development of new automated air traffic control systems, airborne collision avoidance systems, intelligent air traffic control simulators and for research activities.

Optimization of rotor blades for combined structural, dynamic, and aerodynamic properties

Science.gov (United States)

He, Cheng-Jian; Peters, David A.

1990-01-01

Optimal helicopter blade design with computer-based mathematical programming has received more and more attention in recent years. Most of the research has focused on optimum dynamic characteristics of rotor blades to reduce vehicle vibration. There is also work on optimization of aerodynamic performance and on composite structural design. This research has greatly increased our understanding of helicopter optimum design in each of these aspects. Helicopter design is an inherently multidisciplinary process involving strong interactions among various disciplines which can appropriately include aerodynamics; dynamics, both flight dynamics and structural dynamics; aeroelasticity: vibrations and stability; and even acoustics. Therefore, the helicopter design process must satisfy manifold requirements related to the aforementioned diverse disciplines. In our present work, we attempt to combine several of these important effects in a unified manner. First, we design a blade with optimum aerodynamic performance by proper layout of blade planform and spanwise twist. Second, the blade is designed to have natural frequencies that are placed away from integer multiples of the rotor speed for a good dynamic characteristics. Third, the structure is made as light as possible with sufficient rotational inertia to allow for autorotational landing, with safe stress margins and flight fatigue life at each cross-section, and with aeroelastical stability and low vibrations. Finally, a unified optimization refines the solution.

Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power

Institute of Scientific and Technical Information of China (English)

Feng Zhao; Chenghui Zhang; Bo Sun

2016-01-01

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power(CCHP) with storage systems.Initially,the initiative optimization operation strategy of CCHP system in the cooling season,the heating season and the transition season was formulated.The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency,minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy.Furthermore,the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm.Ultimately,the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution(TOPSIS) method.A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method.The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method.The CCHP system has achieved better energy efficiency,environmental protection and economic benefits.

Optimization of food materials for development of nutritious pasta utilizing groundnut meal and beetroot.

Science.gov (United States)

Mridula, D; Gupta, R K; Bhadwal, Sheetal; Khaira, Harjot; Tyagi, S K

2016-04-01

Present study was undertaken to optimize the level of food materials viz. groundnut meal, beetroot juice and refined wheat flour for development of nutritious pasta using response surface methodology. Box-benken design of experiments was used to design different experimental combinations considering 10 to 20 g groundnut meal, 6 to 18 mL beetroot juice and 80 to 90 g refined wheat flour. Quality attributes such as protein content, antioxidant activity, colour, cooking quality (solid loss, rehydration ratio and cooking time) and sensory acceptability of pasta samples were the dependent variables for the study. The results revealed that pasta samples with higher levels of groundnut meal and beetroot juice were high in antioxidant activity and overall sensory acceptability. The samples with higher content of groundnut meal indicated higher protein contents in them. On the other hand, the samples with higher beetroot juice content were high in rehydration ratio and lesser cooking time along with low solid loss in cooking water. The different level of studied food materials significantly affected the colour quality of pasta samples. Optimized combination for development of nutritious pasta consisted of 20 g groundnut meal, 18 mL beetroot juice and 83.49 g refined wheat flour with overall desirability as 0.905. This pasta sample required 5.5 min to cook and showed 1.37 % solid loss and rehydration ratio as 6.28. Pasta sample prepared following optimized formulation provided 19.56 % protein content, 23.95 % antioxidant activity and 125.89 mg/100 g total phenols with overall sensory acceptability scores 8.71.

Multidisciplinary Inverse Reliability Analysis Based on Collaborative Optimization with Combination of Linear Approximations

Directory of Open Access Journals (Sweden)

Xin-Jia Meng

2015-01-01

Full Text Available Multidisciplinary reliability is an important part of the reliability-based multidisciplinary design optimization (RBMDO. However, it usually has a considerable amount of calculation. The purpose of this paper is to improve the computational efficiency of multidisciplinary inverse reliability analysis. A multidisciplinary inverse reliability analysis method based on collaborative optimization with combination of linear approximations (CLA-CO is proposed in this paper. In the proposed method, the multidisciplinary reliability assessment problem is first transformed into a problem of most probable failure point (MPP search of inverse reliability, and then the process of searching for MPP of multidisciplinary inverse reliability is performed based on the framework of CLA-CO. This method improves the MPP searching process through two elements. One is treating the discipline analyses as the equality constraints in the subsystem optimization, and the other is using linear approximations corresponding to subsystem responses as the replacement of the consistency equality constraint in system optimization. With these two elements, the proposed method realizes the parallel analysis of each discipline, and it also has a higher computational efficiency. Additionally, there are no difficulties in applying the proposed method to problems with nonnormal distribution variables. One mathematical test problem and an electronic packaging problem are used to demonstrate the effectiveness of the proposed method.

Coupled multiscale simulation and optimization in nanoelectronics

CERN Document Server

2015-01-01

Designing complex integrated circuits relies heavily on mathematical methods and calls for suitable simulation and optimization tools. The current design approach involves simulations and optimizations in different physical domains (device, circuit, thermal, electromagnetic) and in a range of electrical engineering disciplines (logic, timing, power, crosstalk, signal integrity, system functionality). COMSON was a Marie Curie Research Training Network created to meet these new scientific and training challenges by (a) developing new descriptive models that take these mutual dependencies into account, (b) combining these models with existing circuit descriptions in new simulation strategies, and (c) developing new optimization techniques that will accommodate new designs. The book presents the main project results in the fields of PDAE modeling and simulation, model order reduction techniques and optimization, based on merging the know-how of three major European semiconductor companies with the combined expe...

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease.

Science.gov (United States)

Shamir, Reuben R; Dolber, Trygve; Noecker, Angela M; Walter, Benjamin L; McIntyre, Cameron C

2015-01-01

Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization. Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication. Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: (1) information retrieval; (2) visualization of treatment, and; (3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest. Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery. Using patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients. Copyright © 2015 Elsevier Inc. All rights reserved.

Simulation of neuro-fuzzy model for optimization of combine header setting

Directory of Open Access Journals (Sweden)

S Zareei

2016-09-01

Full Text Available Introduction The noticeable proportion of producing wheat losses occur during production and consumption steps and the loss due to harvesting with combine harvester is regarded as one of the main factors. A grain combines harvester consists of different sets of equipment and one of the most important parts is the header which comprises more than 50% of the entire harvesting losses. Some researchers have presented regression equation to estimate grain loss of combine harvester. The results of their study indicated that grain moisture content, reel index, cutter bar speed, service life of cutter bar, tine spacing, tine clearance over cutter bar, stem length were the major parameters affecting the losses. On the other hand, there are several researchswhich have used the variety of artificial intelligence methods in the different aspects of combine harvester. In neuro-fuzzy control systems, membership functions and if-then rules were defined through neural networks. Sugeno- type fuzzy inference model was applied to generate fuzzy rules from a given input-output data set due to its less time-consuming and mathematically tractable defuzzification operation for sample data-based fuzzy modeling. In this study, neuro-fuzzy model was applied to develop forecasting models which can predict the combine header loss for each set of the header parameter adjustments related to site-specific information and therefore can minimize the header loss. Materials and Methods The field experiment was conducted during the harvesting season of 2011 at the research station of the Faulty of Agriculture, Shiraz University, Shiraz, Iran. The wheat field (CV. Shiraz was harvested with a Claas Lexion-510 combine harvester. The factors which were selected as main factors influenced the header performance were three levels of reel index (RI (forward speed of combine harvester divided by peripheral speed of reel (1, 1.2, 1.5, three levels of cutting height (CH(25, 30, 35 cm, three

Research on hybrid transmission mode for HVDC with optimal thermal power and renewable energy combination

Science.gov (United States)

Zhang, Jinfang; Yan, Xiaoqing; Wang, Hongfu

2018-02-01

With the rapid development of renewable energy in Northwest China, curtailment phenomena is becoming more and more serve owing to lack of adjustment ability and enough transmission capacity. Based on the existing HVDC projects, exploring the hybrid transmission mode associated with thermal power and renewable power will be necessary and important. This paper has proposed a method on optimal thermal power and renewable energy combination for HVDC lines, based on multi-scheme comparison. Having established the mathematic model for electric power balance in time series mode, ten different schemes have been picked for figuring out the suitable one by test simulation. By the proposed related discriminated principle, including generation device utilization hours, renewable energy electricity proportion and curtailment level, the recommendation scheme has been found. The result has also validated the efficiency of the method.

A Local and Global Search Combine Particle Swarm Optimization Algorithm for Job-Shop Scheduling to Minimize Makespan

Directory of Open Access Journals (Sweden)

Zhigang Lian

2010-01-01

Full Text Available The Job-shop scheduling problem (JSSP is a branch of production scheduling, which is among the hardest combinatorial optimization problems. Many different approaches have been applied to optimize JSSP, but for some JSSP even with moderate size cannot be solved to guarantee optimality. The original particle swarm optimization algorithm (OPSOA, generally, is used to solve continuous problems, and rarely to optimize discrete problems such as JSSP. In OPSOA, through research I find that it has a tendency to get stuck in a near optimal solution especially for middle and large size problems. The local and global search combine particle swarm optimization algorithm (LGSCPSOA is used to solve JSSP, where particle-updating mechanism benefits from the searching experience of one particle itself, the best of all particles in the swarm, and the best of particles in neighborhood population. The new coding method is used in LGSCPSOA to optimize JSSP, and it gets all sequences are feasible solutions. Three representative instances are made computational experiment, and simulation shows that the LGSCPSOA is efficacious for JSSP to minimize makespan.

Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimization Model for Stand-Alone Microgrid Operation

Directory of Open Access Journals (Sweden)

Fei Wang

2017-11-01

Full Text Available The optimal dispatching model for a stand-alone microgrid (MG is of great importance to its operation reliability and economy. This paper aims at addressing the difficulties in improving the operational economy and maintaining the power balance under uncertain load demand and renewable generation, which could be even worse in such abnormal conditions as storms or abnormally low or high temperatures. A new two-time scale multi-objective optimization model, including day-ahead cursory scheduling and real-time scheduling for finer adjustments, is proposed to optimize the operational cost, load shedding compensation and environmental benefit of stand-alone MG through controllable load (CL and multi-distributed generations (DGs. The main novelty of the proposed model is that the synergetic response of CL and energy storage system (ESS in real-time scheduling offset the operation uncertainty quickly. And the improved dispatch strategy for combined cooling-heating-power (CCHP enhanced the system economy while the comfort is guaranteed. An improved algorithm, Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy (SIP-CO-PSO-ERS algorithm with strong searching capability and fast convergence speed, was presented to deal with the problem brought by the increased errors between actual renewable generation and load and prior predictions. Four typical scenarios are designed according to the combinations of day types (work day or weekend and weather categories (sunny or rainy to verify the performance of the presented dispatch strategy. The simulation results show that the proposed two-time scale model and SIP-CO-PSO-ERS algorithm exhibit better performance in adaptability, convergence speed and search ability than conventional methods for the stand-alone MG’s operation.

An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

Energy Technology Data Exchange (ETDEWEB)

Lu, Songfeng; Sun, Chengfu; Lu, Zhengding [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China)

2010-03-15

This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms. (author)

An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

Energy Technology Data Exchange (ETDEWEB)

Lu Songfeng [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Sun Chengfu, E-mail: ajason_369@sina.co [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Lu Zhengding [School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China)

2010-03-15

This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.

An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

International Nuclear Information System (INIS)

Lu Songfeng; Sun Chengfu; Lu Zhengding

2010-01-01

This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (i) minimizing fuel cost and (ii) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.

Pharmacodynamically optimized erythropoietin treatment combined with phlebotomy reduction predicted to eliminate blood transfusions in selected preterm infants.

Science.gov (United States)

Rosebraugh, Matthew R; Widness, John A; Nalbant, Demet; Cress, Gretchen; Veng-Pedersen, Peter

2014-02-01

Preterm very-low-birth-weight (VLBW) infants weighing eliminated by reducing laboratory blood loss in combination with pharmacodynamically optimized erythropoietin (Epo) treatment. Twenty-six VLBW ventilated infants receiving RBCTx were studied during the first month of life. RBCTx simulations were based on previously published RBCTx criteria and data-driven Epo pharmacodynamic optimization of literature-derived RBC life span and blood volume data corrected for phlebotomy loss. Simulated pharmacodynamic optimization of Epo administration and reduction in phlebotomy by ≥ 55% predicted a complete elimination of RBCTx in 1.0-1.5 kg infants. In infants 1.0 kg.

Anatomy-based inverse optimization in high-dose-rate brachytherapy combined with hypofractionated external beam radiotherapy for localized prostate cancer: Comparison of incidence of acute genitourinary toxicity between anatomy-based inverse optimization and geometric optimization

International Nuclear Information System (INIS)

Akimoto, Tetsuo; Katoh, Hiroyuki; Kitamoto, Yoshizumi; Shirai, Katsuyuki; Shioya, Mariko; Nakano, Takashi

2006-01-01

Purpose: To evaluate the advantages of anatomy-based inverse optimization (IO) in planning high-dose-rate (HDR) brachytherapy. Methods and Materials: A total of 114 patients who received HDR brachytherapy (9 Gy in two fractions) combined with hypofractionated external beam radiotherapy (EBRT) were analyzed. The dose distributions of HDR brachytherapy were optimized using geometric optimization (GO) in 70 patients and by anatomy-based IO in the remaining 44 patients. The correlation between the dose-volume histogram parameters, including the urethral dose and the incidence of acute genitourinary (GU) toxicity, was evaluated. Results: The averaged values of the percentage of volume receiving 80-150% of the prescribed minimal peripheral dose (V 8 -V 15 ) of the urethra generated by anatomy-based IO were significantly lower than the corresponding values generated by GO. Similarly, the averaged values of the minimal dose received by 5-50% of the target volume (D 5 -D 5 ) obtained using anatomy-based IO were significantly lower than those obtained using GO. Regarding acute toxicity, Grade 2 or worse acute GU toxicity developed in 23% of all patients, but was significantly lower in patients for whom anatomy-based IO (16%) was used than in those for whom GO was used (37%), consistent with the reduced urethral dose (p <0.01). Conclusion: The results of this study suggest that anatomy-based IO is superior to GO for dose optimization in HDR brachytherapy for prostate cancer

Optimal Combination of VNTR Typing for Discrimination of Isolated Mycobacterium tuberculosis in Korea

OpenAIRE

Lee, Jihye; Kang, Heeyoon; Kim, Sarang; Yoo, Heekyung; Kim, Hee Jin; Park, Young Kil

2014-01-01

Background Variable-number tandem repeat (VNTR) typing is a promising method to discriminate the Mycobacterium tuberculosis isolates in molecular epidemiology. The purpose of this study is to determine the optimal VNTR combinations for discriminating isolated M. tuberculosis strains in Korea. Methods A total of 317 clinical isolates collected throughout Korea were genotyped by using the IS6110 restriction fragment length polymorphism (RFLP), and then analysed for the number of VNTR copies fro...

Exergy, exergoeconomic and environmental analyses and evolutionary algorithm based multi-objective optimization of combined cycle power plants

International Nuclear Information System (INIS)

Ahmadi, Pouria; Dincer, Ibrahim; Rosen, Marc A.

2011-01-01

A comprehensive exergy, exergoeconomic and environmental impact analysis and optimization is reported of several combined cycle power plants (CCPPs). In the first part, thermodynamic analyses based on energy and exergy of the CCPPs are performed, and the effect of supplementary firing on the natural gas-fired CCPP is investigated. The latter step includes the effect of supplementary firing on the performance of bottoming cycle and CO 2 emissions, and utilizes the first and second laws of thermodynamics. In the second part, a multi-objective optimization is performed to determine the 'best' design parameters, accounting for exergetic, economic and environmental factors. The optimization considers three objective functions: CCPP exergy efficiency, total cost rate of the system products and CO 2 emissions of the overall plant. The environmental impact in terms of CO 2 emissions is integrated with the exergoeconomic objective function as a new objective function. The results of both exergy and exergoeconomic analyses show that the largest exergy destructions occur in the CCPP combustion chamber, and that increasing the gas turbine inlet temperature decreases the CCPP cost of exergy destruction. The optimization results demonstrates that CO 2 emissions are reduced by selecting the best components and using a low fuel injection rate into the combustion chamber. -- Highlights: → Comprehensive thermodynamic modeling of a combined cycle power plant. → Exergy, economic and environmental analyses of the system. → Investigation of the role of multiobjective exergoenvironmental optimization as a tool for more environmentally-benign design.

Optimal Solubility of Diclofenac β-Cyclodextrin in Combination with Local Anaesthetics for Mesotherapy Applications.

Science.gov (United States)

Tringali, Giuseppe; Navarra, Pierluigi

2017-01-01

Because of low injection volume, the recently marketed injectable solution of diclofenac in complex with β -cyclodextrin (Akis®, IBSA Farmaceutici Italia) is an ideal candidate for mesotherapy applications. In this study, we investigated the solubility of Akis, 25 and 50 mg/kg, in combination with various local anaesthetics (lidocaine, mepivacaine, bupivacaine, levobupivacaine, and ropivacaine) at different concentrations in aqueous vehicles (normal saline, sterile water, or bicarbonate). Final injection mixtures were classified as limpid, turbid, or milky at visual analysis under standardized conditions. We found that (i) the use of sterile water for injections or normal saline as vehicles to dilute Akis in combination with whatever local anaesthetic normally results in milky solutions and therefore is not recommended; (ii) using bicarbonate, optimal solubility was obtained combining Akis with lidocaine, both 1 and 2%, or mepivacaine, both 1 and 2%, whereas solutions were turbid in combination with bupivacaine, levobupivacaine, or ropivacaine. Thus, we recommend that Akis is used in combination with lidocaine or mepivacaine in a bicarbonate vehicle.

Optimal Solubility of Diclofenac β-Cyclodextrin in Combination with Local Anaesthetics for Mesotherapy Applications

Directory of Open Access Journals (Sweden)

Giuseppe Tringali

2017-01-01

Full Text Available Because of low injection volume, the recently marketed injectable solution of diclofenac in complex with β-cyclodextrin (Akis®, IBSA Farmaceutici Italia is an ideal candidate for mesotherapy applications. In this study, we investigated the solubility of Akis, 25 and 50 mg/kg, in combination with various local anaesthetics (lidocaine, mepivacaine, bupivacaine, levobupivacaine, and ropivacaine at different concentrations in aqueous vehicles (normal saline, sterile water, or bicarbonate. Final injection mixtures were classified as limpid, turbid, or milky at visual analysis under standardized conditions. We found that (i the use of sterile water for injections or normal saline as vehicles to dilute Akis in combination with whatever local anaesthetic normally results in milky solutions and therefore is not recommended; (ii using bicarbonate, optimal solubility was obtained combining Akis with lidocaine, both 1 and 2%, or mepivacaine, both 1 and 2%, whereas solutions were turbid in combination with bupivacaine, levobupivacaine, or ropivacaine. Thus, we recommend that Akis is used in combination with lidocaine or mepivacaine in a bicarbonate vehicle.

Comparative evaluation of various optimization methods and the development of an optimization code system SCOOP

International Nuclear Information System (INIS)

Suzuki, Tadakazu

1979-11-01

Thirty two programs for linear and nonlinear optimization problems with or without constraints have been developed or incorporated, and their stability, convergence and efficiency have been examined. On the basis of these evaluations, the first version of the optimization code system SCOOP-I has been completed. The SCOOP-I is designed to be an efficient, reliable, useful and also flexible system for general applications. The system enables one to find global optimization point for a wide class of problems by selecting the most appropriate optimization method built in it. (author)

Optimal Energy Management of Combined Cooling, Heat and Power in Different Demand Type Buildings Considering Seasonal Demand Variations

Directory of Open Access Journals (Sweden)

Akhtar Hussain

2017-06-01

Full Text Available In this paper, an optimal energy management strategy for a cooperative multi-microgrid system with combined cooling, heat and power (CCHP is proposed and has been verified for a test case of building microgrids (BMGs. Three different demand types of buildings are considered and the BMGs are assumed to be equipped with their own combined heat and power (CHP generators. In addition, the BMGs are also connected to an external energy network (EEN, which contains a large CHP, an adsorption chiller (ADC, a thermal storage tank, and an electric heat pump (EHP. By trading the excess electricity and heat energy with the utility grid and EEN, each BMG can fulfill its energy demands. Seasonal energy demand variations have been evaluated by selecting a representative day for the two extreme seasons (summer and winter of the year, among the real profiles of year-round data on electricity, heating, and cooling usage of all the three selected buildings. Especially, the thermal energy management aspect is emphasized where, bi-lateral heat trading between the energy supplier and the consumers, so-called energy prosumer concept, has been realized. An optimization model based on mixed integer linear programming has been developed for minimizing the daily operation cost of the EEN while fulfilling the energy demands of the BMGs. Simulation results have demonstrated the effectiveness of the proposed strategy.

Optimization in underground mine planning - developments and opportunities

OpenAIRE

Musingwini, C.

2016-01-01

The application of mining-specific and generic optimization techniques in the mining industry is deeply rooted in the discipline of operations research (OR). OR has its origins in the British Royal Air Force and Army around the early 1930s. Its development continued during and after World War II. The application of OR techniques to optimization in the mining industry started to emerge in the early 1960s. Since then, optimization techniques have been applied to solve widely different mine plan...

An interconnecting bus power optimization method combining interconnect wire spacing with wire ordering

International Nuclear Information System (INIS)

Zhu Zhang-Ming; Hao Bao-Tian; En Yun-Fei; Yang Yin-Tang; Li Yue-Jin

2011-01-01

On-chip interconnect buses consume tens of percents of dynamic power in a nanometer scale integrated circuit and they will consume more power with the rapid scaling down of technology size and continuously rising clock frequency, therefore it is meaningful to lower the interconnecting bus power in design. In this paper, a simple yet accurate interconnect parasitic capacitance model is presented first and then, based on this model, a novel interconnecting bus optimization method is proposed. Wire spacing is a process for spacing wires for minimum dynamic power, while wire ordering is a process that searches for wire orders that maximally enhance it. The method, i.e., combining wire spacing with wire ordering, focuses on bus dynamic power optimization with a consideration of bus performance requirements. The optimization method is verified based on various nanometer technology parameters, showing that with 50% slack of routing space, 25.71% and 32.65% of power can be saved on average by the proposed optimization method for a global bus and an intermediate bus, respectively, under a 65-nm technology node, compared with 21.78% and 27.68% of power saved on average by uniform spacing technology. The proposed method is especially suitable for computer-aided design of nanometer scale on-chip buses. (interdisciplinary physics and related areas of science and technology)

Optimization of wind turbine rotors - using advanced aerodynamic and aeroelastic models and numerical optimization

DEFF Research Database (Denmark)

Døssing, Mads

of very large machines introduces new problems in the practical design, and optimization tools are necessary. These must combine the dynamic eects of both aerodynamics and structure in an integrated optimization environment. This is referred to as aeroelastic optimization. The Ris DTU optimization...... software HAWTOPT has been used in this project. The quasi-steady aerodynamic module have been improved with a corrected blade element momentum method. A structure module has also been developed which lays out the blade structural properties. This is done in a simplied way allowing fast conceptual design...... studies and with focus on the overall properties relevant for the aeroelastic properties. Aeroelastic simulations in the time domain were carried out using the aeroelastic code HAWC2. With these modules coupled to HAWTOPT, optimizations have been made. In parallel with the developments of the mentioned...

Optimization Models and Methods Developed at the Energy Systems Institute

OpenAIRE

N.I. Voropai; V.I. Zorkaltsev

2013-01-01

The paper presents shortly some optimization models of energy system operation and expansion that have been created at the Energy Systems Institute of the Siberian Branch of the Russian Academy of Sciences. Consideration is given to the optimization models of energy development in Russia, a software package intended for analysis of power system reliability, and model of flow distribution in hydraulic systems. A general idea of the optimization methods developed at the Energy Systems Institute...

Optimal operation of a micro-combined cooling, heating and power system driven by a gas engine

International Nuclear Information System (INIS)

Kong, X.Q.; Wang, R.Z.; Li, Y.; Huang, X.H.

2009-01-01

The objective of this paper is to investigate the problem of energy management and optimal operation of cogeneration system for micro-combined cooling, heating and power production (CCHP). The energy system mainly consists of a gas engine, an adsorption chiller, a gas boiler, a heat exchanger and an electric chiller. On the basis of an earlier experimental research of the micro-CCHP system, a non-linear-programming cost-minimization optimization model is presented to determine the optimum operational strategies for the system. It is shown that energy management and optimal operation of the micro-CCHP system is dependent upon load conditions to be satisfied and energy cost. In view of energy cost, it would not be optimal to operate the gas engine when the electric-to-gas cost ratio (EGCR) is very low. With higher EGCR, the optimum operational strategy of the micro-CCHP system is independent of energy cost

Landslide Susceptibility Mapping Based on Selected Optimal Combination of Landslide Predisposing Factors in a Large Catchment

Directory of Open Access Journals (Sweden)

Qianqian Wang

2015-12-01

Full Text Available Landslides are usually initiated under complex geological conditions. It is of great significance to find out the optimal combination of predisposing factors and create an accurate landslide susceptibility map based on them. In this paper, the Information Value Model was modified to make the Modified Information Value (MIV Model, and together with GIS (Geographical Information System and AUC (Area Under Receiver Operating Characteristic Curve test, 32 factor combinations were evaluated separately, and factor combination group with members Slope, Lithology, Drainage network, Annual precipitation, Faults, Road and Vegetation was selected as the optimal combination group with an accuracy of 95.0%. Based on this group, a landslide susceptibility zonation map was drawn, where the study area was reclassified into five classes, presenting an accurate description of different levels of landslide susceptibility, with 79.41% and 13.67% of the validating field survey landslides falling in the Very High and High zones, respectively, mainly distributed in the south and southeast of the catchment. It showed that MIV model can tackle the problem of “no data in subclass” well, generate the true information value and show real running trend, which performs well in showing the relationship between predisposing factors and landslide occurrence and can be used for preliminary landslide susceptibility assessment in the study area.

Analysis of combination drug therapy to develop regimens with shortened duration of treatment for tuberculosis.

Directory of Open Access Journals (Sweden)

George L Drusano

Full Text Available Tuberculosis remains a worldwide problem, particularly with the advent of multi-drug resistance. Shortening therapy duration for Mycobacterium tuberculosis is a major goal, requiring generation of optimal kill rate and resistance-suppression. Combination therapy is required to attain the goal of shorter therapy.Our objective was to identify a method for identifying optimal combination chemotherapy. We developed a mathematical model for attaining this end. This is accomplished by identifying drug effect interaction (synergy, additivity, antagonism for susceptible organisms and subpopulations resistant to each drug in the combination.We studied the combination of linezolid plus rifampin in our hollow fiber infection model. We generated a fully parametric drug effect interaction mathematical model. The results were subjected to Monte Carlo simulation to extend the findings to a population of patients by accounting for between-patient variability in drug pharmacokinetics.All monotherapy allowed emergence of resistance over the first two weeks of the experiment. In combination, the interaction was additive for each population (susceptible and resistant. For a 600 mg/600 mg daily regimen of linezolid plus rifampin, we demonstrated that >50% of simulated subjects had eradicated the susceptible population by day 27 with the remaining organisms resistant to one or the other drug. Only 4% of patients had complete organism eradication by experiment end.These data strongly suggest that in order to achieve the goal of shortening therapy, the original regimen may need to be changed at one month to a regimen of two completely new agents with resistance mechanisms independent of the initial regimen. This hypothesis which arose from the analysis is immediately testable in a clinical trial.

Development of a fuzzy logic method to build objective functions in optimization problems: application to BWR fuel lattice design

International Nuclear Information System (INIS)

Martin-del-Campo, C.; Francois, J.L.; Barragan, A.M.; Palomera, M.A.

2005-01-01

In this paper we develop a methodology based on the use of the Fuzzy Logic technique to build multi-objective functions to be used in optimization processes applied to in-core nuclear fuel management. As an example, we selected the problem of determining optimal radial fuel enrichment and gadolinia distributions in a typical 'Boiling Water Reactor (BWR)' fuel lattice. The methodology is based on the use of the mathematical capability of Fuzzy Logic to model nonlinear functions of arbitrary complexity. The utility of Fuzzy Logic is to map an input space into an output space, and the primary mechanism for doing this is a list of if-then statements called rules. The rules refer to variables and adjectives that describe those variables and, the Fuzzy Logic technique interprets the values in the input vectors and, based on the set of rules assigns values to the output vector. The methodology was developed for the radial optimization of a BWR lattice where the optimization algorithm employed is Tabu Search. The global objective is to find the optimal distribution of enrichments and burnable poison concentrations in a 10*10 BWR lattice. In order to do that, a fuzzy control inference system was developed using the Fuzzy Logic Toolbox of Matlab and it has been linked to the Tabu Search optimization process. Results show that Tabu Search combined with Fuzzy Logic performs very well, obtaining lattices with optimal fuel utilization. (authors)

Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

Science.gov (United States)

Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Application of simplex-centroid mixture design to optimize stabilizer combinations for ice cream manufacture.

Science.gov (United States)

BahramParvar, Maryam; Tehrani, Mostafa Mazaheri; Razavi, Seyed M A; Koocheki, Arash

2015-03-01

This study aimed to obtain the optimum formulation for stabilizers in ice cream that could contest with blends presented nowadays. Thus, different mixtures of three stabilizers, i.e. basil seed gum, carboxymethyl cellulose, and guar gum, at two concentrations (0.15 % & 0.35 %) were studied using mixture design methodology. The influence of these mixtures on some properties of ice cream and the regression models for them were also determined. Generally, high ratios of basil seed gum in mixture developed the apparent viscosity of ice cream mixes and decreased the melting rate. Increasing proportion of this stabilizer as well as guar gum in the mixtures at concentration of 0.15 % enhanced the overrun of samples. Based on the optimization criteria, the most excellent combination was 84.43 % basil seed gum and 15.57 % guar gum at concentration of 0.15 %. This research proved the capability of basil seed gum as a novel stabilizer in ice cream stabilization.

Combined treatment: impact of optimal psychotherapy and medication in bipolar disorder.

Science.gov (United States)

Parikh, Sagar V; Hawke, Lisa D; Velyvis, Vytas; Zaretsky, Ari; Beaulieu, Serge; Patelis-Siotis, Irene; MacQueen, Glenda; Young, L Trevor; Yatham, Lakshmi N; Cervantes, Pablo

2015-02-01

The current study investigated the longitudinal course of symptoms in bipolar disorder among individuals receiving optimal treatment combining pharmacotherapy and psychotherapy, as well as predictors of the course of illness. A total of 160 participants with bipolar disorder (bipolar I disorder: n = 115; bipolar II disorder: n = 45) received regular pharmacological treatment, complemented by a manualized, evidence-based psychosocial treatment - that is, cognitive behavioral therapy or psychoeducation. Participants were assessed at baseline and prospectively for 72 weeks using the Longitudinal Interval Follow-up Evaluation (LIFE) scale scores for mania/hypomania and depression, as well as comparison measures (clinicaltrials.gov identifier: NCT00188838). Over a 72-week period, patients spent a clear majority (about 65%) of time euthymic. Symptoms were experienced more than 50% of the time by only a quarter of the sample. Depressive symptoms strongly dominated over (hypo)manic symptoms, while subsyndromal symptoms were more common than full diagnosable episodes for both polarities. Mixed symptoms were rare, but present for a minority of participants. Individuals experienced approximately six significant mood changes per year, with a full relapse on average every 7.5 months. Participants who had fewer depressive symptoms at intake, a later age at onset, and no history of psychotic symptoms spent more weeks well over the course of the study. Combined pharmacological and adjunctive psychosocial treatments appeared to provide an improved course of illness compared to the results of previous studies. Efforts to further improve the course of illness beyond that provided by current optimal treatment regimens will require a substantial focus on both subsyndromal and syndromal depressive symptoms. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Themoeconomic optimization of triple pressure heat recovery steam generator operating parameters for combined cycle plants

Directory of Open Access Journals (Sweden)

Mohammd Mohammed S.

2015-01-01

Full Text Available The aim of this work is to develop a method for optimization of operating parameters of a triple pressure heat recovery steam generator. Two types of optimization: (a thermodynamic and (b thermoeconomic were preformed. The purpose of the thermodynamic optimization is to maximize the efficiency of the plant. The selected objective for this purpose is minimization of the exergy destruction in the heat recovery steam generator (HRSG. The purpose of the thermoeconomic optimization is to decrease the production cost of electricity. Here, the total annual cost of HRSG, defined as a sum of annual values of the capital costs and the cost of the exergy destruction, is selected as the objective function. The optimal values of the most influencing variables are obtained by minimizing the objective function while satisfying a group of constraints. The optimization algorithm is developed and tested on a case of CCGT plant with complex configuration. Six operating parameters were subject of optimization: pressures and pinch point temperatures of every three (high, intermediate and low pressure steam stream in the HRSG. The influence of these variables on the objective function and production cost are investigated in detail. The differences between results of thermodynamic and the thermoeconomic optimization are discussed.

Development and testing of new candidate psoriatic arthritis screening questionnaires combining optimal questions from existing tools.

Science.gov (United States)

Coates, Laura C; Walsh, Jessica; Haroon, Muhammad; FitzGerald, Oliver; Aslam, Tariq; Al Balushi, Farida; Burden, A D; Burden-Teh, Esther; Caperon, Anna R; Cerio, Rino; Chattopadhyay, Chandrabhusan; Chinoy, Hector; Goodfield, Mark J D; Kay, Lesley; Kelly, Stephen; Kirkham, Bruce W; Lovell, Christopher R; Marzo-Ortega, Helena; McHugh, Neil; Murphy, Ruth; Reynolds, Nick J; Smith, Catherine H; Stewart, Elizabeth J C; Warren, Richard B; Waxman, Robin; Wilson, Hilary E; Helliwell, Philip S

2014-09-01

Several questionnaires have been developed to screen for psoriatic arthritis (PsA), but head-to-head studies have found limitations. This study aimed to develop new questionnaires encompassing the most discriminative questions from existing instruments. Data from the CONTEST study, a head-to-head comparison of 3 existing questionnaires, were used to identify items with a Youden index score of ≥0.1. These were combined using 4 approaches: CONTEST (simple additions of questions), CONTESTw (weighting using logistic regression), CONTESTjt (addition of a joint manikin), and CONTESTtree (additional questions identified by classification and regression tree [CART] analysis). These candidate questionnaires were tested in independent data sets. Twelve individual questions with a Youden index score of ≥0.1 were identified, but 4 of these were excluded due to duplication and redundancy. Weighting for 2 of these questions was included in CONTESTw. Receiver operating characteristic (ROC) curve analysis showed that involvement in 6 joint areas on the manikin was predictive of PsA for inclusion in CONTESTjt. CART analysis identified a further 5 questions for inclusion in CONTESTtree. CONTESTtree was not significant on ROC curve analysis and discarded. The other 3 questionnaires were significant in all data sets, although CONTESTw was slightly inferior to the others in the validation data sets. Potential cut points for referral were also discussed. Of 4 candidate questionnaires combining existing discriminatory items to identify PsA in people with psoriasis, 3 were found to be significant on ROC curve analysis. Testing in independent data sets identified 2 questionnaires (CONTEST and CONTESTjt) that should be pursued for further prospective testing. Copyright © 2014 by the American College of Rheumatology.

Combining kernel matrix optimization and regularization to improve particle size distribution retrieval

Science.gov (United States)

Ma, Qian; Xia, Houping; Xu, Qiang; Zhao, Lei

2018-05-01

A new method combining Tikhonov regularization and kernel matrix optimization by multi-wavelength incidence is proposed for retrieving particle size distribution (PSD) in an independent model with improved accuracy and stability. In comparison to individual regularization or multi-wavelength least squares, the proposed method exhibited better anti-noise capability, higher accuracy and stability. While standard regularization typically makes use of the unit matrix, it is not universal for different PSDs, particularly for Junge distributions. Thus, a suitable regularization matrix was chosen by numerical simulation, with the second-order differential matrix found to be appropriate for most PSD types.

Optimal Control Allocation with Load Sensor Feedback for Active Load Suppression, Experiment Development

Science.gov (United States)

Miller, Christopher J.; Goodrick, Dan

2017-01-01

The problem of control command and maneuver induced structural loads is an important aspect of any control system design. The aircraft structure and the control architecture must be designed to achieve desired piloted control responses while limiting the imparted structural loads. The classical approach is to utilize high structural margins, restrict control surface commands to a limited set of analyzed combinations, and train pilots to follow procedural maneuvering limitations. With recent advances in structural sensing and the continued desire to improve safety and vehicle fuel efficiency, it is both possible and desirable to develop control architectures that enable lighter vehicle weights while maintaining and improving protection against structural damage. An optimal control technique has been explored and shown to achieve desirable vehicle control performance while limiting sensed structural loads. The subject of this paper is the design of the optimal control architecture, and provides the reader with some techniques for tailoring the architecture, along with detailed simulation results.

Optimized production of vanillin from green vanilla pods by enzyme-assisted extraction combined with pre-freezing and thawing.

Science.gov (United States)

Zhang, Yanjun; Mo, Limei; Chen, Feng; Lu, Minquan; Dong, Wenjiang; Wang, Qinghuang; Xu, Fei; Gu, Fenglin

2014-02-19

Production of vanillin from natural green vanilla pods was carried out by enzyme-assisted extraction combined with pre-freezing and thawing. In the first step the green vanilla pods were pre-frozen and then thawed to destroy cellular compartmentation. In the second step pectinase from Aspergillus niger was used to hydrolyze the pectin between the glucovanillin substrate and β-glucosidase. Four main variables, including enzyme amount, reaction temperature, time and pH, which were of significance for the vanillin content were studied and a central composite design (CCD) based on the results of a single-factor tests was used. Response surface methodology based on CCD was employed to optimize the combination of enzyme amount, reaction temperature, time, and pH for maximum vanillin production. This resulted in the optimal condition in regards of the enzyme amount, reaction temperature, time, and pH at 84.2 mg, 49.5 °C, 7.1 h, and 4.2, respectively. Under the optimal condition, the experimental yield of vanillin was 4.63% ± 0.11% (dwb), which was in good agreement with the value predicted by the model. Compared to the traditional curing process (1.98%) and viscozyme extract (2.36%), the optimized method for the vanillin production significantly increased the yield by 133.85% and 96%, respectively.

How should immunomodulators be optimized when used as combination therapy with anti-tumor necrosis factor agents in the management of inflammatory bowel disease?

Science.gov (United States)

Ward, Mark G; Irving, Peter M; Sparrow, Miles P

2015-10-28

In the last 15 years the management of inflammatory bowel disease has evolved greatly, largely through the increased use of immunomodulators and, especially, anti-tumor necrosis factor (anti-TNF) biologic agents. Within this time period, confidence in the use of anti-TNFs has increased, whilst, especially in recent years, the efficacy and safety of thiopurines has been questioned. Yet despite recent concerns regarding the risk: benefit profile of thiopurines, combination therapy with an immunomodulator and an anti-TNF has emerged as the recommended treatment strategy for the majority of patients with moderate-severe disease, especially those who are recently diagnosed. Concurrently, therapeutic drug monitoring has emerged as a means of optimizing the dosage of both immunomodulators and anti-TNFs. However the recommended therapeutic target levels for both drug classes were largely derived from studies of monotherapy with either agent, or studies underpowered to analyze outcomes in combination therapy patients. It has been assumed that these target levels are applicable to patients on combination therapy also, however there are few data to support this. Similarly, the timing and duration of treatment with immunomodulators when used in combination therapy remains unknown. Recent attention, including post hoc analyses of the pivotal registration trials, has focused on the optimization of anti-TNF agents, when used as either monotherapy or combination therapy. This review will instead focus on how best to optimize immunomodulators when used in combination therapy, including an evaluation of recent data addressing unanswered questions regarding the optimal timing, dosage and duration of immunomodulator therapy in combination therapy patients.

Experience of web-complex development of NPP thermophysical optimization

International Nuclear Information System (INIS)

Nikolaev, M.A.

2014-01-01

Current state of developing computation web complex (CWC) of thermophysical optimization of nuclear power plants is described. Main databases of CWC is realized on the MySQL platform. CWC information architecture, its functionality, optimization algorithms and CWC user interface are under consideration [ru

Risk Assessment and Optimization for New or Novel Processes: Combining task analysis with 4D process simulation-framework and case study.

OpenAIRE

Leva, Maria Chiara; Gerbec, Marko; Balfe, Nora; Demichela, Micaela

2015-01-01

This paper describes work undertaken as part of the TOSCA project to develop approaches in-tegrating and enhancing safety, quality and productivity. The work reported here combines two existing ap-proaches: task analysis and 4D process simulation to model tasks in a 3D environment, thus creating a 4D model. The 4D model is next used for safety analysis (e.g., HAZOP a structured Hazid study) and optimiza-tion (e.g., Pareto type). The approach is demonstrated on an industrial case study involvi...

Solving Optimization Problems via Vortex Optimization Algorithm and Cognitive Development Optimization Algorithm

OpenAIRE

Ahmet Demir; Utku kose

2017-01-01

In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an...

Evidence-Based Design of Fixed-Dose Combinations: Principles and Application to Pediatric Anti-Tuberculosis Therapy.

Science.gov (United States)

Svensson, Elin M; Yngman, Gunnar; Denti, Paolo; McIlleron, Helen; Kjellsson, Maria C; Karlsson, Mats O

2018-05-01

Fixed-dose combination formulations where several drugs are included in one tablet are important for the implementation of many long-term multidrug therapies. The selection of optimal dose ratios and tablet content of a fixed-dose combination and the design of individualized dosing regimens is a complex task, requiring multiple simultaneous considerations. In this work, a methodology for the rational design of a fixed-dose combination was developed and applied to the case of a three-drug pediatric anti-tuberculosis formulation individualized on body weight. The optimization methodology synthesizes information about the intended use population, the pharmacokinetic properties of the drugs, therapeutic targets, and practical constraints. A utility function is included to penalize deviations from the targets; a sequential estimation procedure was developed for stable estimation of break-points for individualized dosing. The suggested optimized pediatric anti-tuberculosis fixed-dose combination was compared with the recently launched World Health Organization-endorsed formulation. The optimized fixed-dose combination included 15, 36, and 16% higher amounts of rifampicin, isoniazid, and pyrazinamide, respectively. The optimized fixed-dose combination is expected to result in overall less deviation from the therapeutic targets based on adult exposure and substantially fewer children with underexposure (below half the target). The development of this design tool can aid the implementation of evidence-based formulations, integrating available knowledge and practical considerations, to optimize drug exposures and thereby treatment outcomes.

Novel, cost-effective configurations of combined power plants for small-scale cogeneration from biomass: Feasibility study and performance optimization

International Nuclear Information System (INIS)

Amirante, Riccardo; Tamburrano, Paolo

2015-01-01

Highlights: • A cheap small combined cycle for cogeneration from biomass is proposed. • An optimization procedure is utilized to explore its potential. • Two configurations employing two different heat exchangers are considered. • The maximum electrical efficiency is 25%, the maximum overall efficiency is 70%. • The operation in load following mode is effective for both configurations. - Abstract: The aim of this paper is to demonstrate that, thanks to recent advances in designing micro steam expanders and gas to gas heat exchangers, the use of small combined cycles for simultaneous generation of heat and power from the external combustion of solid biomass and low quality biofuels is feasible. In particular, a novel typology of combined cycle that has the potential both to be cost-effective and to achieve a high level of efficiency is presented. In the small combined cycle proposed, a commercially available micro-steam turbine is utilized as the steam expander of the bottoming cycle, while the conventional microturbine of the topping cycle is replaced by a cheaper automotive turbocharger. The feasibility, reliability and availability of the required mechanical and thermal components are thoroughly investigated. In order to explore the potential of such a novel typology of power plant, an optimization procedure, based on a genetic algorithm combined with a computing code, is utilized to analyze the trade-off between the maximization of the electrical efficiency and the maximization of the thermal efficiency. Two design optimizations are performed: the first one makes use of the innovative “Immersed Particle Heat Exchanger”, whilst a nickel alloy heat exchanger is used in the other one. After selecting the optimum combination of the design parameters, the operation in load following mode is also assessed for both configurations

Financial stability, wealth effects and optimal macroeconomic policy combination in the United Kingdom: A new-Keynesian dynamic stochastic general equilibrium framework

Directory of Open Access Journals (Sweden)

Muhammad Ali Nasir

2016-12-01

Full Text Available This study derives an optimal macroeconomic policy combination for financial sector stability in the United Kingdom by employing a New Keynesian Dynamic Stochastic General Equilibrium (NK-DSGE framework. The empirical results obtained show that disciplined fiscal and accommodative monetary policies stance is optimal for financial sector stability. Furthermore, fiscal indiscipline countered by contractionary monetary stance adversely affects financial sector stability. Financial markets, e.g. stocks and Gilts show a short-term asymmetric response to macroeconomic policy interaction and to each other. The asymmetry is a reflection of portfolio adjustment. However in the long-run, the responses to suggested optimal policy combination had homogenous effects and there was evidence of co-movement in the stock and Gilt markets.

Applicability of initial optimal maternal and fetal electrocardiogram combination vectors to subsequent recordings

International Nuclear Information System (INIS)

Yan Hua-Wen; Huang Xiao-Lin; Zhao Ying; Si Jun-Feng; Liu Hong-Xing; Liu Tie-Bing

2014-01-01

A series of experiments are conducted to confirm whether the vectors calculated for an early section of a continuous non-invasive fetal electrocardiogram (fECG) recording can be directly applied to subsequent sections in order to reduce the computation required for real-time monitoring. Our results suggest that it is generally feasible to apply the initial optimal maternal and fetal ECG combination vectors to extract the fECG and maternal ECG in subsequent recorded sections. (interdisciplinary physics and related areas of science and technology)

Recent developments of discrete material optimization of laminated composite structures

DEFF Research Database (Denmark)

Lund, Erik; Sørensen, Rene

2015-01-01

This work will give a quick summary of recent developments of the Discrete Material Optimization approach for structural optimization of laminated composite structures. This approach can be seen as a multi-material topology optimization approach for selecting the best ply material and number...... of plies in a laminated composite structure. The conceptual combinatorial design problem is relaxed to a continuous problem such that well-established gradient based optimization techniques can be applied, and the optimization problem is solved on basis of interpolation schemes with penalization...

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization

Science.gov (United States)

Li, Zukui; Ding, Ran; Floudas, Christodoulos A.

2011-01-01

Robust counterpart optimization techniques for linear optimization and mixed integer linear optimization problems are studied in this paper. Different uncertainty sets, including those studied in literature (i.e., interval set; combined interval and ellipsoidal set; combined interval and polyhedral set) and new ones (i.e., adjustable box; pure ellipsoidal; pure polyhedral; combined interval, ellipsoidal, and polyhedral set) are studied in this work and their geometric relationship is discussed. For uncertainty in the left hand side, right hand side, and objective function of the optimization problems, robust counterpart optimization formulations induced by those different uncertainty sets are derived. Numerical studies are performed to compare the solutions of the robust counterpart optimization models and applications in refinery production planning and batch process scheduling problem are presented. PMID:21935263

An application of the Proper Orthogonal Decomposition method to the thermo-economic optimization of a dual pressure, combined cycle powerplant

International Nuclear Information System (INIS)

Melli, Roberto; Sciubba, Enrico; Toro, Claudia

2014-01-01

Highlights: • The CCGT is modelled and simulated in CAMEL-Pro. • Economic costs of the system product are computed. • The POD–RBF procedure is applied to the thermoeconomic optimization of a CCGT power plant. • Economic optimal configuration is identified with POD–RBF procedure. - Abstract: This paper presents a thermo-economic optimization of a combined cycle power plant obtained via the Proper Orthogonal Decomposition–Radial Basis Functions (POD–RBF) procedure. POD, also known as “Karhunen–Loewe decomposition” or as “Method of Snapshots” is a powerful mathematical method for the low-order approximation of highly dimensional processes for which a set of initial data is known in the form of a discrete and finite set of experimental (or simulated) data: the procedure consists in constructing an approximated representation of a matricial operator that optimally “represents” the original data set on the basis of the eigenvalues and eigenvectors of the properly re-assembled data set. By combining POD and RBF it is possible to construct, by interpolation, a functional (parametric) approximation of such a representation. In this paper the set of starting data for the POD–RBF procedure has been obtained by the CAMEL-Pro™ process simulator. The proposed procedure does not require the generation of a complete simulated set of results at each iteration step of the optimization, because POD constructs a very accurate approximation to the function described by a relatively small number of initial simulations, and thus “new” points in design space can be extrapolated without recurring to additional and expensive process simulations. Thus, the often taxing computational effort needed to iteratively generate numerical process simulations of incrementally different configurations is substantially reduced by replacing much of it by easy-to-perform matrix operations. The object of the study was a fossil-fuelled, combined cycle powerplant of

Optimal Isolation Control of Three-Port Active Converters as a Combined Charger for Electric Vehicles

Directory of Open Access Journals (Sweden)

Zhixiang Ling

2016-09-01

Full Text Available The three-port converter has three H-bridge ports that can interface with three different energy sources and offers the advantages of flexible power transmission, galvanic isolation ability and high power density. The three-port full-bridge converter can be used in electric vehicles as a combined charger that consists of a battery charger and a DC-DC converter. Power transfer occurs between two ports while the third port is isolated, i.e., the average power is zero. The purpose of this paper is to apply an optimal phase shift strategy in isolation control and provide a detailed comparison between traditional phase shift control and optimal phase shift control under the proposed isolation control scheme, including comparison of the zero-voltage-switching range and the root mean square current for the two methods. Based on this analysis, the optimal parameters are selected. The results of simulations and experiments are given to verify the advantages of dual-phase-shift control in isolation control.

Stiffened Composite Fuselage Barrel Optimization

Science.gov (United States)

Movva, R. G.; Mittal, A.; Agrawal, K.; Upadhyay, C. S.

2012-07-01

In a typical commercial transport aircraft, Stiffened skin panels and frames contribute around 40% of the fuselage weight. In the current study a stiffened composite fuselage skin panel optimization engine is developed for optimization of the layups of composite panels and stringers using Genetic Algorithm (GA). The skin and stringers of the fuselage section are optimized for the strength and the stability requirements. The selection of the GA parameters considered for the optimization is arrived by performing case studies on selected problems. The optimization engine facilitates in carrying out trade studies for selection of the optimum ply layup and material combination for the configuration being analyzed. The optimization process is applied on a sample model and the results are presented.

Development of Ultraviolet (UV) Radiation Protective Fabric Using Combined Electrospinning and Electrospraying Technique

Science.gov (United States)

Sinha, Mukesh Kumar; Das, B. R.; Kumar, Kamal; Kishore, Brij; Prasad, N. Eswara

2017-06-01

The article reports a novel technique for functionization of nanoweb to develop ultraviolet (UV) radiation protective fabric. UV radiation protection effect is produced by combination of electrospinning and electrospraying technique. A nanofibrous web of polyvinylidene difluoride (PVDF) coated on polypropylene nonwoven fabric is produced by latest nanospider technology. Subsequently, web is functionalized by titanium dioxide (TiO2). The developed web is characterized for evaluation of surface morphology and other functional properties; mechanical, chemical, crystalline and thermal. An optimal (judicious) nanofibre spinning condition is achieved and established. The produced web is uniformly coated by defect free functional nanofibres in a continuous form of useable textile structural membrane for ultraviolet (UV) protective clothing. This research initiative succeeds in preparation and optimization of various nanowebs for UV protection. Field Emission Scanning Electron Microscope (FESEM) result reveals that PVDF webs photo-degradative behavior is non-accelerated, as compared to normal polymeric grade fibres. Functionalization with TiO2 has enhanced the photo-stability of webs. The ultraviolet protection factor of functionalized and non-functionalized nanowebs empirically evaluated to be 65 and 24 respectively. The developed coated layer could be exploited for developing various defence, para-military and civilian UV protective light weight clothing (tent, covers and shelter segments, combat suit, snow bound camouflaging nets). This research therefore, is conducted in an attempt to develop a scientific understanding of PVDF fibre coated webs for photo-degradation and applications for defence protective textiles. This technological research in laboratory scale could be translated into bulk productionization.

Structure optimization of a grain impact piezoelectric sensor and its application for monitoring separation losses on tangential-axial combine harvesters.

Science.gov (United States)

Liang, Zhenwei; Li, Yaoming; Zhao, Zhan; Xu, Lizhang

2015-01-14

Grain separation losses is a key parameter to weigh the performance of combine harvesters, and also a dominant factor for automatically adjusting their major working parameters. The traditional separation losses monitoring method mainly rely on manual efforts, which require a high labor intensity. With recent advancements in sensor technology, electronics and computational processing power, this paper presents an indirect method for monitoring grain separation losses in tangential-axial combine harvesters in real-time. Firstly, we developed a mathematical monitoring model based on detailed comparative data analysis of different feeding quantities. Then, we developed a grain impact piezoelectric sensor utilizing a YT-5 piezoelectric ceramic as the sensing element, and a signal process circuit designed according to differences in voltage amplitude and rise time of collision signals. To improve the sensor performance, theoretical analysis was performed from a structural vibration point of view, and the optimal sensor structural has been selected. Grain collide experiments have shown that the sensor performance was greatly improved. Finally, we installed the sensor on a tangential-longitudinal axial combine harvester, and grain separation losses monitoring experiments were carried out in North China, which results have shown that the monitoring method was feasible, and the biggest measurement relative error was 4.63% when harvesting rice.

Structure Optimization of a Grain Impact Piezoelectric Sensor and Its Application for Monitoring Separation Losses on Tangential-Axial Combine Harvesters

Science.gov (United States)

Liang, Zhenwei; Li, Yaoming; Zhao, Zhan; Xu, Lizhang

2015-01-01

Grain separation losses is a key parameter to weigh the performance of combine harvesters, and also a dominant factor for automatically adjusting their major working parameters. The traditional separation losses monitoring method mainly rely on manual efforts, which require a high labor intensity. With recent advancements in sensor technology, electronics and computational processing power, this paper presents an indirect method for monitoring grain separation losses in tangential-axial combine harvesters in real-time. Firstly, we developed a mathematical monitoring model based on detailed comparative data analysis of different feeding quantities. Then, we developed a grain impact piezoelectric sensor utilizing a YT-5 piezoelectric ceramic as the sensing element, and a signal process circuit designed according to differences in voltage amplitude and rise time of collision signals. To improve the sensor performance, theoretical analysis was performed from a structural vibration point of view, and the optimal sensor structural has been selected. Grain collide experiments have shown that the sensor performance was greatly improved. Finally, we installed the sensor on a tangential-longitudinal axial combine harvester, and grain separation losses monitoring experiments were carried out in North China, which results have shown that the monitoring method was feasible, and the biggest measurement relative error was 4.63% when harvesting rice. PMID:25594592

Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

Directory of Open Access Journals (Sweden)

Chia-Hung Lin

2010-01-01

Full Text Available This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katz's algorithm is employed to estimate the fractal dimension (FD from a two-dimensional (2D image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Pareto optimal pairwise sequence alignment.

Science.gov (United States)

DeRonne, Kevin W; Karypis, George

2013-01-01

Sequence alignment using evolutionary profiles is a commonly employed tool when investigating a protein. Many profile-profile scoring functions have been developed for use in such alignments, but there has not yet been a comprehensive study of Pareto optimal pairwise alignments for combining multiple such functions. We show that the problem of generating Pareto optimal pairwise alignments has an optimal substructure property, and develop an efficient algorithm for generating Pareto optimal frontiers of pairwise alignments. All possible sets of two, three, and four profile scoring functions are used from a pool of 11 functions and applied to 588 pairs of proteins in the ce_ref data set. The performance of the best objective combinations on ce_ref is also evaluated on an independent set of 913 protein pairs extracted from the BAliBASE RV11 data set. Our dynamic-programming-based heuristic approach produces approximated Pareto optimal frontiers of pairwise alignments that contain comparable alignments to those on the exact frontier, but on average in less than 1/58th the time in the case of four objectives. Our results show that the Pareto frontiers contain alignments whose quality is better than the alignments obtained by single objectives. However, the task of identifying a single high-quality alignment among those in the Pareto frontier remains challenging.

Multi-Objective Optimization for Analysis of Changing Trade-Offs in the Nepalese Water-Energy-Food Nexus with Hydropower Development

DEFF Research Database (Denmark)

Dhaubanjar, Sanita; Davidsen, Claus; Bauer-Gottwein, Peter

2017-01-01

transmission constraints using an optimal power flow approach. Basin inflows, hydropower plant specifications, reservoir characteristics, reservoir rules, irrigation water demand, environmental flow requirements, power demand, and transmission line properties are provided as model inputs. The trade......-established water and power system models to develop a decision support tool combining multiple nexus objectives in a linear objective function. To demonstrate our framework, we compare eight Nepalese power development scenarios based on five nexus objectives: minimization of power deficit, maintenance of water...... availability for irrigation to support food self-sufficiency, reduction in flood risk, maintenance of environmental flows, and maximization of power export. The deterministic multi-objective optimization model is spatially resolved to enable realistic representation of the nexus linkages and accounts for power...

Optimizing decentralized renewable energy production by combining potentials and integrated environmental impact analysis. A case study in the Hannover region

Energy Technology Data Exchange (ETDEWEB)

Palmas, Claudia; Siewert, Almut [Leibniz Univ. of Hannover (Germany). Dept. of Environmental Planning

2013-07-01

In Europe, the integration of decentralized renewable energy production in regional planning processes plays a crucial role. In particular, regions face a major challenge in order to set up renewable decentralized energy systems and incorporate them into the electricity grid. This paper presents a methodological concept and preliminary tests of applications in order to create an optimization model for an improved renewable energy development and planning practice: firstly, the energy potentials of micro renewable resources are estimated, and secondly the outcomes are combined with an estimation of resulting environmental impacts. Including these data into the spatial analysis, different scenarios can be developed in order to support decision making in landscape planning on the basis of environmental and landscape criteria as well as energy issues, including technical aspects and costs. The case study area is the Hannover region. First results show good energy potentials, which will be in a next step evaluated and combined with environmental impacts in order to improve energy efficiency by integrated renewable, decentralized power plants and energy mix. (orig.)

Developing Optimal Combination of Bulking Agents in an In-Vessel Composting of Vegetable Waste

Directory of Open Access Journals (Sweden)

C. C. Monson

2010-01-01

Full Text Available The objective of the study is to determine the optimum combination of feed stock components for composting the organic solid waste, a prerequisite for effective microbial degradation and for obtaining quality compost. Combination of dry leaves with locally available bulking agents like sawdust, wood shavings, paddy straw, sugarcane bagasse and rice husk are composted along with vegetable waste in a laboratory scale reactor for the study. The central core of composting process is replicated in controlled conditions in the in-vessel by keeping initial feed stock C/N ratio fixed between 30 and 35. The study is monitored for 14 days for the variations in temperature, pH, moisture and macronutrients C and N of the compost. It is found that composting vegetable waste with the combination of paddy straw and dry leaves provided best results of C/N ratio of 17.58 confirming that, if right feedstock components are provided, an effective environment for the growth of microorganisms is achieved to accelerate the process to produce a resultant C/N ratio acceptable to be used as compost.

Optimization Analysis on Parameters of Cleaning Sieve of Rape Combine of "Bi Lang 4LZ(Y)-1.0"

OpenAIRE

Wu Mingliang; Tang Lun; Guan Chunyun; Tang Chuzhou

2014-01-01

Against the phenomenon of high impurity rate and cane and pod shell are difficult to discharge at the end of the sieve for rape combine of "Bi Lang 4LZ(Y)-1.0". This study take cleaning sieve of rape combine of "Bi Lang 4LZ(Y)-1.0" as study object, analyzed the movement of materials on sieve, established the virtual prototype model of the cleaning sieve of this rape combine, taken materials and cleaning sieve all at the best motion state as constraint conditions and optimized the structure an...

Combined Turbine and Cycle Optimization for Organic Rankine Cycle Power Systems—Part A

DEFF Research Database (Denmark)

Meroni, Andrea; La Seta, Angelo; Andreasen, Jesper Graa

2016-01-01

Axial-flow turbines represent a well-established technology for a wide variety of power generation systems. Compactness, flexibility, reliability and high efficiency have been key factors for the extensive use of axial turbines in conventional power plants and, in the last decades, in organic...... Rankine cycle power systems. In this two-part paper, an overall cycle model and a model of an axial turbine were combined in order to provide a comprehensive preliminary design of the organic Rankine cycle unit, taking into account both cycle and turbine optimal designs. Part A presents the preliminary...

Combined optimal-pathlengths method for near-infrared spectroscopy analysis

International Nuclear Information System (INIS)

Liu Rong; Xu Kexin; Lu Yanhui; Sun Huili

2004-01-01

Near-infrared (NIR) spectroscopy is a rapid, reagent-less and nondestructive analytical technique, which is being increasingly employed for quantitative application in chemistry, pharmaceutics and food industry, and for the optical analysis of biological tissue. The performance of NIR technology greatly depends on the abilities to control and acquire data from the instrument and to calibrate and analyse data. Optical pathlength is a key parameter of the NIR instrument, which has been thoroughly discussed in univariate quantitative analysis in the presence of photometric errors. Although multiple wavelengths can provide more chemical information, it is difficult to determine a single pathlength that is suitable for each wavelength region. A theoretical investigation of a selection procedure for multiple pathlengths, called the combined optimal-pathlengths (COP) method, is identified in this paper and an extensive comparison with the single pathlength method is also performed on simulated and experimental NIR spectral data sets. The results obtained show that the COP method can greatly improve the prediction accuracy in NIR spectroscopy quantitative analysis

Penumbra modifier for optimal electron field combination

International Nuclear Information System (INIS)

El-Sherbini, N.; Hejazy, M.; Khalil, W.

2008-01-01

Treatment with megavoltage electron beam is ideal for irradiating shallow seated tumors because of their limited range in tissues. However, the treatment of extended areas with electrons requires the use of two or more adjacent fields. Variations may arise at the junction of the fields. These dose variations come from the presence of large bulges in the low value isodose curves created by electron beam divergence and lateral scattering in tissues. Overlapping of these bulges, creates a high dose region at depths. While constriction of the isodose curves near the surface may produce a Long-term follow-up study critically on the fields separation. To overcome this problem, several authors have proposed techniques for matching electron beam edge in such a way as to make the overlap region as uniform as possible. The simplest approach to the problem is to optimize the skin gap between the two adjacent electron field edges. The increased lateral scatter of low-energy electrons and the machine specific characteristics of an electron beam penumbra make the determination of an optimized skin gap somewhat complicated. Optimization is achieved by a complete set of trial and error measurements. The main limitation to the usefulness of the optimized skin gap technique is the strong sensitivity of the dose distribution in the field junction region to small deviation in field separation or in the angulation of the incident electron beams, making it strongly dependent on positioning. The present study is done at electron beam energies of 6, 8, and 15 MeV. The method depends on the abutment of different field areas using beam edge modifier (Penumbra Generator) made of cerrobend. The objectives of this study are to present a systematic study of the modified electron field for better under standing of the behavior and physical characteristics of the penumbra generator, and to investigate the feasibility of using this technique for large electron fields. Also to obtain a quantitative

Dependability of self-optimizing mechatronic systems

CERN Document Server

Rammig, Franz; Schäfer, Wilhelm; Sextro, Walter

2014-01-01

Intelligent technical systems, which combine mechanical, electrical and software engineering with control engineering and advanced mathematics, go far beyond the state of the art in mechatronics and open up fascinating perspectives. Among these systems are so-called self-optimizing systems, which are able to adapt their behavior autonomously and flexibly to changing operating conditions. Self-optimizing systems create high value for example in terms of energy and resource efficiency as well as reliability. The Collaborative Research Center 614 "Self-optimizing Concepts and Structures in Mechanical Engineering" pursued the long-term aim to open up the active paradigm of self-optimization for mechanical engineering and to enable others to develop self-optimizing systems. This book is directed to researchers and practitioners alike. It provides a design methodology for the development of self-optimizing systems consisting of a reference process, methods, and tools. The reference process is divided into two phase...

Optimized Production of Vanillin from Green Vanilla Pods by Enzyme-Assisted Extraction Combined with Pre-Freezing and Thawing

Directory of Open Access Journals (Sweden)

Yanjun Zhang

2014-02-01

Full Text Available Production of vanillin from natural green vanilla pods was carried out by enzyme-assisted extraction combined with pre-freezing and thawing. In the first step the green vanilla pods were pre-frozen and then thawed to destroy cellular compartmentation. In the second step pectinase from Aspergillus niger was used to hydrolyze the pectin between the glucovanillin substrate and β-glucosidase. Four main variables, including enzyme amount, reaction temperature, time and pH, which were of significance for the vanillin content were studied and a central composite design (CCD based on the results of a single-factor tests was used. Response surface methodology based on CCD was employed to optimize the combination of enzyme amount, reaction temperature, time, and pH for maximum vanillin production. This resulted in the optimal condition in regards of the enzyme amount, reaction temperature, time, and pH at 84.2 mg, 49.5 °C, 7.1 h, and 4.2, respectively. Under the optimal condition, the experimental yield of vanillin was 4.63% ± 0.11% (dwb, which was in good agreement with the value predicted by the model. Compared to the traditional curing process (1.98% and viscozyme extract (2.36%, the optimized method for the vanillin production significantly increased the yield by 133.85% and 96%, respectively.

Optimizing Placement of Weather Stations: Exploring Objective Functions of Meaningful Combinations of Multiple Weather Variables

Science.gov (United States)

Snyder, A.; Dietterich, T.; Selker, J. S.

2017-12-01

Many regions of the world lack ground-based weather data due to inadequate or unreliable weather station networks. For example, most countries in Sub-Saharan Africa have unreliable, sparse networks of weather stations. The absence of these data can have consequences on weather forecasting, prediction of severe weather events, agricultural planning, and climate change monitoring. The Trans-African Hydro-Meteorological Observatory (TAHMO.org) project seeks to address these problems by deploying and operating a large network of weather stations throughout Sub-Saharan Africa. To design the TAHMO network, we must determine where to place weather stations within each country. We should consider how we can create accurate spatio-temporal maps of weather data and how to balance the desired accuracy of each weather variable of interest (precipitation, temperature, relative humidity, etc.). We can express this problem as a joint optimization of multiple weather variables, given a fixed number of weather stations. We use reanalysis data as the best representation of the "true" weather patterns that occur in the region of interest. For each possible combination of sites, we interpolate the reanalysis data between selected locations and calculate the mean average error between the reanalysis ("true") data and the interpolated data. In order to formulate our multi-variate optimization problem, we explore different methods of weighting each weather variable in our objective function. These methods include systematic variation of weights to determine which weather variables have the strongest influence on the network design, as well as combinations targeted for specific purposes. For example, we can use computed evapotranspiration as a metric that combines many weather variables in a way that is meaningful for agricultural and hydrological applications. We compare the errors of the weather station networks produced by each optimization problem formulation. We also compare these

A combination of genetic algorithm and particle swarm optimization method for solving traveling salesman problem

Directory of Open Access Journals (Sweden)

Keivan Borna

2015-12-01

Full Text Available Traveling salesman problem (TSP is a well-established NP-complete problem and many evolutionary techniques like particle swarm optimization (PSO are used to optimize existing solutions for that. PSO is a method inspired by the social behavior of birds. In PSO, each member will change its position in the search space, according to personal or social experience of the whole society. In this paper, we combine the principles of PSO and crossover operator of genetic algorithm to propose a heuristic algorithm for solving the TSP more efficiently. Finally, some experimental results on our algorithm are applied in some instances in TSPLIB to demonstrate the effectiveness of our methods which also show that our algorithm can achieve better results than other approaches.

Optimal capacitor placement and sizing using combined fuzzy ...

African Journals Online (AJOL)

Then the sizing of the capacitors is modeled as an optimization problem and the objective function (loss minimization) is solved using Hybrid Particle Swarm Optimization (HPSO) technique. A case study with an IEEE 34 bus distribution feeder is presented to illustrate the applicability of the algorithm. A comparison is made ...

Optimization of capillary zone electrophoresis for charge heterogeneity testing of biopharmaceuticals using enhanced method development principles.

Science.gov (United States)

Moritz, Bernd; Locatelli, Valentina; Niess, Michele; Bathke, Andrea; Kiessig, Steffen; Entler, Barbara; Finkler, Christof; Wegele, Harald; Stracke, Jan

2017-12-01

CZE is a well-established technique for charge heterogeneity testing of biopharmaceuticals. It is based on the differences between the ratios of net charge and hydrodynamic radius. In an extensive intercompany study, it was recently shown that CZE is very robust and can be easily implemented in labs that did not perform it before. However, individual characteristics of some examined proteins resulted in suboptimal resolution. Therefore, enhanced method development principles were applied here to investigate possibilities for further method optimization. For this purpose, a high number of different method parameters was evaluated with the aim to improve CZE separation. For the relevant parameters, design of experiments (DoE) models were generated and optimized in several ways for different sets of responses like resolution, peak width and number of peaks. In spite of product specific DoE optimization it was found that the resulting combination of optimized parameters did result in significant improvement of separation for 13 out of 16 different antibodies and other molecule formats. These results clearly demonstrate generic applicability of the optimized CZE method. Adaptation to individual molecular properties may sometimes still be required in order to achieve optimal separation but the set screws discussed in this study [mainly pH, identity of the polymer additive (HPC versus HPMC) and the concentrations of additives like acetonitrile, butanolamine and TETA] are expected to significantly reduce the effort for specific optimization. 2017 The Authors. Electrophoresis published by Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Promoting the energy structure optimization around Chinese Beijing-Tianjin area by developing biomass energy

Science.gov (United States)

Zhao, Li; Sun, Du; Wang, Shi-Yu; Zhao, Feng-Qing

2017-06-01

In recent years, remarkable achievements in the utilization of biomass energy have been made in China. However, there are still some problems, such as irrational industry layout, immature existing market survival mechanism and lack of core competitiveness. On the basis of investigation and research, some recommendations and strategies are proposed for the development of biomass energy around Chinese Beijing-Tianjin area: scientific planning and precise laying out of biomass industry; rationalizing the relationship between government and enterprises and promoting the establishment of a market-oriented survival mechanism; combining ‘supply side’ with ‘demand side’ to optimize product structure; extending industrial chain to promote industry upgrading and sustainable development; and comprehensive co-ordinating various types of biomass resources and extending product chain to achieve better economic benefits.

Optimal combination of signals from colocated gravitational wave interferometers for use in searches for a stochastic background

International Nuclear Information System (INIS)

Lazzarini, Albert; Reilly, Kaice; Whitcomb, Stan; Bose, Sukanta; Fritschel, Peter; McHugh, Martin; Whelan, John T.; Regimbau, Tania; Romano, Joseph D.; Whiting, Bernard F.

2004-01-01

This article derives an optimal (i.e., unbiased, minimum variance) estimator for the pseudodetector strain for a pair of colocated gravitational wave interferometers (such as the pair of LIGO interferometers at its Hanford Observatory), allowing for possible instrumental correlations between the two detectors. The technique is robust and does not involve any assumptions or approximations regarding the relative strength of gravitational wave signals in the Hanford pair with respect to other sources of correlated instrumental or environmental noise. An expression is given for the effective power spectral density of the combined noise in the pseudodetector. This can then be introduced into the standard optimal Wiener filter used to cross-correlate detector data streams in order to obtain an optimal estimate of the stochastic gravitational wave background. In addition, a dual to the optimal estimate of strain is derived. This dual is constructed to contain no gravitational wave signature and can thus be used as an 'off-source' measurement to test algorithms used in the 'on-source' observation

Combined and Relative Effect Levels of Perceived Risk, Knowledge, Optimism, Pessimism, and Social Trust on Anxiety among Inhabitants Concerning Living on Heavy Metal Contaminated Soil.

Science.gov (United States)

Tang, Zhongjun; Guo, Zengli; Zhou, Li; Xue, Shengguo; Zhu, Qinfeng; Zhu, Huike

2016-11-02

This research aims at combined and relative effect levels on anxiety of: (1) perceived risk, knowledge, optimism, pessimism, and social trust; and (2) four sub-variables of social trust among inhabitants concerning living on heavy metal contaminated soil. On the basis of survey data from 499 Chinese respondents, results suggest that perceived risk, pessimism, optimism, and social trust have individual, significant, and direct effects on anxiety, while knowledge does not. Knowledge has significant, combined, and interactive effects on anxiety together with social trust and pessimism, respectively, but does not with perceived risk and optimism. Social trust, perceived risk, pessimism, knowledge, and optimism have significantly combined effects on anxiety; the five variables as a whole have stronger predictive values than each one individually. Anxiety is influenced firstly by social trust and secondly by perceived risk, pessimism, knowledge, and optimism. Each of four sub-variables of social trust has an individual, significant, and negative effect on anxiety. When introducing four sub-variables into one model, trust in social organizations and in the government have significantly combined effects on anxiety, while trust in experts and in friends and relatives do not; anxiety is influenced firstly by trust in social organization, and secondly by trust in the government.

Visual gene developer: a fully programmable bioinformatics software for synthetic gene optimization

Directory of Open Access Journals (Sweden)

McDonald Karen

2011-08-01

Full Text Available Abstract Background Direct gene synthesis is becoming more popular owing to decreases in gene synthesis pricing. Compared with using natural genes, gene synthesis provides a good opportunity to optimize gene sequence for specific applications. In order to facilitate gene optimization, we have developed a stand-alone software called Visual Gene Developer. Results The software not only provides general functions for gene analysis and optimization along with an interactive user-friendly interface, but also includes unique features such as programming capability, dedicated mRNA secondary structure prediction, artificial neural network modeling, network & multi-threaded computing, and user-accessible programming modules. The software allows a user to analyze and optimize a sequence using main menu functions or specialized module windows. Alternatively, gene optimization can be initiated by designing a gene construct and configuring an optimization strategy. A user can choose several predefined or user-defined algorithms to design a complicated strategy. The software provides expandable functionality as platform software supporting module development using popular script languages such as VBScript and JScript in the software programming environment. Conclusion Visual Gene Developer is useful for both researchers who want to quickly analyze and optimize genes, and those who are interested in developing and testing new algorithms in bioinformatics. The software is available for free download at http://www.visualgenedeveloper.net.

Review of design optimization methods for turbomachinery aerodynamics

Science.gov (United States)

Li, Zhihui; Zheng, Xinqian

2017-08-01

In today's competitive environment, new turbomachinery designs need to be not only more efficient, quieter, and ;greener; but also need to be developed at on much shorter time scales and at lower costs. A number of advanced optimization strategies have been developed to achieve these requirements. This paper reviews recent progress in turbomachinery design optimization to solve real-world aerodynamic problems, especially for compressors and turbines. This review covers the following topics that are important for optimizing turbomachinery designs. (1) optimization methods, (2) stochastic optimization combined with blade parameterization methods and the design of experiment methods, (3) gradient-based optimization methods for compressors and turbines and (4) data mining techniques for Pareto Fronts. We also present our own insights regarding the current research trends and the future optimization of turbomachinery designs.

Optimization and Development of Swellable Controlled Porosity ...

African Journals Online (AJOL)

Purpose: To develop swellable controlled porosity osmotic pump tablet of theophylline and to define the formulation and process variables responsible for drug release by applying statistical optimization technique. Methods: Formulations were prepared based on Taguchi Orthogonal Array design and Fraction Factorial ...

Optimal control of open quantum systems: a combined surrogate hamiltonian optimal control theory approach applied to photochemistry on surfaces.

Science.gov (United States)

Asplund, Erik; Klüner, Thorsten

2012-03-28

In this paper, control of open quantum systems with emphasis on the control of surface photochemical reactions is presented. A quantum system in a condensed phase undergoes strong dissipative processes. From a theoretical viewpoint, it is important to model such processes in a rigorous way. In this work, the description of open quantum systems is realized within the surrogate hamiltonian approach [R. Baer and R. Kosloff, J. Chem. Phys. 106, 8862 (1997)]. An efficient and accurate method to find control fields is optimal control theory (OCT) [W. Zhu, J. Botina, and H. Rabitz, J. Chem. Phys. 108, 1953 (1998); Y. Ohtsuki, G. Turinici, and H. Rabitz, J. Chem. Phys. 120, 5509 (2004)]. To gain control of open quantum systems, the surrogate hamiltonian approach and OCT, with time-dependent targets, are combined. Three open quantum systems are investigated by the combined method, a harmonic oscillator immersed in an ohmic bath, CO adsorbed on a platinum surface, and NO adsorbed on a nickel oxide surface. Throughout this paper, atomic units, i.e., ℏ = m(e) = e = a(0) = 1, have been used unless otherwise stated.

Optimal coordinated scheduling of combined heat and power fuel cell, wind, and photovoltaic units in micro grids considering uncertainties

International Nuclear Information System (INIS)

Bornapour, Mosayeb; Hooshmand, Rahmat-Allah; Khodabakhshian, Amin; Parastegari, Moein

2016-01-01

In this paper, a stochastic model is proposed for coordinated scheduling of combined heat and power units in micro grid considering wind turbine and photovoltaic units. Uncertainties of electrical market price; the speed of wind and solar radiation are considered using a scenario-based method. In the method, scenarios are generated using roulette wheel mechanism based on probability distribution functions of input random variables. Using this method, the probabilistic specifics of the problem are distributed and the problem is converted to a deterministic one. The type of the objective function, coordinated scheduling of combined heat and power, wind turbine, and photovoltaic units change this problem to a mixed integer nonlinear one. Therefore to solve this problem modified particle swarm optimization algorithm is employed. The mentioned uncertainties lead to an increase in profit. Moreover, the optimal coordinated scheduling of renewable energy resources and thermal units in micro grids increase the total profit. In order to evaluate the performance of the proposed method, its performance is executed on modified 33 bus distributed system as a micro grid. - Highlights: • Stochastic model is proposed for coordinated scheduling of renewable energy sources. • The effect of combined heat and power is considered. • Maximizing profits of micro grid is considered as objective function. • Considering the uncertainties of problem lead to profit increasing. • Optimal scheduling of renewable energy sources and thermal units increases profit.

Evaluation of the optimal combinations of modulation factor and pitch for Helical TomoTherapy plans made with TomoEdge using Pareto optimal fronts.

Science.gov (United States)

De Kerf, Geert; Van Gestel, Dirk; Mommaerts, Lobke; Van den Weyngaert, Danielle; Verellen, Dirk

2015-09-17

Modulation factor (MF) and pitch have an impact on Helical TomoTherapy (HT) plan quality and HT users mostly use vendor-recommended settings. This study analyses the effect of these two parameters on both plan quality and treatment time for plans made with TomoEdge planning software by using the concept of Pareto optimal fronts. More than 450 plans with different combinations of pitch [0.10-0.50] and MF [1.2-3.0] were produced. These HT plans, with a field width (FW) of 5 cm, were created for five head and neck patients and homogeneity index, conformity index, dose-near-maximum (D2), and dose-near-minimum (D98) were analysed for the planning target volumes, as well as the mean dose and D2 for most critical organs at risk. For every dose metric the median value will be plotted against treatment time. A Pareto-like method is used in the analysis which will show how pitch and MF influence both treatment time and plan quality. For small pitches (≤0.20), MF does not influence treatment time. The contrary is true for larger pitches (≥0.25) as lowering MF will both decrease treatment time and plan quality until maximum gantry speed is reached. At this moment, treatment time is saturated and only plan quality will further decrease. The Pareto front analysis showed optimal combinations of pitch [0.23-0.45] and MF > 2.0 for a FW of 5 cm. Outside this range, plans will become less optimal. As the vendor-recommended settings fall within this range, the use of these settings is validated.

Service Operations Optimization: Recent Development in Supply Chain Management

Directory of Open Access Journals (Sweden)

Bin Shen

2015-01-01

Full Text Available Services are the key of success in operation management. Designing the effective strategies by optimization techniques is the fundamental and important condition for performance increase in service operations (SOs management. In this paper, we mainly focus on investigating SOs optimization in the areas of supply chain management, which create the greatest business values. Specifically, we study the recent development of SOs optimization associated with supply chain by categorizing them into four different industries (i.e., e-commerce industry, consumer service industry, public sector, and fashion industry and four various SOs features (i.e., advertising, channel coordination, pricing, and inventory. Moreover, we conduct the technical review on the stylish industries/topics and typical optimization models. The classical optimization approaches for SOs management in supply chain are presented. The managerial implications of SOs in supply chain are discussed.

Particle Swarm Optimization Toolbox

Science.gov (United States)

Grant, Michael J.

2010-01-01

The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry

Optimally Stopped Optimization

Science.gov (United States)

Vinci, Walter; Lidar, Daniel

We combine the fields of heuristic optimization and optimal stopping. We propose a strategy for benchmarking randomized optimization algorithms that minimizes the expected total cost for obtaining a good solution with an optimal number of calls to the solver. To do so, rather than letting the objective function alone define a cost to be minimized, we introduce a further cost-per-call of the algorithm. We show that this problem can be formulated using optimal stopping theory. The expected cost is a flexible figure of merit for benchmarking probabilistic solvers that can be computed when the optimal solution is not known, and that avoids the biases and arbitrariness that affect other measures. The optimal stopping formulation of benchmarking directly leads to a real-time, optimal-utilization strategy for probabilistic optimizers with practical impact. We apply our formulation to benchmark the performance of a D-Wave 2X quantum annealer and the HFS solver, a specialized classical heuristic algorithm designed for low tree-width graphs. On a set of frustrated-loop instances with planted solutions defined on up to N = 1098 variables, the D-Wave device is between one to two orders of magnitude faster than the HFS solver.

The development of multi-objective optimization model for excess bagasse utilization: A case study for Thailand

International Nuclear Information System (INIS)

Buddadee, Bancha; Wirojanagud, Wanpen; Watts, Daniel J.; Pitakaso, Rapeepan

2008-01-01

In this paper, a multi-objective optimization model is proposed as a tool to assist in deciding for the proper utilization scheme of excess bagasse produced in sugarcane industry. Two major scenarios for excess bagasse utilization are considered in the optimization. The first scenario is the typical situation when excess bagasse is used for the onsite electricity production. In case of the second scenario, excess bagasse is processed for the offsite ethanol production. Then the ethanol is blended with an octane rating of 91 gasoline by a portion of 10% and 90% by volume respectively and the mixture is used as alternative fuel for gasoline vehicles in Thailand. The model proposed in this paper called 'Environmental System Optimization' comprises the life cycle impact assessment of global warming potential (GWP) and the associated cost followed by the multi-objective optimization which facilitates in finding out the optimal proportion of the excess bagasse processed in each scenario. Basic mathematical expressions for indicating the GWP and cost of the entire process of excess bagasse utilization are taken into account in the model formulation and optimization. The outcome of this study is the methodology developed for decision-making concerning the excess bagasse utilization available in Thailand in view of the GWP and economic effects. A demonstration example is presented to illustrate the advantage of the methodology which may be used by the policy maker. The methodology developed is successfully performed to satisfy both environmental and economic objectives over the whole life cycle of the system. It is shown in the demonstration example that the first scenario results in positive GWP while the second scenario results in negative GWP. The combination of these two scenario results in positive or negative GWP depending on the preference of the weighting given to each objective. The results on economics of all scenarios show the satisfied outcomes

Design and optimization of a combined fuel reforming and solid oxide fuel cell system with anode off-gas recycling

International Nuclear Information System (INIS)

Lee, Tae Seok; Chung, J.N.; Chen, Yen-Cho

2011-01-01

Highlights: → In this work, an analytical, parametric study is performed to evaluate the feasibility and performance of a combined fuel reforming and SOFC system. → Specifically the effects of adding the anode off-gas recycling and recirculation components and the CO 2 absorbent unit are investigated. → The AOG recycle ratio increases with increasing S/C ratio and the addition of AOG recycle eliminates the need for external water consumption. → The key finding is that for the SOFC operating at 900 deg. C with the steam to carbon ratio at 5 and no AOG recirculation, the system efficiency peaks. - Abstract: An energy conversion and management concept for a combined system of a solid oxide fuel cell coupled with a fuel reforming device is developed and analyzed by a thermodynamic and electrochemical model. The model is verified by an experiment and then used to evaluate the overall system performance and to further suggest an optimal design strategy. The unique feature of the system is the inclusion of the anode off-gas recycle that eliminates the need of external water consumption for practical applications. The system performance is evaluated as a function of the steam to carbon ratio, fuel cell temperature, anode off gas recycle ratio and CO 2 adsorption percentage. For most of the operating conditions investigated, the system efficiency starts at around 70% and then monotonically decreases to the average of 50% at the peak power density before dropping down to zero at the limiting current density point. From an engineering application point of view, the proposed combined fuel reforming and SOFC system with a range of efficiency between 50% and 70% is considered very attractive. It is suggested that the optimal system is the one where the SOFC operates around 900 deg. C with S/C ratio higher than 3, maximum CO 2 capture, and minimum AOG recirculation.

Immuno-pharmacodynamics for evaluating mechanism of action and developing immunotherapy combinations.

Science.gov (United States)

Parchment, Ralph E; Voth, Andrea Regier; Doroshow, James H; Berzofsky, Jay A

2016-08-01

importance of the location of the PD-biomarker response within the cellular landscape of a tumor specimen. We discuss herein the major modes of immunotherapy, and lay out a blueprint for using PD assessment to optimize dosage regimens of single agents and guide development of combination immunotherapy regimens, using PD1/PD-L1 immune checkpoint inhibition as a case study. Copyright © 2016 Elsevier Inc. All rights reserved.

Combined Optimal Sizing and Control for a Hybrid Tracked Vehicle

Directory of Open Access Journals (Sweden)

Huei Peng

2012-11-01

Full Text Available The optimal sizing and control of a hybrid tracked vehicle is presented and solved in this paper. A driving schedule obtained from field tests is used to represent typical tracked vehicle operations. Dynamics of the diesel engine-permanent magnetic AC synchronous generator set, the lithium-ion battery pack, and the power split between them are modeled and validated through experiments. Two coupled optimizations, one for the plant parameters, forming the outer optimization loop and one for the control strategy, forming the inner optimization loop, are used to achieve minimum fuel consumption under the selected driving schedule. The dynamic programming technique is applied to find the optimal controller in the inner loop while the component parameters are optimized iteratively in the outer loop. The results are analyzed, and the relationship between the key parameters is observed to keep the optimal sizing and control simultaneously.

Critical overview of the development of the optimization requirement and its implementation

International Nuclear Information System (INIS)

Gonzalez, A.J.

1988-01-01

This paper is intended to provide a critical overview of the development of the optimization requirement of the system of dose limitation recommended by the International Commission on Radiological Protection (ICRP), as well as of its implementation. The concept of optimization began to evolve in the mid-1950s and was formally introduced as a requirement for radiation protection in 1978. Recommendations on its practical implementation have been available for five years. After such a long evolution, it seems reasonable to make a critical assessment of the development of the optimization concept, and this summary paper is intended to provide such an assessment. It does not include a description of the requirement or of any method for implementing it, since it is assumed that optimization is familiar by now. The paper concentrates rather on misunderstandings of and achievements owed to optimization and explores some of their underlying causes, the intention being to draw lessons from past experience and to apply them to future developments in this area. The paper presents an outlook on some remaining policy issues that still pending solution as well as some suggestions on prioritization of implementation of optimization and on standardizing the optimization protection

Structural Optimization Design of Horizontal-Axis Wind Turbine Blades Using a Particle Swarm Optimization Algorithm and Finite Element Method

Directory of Open Access Journals (Sweden)

Pan Pan

2012-11-01

Full Text Available This paper presents an optimization method for the structural design of horizontal-axis wind turbine (HAWT blades based on the particle swarm optimization algorithm (PSO combined with the finite element method (FEM. The main goal is to create an optimization tool and to demonstrate the potential improvements that could be brought to the structural design of HAWT blades. A multi-criteria constrained optimization design model pursued with respect to minimum mass of the blade is developed. The number and the location of layers in the spar cap and the positions of the shear webs are employed as the design variables, while the strain limit, blade/tower clearance limit and vibration limit are taken into account as the constraint conditions. The optimization of the design of a commercial 1.5 MW HAWT blade is carried out by combining the above method and design model under ultimate (extreme flap-wise load conditions. The optimization results are described and compared with the original design. It shows that the method used in this study is efficient and produces improved designs.

The Czech longitudinal study of optimal development

Czech Academy of Sciences Publication Activity Database

Kebza, V.; Šolcová, Iva; Kodl, M.; Kernová, V.

2012-01-01

Roč. 47, Suppl. 1 (2012), s. 266-266 ISSN 0020-7594. [International Congress of Psychology /30./. 22.07.2012-27.07.2012, Cape Town] R&D Projects: GA ČR GAP407/10/2410 Institutional support: RVO:68081740 Keywords : optimal development * Prague longitudinal study Subject RIV: AN - Psychology

A novel heuristic method for optimization of straight blade vertical axis wind turbine

International Nuclear Information System (INIS)

Tahani, Mojtaba; Babayan, Narek; Mehrnia, Seyedmajid; Shadmehri, Mehran

2016-01-01

Highlights: • A novel heuristic method has been proposed for optimization of VAWTs. • The proposed method is the combination of DMST model with heuristic algorithms. • A continuous/discrete optimization problem has been solved. • A novel continuous optimization algorithm has been developed. • The CFD simulation of the optimized geometry has been carried out. - Abstract: In this research study it is aimed to propose a novel heuristic method for optimizing the VAWT design. The method is the combination of continuous/discrete optimization algorithms with double multiple stream tube (DMST) theory. For this purpose a DMST code has been developed and is validated using available experimental data in literature. A novel continuous optimization algorithm is proposed which can be considered as the combination of three heuristic optimization algorithms namely elephant herding optimization, flower pollination algorithm and grey wolf optimizer. The continuous algorithm is combined with popular discrete ant colony optimization algorithm (ACO). The proposed method can be utilized for several engineering problems which are dealing with continuous and discrete variables. In this research study, chord and diameter of the turbine are selected as continuous decision variables and airfoil types and number of blades are selected as discrete decision variables. The average power coefficient between tip speed rations from 1.5 to 9.5 is considered as the objective function. The optimization results indicated that the optimized geometry can produce a maximum power coefficient, 44% higher than the maximum power coefficient of the original turbine. Also a CFD simulation of the optimized geometry is carried out. The CFD results indicated that the average vorticity magnitude around the optimized blade is larger than the original blade and this results greater momentum and power coefficient.

A Framework for Constrained Optimization Problems Based on a Modified Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Biwei Tang

2016-01-01

Full Text Available This paper develops a particle swarm optimization (PSO based framework for constrained optimization problems (COPs. Aiming at enhancing the performance of PSO, a modified PSO algorithm, named SASPSO 2011, is proposed by adding a newly developed self-adaptive strategy to the standard particle swarm optimization 2011 (SPSO 2011 algorithm. Since the convergence of PSO is of great importance and significantly influences the performance of PSO, this paper first theoretically investigates the convergence of SASPSO 2011. Then, a parameter selection principle guaranteeing the convergence of SASPSO 2011 is provided. Subsequently, a SASPSO 2011-based framework is established to solve COPs. Attempting to increase the diversity of solutions and decrease optimization difficulties, the adaptive relaxation method, which is combined with the feasibility-based rule, is applied to handle constraints of COPs and evaluate candidate solutions in the developed framework. Finally, the proposed method is verified through 4 benchmark test functions and 2 real-world engineering problems against six PSO variants and some well-known methods proposed in the literature. Simulation results confirm that the proposed method is highly competitive in terms of the solution quality and can be considered as a vital alternative to solve COPs.

Optimization of systems with the combination of ground-source heat pump and solar collectors in dwellings

DEFF Research Database (Denmark)

Kjellsson, Elisabeth; Hellström, Göran; Perers, Bengt

2010-01-01

The use of ground-source heat pumps for heating and domestic hot water in dwellings is common in Sweden. The combination with solar collectors has been introduced to reduce the electricity demand in the system. In order to analyze different systems with combinations of solar collectors and ground......-source heat pumps, computer simulations have been carried out with the simulation program TRNSYS. Large differences were found between the system alternatives. The optimal design is when solar heat produces domestic hot water during summertime and recharges the borehole during wintertime. The advantage...... is related to the rate of heat extraction from the borehole as well as the overall design of the system. The demand of electricity may increase with solar recharging, because of the increased operating time of the circulation pumps. Another advantage with solar heat in combination with heat pumps is when...

Development and Optimization of controlled drug release ...

African Journals Online (AJOL)

The aim of this study is to develop and optimize an osmotically controlled drug delivery system of diclofenac sodium. Osmotically controlled oral drug delivery systems utilize osmotic pressure for controlled delivery of active drugs. Drug delivery from these systems, to a large extent, is independent of the physiological factors ...

Fermentation of Saccharomyces cerevisiae - Combining kinetic modeling and optimization techniques points out avenues to effective process design.

Science.gov (United States)

Scheiblauer, Johannes; Scheiner, Stefan; Joksch, Martin; Kavsek, Barbara

2018-09-14

A combined experimental/theoretical approach is presented, for improving the predictability of Saccharomyces cerevisiae fermentations. In particular, a mathematical model was developed explicitly taking into account the main mechanisms of the fermentation process, allowing for continuous computation of key process variables, including the biomass concentration and the respiratory quotient (RQ). For model calibration and experimental validation, batch and fed-batch fermentations were carried out. Comparison of the model-predicted biomass concentrations and RQ developments with the corresponding experimentally recorded values shows a remarkably good agreement for both batch and fed-batch processes, confirming the adequacy of the model. Furthermore, sensitivity studies were performed, in order to identify model parameters whose variations have significant effects on the model predictions: our model responds with significant sensitivity to the variations of only six parameters. These studies provide a valuable basis for model reduction, as also demonstrated in this paper. Finally, optimization-based parametric studies demonstrate how our model can be utilized for improving the efficiency of Saccharomyces cerevisiae fermentations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Further development of mathematical description for combined toxicity: A case study of lead–fluoride combination

Directory of Open Access Journals (Sweden)

Vladimir G. Panov

2015-01-01

Full Text Available In this article, we check and develop further some postulates of the theory and mathematical modeling of combined toxic effect that we proposed earlier [1]. To this end, we have analyzed the results of an experiment on rats exposed during 6 weeks to repeated intraperitoneal injections of lead acetate, sodium fluoride or both. The development of intoxication was estimated quantitatively with 54 functional, biochemical and morphometric indices. For mathematical description of the effect that lead and fluorine doses produced alone or in combination, we used a response surface regression model containing linear and cross terms (hyperbolic paraboloid. It is shown that the combination of lead and fluoride features the same 10 types of combined effect that we found previously for the lead and cadmium combination. Special attention is given to indices on which lead and fluorine produce an opposite effect.

Developing optimal search strategies for detecting clinically sound prognostic studies in MEDLINE: an analytic survey

Directory of Open Access Journals (Sweden)

Haynes R Brian

2004-06-01

Full Text Available Abstract Background Clinical end users of MEDLINE have a difficult time retrieving articles that are both scientifically sound and directly relevant to clinical practice. Search filters have been developed to assist end users in increasing the success of their searches. Many filters have been developed for the literature on therapy and reviews but little has been done in the area of prognosis. The objective of this study is to determine how well various methodologic textwords, Medical Subject Headings, and their Boolean combinations retrieve methodologically sound literature on the prognosis of health disorders in MEDLINE. Methods An analytic survey was conducted, comparing hand searches of journals with retrievals from MEDLINE for candidate search terms and combinations. Six research assistants read all issues of 161 journals for the publishing year 2000. All articles were rated using purpose and quality indicators and categorized into clinically relevant original studies, review articles, general papers, or case reports. The original and review articles were then categorized as 'pass' or 'fail' for methodologic rigor in the areas of prognosis and other clinical topics. Candidate search strategies were developed for prognosis and run in MEDLINE – the retrievals being compared with the hand search data. The sensitivity, specificity, precision, and accuracy of the search strategies were calculated. Results 12% of studies classified as prognosis met basic criteria for scientific merit for testing clinical applications. Combinations of terms reached peak sensitivities of 90%. Compared with the best single term, multiple terms increased sensitivity for sound studies by 25.2% (absolute increase, and increased specificity, but by a much smaller amount (1.1% when sensitivity was maximized. Combining terms to optimize both sensitivity and specificity achieved sensitivities and specificities of approximately 83% for each. Conclusion Empirically derived

Realizing an Optimization Approach Inspired from Piaget’s Theory on Cognitive Development

Directory of Open Access Journals (Sweden)

Utku Kose

2015-09-01

Full Text Available The objective of this paper is to introduce an artificial intelligence based optimization approach, which is inspired from Piaget’s theory on cognitive development. The approach has been designed according to essential processes that an individual may experience while learning something new or improving his / her knowledge. These processes are associated with the Piaget’s ideas on an individual’s cognitive development. The approach expressed in this paper is a simple algorithm employing swarm intelligence oriented tasks in order to overcome single-objective optimization problems. For evaluating effectiveness of this early version of the algorithm, test operations have been done via some benchmark functions. The obtained results show that the approach / algorithm can be an alternative to the literature in terms of single-objective optimization.The authors have suggested the name: Cognitive Development Optimization Algorithm (CoDOA for the related intelligent optimization approach.

Multi-objective optimization design method of radiation shielding

International Nuclear Information System (INIS)

Yang Shouhai; Wang Weijin; Lu Daogang; Chen Yixue

2012-01-01

Due to the shielding design goals of diversification and uncertain process of many factors, it is necessary to develop an optimization design method of intelligent shielding by which the shielding scheme selection will be achieved automatically and the uncertainties of human impact will be reduced. For economical feasibility to achieve a radiation shielding design for automation, the multi-objective genetic algorithm optimization of screening code which combines the genetic algorithm and discrete-ordinate method was developed to minimize the costs, size, weight, and so on. This work has some practical significance for gaining the optimization design of shielding. (authors)

Combined Turbine and Cycle Optimization for Organic Rankine Cycle Power Systems—Part B: Application on a Case Study

Directory of Open Access Journals (Sweden)

Angelo La Seta

2016-05-01

Full Text Available Organic Rankine cycle (ORC power systems have recently emerged as promising solutions for waste heat recovery in low- and medium-size power plants. Their performance and economic feasibility strongly depend on the expander. The design process and efficiency estimation are particularly challenging due to the peculiar physical properties of the working fluid and the gas-dynamic phenomena occurring in the machine. Unlike steam Rankine and Brayton engines, organic Rankine cycle expanders combine small enthalpy drops with large expansion ratios. These features yield turbine designs with few highly-loaded stages in supersonic flow regimes. Part A of this two-part paper has presented the implementation and validation of the simulation tool TURAX, which provides the optimal preliminary design of single-stage axial-flow turbines. The authors have also presented a sensitivity analysis on the decision variables affecting the turbine design. Part B of this two-part paper presents the first application of a design method where the thermodynamic cycle optimization is combined with calculations of the maximum expander performance using the mean-line design tool described in part A. The high computational cost of the turbine optimization is tackled by building a model which gives the optimal preliminary design of an axial-flow turbine as a function of the cycle conditions. This allows for estimating the optimal expander performance for each operating condition of interest. The test case is the preliminary design of an organic Rankine cycle turbogenerator to increase the overall energy efficiency of an offshore platform. For an increase in expander pressure ratio from 10 to 35, the results indicate up to 10% point reduction in expander performance. This corresponds to a relative reduction in net power output of 8.3% compared to the case when the turbine efficiency is assumed to be 80%. This work also demonstrates that this approach can support the plant designer

Data Mining and Optimization Tools for Developing Engine Parameters Tools

Science.gov (United States)

Dhawan, Atam P.

1998-01-01

This project was awarded for understanding the problem and developing a plan for Data Mining tools for use in designing and implementing an Engine Condition Monitoring System. Tricia Erhardt and I studied the problem domain for developing an Engine Condition Monitoring system using the sparse and non-standardized datasets to be available through a consortium at NASA Lewis Research Center. We visited NASA three times to discuss additional issues related to dataset which was not made available to us. We discussed and developed a general framework of data mining and optimization tools to extract useful information from sparse and non-standard datasets. These discussions lead to the training of Tricia Erhardt to develop Genetic Algorithm based search programs which were written in C++ and used to demonstrate the capability of GA algorithm in searching an optimal solution in noisy, datasets. From the study and discussion with NASA LeRC personnel, we then prepared a proposal, which is being submitted to NASA for future work for the development of data mining algorithms for engine conditional monitoring. The proposed set of algorithm uses wavelet processing for creating multi-resolution pyramid of tile data for GA based multi-resolution optimal search.

Optimal control of open quantum systems: A combined surrogate Hamiltonian optimal control theory approach applied to photochemistry on surfaces

International Nuclear Information System (INIS)

Asplund, Erik; Kluener, Thorsten

2012-01-01

In this paper, control of open quantum systems with emphasis on the control of surface photochemical reactions is presented. A quantum system in a condensed phase undergoes strong dissipative processes. From a theoretical viewpoint, it is important to model such processes in a rigorous way. In this work, the description of open quantum systems is realized within the surrogate Hamiltonian approach [R. Baer and R. Kosloff, J. Chem. Phys. 106, 8862 (1997)]. An efficient and accurate method to find control fields is optimal control theory (OCT) [W. Zhu, J. Botina, and H. Rabitz, J. Chem. Phys. 108, 1953 (1998); Y. Ohtsuki, G. Turinici, and H. Rabitz, J. Chem. Phys. 120, 5509 (2004)]. To gain control of open quantum systems, the surrogate Hamiltonian approach and OCT, with time-dependent targets, are combined. Three open quantum systems are investigated by the combined method, a harmonic oscillator immersed in an ohmic bath, CO adsorbed on a platinum surface, and NO adsorbed on a nickel oxide surface. Throughout this paper, atomic units, i.e., (ℎ/2π)=m e =e=a 0 = 1, have been used unless otherwise stated.

Comparison of particle swarm optimization and simulated annealing for locating additional boreholes considering combined variance minimization

Science.gov (United States)

Soltani-Mohammadi, Saeed; Safa, Mohammad; Mokhtari, Hadi

2016-10-01

One of the most important stages in complementary exploration is optimal designing the additional drilling pattern or defining the optimum number and location of additional boreholes. Quite a lot research has been carried out in this regard in which for most of the proposed algorithms, kriging variance minimization as a criterion for uncertainty assessment is defined as objective function and the problem could be solved through optimization methods. Although kriging variance implementation is known to have many advantages in objective function definition, it is not sensitive to local variability. As a result, the only factors evaluated for locating the additional boreholes are initial data configuration and variogram model parameters and the effects of local variability are omitted. In this paper, with the goal of considering the local variability in boundaries uncertainty assessment, the application of combined variance is investigated to define the objective function. Thus in order to verify the applicability of the proposed objective function, it is used to locate the additional boreholes in Esfordi phosphate mine through the implementation of metaheuristic optimization methods such as simulated annealing and particle swarm optimization. Comparison of results from the proposed objective function and conventional methods indicates that the new changes imposed on the objective function has caused the algorithm output to be sensitive to the variations of grade, domain's boundaries and the thickness of mineralization domain. The comparison between the results of different optimization algorithms proved that for the presented case the application of particle swarm optimization is more appropriate than simulated annealing.

Modeling and optimization of laser cutting operations

Directory of Open Access Journals (Sweden)

Gadallah Mohamed Hassan

2015-01-01

Full Text Available Laser beam cutting is one important nontraditional machining process. This paper optimizes the parameters of laser beam cutting parameters of stainless steel (316L considering the effect of input parameters such as power, oxygen pressure, frequency and cutting speed. Statistical design of experiments is carried in three different levels and process responses such as average kerf taper (Ta, surface roughness (Ra and heat affected zones are measured accordingly. A response surface model is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi’s L27OA are employed to search for an optimal combination to achieve desired process yield. Response Surface Models (RSMs are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective optimization problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA and optimized using Matlab developed environment. Optimum solutions are compared with Taguchi Methodology results. As such, practicing engineers have means to model, analyze and optimize nontraditional machining processes. Validation experiments are carried to verify the developed models with success.

Design and development of bio-inspired framework for reservoir operation optimization

Science.gov (United States)

Asvini, M. Sakthi; Amudha, T.

2017-12-01

Frameworks for optimal reservoir operation play an important role in the management of water resources and delivery of economic benefits. Effective utilization and conservation of water from reservoirs helps to manage water deficit periods. The main challenge in reservoir optimization is to design operating rules that can be used to inform real-time decisions on reservoir release. We develop a bio-inspired framework for the optimization of reservoir release to satisfy the diverse needs of various stakeholders. In this work, single-objective optimization and multiobjective optimization problems are formulated using an algorithm known as "strawberry optimization" and tested with actual reservoir data. Results indicate that well planned reservoir operations lead to efficient deployment of the reservoir water with the help of optimal release patterns.

Combining nutrition and exercise to optimize survival and recovery from critical illness: Conceptual and methodological issues.

Science.gov (United States)

Heyland, Daren K; Stapleton, Renee D; Mourtzakis, Marina; Hough, Catherine L; Morris, Peter; Deutz, Nicolaas E; Colantuoni, Elizabeth; Day, Andrew; Prado, Carla M; Needham, Dale M

2016-10-01

Survivors of critical illness commonly experience neuromuscular abnormalities, including muscle weakness known as ICU-acquired weakness (ICU-AW). ICU-AW is associated with delayed weaning from mechanical ventilation, extended ICU and hospital stays, more healthcare-related hospital costs, a higher risk of death, and impaired physical functioning and quality of life in the months after ICU admission. These observations speak to the importance of developing new strategies to aid in the physical recovery of acute respiratory failure patients. We posit that to maintain optimal muscle mass, strength and physical function, the combination of nutrition and exercise may have the greatest impact on physical recovery of survivors of critical illness. Randomized trials testing this and related hypotheses are needed. We discussed key methodological issues and proposed a common evaluation framework to stimulate work in this area and standardize our approach to outcome assessments across future studies. Copyright © 2015 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

Optimally Controlled Flexible Fuel Powertrain System

Energy Technology Data Exchange (ETDEWEB)

Hakan Yilmaz; Mark Christie; Anna Stefanopoulou

2010-12-31

The primary objective of this project was to develop a true Flex Fuel Vehicle capable of running on any blend of ethanol from 0 to 85% with reduced penalty in usable vehicle range. A research and development program, targeting 10% improvement in fuel economy using a direct injection (DI) turbocharged spark ignition engine was conducted. In this project a gasoline-optimized high-technology engine was considered and the hardware and configuration modifications were defined for the engine, fueling system, and air path. Combined with a novel engine control strategy, control software, and calibration this resulted in a highly efficient and clean FFV concept. It was also intended to develop robust detection schemes of the ethanol content in the fuel integrated with adaptive control algorithms for optimized turbocharged direct injection engine combustion. The approach relies heavily on software-based adaptation and optimization striving for minimal modifications to the gasoline-optimized engine hardware system. Our ultimate objective was to develop a compact control methodology that takes advantage of any ethanol-based fuel mixture and not compromise the engine performance under gasoline operation.

Combined shape and topology optimization of 3D structures

DEFF Research Database (Denmark)

Christiansen, Asger Nyman; Bærentzen, Jakob Andreas; Nobel-Jørgensen, Morten

2015-01-01

We present a method for automatic generation of 3D models based on shape and topology optimization. The optimization procedure, or model generation process, is initialized by a set of boundary conditions, an objective function, constraints and an initial structure. Using this input, the method...... will automatically deform and change the topology of the initial structure such that the objective function is optimized subject to the specified constraints and boundary conditions. For example, this tool can be used to improve the stiffness of a structure before printing, reduce the amount of material needed...

Scalable implementation of ancilla-free optimal 1→M phase-covariant quantum cloning by combining quantum Zeno dynamics and adiabatic passage

International Nuclear Information System (INIS)

Shao, Xiao-Qiang; Zheng, Tai-Yu; Zhang, Shou

2011-01-01

A scalable way for implementation of ancilla-free optimal 1→M phase-covariant quantum cloning (PCC) is proposed by combining quantum Zeno dynamics and adiabatic passage. An optimal 1→M PCC can be achieved directly from the existed optimal 1→(M-1) PCC without excited states population during the whole process. The cases for optimal 1→3 (4) PCCs are discussed detailedly to show that the scheme is robust against the effect of decoherence. Moreover, the time for carrying out each cloning transformation is regular, which may reduce the complexity for achieving the optimal PCC in experiment. -- Highlights: → We implement the ancilla-free optimal 1→M phase-covariant quantum cloning machine. → This scheme is robust against the cavity decay and the spontaneous emission of atom. → The time for carrying out each cloning transformation is regular.

Scalable implementation of ancilla-free optimal 1→M phase-covariant quantum cloning by combining quantum Zeno dynamics and adiabatic passage

Energy Technology Data Exchange (ETDEWEB)

Shao, Xiao-Qiang, E-mail: xqshao83@yahoo.cn [School of Physics, Northeast Normal University, Changchun 130024 (China); Zheng, Tai-Yu, E-mail: zhengty@nenu.edu.cn [School of Physics, Northeast Normal University, Changchun 130024 (China); Zhang, Shou [Department of Physics, College of Science, Yanbian University, Yanji, Jilin 133002 (China)

2011-09-19

A scalable way for implementation of ancilla-free optimal 1→M phase-covariant quantum cloning (PCC) is proposed by combining quantum Zeno dynamics and adiabatic passage. An optimal 1→M PCC can be achieved directly from the existed optimal 1→(M-1) PCC without excited states population during the whole process. The cases for optimal 1→3 (4) PCCs are discussed detailedly to show that the scheme is robust against the effect of decoherence. Moreover, the time for carrying out each cloning transformation is regular, which may reduce the complexity for achieving the optimal PCC in experiment. -- Highlights: → We implement the ancilla-free optimal 1→M phase-covariant quantum cloning machine. → This scheme is robust against the cavity decay and the spontaneous emission of atom. → The time for carrying out each cloning transformation is regular.

Development of shelf stable, processed, low acid food products using heat-irradiation combination treatments

International Nuclear Information System (INIS)

Minnaar, A.

1998-01-01

The amount of ionizing irradiation needed to sterilize low acid vegetable and starch products (with and without sauces) commercially impairs their sensorial and nutritive qualities, and use of thermal processes for the same purpose may also have an adverse effect on the product quality. A systematic approach to the establishment of optimized combination parameters was developed for heat-irradiation processing to produce high quality, shelf stable, low acid food products. The effects of selected heat, heat-irradiation combination and irradiation treatments on the quality of shelf stable mushrooms in brine and rice, stored at ambient temperature, were studied. From a quality viewpoint, use of heat-irradiation combination treatments favouring low irradiation dose levels offered a feasible alternative to thermally processed or radappertized mushrooms in brine. However, shelf stable rice produced by heat-irradiation combination treatments offered a feasible alternative only to radappertized rice from the standpoint of quality. The technical requirements for the heat and irradiation processing of a long grain rice cultivar from the United States of America oppose each other directly, thereby reducing the feasibility of using heat-irradiation combination processing to produce shelf stable rice. The stability of starch thickened white sauces was found to be affected severely during high dose irradiation and subsequent storage at ambient temperature. However, use of pea protein isolate as a thickener in white sauces was found to have the potential to maintain the viscosity of sauces for irradiated meat and sauce products throughout processing and storage. (author)

Optimizing combination of vascular endothelial growth factor and mesenchymal stem cells on ectopic bone formation in SCID mice

DEFF Research Database (Denmark)

Dreyer, Chris H; Kjaergaard, Kristian; Ditzel, Nicholas

2017-01-01

combined immunodeficient (SCID) mice were used in this study to evaluate optimal time points for VEGF stimulation to increase bone formation. METHODS: Twenty-eight SCID (NOD.CB17-Prkdcscid/J) mice had hydroxyapatite granules seeded with 5 × 105MSCs inserted subcutaneous. Pellets released VEGF on days 1...

BRAIN Journal - Solving Optimization Problems via Vortex Optimization Algorithm and Cognitive Development Optimization Algorithm

OpenAIRE

Ahmet Demir; Utku Kose

2016-01-01

ABSTRACT In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Sc...

A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design.

Directory of Open Access Journals (Sweden)

Maryam M Shanechi

Full Text Available Real-time brain-machine interfaces (BMI have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.

Combined emission economic dispatch of power system including solar photo voltaic generation

International Nuclear Information System (INIS)

Khan, Naveed Ahmed; Awan, Ahmed Bilal; Mahmood, Anzar; Razzaq, Sohail; Zafar, Adnan; Sidhu, Guftaar Ahmed Sardar

2015-01-01

Highlights: • Combined Emission Economic Dispatch Problem has been solved with inclusion of solar power plants. • Mixed Integer Optimization Problem has been solved using Particle Swarm Optimization. • Static and dynamic case studies have been considered. • Clouds effect with 15% and 30% reduced radiations has also been taken into account. • Simulation results prove the effectiveness of proposed model. - Abstract: Reliable and inexpensive electricity provision is one of the significant research objectives since decades. Various Economic Dispatch (ED) methods have been developed in order to address the challenge of continuous and sustainable electricity provision at optimized cost. Rapid escalation of fuel prices, depletion of fossil fuel reserves and environmental concerns have compelled us to incorporate the Renewable Energy (RE) resources in the energy mix. This paper presents Combined Emission Economic Dispatch (CEED) models developed for a system consisting of multiple Photo Voltaic (PV) plants and thermal units. Based on the nature of decision variables, our proposed model is essentially a Mixed Integer Optimization Problem (MIOP). Particle Swarm Optimization (PSO) is used to solve the optimization problem for a scenario involving six conventional and thirteen PV plants. Two test cases, Combined Static Emission Economic Dispatch (SCEED) and Combined Dynamic Emission Economic Dispatch (DCEED), have been considered. SCEED is performed for full solar radiation level as well as for reduced radiation level due to clouds effect. Simulation results have proved the effectiveness of the proposed model

Optimal placement of combined heat and power scheme (cogeneration): application to an ethylbenzene plant

International Nuclear Information System (INIS)

Zainuddin Abd Manan; Lim Fang Yee

2001-01-01

Combined heat and power (CHP) scheme, also known as cogeneration is widely accepted as a highly efficient energy saving measure, particularly in medium to large scale chemical process plants. To date, CHP application is well established in the developed countries. The advantage of a CHP scheme for a chemical plant is two-fold: (i) drastically cut down on the electricity bill from on-site power generation (ii) to save the fuel bills through recovery of the quality waste heat from power generation for process heating. In order to be effective, a CHP scheme must be placed at the right temperature level in the context of the overall process. Failure to do so might render a CHP venture worthless. This paper discusses the procedure for an effective implementation of a CHP scheme. An ethylbenzene process is used as a case study. A key visualization tool known as the grand composite curves is used to provide an overall picture of the process heat source and heat sink profiles. The grand composite curve, which is generated based on the first principles of Pinch Analysis enables the CHP scheme to be optimally placed within the overall process scenario. (Author)

Combined application of mixture experimental design and artificial neural networks in the solid dispersion development.

Science.gov (United States)

Medarević, Djordje P; Kleinebudde, Peter; Djuriš, Jelena; Djurić, Zorica; Ibrić, Svetlana

2016-01-01

This study for the first time demonstrates combined application of mixture experimental design and artificial neural networks (ANNs) in the solid dispersions (SDs) development. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs were prepared by solvent casting method to improve carbamazepine dissolution rate. The influence of the composition of prepared SDs on carbamazepine dissolution rate was evaluated using d-optimal mixture experimental design and multilayer perceptron ANNs. Physicochemical characterization proved the presence of the most stable carbamazepine polymorph III within the SD matrix. Ternary carbamazepine-Soluplus®-poloxamer 188 SDs significantly improved carbamazepine dissolution rate compared to pure drug. Models developed by ANNs and mixture experimental design well described the relationship between proportions of SD components and percentage of carbamazepine released after 10 (Q10) and 20 (Q20) min, wherein ANN model exhibit better predictability on test data set. Proportions of carbamazepine and poloxamer 188 exhibited the highest influence on carbamazepine release rate. The highest carbamazepine release rate was observed for SDs with the lowest proportions of carbamazepine and the highest proportions of poloxamer 188. ANNs and mixture experimental design can be used as powerful data modeling tools in the systematic development of SDs. Taking into account advantages and disadvantages of both techniques, their combined application should be encouraged.

Research on loading pattern optimization for VVER reactor

International Nuclear Information System (INIS)

Tran Viet Phu; Nguyen Thi Mai Huong; Nguyen Huu Tiep; Ta Duy Long; Tran Vinh Thanh; Tran Hoai Nam

2017-01-01

A study on fuel loading pattern optimization of a VVER reactor was performed. In this study, a core physics simulator was developed based on a multi-group diffusion theory for the use in the problem of fuel loading optimization of VVER reactors. The core simulator could handle the triangular meshes of the core and the computational speed is fast. Verification of the core simulator was confirmed against a benchmark problem of a VVER-1000 reactor. Several optimization methods such as DS, SA, TS and a combination of them were investigated and implemented in coupling with the core simulator. Calculations was performed for optimizing the fuel loading pattern of the core using these methods based on a benchmark core model in comparison with the reference core. Comparison among these methods have shown that a combination of SA+TS is the most effective for the problem of fuel loading pattern optimization. Advanced methods are being researched continuously. (author)

Contingency Contractor Optimization Phase 3 Sustainment Software Design Document - Contingency Contractor Optimization Tool - Prototype

Energy Technology Data Exchange (ETDEWEB)

Durfee, Justin David; Frazier, Christopher Rawls; Bandlow, Alisa; Jones, Katherine A

2016-05-01

This document describes the final software design of the Contingency Contractor Optimization Tool - Prototype. Its purpose is to provide the overall architecture of the software and the logic behind this architecture. Documentation for the individual classes is provided in the application Javadoc. The Contingency Contractor Optimization project is intended to address Department of Defense mandates by delivering a centralized strategic planning tool that allows senior decision makers to quickly and accurately assess the impacts, risks, and mitigation strategies associated with utilizing contract support. The Contingency Contractor Optimization Tool - Prototype was developed in Phase 3 of the OSD ATL Contingency Contractor Optimization project to support strategic planning for contingency contractors. The planning tool uses a model to optimize the Total Force mix by minimizing the combined total costs for selected mission scenarios. The model optimizes the match of personnel types (military, DoD civilian, and contractors) and capabilities to meet mission requirements as effectively as possible, based on risk, cost, and other requirements.

Modeling the ultrasonic testing echoes by a combination of particle swarm optimization and Levenberg–Marquardt algorithms

International Nuclear Information System (INIS)

Gholami, Ali; Honarvar, Farhang; Moghaddam, Hamid Abrishami

2017-01-01

This paper presents an accurate and easy-to-implement algorithm for estimating the parameters of the asymmetric Gaussian chirplet model (AGCM) used for modeling echoes measured in ultrasonic nondestructive testing (NDT) of materials. The proposed algorithm is a combination of particle swarm optimization (PSO) and Levenberg–Marquardt (LM) algorithms. PSO does not need an accurate initial guess and quickly converges to a reasonable output while LM needs a good initial guess in order to provide an accurate output. In the combined algorithm, PSO is run first to provide a rough estimate of the output and this result is consequently inputted to the LM algorithm for more accurate estimation of parameters. To apply the algorithm to signals with multiple echoes, the space alternating generalized expectation maximization (SAGE) is used. The proposed combined algorithm is robust and accurate. To examine the performance of the proposed algorithm, it is applied to a number of simulated echoes having various signal to noise ratios. The combined algorithm is also applied to a number of experimental ultrasonic signals. The results corroborate the accuracy and reliability of the proposed combined algorithm. (paper)

Optimally combining dynamical decoupling and quantum error correction.

Science.gov (United States)

Paz-Silva, Gerardo A; Lidar, D A

2013-01-01

Quantum control and fault-tolerant quantum computing (FTQC) are two of the cornerstones on which the hope of realizing a large-scale quantum computer is pinned, yet only preliminary steps have been taken towards formalizing the interplay between them. Here we explore this interplay using the powerful strategy of dynamical decoupling (DD), and show how it can be seamlessly and optimally integrated with FTQC. To this end we show how to find the optimal decoupling generator set (DGS) for various subspaces relevant to FTQC, and how to simultaneously decouple them. We focus on stabilizer codes, which represent the largest contribution to the size of the DGS, showing that the intuitive choice comprising the stabilizers and logical operators of the code is in fact optimal, i.e., minimizes a natural cost function associated with the length of DD sequences. Our work brings hybrid DD-FTQC schemes, and their potentially considerable advantages, closer to realization.

Proposing a novel combined cycle for optimal exergy recovery of liquefied natural gas

Energy Technology Data Exchange (ETDEWEB)

Salimpour, M.R.; Zahedi, M.A. [Isfahan University of Technology (Iran, Islamic Republic of). Department of Mechanical Engineering

2012-08-15

The effective utilization of the cryogenic exergy associated with liquefied natural gas (LNG) vaporization is important. In this paper, a novel combined power cycle is proposed which utilizes LNG in different ways to enhance the power generation of a power plant. In addition to the direct expansion in the appropriate expander, LNG is used as a low-temperature heat sink for a middle-pressure gas cycle which uses nitrogen as working fluid. Also, LNG is used to cool the inlet air of an open Brayton gas turbine cycle. These measures are accomplished to improve the exergy recovery of LNG. In order to analyze the performance of the system, the influence of several key parameters such as pressure ratio of LNG turbine, ratio of the mass flow rate of LNG to the mass flow rate of air, pressure ratio of different compressors, LNG pressure and inlet pressure of nitrogen compressor, on the thermal efficiency and exergy efficiency of the offered cycle is investigated. Finally, the proposed combined cycle is optimized on the basis of first and second laws of thermodynamics. (orig.)

A method to optimize the shield compact and lightweight combining the structure with components together by genetic algorithm and MCNP code.

Science.gov (United States)

Cai, Yao; Hu, Huasi; Pan, Ziheng; Hu, Guang; Zhang, Tao

2018-05-17

To optimize the shield for neutrons and gamma rays compact and lightweight, a method combining the structure and components together was established employing genetic algorithms and MCNP code. As a typical case, the fission energy spectrum of 235 U which mixed neutrons and gamma rays was adopted in this study. Six types of materials were presented and optimized by the method. Spherical geometry was adopted in the optimization after checking the geometry effect. Simulations have made to verify the reliability of the optimization method and the efficiency of the optimized materials. To compare the materials visually and conveniently, the volume and weight needed to build a shield are employed. The results showed that, the composite multilayer material has the best performance. Copyright © 2018 Elsevier Ltd. All rights reserved.

Optimized Treatment Schedules for Chronic Myeloid Leukemia.

Directory of Open Access Journals (Sweden)

Qie He

2016-10-01

Full Text Available Over the past decade, several targeted therapies (e.g. imatinib, dasatinib, nilotinib have been developed to treat Chronic Myeloid Leukemia (CML. Despite an initial response to therapy, drug resistance remains a problem for some CML patients. Recent studies have shown that resistance mutations that preexist treatment can be detected in a substantial number of patients, and that this may be associated with eventual treatment failure. One proposed method to extend treatment efficacy is to use a combination of multiple targeted therapies. However, the design of such combination therapies (timing, sequence, etc. remains an open challenge. In this work we mathematically model the dynamics of CML response to combination therapy and analyze the impact of combination treatment schedules on treatment efficacy in patients with preexisting resistance. We then propose an optimization problem to find the best schedule of multiple therapies based on the evolution of CML according to our ordinary differential equation model. This resulting optimization problem is nontrivial due to the presence of ordinary different equation constraints and integer variables. Our model also incorporates drug toxicity constraints by tracking the dynamics of patient neutrophil counts in response to therapy. We determine optimal combination strategies that maximize time until treatment failure on hypothetical patients, using parameters estimated from clinical data in the literature.

Development of a Multi-Event Trajectory Optimization Tool for Noise-Optimized Approach Route Design

NARCIS (Netherlands)

Braakenburg, M.L.; Hartjes, S.; Visser, H.G.; Hebly, S.J.

2011-01-01

This paper presents preliminary results from an ongoing research effort towards the development of a multi-event trajectory optimization methodology that allows to synthesize RNAV approach routes that minimize a cumulative measure of noise, taking into account the total noise effect aggregated for

Management Methods by Development of Objects of Energy Supply Taking into Account the Combined Risks

Directory of Open Access Journals (Sweden)

Kirillova Ariadna

2016-01-01

Full Text Available In the article the methods of choice of optimal administrative decisions are examined on the development of objects energy supplies of housing and communal services. That would take into account nascent risks related to investment-building activity of energy supplying enterprises. It is shown that the basic condition of housing and utilities on an energy supply is the building of objects with a subsequent production, distribution and realization of electric energy, that allows at their reproduction to provide quality and reliability of energy supply to the consumers. Also the questions of decline of risks and exposure of factors, influencing on the processes of management development of objects of energy supply in the field of housing and utilities, estimation of organizationally-economic reliability and combined risks, estimation of possibility of the use of innovative energy technologies, alternative sources of energy supply, are investigated in the article. It is fixed, that risk indexes must be appraised not only on the investment stage of building object, but also on his operating stage (after commissioning on the basis of account of the great number of risks characterizing the combined risk.

A Combination of Genetic Algorithm and Particle Swarm Optimization for Vehicle Routing Problem with Time Windows.

Science.gov (United States)

Xu, Sheng-Hua; Liu, Ji-Ping; Zhang, Fu-Hao; Wang, Liang; Sun, Li-Jian

2015-08-27

A combination of genetic algorithm and particle swarm optimization (PSO) for vehicle routing problems with time windows (VRPTW) is proposed in this paper. The improvements of the proposed algorithm include: using the particle real number encoding method to decode the route to alleviate the computation burden, applying a linear decreasing function based on the number of the iterations to provide balance between global and local exploration abilities, and integrating with the crossover operator of genetic algorithm to avoid the premature convergence and the local minimum. The experimental results show that the proposed algorithm is not only more efficient and competitive with other published results but can also obtain more optimal solutions for solving the VRPTW issue. One new well-known solution for this benchmark problem is also outlined in the following.

Application of colony complex algorithm to nuclear component optimization design

International Nuclear Information System (INIS)

Yan Changqi; Li Guijing; Wang Jianjun

2014-01-01

Complex algorithm (CA) has got popular application to the region of nuclear engineering. In connection with the specific features of the application of traditional complex algorithm (TCA) to the optimization design in engineering structures, an improved method, colony complex algorithm (CCA), was developed based on the optimal combination of many complexes, in which the disadvantages of TCA were overcame. The optimized results of benchmark function show that CCA has better optimizing performance than TCA. CCA was applied to the high-pressure heater optimization design, and the optimization effect is obvious. (authors)

Controlling T2 blurring in 3D RARE arterial spin labeling acquisition through optimal combination of variable flip angles and k-space filtering.

Science.gov (United States)

Zhao, Li; Chang, Ching-Di; Alsop, David C

2018-02-09

To improve the SNR efficiency and reduce the T 2 blurring of 3D rapid acquisition with relaxation enhancement stack-of-spiral arterial spin labeling imaging by using variable refocusing flip angles and k-space filtering. An algorithm for determining the optimal combination of variable flip angles and filtering correction is proposed. The flip angles are designed using extended phase graph physical simulations in an analytical and global optimization framework, with an optional constraint on deposited power. Optimal designs for correcting to Hann and Fermi window functions were compared with conventional constant amplitude or variable flip angle only designs on 6 volunteers. With the Fermi window correction, the proposed optimal designs provided 39.8 and 27.3% higher SNR (P variable flip angle designs. Even when power deposition was limited to 50% of the constant amplitude design, the proposed method outperformed the SNR (P variable flip angles can be derived as the output of an optimization problem. The combined design of variable flip angle and k-space filtering provided superior SNR to designs primarily emphasizing either approach singly. © 2018 International Society for Magnetic Resonance in Medicine.

Development of a Deterministic Optimization Model for Design of an Integrated Utility and Hydrogen Supply Network

International Nuclear Information System (INIS)

Hwangbo, Soonho; Lee, In-Beum; Han, Jeehoon

2014-01-01

Lots of networks are constructed in a large scale industrial complex. Each network meet their demands through production or transportation of materials which are needed to companies in a network. Network directly produces materials for satisfying demands in a company or purchase form outside due to demand uncertainty, financial factor, and so on. Especially utility network and hydrogen network are typical and major networks in a large scale industrial complex. Many studies have been done mainly with focusing on minimizing the total cost or optimizing the network structure. But, few research tries to make an integrated network model by connecting utility network and hydrogen network. In this study, deterministic mixed integer linear programming model is developed for integrating utility network and hydrogen network. Steam Methane Reforming process is necessary for combining two networks. After producing hydrogen from Steam-Methane Reforming process whose raw material is steam vents from utility network, produced hydrogen go into hydrogen network and fulfill own needs. Proposed model can suggest optimized case in integrated network model, optimized blueprint, and calculate optimal total cost. The capability of the proposed model is tested by applying it to Yeosu industrial complex in Korea. Yeosu industrial complex has the one of the biggest petrochemical complex and various papers are based in data of Yeosu industrial complex. From a case study, the integrated network model suggests more optimal conclusions compared with previous results obtained by individually researching utility network and hydrogen network

Forecast Combinations

OpenAIRE

Timmermann, Allan G

2005-01-01

Forecast combinations have frequently been found in empirical studies to produce better forecasts on average than methods based on the ex-ante best individual forecasting model. Moreover, simple combinations that ignore correlations between forecast errors often dominate more refined combination schemes aimed at estimating the theoretically optimal combination weights. In this paper we analyse theoretically the factors that determine the advantages from combining forecasts (for example, the d...

To an optimal electricity supply system. Possible bottlenecks in the development to an optimal electricity supply system in northwest Europe

International Nuclear Information System (INIS)

Van Werven, M.J.N.; De Joode, J.; Scheepers, M.J.J.

2006-02-01

It is uncertain how the electricity system in Europe, and in particular northwest Europe and the Netherlands, will develop in the next fifteen years. The main objective of this report is to identify possible bottlenecks that may hamper the northwest European electricity system to develop into an optimal system in the long term (until 2020). Subsequently, based on the identified bottlenecks, the report attempts to indicate relevant market response and policy options. To be able to identify possible bottlenecks in the development to an optimal electricity system, an analytical framework has been set up with the aim to identify possible (future) problems in a structured way. The segments generation, network, demand, balancing, and policy and regulation are analysed, as well as the interactions between these segments. Each identified bottleneck is assessed on the criteria reliability, sustainability and affordability. Three bottlenecks are analysed in more detail: (1) The increasing penetration of distributed generation (DG) and its interaction with the electricity network. Dutch policy could be aimed at: (a) Gaining more insight in the costs and benefits that result from the increasing penetration of DG; (b) Creating possibilities for DSOs to experiment with innovative (network management) concepts; (c) Introducing locational signals; and (d) Further analyse the possibility of ownership unbundling; (2) The problem of intermittency and its implications for balancing the electricity system. Dutch policy could be aimed at: (a) Creating the environment in which the market is able to respond in an efficient way; (b) Monitoring market responses; (c) Market coupling; and (d) Discussing the timing of the gate closure; and (3) Interconnection and congestion issues in combination with generation. Dutch policy could be aimed at: (a) Using the existing interconnection capacity as efficient as possible; (b) Identifying the causes behind price differences; and (c) Harmonise market

Optimization and inverse problems in electromagnetism

CERN Document Server

Wiak, Sławomir

2003-01-01

From 12 to 14 September 2002, the Academy of Humanities and Economics (AHE) hosted the workshop "Optimization and Inverse Problems in Electromagnetism". After this bi-annual event, a large number of papers were assembled and combined in this book. During the workshop recent developments and applications in optimization and inverse methodologies for electromagnetic fields were discussed. The contributions selected for the present volume cover a wide spectrum of inverse and optimal electromagnetic methodologies, ranging from theoretical to practical applications. A number of new optimal and inverse methodologies were proposed. There are contributions related to dedicated software. Optimization and Inverse Problems in Electromagnetism consists of three thematic chapters, covering: -General papers (survey of specific aspects of optimization and inverse problems in electromagnetism), -Methodologies, -Industrial Applications. The book can be useful to students of electrical and electronics engineering, computer sci...

Optimal combination of illusory and luminance-defined 3-D surfaces: A role for ambiguity.

Science.gov (United States)

Hartle, Brittney; Wilcox, Laurie M; Murray, Richard F

2018-04-01

The shape of the illusory surface in stereoscopic Kanizsa figures is determined by the interpolation of depth from the luminance edges of adjacent inducing elements. Despite ambiguity in the position of illusory boundaries, observers reliably perceive a coherent three-dimensional (3-D) surface. However, this ambiguity may contribute additional uncertainty to the depth percept beyond what is expected from measurement noise alone. We evaluated the intrinsic ambiguity of illusory boundaries by using a cue-combination paradigm to measure the reliability of depth percepts elicited by stereoscopic illusory surfaces. We assessed the accuracy and precision of depth percepts using 3-D Kanizsa figures relative to luminance-defined surfaces. The location of the surface peak was defined by illusory boundaries, luminance-defined edges, or both. Accuracy and precision were assessed using a depth-discrimination paradigm. A maximum likelihood linear cue combination model was used to evaluate the relative contribution of illusory and luminance-defined signals to the perceived depth of the combined surface. Our analysis showed that the standard deviation of depth estimates was consistent with an optimal cue combination model, but the points of subjective equality indicated that observers consistently underweighted the contribution of illusory boundaries. This systematic underweighting may reflect a combination rule that attributes additional intrinsic ambiguity to the location of the illusory boundary. Although previous studies show that illusory and luminance-defined contours share many perceptual similarities, our model suggests that ambiguity plays a larger role in the perceptual representation of illusory contours than of luminance-defined contours.

Combined Municipal Solid Waste and biomass system optimization for district energy applications

International Nuclear Information System (INIS)

Rentizelas, Athanasios A.; Tolis, Athanasios I.; Tatsiopoulos, Ilias P.

2014-01-01

Highlights: • Combined energy conversion of MSW and agricultural residue biomass is examined. • The model optimizes the financial yield of the investment. • Several system specifications are optimally defined by the optimization model. • The application to a case study in Greece shows positive financial yield. • The investment is mostly sensitive on the interest rate, the investment cost and the heating oil price. - Abstract: Municipal Solid Waste (MSW) disposal has been a controversial issue in many countries over the past years, due to disagreement among the various stakeholders on the waste management policies and technologies to be adopted. One of the ways of treating/disposing MSW is energy recovery, as waste is considered to contain a considerable amount of bio-waste and therefore can lead to renewable energy production. The overall efficiency can be very high in the cases of co-generation or tri-generation. In this paper a model is presented, aiming to support decision makers in issues relating to Municipal Solid Waste energy recovery. The idea of using more fuel sources, including MSW and agricultural residue biomass that may exist in a rural area, is explored. The model aims at optimizing the system specifications, such as the capacity of the base-load Waste-to-Energy facility, the capacity of the peak-load biomass boiler and the location of the facility. Furthermore, it defines the quantity of each potential fuel source that should be used annually, in order to maximize the financial yield of the investment. The results of an energy tri-generation case study application at a rural area of Greece, using mixed MSW and biomass, indicate positive financial yield of investment. In addition, a sensitivity analysis is performed on the effect of the most important parameters of the model on the optimum solution, pinpointing the parameters of interest rate, investment cost and heating oil price, as those requiring the attention of the decision makers

Combined Municipal Solid Waste and biomass system optimization for district energy applications

Energy Technology Data Exchange (ETDEWEB)

Rentizelas, Athanasios A., E-mail: arent@central.ntua.gr; Tolis, Athanasios I., E-mail: atol@central.ntua.gr; Tatsiopoulos, Ilias P., E-mail: itat@central.ntua.gr

2014-01-15

Highlights: • Combined energy conversion of MSW and agricultural residue biomass is examined. • The model optimizes the financial yield of the investment. • Several system specifications are optimally defined by the optimization model. • The application to a case study in Greece shows positive financial yield. • The investment is mostly sensitive on the interest rate, the investment cost and the heating oil price. - Abstract: Municipal Solid Waste (MSW) disposal has been a controversial issue in many countries over the past years, due to disagreement among the various stakeholders on the waste management policies and technologies to be adopted. One of the ways of treating/disposing MSW is energy recovery, as waste is considered to contain a considerable amount of bio-waste and therefore can lead to renewable energy production. The overall efficiency can be very high in the cases of co-generation or tri-generation. In this paper a model is presented, aiming to support decision makers in issues relating to Municipal Solid Waste energy recovery. The idea of using more fuel sources, including MSW and agricultural residue biomass that may exist in a rural area, is explored. The model aims at optimizing the system specifications, such as the capacity of the base-load Waste-to-Energy facility, the capacity of the peak-load biomass boiler and the location of the facility. Furthermore, it defines the quantity of each potential fuel source that should be used annually, in order to maximize the financial yield of the investment. The results of an energy tri-generation case study application at a rural area of Greece, using mixed MSW and biomass, indicate positive financial yield of investment. In addition, a sensitivity analysis is performed on the effect of the most important parameters of the model on the optimum solution, pinpointing the parameters of interest rate, investment cost and heating oil price, as those requiring the attention of the decision makers

Optimizing Water Allocation under Uncertain System Conditions for Water and Agriculture Future Scenarios in Alfeios River Basin (Greece—Part B: Fuzzy-Boundary Intervals Combined with Multi-Stage Stochastic Programming Model

Directory of Open Access Journals (Sweden)

Eleni Bekri

2015-11-01

Full Text Available Optimal water allocation within a river basin still remains a great modeling challenge for engineers due to various hydrosystem complexities, parameter uncertainties and their interactions. Conventional deterministic optimization approaches have given their place to stochastic, fuzzy and interval-parameter programming approaches and their hybrid combinations for overcoming these difficulties. In many countries, including Mediterranean countries, water resources management is characterized by uncertain, imprecise and limited data because of the absence of permanent measuring systems, inefficient river monitoring and fragmentation of authority responsibilities. A fuzzy-boundary-interval linear programming methodology developed by Li et al. (2010 is selected and applied in the Alfeios river basin (Greece for optimal water allocation under uncertain system conditions. This methodology combines an ordinary multi-stage stochastic programming with uncertainties expressed as fuzzy-boundary intervals. Upper- and lower-bound solution intervals for optimized water allocation targets and probabilistic water allocations and shortages are estimated under a baseline scenario and four water and agricultural policy future scenarios for an optimistic and a pessimistic attitude of the decision makers. In this work, the uncertainty of the random water inflows is incorporated through the simultaneous generation of stochastic equal-probability hydrologic scenarios at various inflow positions instead of using a scenario-tree approach in the original methodology.

The PWR loading pattern optimization in X-IMAGE

International Nuclear Information System (INIS)

Stevens, J.G.; Smith, K.S.; Rempe, K.R.; Downar, T.J.

1993-01-01

The design of reactor core loading patterns is difficult due to the staggering number of patterns. The integer nature and nonlinear neutronic response of core design preclude simple prescriptions for generation of the feasible patterns, much less optimization among feasible candidates. Fortunately, recent developments in optimization, graphical user interfaces (GUIs), and the speed and low cost of engineering workstations combine to make loading pattern automation possible. The optimization module SIMAN has been added to X-IMAGE to automatically generate high-quality core loadings

Optimized molecular reconstruction procedure combining hybrid reverse Monte Carlo and molecular dynamics

Energy Technology Data Exchange (ETDEWEB)

Bousige, Colin; Boţan, Alexandru; Coasne, Benoît, E-mail: coasne@mit.edu [Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); UMI 3466 CNRS-MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); Ulm, Franz-Josef [Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); Pellenq, Roland J.-M. [Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); UMI 3466 CNRS-MIT, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139 (United States); CINaM, CNRS/Aix Marseille Université, Campus de Luminy, 13288 Marseille Cedex 09 (France)

2015-03-21

We report an efficient atom-scale reconstruction method that consists of combining the Hybrid Reverse Monte Carlo algorithm (HRMC) with Molecular Dynamics (MD) in the framework of a simulated annealing technique. In the spirit of the experimentally constrained molecular relaxation technique [Biswas et al., Phys. Rev. B 69, 195207 (2004)], this modified procedure offers a refined strategy in the field of reconstruction techniques, with special interest for heterogeneous and disordered solids such as amorphous porous materials. While the HRMC method generates physical structures, thanks to the use of energy penalties, the combination with MD makes the method at least one order of magnitude faster than HRMC simulations to obtain structures of similar quality. Furthermore, in order to ensure the transferability of this technique, we provide rational arguments to select the various input parameters such as the relative weight ω of the energy penalty with respect to the structure optimization. By applying the method to disordered porous carbons, we show that adsorption properties provide data to test the global texture of the reconstructed sample but are only weakly sensitive to the presence of defects. In contrast, the vibrational properties such as the phonon density of states are found to be very sensitive to the local structure of the sample.

Infrared Drying Parameter Optimization

Science.gov (United States)

Jackson, Matthew R.

In recent years, much research has been done to explore direct printing methods, such as screen and inkjet printing, as alternatives to the traditional lithographic process. The primary motivation is reduction of the material costs associated with producing common electronic devices. Much of this research has focused on developing inkjet or screen paste formulations that can be printed on a variety of substrates, and which have similar conductivity performance to the materials currently used in the manufacturing of circuit boards and other electronic devices. Very little research has been done to develop a process that would use direct printing methods to manufacture electronic devices in high volumes. This study focuses on developing and optimizing a drying process for conductive copper ink in a high volume manufacturing setting. Using an infrared (IR) dryer, it was determined that conductive copper prints could be dried in seconds or minutes as opposed to tens of minutes or hours that it would take with other drying devices, such as a vacuum oven. In addition, this study also identifies significant parameters that can affect the conductivity of IR dried prints. Using designed experiments and statistical analysis; the dryer parameters were optimized to produce the best conductivity performance for a specific ink formulation and substrate combination. It was determined that for an ethylene glycol, butanol, 1-methoxy 2- propanol ink formulation printed on Kapton, the optimal drying parameters consisted of a dryer height of 4 inches, a temperature setting between 190 - 200°C, and a dry time of 50-65 seconds depending on the printed film thickness as determined by the number of print passes. It is important to note that these parameters are optimized specifically for the ink formulation and substrate used in this study. There is still much research that needs to be done into optimizing the IR dryer for different ink substrate combinations, as well as developing a

Exergoeconomic performance optimization of an endoreversible intercooled regenerative Brayton combined heat and power plant coupled to variable-temperature heat reservoirs

Energy Technology Data Exchange (ETDEWEB)

Yang, Bo; Chen, Lingen; Sun, Fengrui [College of Naval Architecture and Power, Naval University of Engineering, Wuhan 430033 (China)

2012-07-01

An endoreversible intercooled regenerative Brayton combined heat and power (CHP) plant model coupled to variable-temperature heat reservoirs is established. The exergoeconomic performance of the CHP plant is investigated using finite time thermodynamics. The analytical formulae about dimensionless profit rate and exergy efficiency of the CHP plant with the heat resistance losses in the hot-, cold- and consumer-side heat exchangers, the intercooler and the regenerator are deduced. By taking the maximum profit rate as the objective, the heat conductance allocation among the five heat exchangers and the choice of intercooling pressure ratio are optimized by numerical examples, the characteristic of the optimal dimensionless profit rate versus corresponding exergy efficiency is investigated. When the optimization is performed further with respect to the total pressure ratio, a double-maximum profit rate is obtained. The effects of the design parameters on the double-maximum dimensionless profit rate and corresponding exergy efficiency, optimal total pressure ratio and optimal intercooling pressure ratio are analyzed in detail, and it is found that there exist an optimal consumer-side temperature and an optimal thermal capacitance rate matching between the working fluid and the heat reservoir, respectively, corresponding to a thrice-maximum dimensionless profit rate.

Optimization of a reversed-phase-high-performance thin-layer chromatography method for the separation of isoniazid, ethambutol, rifampicin and pyrazinamide in fixed-dose combination antituberculosis tablets.

Science.gov (United States)

Shewiyo, D H; Kaale, E; Risha, P G; Dejaegher, B; Smeyers-Verbeke, J; Vander Heyden, Y

2012-10-19

This paper presents the development of a new RP-HPTLC method for the separation of pyrazinamide, isoniazid, rifampicin and ethambutol in a four fixed-dose combination (4 FDC) tablet formulation. It is a single method with two steps in which after plate development pyrazinamide, isoniazid and rifampicin are detected at an UV wavelength of 280 nm. Then ethambutol is derivatized and detected at a VIS wavelength of 450 nm. Methanol, ethanol and propan-1-ol were evaluated modifiers to form alcohol-water mobile phases. Systematic optimization of the composition of each alcohol in the mobile phase was carried out using the window diagramming concept to obtain the best separation. Examination of the Rf distribution of the separated compounds showed that separation of the compounds with the mobile phase containing ethanol at the optimal fraction was almost situated within the optimal Rf-values region of 0.20-0.80. Therefore, ethanol was selected as organic modifier and the optimal mobile phase composition was found to be ethanol, water, glacial acetic acid (>99% acetic acid) and 37% ammonia solution (70/30/5/1, v/v/v/v). The method is new, quick and cheap compared to the actual method in the International Pharmacopoeia for the assay of the 4 FDC tablets, which involves the use of two separate HPLC methods. Copyright © 2012 Elsevier B.V. All rights reserved.

Optimal gasoline tax in developing, oil-producing countries: The case of Mexico

International Nuclear Information System (INIS)

Antón-Sarabia, Arturo; Hernández-Trillo, Fausto

2014-01-01

This paper uses the methodology of Parry and Small (2005) to estimate the optimal gasoline tax for a less-developed oil-producing country. The relevance of the estimation relies on the differences between less-developed countries (LDCs) and industrial countries. We argue that lawless roads, general subsidies on gasoline, poor mass transportation systems, older vehicle fleets and unregulated city growth make the tax rates in LDCs differ substantially from the rates in the developed world. We find that the optimal gasoline tax is $1.90 per gallon at 2011 prices and show that the estimate differences are in line with the factors hypothesized. In contrast to the existing literature on industrial countries, we show that the relative gasoline tax incidence may be progressive in Mexico and, more generally, in LDCs. - Highlights: • We estimate the optimal gasoline tax for a typical less-developed, oil-producing country like Mexico. • The relevance of the estimation relies on the differences between less-developed and industrial countries. • The optimal gasoline tax is $1.90 per gallon at 2011 prices. • Distance-related pollution damages, accident costs and gas subsidies account for the major differences. • Gasoline tax incidence may be progressive in less developed countries

Optimizing sales areas of combined transport chains

Directory of Open Access Journals (Sweden)

Philip Michalk

2013-12-01

Full Text Available Background: Combined transport chains (such as intermodal transport, have certain advantages. The main advantage from customer points of view is the possibility to bundle freight and thereby decrease transport costs. On the other hand, a combined transport chain can cause longer transport times, due to the necessary transshipment processes. Methods: The area around a terminal, in which a combined service has favourable properties to a customer in comparison to a direct transport, can be understood as a sales-area, in which a combined transport product is marketable. The aim of this paper was to find a method to determine the best shape and size of this area. Results and conclusions: The paper at hand lined out a method in order to calculate such a sales area and determine which geographical points around a terminal have an advantage in comparison to a direct transport service.

Optimization of wind turbine rotors - using advanced aerodynamic and aeroelastic models and numerical optimization

Energy Technology Data Exchange (ETDEWEB)

Doessing, M.

2011-05-15

During the last decades the annual energy produced by wind turbines has increased dramatically and wind turbines are now available in the 5MW range. Turbines in this range are constantly being developed and it is also being investigated whether turbines as large as 10-20MW are feasible. The design of very large machines introduces new problems in the practical design, and optimization tools are necessary. These must combine the dynamic effects of both aerodynamics and structure in an integrated optimization environment. This is referred to as aeroelastic optimization. The Risoe DTU optimization software HAWTOPT has been used in this project. The quasi-steady aerodynamic module have been improved with a corrected blade element momentum method. A structure module has also been developed which lays out the blade structural properties. This is done in a simplified way allowing fast conceptual design studies and with focus on the overall properties relevant for the aeroelastic properties. Aeroelastic simulations in the time domain were carried out using the aeroelastic code HAWC2. With these modules coupled to HAWTOPT, optimizations have been made. In parallel with the developments of the mentioned numerical modules, focus has been on analysis and a fundamental understanding of the key parameters in wind turbine design. This has resulted in insight and an effective design methodology is presented. Using the optimization environment a 5MW wind turbine rotor has been optimized for reduced fatigue loads due to apwise bending moments. Among other things this has indicated that airfoils for wind turbine blades should have a high lift coefficient. The design methodology proved to be stable and a help in the otherwise challenging task of numerical aeroelastic optimization. (Author)

Optimizing Storage and Renewable Energy Systems with REopt

Energy Technology Data Exchange (ETDEWEB)

Elgqvist, Emma M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Anderson, Katherine H. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Cutler, Dylan S. [National Renewable Energy Lab. (NREL), Golden, CO (United States); DiOrio, Nicholas A. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Laws, Nicholas D. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Olis, Daniel R. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Walker, H. A. [National Renewable Energy Lab. (NREL), Golden, CO (United States)

2017-12-27

Under the right conditions, behind the meter (BTM) storage combined with renewable energy (RE) technologies can provide both cost savings and resiliency. Storage economics depend not only on technology costs and avoided utility rates, but also on how the technology is operated. REopt, a model developed at NREL, can be used to determine the optimal size and dispatch strategy for BTM or off-grid applications. This poster gives an overview of three applications of REopt: Optimizing BTM Storage and RE to Extend Probability of Surviving Outage, Optimizing Off-Grid Energy System Operation, and Optimizing Residential BTM Solar 'Plus'.

Application of Chitosan-Zinc Oxide Nanoparticles for Lead Extraction From Water Samples by Combining Ant Colony Optimization with Artificial Neural Network

Science.gov (United States)

Khajeh, M.; Pourkarami, A.; Arefnejad, E.; Bohlooli, M.; Khatibi, A.; Ghaffari-Moghaddam, M.; Zareian-Jahromi, S.

2017-09-01

Chitosan-zinc oxide nanoparticles (CZPs) were developed for solid-phase extraction. Combined artificial neural network-ant colony optimization (ANN-ACO) was used for the simultaneous preconcentration and determination of lead (Pb2+) ions in water samples prior to graphite furnace atomic absorption spectrometry (GF AAS). The solution pH, mass of adsorbent CZPs, amount of 1-(2-pyridylazo)-2-naphthol (PAN), which was used as a complexing agent, eluent volume, eluent concentration, and flow rates of sample and eluent were used as input parameters of the ANN model, and the percentage of extracted Pb2+ ions was used as the output variable of the model. A multilayer perception network with a back-propagation learning algorithm was used to fit the experimental data. The optimum conditions were obtained based on the ACO. Under the optimized conditions, the limit of detection for Pb2+ ions was found to be 0.078 μg/L. This procedure was also successfully used to determine the amounts of Pb2+ ions in various natural water samples.

Artificial intelligence in drug combination therapy.

Science.gov (United States)

Tsigelny, Igor F

2018-02-09

Currently, the development of medicines for complex diseases requires the development of combination drug therapies. It is necessary because in many cases, one drug cannot target all necessary points of intervention. For example, in cancer therapy, a physician often meets a patient having a genomic profile including more than five molecular aberrations. Drug combination therapy has been an area of interest for a while, for example the classical work of Loewe devoted to the synergism of drugs was published in 1928-and it is still used in calculations for optimal drug combinations. More recently, over the past several years, there has been an explosion in the available information related to the properties of drugs and the biomedical parameters of patients. For the drugs, hundreds of 2D and 3D molecular descriptors for medicines are now available, while for patients, large data sets related to genetic/proteomic and metabolomics profiles of the patients are now available, as well as the more traditional data relating to the histology, history of treatments, pretreatment state of the organism, etc. Moreover, during disease progression, the genetic profile can change. Thus, the ability to optimize drug combinations for each patient is rapidly moving beyond the comprehension and capabilities of an individual physician. This is the reason, that biomedical informatics methods have been developed and one of the more promising directions in this field is the application of artificial intelligence (AI). In this review, we discuss several AI methods that have been successfully implemented in several instances of combination drug therapy from HIV, hypertension, infectious diseases to cancer. The data clearly show that the combination of rule-based expert systems with machine learning algorithms may be promising direction in this field. © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Design optimization and sensitivity analysis of a biomass-fired combined cooling, heating and power system with thermal energy storage systems

International Nuclear Information System (INIS)

Caliano, Martina; Bianco, Nicola; Graditi, Giorgio; Mongibello, Luigi

2017-01-01

Highlights: • A novel operation strategy for biomass-fired combined cooling, heating and power system is presented. • A design optimization of the system is conducted. • The effects of variation of the incentive for the electricity generation are evaluated. • The effects of the variation of the absorption chiller size and the thermal energy storage system one are evaluated. • The inclusion of a cold storage system into the combined cooling, heating and power system is also analyzed. - Abstract: In this work, an operation strategy for a biomass-fired combined cooling, heating and power system, composed of a cogeneration unit, an absorption chiller, and a thermal energy storage system, is formulated in order to satisfy time-varying energy demands of an Italian cluster of residential multi-apartment buildings. This operation strategy is adopted for performing the economical optimization of the design of two of the devices composing the combined cooling, heating and power system, namely the absorption chiller and the storage system. A sensitivity analysis is carried out in order to evaluate the impact of the incentive for the electricity generation on the optimized results, and also to evaluate, separately, the effects of the variation of the absorption chiller size, and the effects of the variation of the thermal energy storage system size on the system performance. In addition, the inclusion into the system of a cold thermal energy storage system is analyzed, as well, assuming different possible values for the cold storage system cost. The results of the sensitivity analysis indicate that the most influencing factors from the economical point of view are represented by the incentive for the electricity generation and the absorption chiller power. Results also show that the combined use of a thermal energy storage and of a cold thermal energy storage during the hot season could represent a viable solution from the economical point of view.

Optimal GENCO bidding strategy

Science.gov (United States)

Gao, Feng

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed

Combining spanwise morphing, inline motion and model based optimization for force magnitude and direction control

Science.gov (United States)

Scheller, Johannes; Braza, Marianna; Triantafyllou, Michael

2016-11-01

Bats and other animals rapidly change their wingspan in order to control the aerodynamic forces. A NACA0013 type airfoil with dynamically changing span is proposed as a simple model to experimentally study these biomimetic morphing wings. Combining this large-scale morphing with inline motion allows to control both force magnitude and direction. Force measurements are conducted in order to analyze the impact of the 4 degree of freedom flapping motion on the flow. A blade-element theory augmented unsteady aerodynamic model is then used to derive optimal flapping trajectories.

Formulation development and optimization of Lamivudine 300 mg and Tenofovir Disoproxil Fumarate (TDF 300 mg FDC tablets by D-optimal mixture design

Directory of Open Access Journals (Sweden)

Prosper Tibalinda

2016-12-01

Full Text Available The usage of fixed dose combination (FDC tablets of Lamivudine and Tenofovir Disoproxil Fumarate (TDF is increasing due to increased incidences of HIV/Hepatitis B and HIV/TB co-infections. This is likely to increase the financial crisis due to limited resources for funding procurement of ready-made products from the pharmaceuticals manufacturing leading countries. Therefore, production of local oral tablets containing Lamivudine and TDF FDC is inevitable. Lamivudine 300 mg/TDF 300 mg tablets were developed and optimized by D-optimal mixture design and produced by direct compression technique. Twenty trial formulations with independent variables, including PVP-CL 1–12.00%, PVP-K30 1–10.00%, starch-1500 2.5–12.5% and Avicel-PH102 2–19.25% were prepared by direct compression technique. The formulations were assessed on assay, dissolution, friability, weight variation and disintegration time. It was found that assay ranged from 98.13–101.95% for Lamivudine, 98.25–102.84 for TDF, both were within the in-house assay specification of 95 to 105%. Dissolution at single point was above 80% for Lamivudine 93.96–100.55% and 95.85–103.15% for TDF, disintegration time was between 1.92–66.33 min and friability 0.06–12.56%. Out of twenty formulation trials, eight formulations had all parameters in proven acceptable range. On optimization, one formulation with independent variables, PVP-CL 5.67%, PVP-K30 1.00%, Starch-1500 5.76% was selected. The optimized formulation was comparable to the reference product on the market with similarity factor (f2 and difference factor (f1 within the acceptable range for both Lamivudine and TDF.

Contributions for larval development optimization of Homarus gammarus

Directory of Open Access Journals (Sweden)

Pedro Tiago Fonseca Sá

2014-06-01

The seawater rising temperature resulted in a decrease of intermoult period in all larval development stages and at all tested temperatures, ranging from 4.77 (Z1 to 16.5 days (Z3 at 16°C, whereas at 23°C, ranged from 3:02 (Z1 and 9.75 days (Z3. The results obtained are an extremely useful guide for future optimization of protocols on larval development of H. gammarus.

Power and efficiency optimization for combined Brayton and inverse Brayton cycles

International Nuclear Information System (INIS)

Zhang Wanli; Chen Lingen; Sun Fengrui

2009-01-01

A thermodynamic model for open combined Brayton and inverse Brayton cycles is established considering the pressure drops of the working fluid along the flow processes and the size constraints of the real power plant using finite time thermodynamics in this paper. There are 11 flow resistances encountered by the gas stream for the combined Brayton and inverse Brayton cycles. Four of these, the friction through the blades and vanes of the compressors and the turbines, are related to the isentropic efficiencies. The remaining flow resistances are always present because of the changes in flow cross-section at the compressor inlet of the top cycle, combustion inlet and outlet, turbine outlet of the top cycle, turbine outlet of the bottom cycle, heat exchanger inlet, and compressor inlet of the bottom cycle. These resistances control the air flow rate and the net power output. The relative pressure drops associated with the flow through various cross-sectional areas are derived as functions of the compressor inlet relative pressure drop of the top cycle. The analytical formulae about the relations between power output, thermal conversion efficiency, and the compressor pressure ratio of the top cycle are derived with the 11 pressure drop losses in the intake, compression, combustion, expansion, and flow process in the piping, the heat transfer loss to the ambient, the irreversible compression and expansion losses in the compressors and the turbines, and the irreversible combustion loss in the combustion chamber. The performance of the model cycle is optimized by adjusting the compressor inlet pressure of the bottom cycle, the air mass flow rate and the distribution of pressure losses along the flow path. It is shown that the power output has a maximum with respect to the compressor inlet pressure of the bottom cycle, the air mass flow rate or any of the overall pressure drops, and the maximized power output has an additional maximum with respect to the compressor pressure

Combining large number of weak biomarkers based on AUC.

Science.gov (United States)

Yan, Li; Tian, Lili; Liu, Song

2015-12-20

Combining multiple biomarkers to improve diagnosis and/or prognosis accuracy is a common practice in clinical medicine. Both parametric and non-parametric methods have been developed for finding the optimal linear combination of biomarkers to maximize the area under the receiver operating characteristic curve (AUC), primarily focusing on the setting with a small number of well-defined biomarkers. This problem becomes more challenging when the number of observations is not order of magnitude greater than the number of variables, especially when the involved biomarkers are relatively weak. Such settings are not uncommon in certain applied fields. The first aim of this paper is to empirically evaluate the performance of existing linear combination methods under such settings. The second aim is to propose a new combination method, namely, the pairwise approach, to maximize AUC. Our simulation studies demonstrated that the performance of several existing methods can become unsatisfactory as the number of markers becomes large, while the newly proposed pairwise method performs reasonably well. Furthermore, we apply all the combination methods to real datasets used for the development and validation of MammaPrint. The implication of our study for the design of optimal linear combination methods is discussed. Copyright © 2015 John Wiley & Sons, Ltd.

Enhancement of L-phenylalanine production by engineered Escherichia coli using phased exponential L-tyrosine feeding combined with nitrogen source optimization.

Science.gov (United States)

Yuan, Peipei; Cao, Weijia; Wang, Zhen; Chen, Kequan; Li, Yan; Ouyang, Pingkai

2015-07-01

Nitrogen source optimization combined with phased exponential L-tyrosine feeding was employed to enhance L-phenylalanine production by a tyrosine-auxotroph strain, Escherichia coli YP1617. The absence of (NH4)2SO4, the use of corn steep powder and yeast extract as composite organic nitrogen source were more suitable for cell growth and L-phenylalanine production. Moreover, the optimal initial L-tyrosine level was 0.3 g L(-1) and exponential L-tyrosine feeding slightly improved L-phenylalanine production. Nerveless, L-phenylalanine production was greatly enhanced by a strategy of phased exponential L-tyrosine feeding, where exponential feeding was started at the set specific growth rate of 0.08, 0.05, and 0.02 h(-1) after 12, 32, and 52 h, respectively. Compared with exponential L-tyrosine feeding at the set specific growth rate of 0.08 h(-1), the developed strategy obtained a 15.33% increase in L-phenylalanine production (L-phenylalanine of 56.20 g L(-1)) and a 45.28% decrease in L-tyrosine supplementation. Copyright © 2014 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization

Energy Technology Data Exchange (ETDEWEB)

Li, Dengwang; Wang, Jie [College of Physics and Electronics, Shandong Normal University, Jinan, Shandong (China); Kapp, Daniel S.; Xing, Lei [Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA (United States)

2015-06-15

Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is

SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization

International Nuclear Information System (INIS)

Li, Dengwang; Wang, Jie; Kapp, Daniel S.; Xing, Lei

2015-01-01

Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is

Design and optimization of a self-developing single axis tracking PV array

International Nuclear Information System (INIS)

Colozza, A.J.

1992-01-01

This paper reports on a study performed in order to design a tracking PV array and optimize the design for maximum specific power. The design considerations were minimal deployment time, high reliability and small stowage volume. The array design was self-deployable, from a compact stowage configuration, using a passive pressurized gas deployment mechanism. The array structural components consist of a combination of beams, columns and cables used to deploy and orient a flexible PV blanket. Each structural component of the design was analyzed to determine the size necessary to withstand the various forces it would be subjected to. An optimization was performed to determine the array dimensions and blanket geometry which produce the maximum specific power

Trends in PDE constrained optimization

CERN Document Server

Benner, Peter; Engell, Sebastian; Griewank, Andreas; Harbrecht, Helmut; Hinze, Michael; Rannacher, Rolf; Ulbrich, Stefan

2014-01-01

Optimization problems subject to constraints governed by partial differential equations (PDEs) are among the most challenging problems in the context of industrial, economical and medical applications. Almost the entire range of problems in this field of research was studied and further explored as part of the Deutsche Forschungsgemeinschaft (DFG) priority program 1253 on “Optimization with Partial Differential Equations” from 2006 to 2013. The investigations were motivated by the fascinating potential applications and challenging mathematical problems that arise in the field of PDE constrained optimization. New analytic and algorithmic paradigms have been developed, implemented and validated in the context of real-world applications. In this special volume, contributions from more than fifteen German universities combine the results of this interdisciplinary program with a focus on applied mathematics.   The book is divided into five sections on “Constrained Optimization, Identification and Control”...

Topology optimization under stochastic stiffness

Science.gov (United States)

Asadpoure, Alireza

Topology optimization is a systematic computational tool for optimizing the layout of materials within a domain for engineering design problems. It allows variation of structural boundaries and connectivities. This freedom in the design space often enables discovery of new, high performance designs. However, solutions obtained by performing the optimization in a deterministic setting may be impractical or suboptimal when considering real-world engineering conditions with inherent variabilities including (for example) variabilities in fabrication processes and operating conditions. The aim of this work is to provide a computational methodology for topology optimization in the presence of uncertainties associated with structural stiffness, such as uncertain material properties and/or structural geometry. Existing methods for topology optimization under deterministic conditions are first reviewed. Modifications are then proposed to improve the numerical performance of the so-called Heaviside Projection Method (HPM) in continuum domains. Next, two approaches, perturbation and Polynomial Chaos Expansion (PCE), are proposed to account for uncertainties in the optimization procedure. These approaches are intrusive, allowing tight and efficient coupling of the uncertainty quantification with the optimization sensitivity analysis. The work herein develops a robust topology optimization framework aimed at reducing the sensitivity of optimized solutions to uncertainties. The perturbation-based approach combines deterministic topology optimization with a perturbation method for the quantification of uncertainties. The use of perturbation transforms the problem of topology optimization under uncertainty to an augmented deterministic topology optimization problem. The PCE approach combines the spectral stochastic approach for the representation and propagation of uncertainties with an existing deterministic topology optimization technique. The resulting compact representations

Optimal day-ahead operational planning of microgrids

International Nuclear Information System (INIS)

Hosseinnezhad, Vahid; Rafiee, Mansour; Ahmadian, Mohammad; Siano, Pierluigi

2016-01-01

Highlights: • A new multi-objective model for optimal day-ahead operational planning of microgrids is proposed. • A new concept called seamlessness is introduced to control the sustainability of microgrid. • A new method is developed to manage the load and renewable energy resources estimation errors. • A new solution based on a combination of numerical and evolutionary approaches is proposed. - Abstract: Providing a cost-efficient, eco-friendly and sustainable energy is one of the main issues in modern societies. In response to this demand, new features of microgrid technology have provided huge potentials while distributing electricity more effectively, economically and securely. Accordingly, this paper presents a new multi-objective generation management model for optimal day-ahead operational planning of medium voltage microgrids. The proposed model optimizes both pollutant emission and operating cost of a microgrid by using multi-objective optimization. Besides, a seamlessness-selective algorithm is integrated into the model, which can be adopted to achieve the desired self-sufficiency level for microgrids along a specified planning horizon. Furthermore, the model is characterized by a reserve-assessment strategy developed to handle the load and renewable energy resources estimation errors. The introduced model is solved using a combination of numerical and evolutionary methods of species-based quantum particle swarm optimization to find the optimal scheduling scheme and minos-based optimal power flow to optimize the operating cost and emission. In addition, the suggested solution approach also incorporates an efficient mechanism for considering energy storage systems and coding the candidate solutions in the evolutionary algorithm. The proposed model is implemented on a test microgrid and is investigated through simulations to study the different aspects of the problem. The results show significant improvements and benefits which are obtained by

Development trends of combined inductance-capacitance electromechanical energy converters

Directory of Open Access Journals (Sweden)

Karayan Hamlet

2018-01-01

Full Text Available In the article the modern state of completely new direction of electromechanical science such as combined inductive-capacitive electromechanics is considered. The wide spectra of its possible practical applications and prospects for further development are analyzed. A new approach for mathematical description of transients in dualcon jugate dynamic systems is proposed. On the basis of the algorithm differential equations for inductive-capacitive compatible electromechanical energy converters are derived. The generalized Lagrangian theory of combined inductively-capacitive electric machines was developed as a union of generalized Lagrangian models of inductive and capacitive electro-mechanical energy converters developed on the basis of the basic principles of binary-conjugate electrophysics. The author gives equations of electrodynamics and electromechanics of combined inductive-capacitive electric machines in case there are active electrotechnical materials of dual purpose (ferroelectromagnets in the structure of their excitation system. At the same time, the necessary Lagrangian for combined inductive-capacitive forces was built using new technologies of interaction between inductive and capacitive subsystems. The joint solution of these equations completely determines the dynamic behavior and energy characteristics of the generalized model of combined machines of any design and in any modes of interaction of their functional elements

Trajectory of Sewerage System Development Optimization

Science.gov (United States)

Chupin, R. V.; Mayzel, I. V.; Chupin, V. R.

2017-11-01

The transition to market relations has determined a new technology for our country to manage the development of urban engineering systems. This technology has shifted to the municipal level and it can, in large, be presented in two stages. The first is the development of a scheme for the development of the water supply and sanitation system, the second is the implementation of this scheme on the basis of investment programs of utilities. In the investment programs, financial support is provided for the development and reconstruction of water disposal systems due to the investment component in the tariff, connection fees for newly commissioned capital construction projects and targeted financing for selected state and municipal programs, loans and credits. Financial provision with the development of sewerage systems becomes limited and the problem arises in their rational distribution between the construction of new water disposal facilities and the reconstruction of existing ones. The paper suggests a methodology for developing options for the development of sewerage systems, selecting the best of them by the life cycle cost criterion, taking into account the limited investments in their construction, models and methods of analysis, optimizing their reconstruction and development, taking into account reliability and seismic resistance.

Combining Innovation Systems and Global Value Chains for Development

DEFF Research Database (Denmark)

Jurowetzki, Roman; Lema, Rasmus; Lundvall, Bengt-Åke

2018-01-01

This paper contributes to ongoing work which seeks to bring together the national innovation system and global value chain literatures for the study of economic development. We depart from the view that such a new combination will be helpful both in enhancing understanding of socioeconomic...... processes in developing countries and in building a more useful knowledge base for action. To this aim, we combine bibliometric analysis with a qualitative review of work from both bodies of literature. The purpose is to inform a research agenda suited for policy-relevant studies of economic development...

Combination of Markov chain and optimal control solved by Pontryagin’s Minimum Principle for a fuel cell/supercapacitor vehicle

International Nuclear Information System (INIS)

Hemi, Hanane; Ghouili, Jamel; Cheriti, Ahmed

2015-01-01

Highlights: • A combination of Markov chain and an optimal control solved by Pontryagin’s Minimum Principle is presented. • This strategy is applied to hybrid electric vehicle dynamic model. • The hydrogen consumption is analyzed for two different vehicle mass and drive cycle. • The supercapacitor and fuel cell behavior is analyzed at high or sudden required power. - Abstract: In this article, a real time optimal control strategy based on Pontryagin’s Minimum Principle (PMP) combined with the Markov chain approach is used for a fuel cell/supercapacitor electrical vehicle. In real time, at high power and at high speed, two phenomena are observed. The first is obtained at higher required power, and the second is observed at sudden power demand. To avoid these situations, the Markov chain model is proposed to predict the future power demand during a driving cycle. The optimal control problem is formulated as an equivalent consumption minimization strategy (ECMS), that has to be solved by using the Pontryagin’s Minimum Principle. A Markov chain model is added as a separate block for a prediction of required power. This approach and the whole system are modeled and implemented using the MATLAB/Simulink. The model without Markov chain block and the model is with it are compared. The results presented demonstrate the importance of a Markov chain block added to a model

A strategy for the economic optimization of combined cycle gas turbine power plants by taking advantage of useful thermodynamic relationships

International Nuclear Information System (INIS)

Godoy, E.; Benz, S.J.; Scenna, N.J.

2011-01-01

Optimal combined cycle gas turbine power plants characterized by minimum specific annual cost values are here determined for wide ranges of market conditions as given by the relative weights of capital investment and operative costs, by means of a non-linear mathematical programming model. On the other hand, as the technical optimization allows identifying trends in the system behavior and unveiling optimization opportunities, selected functional relationships are obtained as the thermodynamic optimal values of the decision variables are systematically linked to the ratio between the total heat transfer area and the net power production (here named as specific transfer area). A strategy for simplifying the resolution of the rigorous economic optimization problem of power plants is proposed based on the economic optima distinctive characteristics which describe the behavior of the decision variables of the power plant on its optima. Such approach results in a novel mathematical formulation shaped as a system of non-linear equations and additional constraints that is able to easily provide accurate estimations of the optimal values of the power plant design and operative variables. Research highlights: → We achieve relationships between power plants' economic and thermodynamic optima. → We achieve functionalities among thermodynamic optimal values of decision variables. → The rigorous optimization problem is reduced to a non-linear equations system. → Accurate estimations of power plants' design and operative variables are obtained.

Optimization and Validation of the Developed Uranium Isotopic Analysis Code

Energy Technology Data Exchange (ETDEWEB)

Kim, J. H.; Kang, M. Y.; Kim, Jinhyeong; Choi, H. D. [Seoul National Univ., Seoul (Korea, Republic of)

2014-10-15

γ-ray spectroscopy is a representative non-destructive assay for nuclear material, and less time-consuming and less expensive than the destructive analysis method. The destructive technique is more precise than NDA technique, however, there is some correction algorithm which can improve the performance of γ-spectroscopy. For this reason, an analysis code for uranium isotopic analysis is developed by Applied Nuclear Physics Group in Seoul National University. Overlapped γ- and x-ray peaks in the 89-101 keV X{sub α}-region are fitted with Gaussian and Lorentzian distribution peak functions, tail and background functions. In this study, optimizations for the full-energy peak efficiency calibration and fitting parameters of peak tail and background are performed, and validated with 24 hour acquisition of CRM uranium samples. The optimization of peak tail and background parameters are performed with the validation by using CRM uranium samples. The analysis performance is improved in HEU samples, but more optimization of fitting parameters is required in LEU sample analysis. In the future, the optimization research about the fitting parameters with various type of uranium samples will be performed. {sup 234}U isotopic analysis algorithms and correction algorithms (coincidence effect, self-attenuation effect) will be developed.

DETERMINATION OF THE OPTIMAL CAPITAL INVESTMENTS TO ENSURE THE SUSTAINABLE DEVELOPMENT OF THE RAILWAY

Directory of Open Access Journals (Sweden)

O. I. Kharchenko

2015-04-01

Full Text Available Purpose. Every year more attention is paid for the theoretical and practical issue of sustainable development of railway transport. But today the mechanisms of financial support of this development are poorly understood. Therefore, the aim of this article is to determine the optimal investment allocation to ensure sustainable development of the railway transport on the example of State Enterprise «Prydniprovsk Railway» and the creation of preconditions for the mathematical model development. Methodology. The ensuring task for sustainable development of railway transport is solved on the basis of the integral indicator of sustainable development effectiveness and defined as the maximization of this criterion. The optimization of measures technological and technical characters are proposed to carry out for increasing values of the integral performance measure components. To the optimization activities of technological nature that enhance the performance criteria belongs: optimization of the number of train and shunting locomotives, optimization of power handling mechanisms at the stations, optimization of routes of train flows. The activities related to the technical nature include: modernization of railways in the direction of their electrification and modernization of the running gear and coupler drawbars of rolling stock, as well as means of separators mechanization at stations to reduce noise impacts on the environment. Findings. The work resulted in the optimal allocation of investments to ensure the sustainable development of railway transportation of State Enterprise «Prydniprovsk Railway». This allows providing such kind of railway development when functioning of State Enterprise «Prydniprovsk Railway» is characterized by a maximum value of the integral indicator of efficiency. Originality. The work was reviewed and the new approach was proposed to determine the optimal allocation of capital investments to ensure sustainable

A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

Directory of Open Access Journals (Sweden)

Qingyang Zhang

2015-02-01

Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

Optimization and Optimal Control

CERN Document Server

Chinchuluun, Altannar; Enkhbat, Rentsen; Tseveendorj, Ider

2010-01-01

During the last four decades there has been a remarkable development in optimization and optimal control. Due to its wide variety of applications, many scientists and researchers have paid attention to fields of optimization and optimal control. A huge number of new theoretical, algorithmic, and computational results have been observed in the last few years. This book gives the latest advances, and due to the rapid development of these fields, there are no other recent publications on the same topics. Key features: Provides a collection of selected contributions giving a state-of-the-art accou

Optimal ex vivo expansion of neutrophils from PBSC CD34+ cells by a combination of SCF, Flt3-L and G-CSF and its inhibition by further addition of TPO

Directory of Open Access Journals (Sweden)

Turner Marc L

2007-10-01

Full Text Available Abstract Background Autologous mobilised peripheral blood stem cell (PBSC transplantation is now a standard approach in the treatment of haematological diseases to reconstitute haematopoiesis following myeloablative chemotherapy. However, there remains a period of severe neutropenia and thrombocytopenia before haematopoietic reconstitution is achieved. Ex vivo expanded PBSC have been employed as an adjunct to unmanipulated HSC transplantation, but have tended to be produced using complex cytokine mixtures aimed at multilineage (neutrophil and megakaryocyte progenitor expansion. These have been reported to reduce or abrogate neutropenia but have little major effect on thrombocytopenia. Selective megakaryocyte expansion has been to date ineffective in reducing thrombocytopenia. This study was implemented to evaluate neutrophil specific rather than multilineage ex vivo expansion of PBSC for specifically focusing on reduction or abrogation of neutropenia. Methods CD34+ cells (PBSC were enriched from peripheral blood mononuclear cells following G-CSF-mobilisation and cultured with different permutations of cytokines to determine optimal cytokine combinations and doses for expansion and functional differentiation and maturation of neutrophils and their progenitors. Results were assessed by cell number, morphology, phenotype and function. Results A simple cytokine combination, SCF + Flt3-L + G-CSF, synergised to optimally expand and mature neutrophil progenitors assessed by cell number, phenotype, morphology and function (superoxide respiratory burst measured by chemiluminescence. G-CSF appears mandatory for functional maturation. Addition of other commonly employed cytokines, IL-3 and IL-6, had no demonstrable additive effect on numbers or function compared to this optimal combination. Addition of TPO, commonly included in multilineage progenitor expansion for development of megakaryocytes, reduced the maturation of neutrophil progenitors as assessed

Optimal Sliding Mode Controllers for Attitude Stabilization of Flexible Spacecraft

Directory of Open Access Journals (Sweden)

Chutiphon Pukdeboon

2011-01-01

Full Text Available The robust optimal attitude control problem for a flexible spacecraft is considered. Two optimal sliding mode control laws that ensure the exponential convergence of the attitude control system are developed. Integral sliding mode control (ISMC is applied to combine the first-order sliding mode with optimal control and is used to control quaternion-based spacecraft attitude manoeuvres with external disturbances and an uncertainty inertia matrix. For the optimal control part the state-dependent Riccati equation (SDRE and optimal Lyapunov techniques are employed to solve the infinite-time nonlinear optimal control problem. The second method of Lyapunov is used to guarantee the stability of the attitude control system under the action of the proposed control laws. An example of multiaxial attitude manoeuvres is presented and simulation results are included to verify the usefulness of the developed controllers.

Nonlinear dynamic simulation of optimal depletion of crude oil in the lower 48 United States

International Nuclear Information System (INIS)

Ruth, M.; Cleveland, C.J.

1993-01-01

This study combines the economic theory of optimal resource use with econometric estimates of demand and supply parameters to develop a nonlinear dynamic model of crude oil exploration, development, and production in the lower 48 United States. The model is simulated with the graphical programming language STELLA, for the years 1985 to 2020. The procedure encourages use of economic theory and econometrics in combination with nonlinear dynamic simulation to enhance our understanding of complex interactions present in models of optimal resource use. (author)

Simplex-based optimization of numerical and categorical inputs in early bioprocess development: Case studies in HT chromatography.

Science.gov (United States)

Konstantinidis, Spyridon; Titchener-Hooker, Nigel; Velayudhan, Ajoy

2017-08-01

Bioprocess development studies often involve the investigation of numerical and categorical inputs via the adoption of Design of Experiments (DoE) techniques. An attractive alternative is the deployment of a grid compatible Simplex variant which has been shown to yield optima rapidly and consistently. In this work, the method is combined with dummy variables and it is deployed in three case studies wherein spaces are comprised of both categorical and numerical inputs, a situation intractable by traditional Simplex methods. The first study employs in silico data and lays out the dummy variable methodology. The latter two employ experimental data from chromatography based studies performed with the filter-plate and miniature column High Throughput (HT) techniques. The solute of interest in the former case study was a monoclonal antibody whereas the latter dealt with the separation of a binary system of model proteins. The implemented approach prevented the stranding of the Simplex method at local optima, due to the arbitrary handling of the categorical inputs, and allowed for the concurrent optimization of numerical and categorical, multilevel and/or dichotomous, inputs. The deployment of the Simplex method, combined with dummy variables, was therefore entirely successful in identifying and characterizing global optima in all three case studies. The Simplex-based method was further shown to be of equivalent efficiency to a DoE-based approach, represented here by D-Optimal designs. Such an approach failed, however, to both capture trends and identify optima, and led to poor operating conditions. It is suggested that the Simplex-variant is suited to development activities involving numerical and categorical inputs in early bioprocess development. © 2017 The Authors. Biotechnology Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Spatio-temporal optimization of agricultural practices to achieve a sustainable development at basin level; framework of a case study in Colombia

Science.gov (United States)

Uribe, Natalia; corzo, Gerald; Solomatine, Dimitri

2016-04-01

The flood events present during the last years in different basins of the Colombian territory have raised questions on the sensitivity of the regions and if this regions have common features. From previous studies it seems important features in the sensitivity of the flood process were: land cover change, precipitation anomalies and these related to impacts of agriculture management and water management deficiencies, among others. A significant government investment in the outreach activities for adopting and promoting the Colombia National Action Plan on Climate Change (NAPCC) is being carried out in different sectors and regions, having as a priority the agriculture sector. However, more information is still needed in the local environment in order to assess were the regions have this sensitivity. Also the continuous change in one region with seasonal agricultural practices have been pointed out as a critical information for optimal sustainable development. This combined spatio-temporal dynamics of crops cycle in relation to climate change (or variations) has an important impact on flooding events at basin areas. This research will develop on the assessment and optimization of the aggregated impact of flood events due to determinate the spatio-temporal dynamic of changes in agricultural management practices. A number of common best agricultural practices have been identified to explore their effect in a spatial hydrological model that will evaluate overall changes. The optimization process consists on the evaluation of best performance in the agricultural production, without having to change crops activities or move to other regions. To achieve this objectives a deep analysis of different models combined with current and future climate scenarios have been planned. An algorithm have been formulated to cover the parametric updates such that the optimal temporal identification will be evaluated in different region on the case study area. Different hydroinformatics

Multidisciplinary Design, Analysis, and Optimization Tool Development Using a Genetic Algorithm

Science.gov (United States)

Pak, Chan-gi; Li, Wesley

2009-01-01

Multidisciplinary design, analysis, and optimization using a genetic algorithm is being developed at the National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) to automate analysis and design process by leveraging existing tools to enable true multidisciplinary optimization in the preliminary design stage of subsonic, transonic, supersonic, and hypersonic aircraft. This is a promising technology, but faces many challenges in large-scale, real-world application. This report describes current approaches, recent results, and challenges for multidisciplinary design, analysis, and optimization as demonstrated by experience with the Ikhana fire pod design.!

Optimized ventilation-on-demand (VOD)

Energy Technology Data Exchange (ETDEWEB)

Masse, M. [Simsmart Technologies Inc., Brossard, PQ (Canada); Cervinka, A. [Newtrax Technologies Inc., Montreal, PQ (Canada)

2008-07-01

This presentation described how the combination of 2 innovative technologies can help optimize mine ventilation. Newtrax Technologies has developed a self-contained battery-powered wireless electronic system designed to operate in harsh industrial environments, including underground mines. Simsmart Technologies has created an advanced process and control simulation based design tool used in industrial applications, including mine ventilation systems. This presentation described the system components and how they work. These included the wireless mesh network designed for dynamic diesel machinery tracking and operating status monitoring; the real-time ventilation model and fan speed optimizer; the OPC server for information interchange; the OPC linkage to existing control infrastructure; a human machine interface that provide data archiving capability; live MS-Excel to interrogate the simulation, controls and optimizer; and, the battery-powered network mesh that provides SCADA functionality to route optimized setpoints. Details of the user interface were also provided. 1 tab., 20 figs.

Developing a simulation framework for safe and optimal trajectories considering drivers’ driving style

DEFF Research Database (Denmark)

Gruber, Thierry; Larue, Grégoire S.; Rakotonirainy, Andry

2017-01-01

drivers with the optimal trajectory considering the motorist's driving style in real time. Travel duration and safety are the main parameters used to find the optimal trajectory. A simulation framework to determine the optimal trajectory was developed in which the ego car travels in a highway environment......Advanced driving assistance systems (ADAS) have huge potential for improving road safety and travel times. However, their take-up in the market is very slow; and these systems should consider driver's preferences to increase adoption rates. The aim of this study is to develop a model providing...

Stress-strain state analysis and optimization of rod system under periodic pulse load

Directory of Open Access Journals (Sweden)

Grebenyuk Grigory

2018-01-01

Full Text Available The paper considers the problem of analysis and optimization of rod systems subjected to combined static and periodic pulse load. As a result of the study the analysis method was developed based on traditional approach to solving homogeneous matrix equations of state and a special algorithm for developing a particular solution. The influence of pulse parameters variations on stress-strain state of a rod system was analyzed. Algorithms for rod systems optimization were developed basing on strength recalculation and statement and solution of optimization problem as a problem of nonlinear mathematical programming. Recommendations are developed for efficient organization of process for optimization of rod systems under static and periodic pulse load.

Optimal diversity in investments with recombinant innovation

NARCIS (Netherlands)

Zeppini-Rossi, P.; van den Bergh, J.C.J.M.

2008-01-01

The notion of dynamic, endogenous diversity and its role in theories of investment and technological innovation is addressed. We develop a formal model of an innovation arising from the combination of two existing modules with the objective to optimize the net benefits of diversity. The model takes

Optimal diversity in investments with recombinant innovation

NARCIS (Netherlands)

Zeppini, P.; van den Bergh, J.C.J.M.

2013-01-01

The notion of dynamic, endogenous diversity and its role in theories of investment and technological innovation is addressed. We develop a formal model of an innovation arising from the combination of two existing modules, with the objective to optimize the net benefits of diversity. The model takes

Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

Energy Technology Data Exchange (ETDEWEB)

Vrugt, Jasper A [Los Alamos National Laboratory; Wohling, Thomas [NON LANL

2008-01-01

Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

Science.gov (United States)

Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

2015-01-01

Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

Optimal policy of energy innovation in developing countries: Development of solar PV in Iran

International Nuclear Information System (INIS)

Shafiei, Ehsan; Saboohi, Yadollah; Ghofrani, Mohammad B.

2009-01-01

The purpose of this study is to apply managerial economics and methods of decision analysis to study the optimal pattern of innovation activities for development of new energy technologies in developing countries. For this purpose, a model of energy research and development (R and D) planning is developed and it is then linked to a bottom-up energy-systems model. The set of interlinked models provide a comprehensive analytical tool for assessment of energy technologies and innovation planning taking into account the specific conditions of developing countries. An energy-system model is used as a tool for the assessment and prioritization of new energy technologies. Based on the results of the technology assessment model, the optimal R and D resources allocation for new energy technologies is estimated with the help of the R and D planning model. The R and D planning model is based on maximization of the total net present value of resulting R and D benefits taking into account the dynamics of technological progress, knowledge and experience spillovers from advanced economies, technology adoption and R and D constraints. Application of the set of interlinked models is explained through the analysis of the development of solar PV in Iranian electricity supply system and then some important policy insights are concluded

Measure Guideline: Combined Space and Water Heating Installation and Optimization

Energy Technology Data Exchange (ETDEWEB)

Schoenbauer, B. [NorthernSTAR Building America Partnership, St. Paul, MN (United States); Bohac, D. [NorthernSTAR Building America Partnership, St. Paul, MN (United States); Huelman, P. [NorthernSTAR Building America Partnership, St. Paul, MN (United States)

2017-03-01

Combined space and water heater (combi or combo) systems are defined by their dual functionality. Combi systems provide both space heating and water heating capabilities with a single heat source. This guideline will focus on the installation and operation of residential systems with forced air heating and domestic hot water (DHW) functionality. Past NorthernSTAR research has used a combi system to replace a natural gas forced air distribution system furnace and tank type water heater (Schoenbauer et al. 2012; Schoenbauer, Bohac, and McAlpine 2014). The combi systems consisted of a water heater or boiler heating plant teamed with a hydronic air handler that included an air handler, water coil, and water pump to circulate water between the heating plant and coil. The combi water heater or boiler had a separate circuit for DHW. Past projects focused on laboratory testing, field characterization, and control optimization of combi systems. Laboratory testing was done to fully characterize and test combi system components; field testing was completed to characterize the installed performance of combi systems; and control methodologies were analyzed to understand the potential of controls to simplify installation and design and to improve system efficiency and occupant comfort. This past work was relied upon on to create this measure guideline.

Measure Guideline: Combined Space and Water Heating Installation and Optimization

Energy Technology Data Exchange (ETDEWEB)

Schoenbauer, B. [Univ. of Minnesota, St. Paul, MN (United States). NorthernSTAR Building America Partnership; Bohac, D. [Univ. of Minnesota, St. Paul, MN (United States). NorthernSTAR Building America Partnership; Huelman, P. [Univ. of Minnesota, St. Paul, MN (United States). NorthernSTAR Building America Partnership

2017-03-03

Combined space and water heater (combi or combo) systems are defined by their dual functionality. Combi systems provide both space heating and water heating capabilities with a single heat source. This guideline will focus on the installation and operation of residential systems with forced air heating and domestic hot water (DHW) functionality. Past NorthernSTAR research has used a combi system to replace a natural gas forced air distribution system furnace and tank type water heater (Schoenbauer et al. 2012; Schoenbauer, Bohac, and McAlpine 2014). The combi systems consisted of a water heater or boiler heating plant teamed with a hydronic air handler that included an air handler, water coil, and water pump to circulate water between the heating plant and coil. The combi water heater or boiler had a separate circuit for DHW. Past projects focused on laboratory testing, field characterization, and control optimization of combi systems. Laboratory testing was done to fully characterize and test combi system components; field testing was completed to characterize the installed performance of combi systems; and control methodologies were analyzed to understand the potential of controls to simplify installation and design and to improve system efficiency and occupant comfort. This past work was relied upon on to create this measure guideline.

Combining agreement and frequency rating scales to optimize psychometrics in measuring behavioral health functioning.

Science.gov (United States)

Marfeo, Elizabeth E; Ni, Pengsheng; Chan, Leighton; Rasch, Elizabeth K; Jette, Alan M

2014-07-01

The goal of this article was to investigate optimal functioning of using frequency vs. agreement rating scales in two subdomains of the newly developed Work Disability Functional Assessment Battery: the Mood & Emotions and Behavioral Control scales. A psychometric study comparing rating scale performance embedded in a cross-sectional survey used for developing a new instrument to measure behavioral health functioning among adults applying for disability benefits in the United States was performed. Within the sample of 1,017 respondents, the range of response category endorsement was similar for both frequency and agreement item types for both scales. There were fewer missing values in the frequency items than the agreement items. Both frequency and agreement items showed acceptable reliability. The frequency items demonstrated optimal effectiveness around the mean ± 1-2 standard deviation score range; the agreement items performed better at the extreme score ranges. Findings suggest an optimal response format requires a mix of both agreement-based and frequency-based items. Frequency items perform better in the normal range of responses, capturing specific behaviors, reactions, or situations that may elicit a specific response. Agreement items do better for those whose scores are more extreme and capture subjective content related to general attitudes, behaviors, or feelings of work-related behavioral health functioning. Copyright © 2014 Elsevier Inc. All rights reserved.

Numerical optimization using flow equations

Science.gov (United States)

Punk, Matthias

2014-12-01

We develop a method for multidimensional optimization using flow equations. This method is based on homotopy continuation in combination with a maximum entropy approach. Extrema of the optimizing functional correspond to fixed points of the flow equation. While ideas based on Bayesian inference such as the maximum entropy method always depend on a prior probability, the additional step in our approach is to perform a continuous update of the prior during the homotopy flow. The prior probability thus enters the flow equation only as an initial condition. We demonstrate the applicability of this optimization method for two paradigmatic problems in theoretical condensed matter physics: numerical analytic continuation from imaginary to real frequencies and finding (variational) ground states of frustrated (quantum) Ising models with random or long-range antiferromagnetic interactions.

Channel Estimation and Optimal Power Allocation for a Multiple-Antenna OFDM System

Directory of Open Access Journals (Sweden)

Yao Kung

2002-01-01

Full Text Available We propose combining channel estimation and optimal power allocation approaches for a multiple-antenna orthogonal frequency division multiplexing (OFDM system in high-speed transmission applications. We develop a least-square channel estimation approach, derive the performance bound of the estimator, and investigate the optimal training sequences for initial channel acquisition. Based on the channel estimates, the optimal power allocation solution which maximizes the bandwidth efficiency is derived under power and quality of service (Qos (symbol error rate constraints. It is shown that combining channel tracking and adaptive power allocation can dramatically enhance the outage capacity of an OFDM multiple-antenna system when severing fading occurs.

Development Of HUD Combiner For Automotive Windshield Application

Science.gov (United States)

Hattori, Akimasa; Makita, Kensuke; Okabayashi, Shigeru

1989-12-01

The head-up display system (HUM) has been developed for the windshield of Nissan Motor's passenger car, '88 model of Silvia (240SX) and '89 model of Maxima. HUD consists of a projector with high brightness VFT and a combiner which is a light-selective reflective film applied on the surface of ' e windshield. The system provides nice display legibility of speed in a three-digit reap at the position more than one meter far from driver's eye even under the bright sunlight. In this report, we present the optical properties and manufacturing process of the advanced combiner. The combiner has to have high transmittance as well as high reflectance so that a driver can see both foreground object and display reading at the same time. The optical design for the combiner is based on the concepts: (a) Visible light transmittance has to be 70% or more in accordance with a legal requirement, and (b) taking both peak wavelengths of Vim' and sensitivity characteristics of human eyes into consideration, 530nm of wave length is chosen as a reflective light. The combiner consists of a dielectric thin layer of Ti02-Si02 system. Its basic structure is decided by simulation with matrix method of the resultant waves. The coating film is applied on the restricted area of the forth surface of laminated windshield by newly developed solgel printing process using a metal alkoxide solution with a relatively long storage life.

Development of Inventory Optimization System for Operation Nuclear Plants

Energy Technology Data Exchange (ETDEWEB)

Jang, Se-Jin; Park, Jong-Hyuk; Yoo, Sung-Soo; Lee, Sang-Guk [Korea Electric Power Research Institutes, Taejon (Korea, Republic of)

2006-07-01

Inventory control of spare parts plays an increasingly important role in operation management. This is why inventory management systems such as manufacturing resources planning(MRP) and enterprise resource planning(ERP) have been added. However, most of these contributions have similar theoretical background. This means the concepts and techniques are mainly based on mathematical assumptions and modeling inventory of spare parts situations. Nuclear utilities in Korea have several problems to manage the optimum level of spare parts though they used MRP System. Because most of items have long lead time and they are imported from United States, Canada, France and so on. We developed the inventory optimization system for Operation Nuclear Plants to resolve these problems. In this paper, we report a data flow process, data load and inventory calculation process. The main contribution of this paper is development of inventory optimization system which can be used in domestic power plants.

Development of Inventory Optimization System for Operation Nuclear Plants

International Nuclear Information System (INIS)

Jang, Se-Jin; Park, Jong-Hyuk; Yoo, Sung-Soo; Lee, Sang-Guk

2006-01-01

Inventory control of spare parts plays an increasingly important role in operation management. This is why inventory management systems such as manufacturing resources planning(MRP) and enterprise resource planning(ERP) have been added. However, most of these contributions have similar theoretical background. This means the concepts and techniques are mainly based on mathematical assumptions and modeling inventory of spare parts situations. Nuclear utilities in Korea have several problems to manage the optimum level of spare parts though they used MRP System. Because most of items have long lead time and they are imported from United States, Canada, France and so on. We developed the inventory optimization system for Operation Nuclear Plants to resolve these problems. In this paper, we report a data flow process, data load and inventory calculation process. The main contribution of this paper is development of inventory optimization system which can be used in domestic power plants

Optimization and Simulation in Drug Development - Review and Analysis

DEFF Research Database (Denmark)

Schjødt-Eriksen, Jens; Clausen, Jens

2003-01-01

We give a review of pharmaceutical R&D and mathematical simulation and optimization methods used to support decision making within the pharmaceutical development process. The complex nature of drug development is pointed out through a description of the various phases of the pharmaceutical develo...... development process. A part of the paper is dedicated to the use of simulation techniques to support clinical trials. The paper ends with a section describing portfolio modelling methods in the context of the pharmaceutical industry....

Experimental observation and combined investigation of high-performance fusion of iron-region isotopes in optimal growing microbiological associations

International Nuclear Information System (INIS)

Vysotskii, Vladimir I.; Kornilova, Alla A.; Tashirev, Alexandr B.; Kornilova, Julia

2006-01-01

The report represents the results of combined (Moessbauer and mass-spectroscopy) examinations of isotopes transmutation process in growing microbiological associations in the iron-region of atomic mass (50 < A < 60). It was shown that the effectiveness of isotopes transmutation during the process of growth of microbiological associations at optimal conditions is by 10-20 times more than the effectiveness of the same transmutation in one-line' (clean) microbiological cultures. (author)

Baseline Glass Development for Combined Fission Products Waste Streams

International Nuclear Information System (INIS)

Crum, Jarrod V.; Billings, Amanda Y.; Lang, Jesse B.; Marra, James C.; Rodriguez, Carmen P.; Ryan, Joseph V.; Vienna, John D.

2009-01-01

Borosilicate glass was selected as the baseline technology for immobilization of the Cs/Sr/Ba/Rb (Cs), lanthanide (Ln) and transition metal fission product (TM) waste steams as part of a cost benefit analysis study.(1) Vitrification of the combined waste streams have several advantages, minimization of the number of waste forms, a proven technology, and similarity to waste forms currently accepted for repository disposal. A joint study was undertaken by Pacific Northwest National Laboratory (PNNL) and Savannah River National Laboratory (SRNL) to develop acceptable glasses for the combined Cs + Ln + TM waste streams (Option 1) and Cs + Ln combined waste streams (Option 2) generated by the AFCI UREX+ set of processes. This study is aimed to develop baseline glasses for both combined waste stream options and identify key waste components and their impact on waste loading. The elemental compositions of the four-corners study were used along with the available separations data to determine the effect of burnup, decay, and separations variability on estimated waste stream compositions.(2-5) Two different components/scenarios were identified that could limit waste loading of the combined Cs + LN + TM waste streams, where as the combined Cs + LN waste stream has no single component that is perceived to limit waste loading. Combined Cs + LN waste stream in a glass waste form will most likely be limited by heat due to the high activity of Cs and Sr isotopes.

Combined Turbine and Cycle Optimization for Organic Rankine Cycle Power Systems—Part A: Turbine Model

Directory of Open Access Journals (Sweden)

Andrea Meroni

2016-04-01

Full Text Available Axial-flow turbines represent a well-established technology for a wide variety of power generation systems. Compactness, flexibility, reliability and high efficiency have been key factors for the extensive use of axial turbines in conventional power plants and, in the last decades, in organic Rankine cycle power systems. In this two-part paper, an overall cycle model and a model of an axial turbine were combined in order to provide a comprehensive preliminary design of the organic Rankine cycle unit, taking into account both cycle and turbine optimal designs. Part A presents the preliminary turbine design model, the details of the validation and a sensitivity analysis on the main parameters, in order to minimize the number of decision variables in the subsequent turbine design optimization. Part B analyzes the application of the combined turbine and cycle designs on a selected case study, which was performed in order to show the advantages of the adopted methodology. Part A presents a one-dimensional turbine model and the results of the validation using two experimental test cases from literature. The first case is a subsonic turbine operated with air and investigated at the University of Hannover. The second case is a small, supersonic turbine operated with an organic fluid and investigated by Verneau. In the first case, the results of the turbine model are also compared to those obtained using computational fluid dynamics simulations. The results of the validation suggest that the model can predict values of efficiency within ± 1.3%-points, which is in agreement with the reliability of classic turbine loss models such as the Craig and Cox correlations used in the present study. Values similar to computational fluid dynamics simulations at the midspan were obtained in the first case of validation. Discrepancy below 12 % was obtained in the estimation of the flow velocities and turbine geometry. The values are considered to be within a

Nuclear fuel management optimization using adaptive evolutionary algorithms with heuristics

International Nuclear Information System (INIS)

Axmann, J.K.; Van de Velde, A.

1996-01-01

Adaptive Evolutionary Algorithms in combination with expert knowledge encoded in heuristics have proved to be a robust and powerful optimization method for the design of optimized PWR fuel loading pattern. Simple parallel algorithmic structures coupled with a low amount of communications between computer processor units in use makes it possible for workstation clusters to be employed efficiently. The extension of classic evolution strategies not only by new and alternative methods but also by the inclusion of heuristics with effects on the exchange probabilities of the fuel assemblies at specific core positions leads to the RELOPAT optimization code of the Technical University of Braunschweig. In combination with the new, neutron-physical 3D nodal core simulator PRISM developed by SIEMENS the PRIMO loading pattern optimization system has been designed. Highly promising results in the recalculation of known reload plans for German PWR's new lead to a commercially usable program. (author)

Automated Multivariate Optimization Tool for Energy Analysis: Preprint

Energy Technology Data Exchange (ETDEWEB)

Ellis, P. G.; Griffith, B. T.; Long, N.; Torcellini, P. A.; Crawley, D.

2006-07-01

Building energy simulations are often used for trial-and-error evaluation of ''what-if'' options in building design--a limited search for an optimal solution, or ''optimization''. Computerized searching has the potential to automate the input and output, evaluate many options, and perform enough simulations to account for the complex interactions among combinations of options. This paper describes ongoing efforts to develop such a tool. The optimization tool employs multiple modules, including a graphical user interface, a database, a preprocessor, the EnergyPlus simulation engine, an optimization engine, and a simulation run manager. Each module is described and the overall application architecture is summarized.

An optimal U.S. biodiesel fuel subsidy

International Nuclear Information System (INIS)

Wu Huiting; Colson, Gregory; Escalante, Cesar; Wetzstein, Michael

2012-01-01

Enhanced environmental quality, fuel security, and economic development, along with reduced prices of blended diesel, are often used as justifications for a U.S. federal excise tax exemption on biodiesel fuels. However, the possible effect of increased overall consumption of fuel in response to lower total prices, mitigating the environmental and fuel security benefits, are generally not considered. Taking this price response into account, the optimal U.S biodiesel subsidy is derived. Estimated values of the optimal subsidy are close to the recently expired subsidy, revealing the subsidy's environmental and security benefits. However, further positive environmental and security benefits from the biodiesel tax-exemption subsidy may be obtained if the subsidy is combined with a federal excise tax on petroleum diesel. - Highlights: ► Taking price response into account, the optimal theoretical U.S biodiesel subsidy is derived. ► Estimated values of the optimal subsidy are close to the recently expired subsidy, revealing the subsidy's environmental and security benefits. ► Further positive environmental and security benefits from the biodiesel tax-exemption subsidy may be obtained if the subsidy is combined with a federal excise tax on petroleum diesel.

[Development of an optimized formulation of damask marmalade with low energy level using Taguchi methodology].

Science.gov (United States)

Villarroel, Mario; Castro, Ruth; Junod, Julio

2003-06-01

The goal of this present study was the development of an optimized formula of damask marmalade low in calories applying Taguchi methodology to improve the quality of this product. The selection of this methodology lies on the fact that in real life conditions the result of an experiment frequently depends on the influence of several variables, therefore, one expedite way to solve this problem is utilizing factorial desings. The influence of acid, thickener, sweetener and aroma additives, as well as time of cooking, and possible interactions among some of them, were studied trying to get the best combination of these factors to optimize the sensorial quality of an experimental formulation of dietetic damask marmalade. An orthogonal array L8 (2(7)) was applied in this experience, as well as level average analysis was carried out according Taguchi methodology to determine the suitable working levels of the design factors previously choiced, to achieve a desirable product quality. A sensory trained panel was utilized to analyze the marmalade samples using a composite scoring test with a descriptive acuantitative scale ranging from 1 = Bad, 5 = Good. It was demonstrated that the design factors sugar/aspartame, pectin and damask aroma had a significant effect (p < 0.05) on the sensory quality of the marmalade with 82% of contribution on the response. The optimal combination result to be: citric acid 0.2%; pectin 1%; 30 g sugar/16 mg aspartame/100 g, damask aroma 0.5 ml/100 g, time of cooking 5 minutes. Regarding chemical composition, the most important results turned out to be the decrease in carbohydrate content compaired with traditional marmalade with a reduction of 56% in coloric value and also the amount of dietary fiber greater than similar commercial products. Assays of storage stability were carried out on marmalade samples submitted to different temperatures held in plastic bags of different density. Non percetible sensorial, microbiological and chemical changes

Advances in combination therapy of lung cancer

DEFF Research Database (Denmark)

Wu, Lan; Leng, Donglei; Cun, Dongmei

2017-01-01

Lung cancer is a complex disease caused by a multitude of genetic and environmental factors. The progression of lung cancer involves dynamic changes in the genome and a complex network of interactions between cancer cells with multiple, distinct cell types that form tumors. Combination therapy......, including small molecule drugs and biopharmaceuticals, which make the optimization of dosing and administration schedule challenging. This article reviews the recent advances in the design and development of combinations of pharmaceuticals for the treatment of lung cancer. Focus is primarily on rationales...... for the selection of specific combination therapies for lung cancer treatment, and state of the art of delivery technologies and dosage regimens for the combinations, tested in preclinical and clinical trials....

Optimizing computed tomography pulmonary angiography using right atrium bolus monitoring combined with spontaneous respiration

Energy Technology Data Exchange (ETDEWEB)

Min, Wang; Jian, Li; Rui, Zhai [Jining No. 1 People' s Hospital, Department of Computed Tomography, Jining City, ShanDong Province (China); Wen, Li [Jining No. 1 People' s Hospital, Department of Gastroenterology, Jining, ShanDong (China); Dai, Lun-Hou [Shandong Chest Hospital, Department of Radiology, Jinan, ShanDong (China)

2015-09-15

CT pulmonary angiography (CTPA) aims to provide pulmonary arterial opacification in the absence of significant pulmonary venous filling. This requires accurate timing of the imaging acquisition to ensure synchronization with the peak pulmonary artery contrast concentration. This study was designed to test the utility of right atrium (RA) monitoring in ensuring optimal timing of CTPA acquisition. Sixty patients referred for CTPA were divided into two groups. Group A (n = 30): CTPA was performed using bolus triggering from the pulmonary trunk, suspended respiration and 70 ml of contrast agent (CA). Group B (n = 30): CTPA image acquisition was triggered using RA monitoring with spontaneous respiration and 40 ml of CA. Image quality was compared. Subjective image quality, average CT values of pulmonary arteries and density difference between artery and vein pairs were significantly higher whereas CT values of pulmonary veins were significantly lower in group B (all P < 0.05). There was no significant difference between the groups in the proportion of subjects where sixth grade pulmonary arteries were opacified (P > 0.05). RA monitoring combined with spontaneous respiration to trigger image acquisition in CTPA produces optimal contrast enhancement in pulmonary arterial structures with minimal venous filling even with reduced doses of CA. (orig.)

Development of a standard methodology for optimizing remote visual display for nuclear-maintenance tasks

International Nuclear Information System (INIS)

Clarke, M.M.; Garin, J.; Preston-Anderson, A.

1981-01-01

The aim of the present study is to develop a methodology for optimizing remote viewing systems for a fuel recycle facility (HEF) being designed at Oak Ridge National Laboratory (ORNL). An important feature of this design involves the Remotex concept: advanced servo-controlled master/slave manipulators, with remote television viewing, will totally replace direct human contact with the radioactive environment. Therefore, the design of optimal viewing conditions is a critical component of the overall man/machine system. A methodology has been developed for optimizing remote visual displays for nuclear maintenance tasks. The usefulness of this approach has been demonstrated by preliminary specification of optimal closed circuit TV systems for such tasks

Optimal Model-Based Fault Estimation and Correction for Particle Accelerators and Industrial Plants Using Combined Support Vector Machines and First Principles Models

International Nuclear Information System (INIS)

2010-01-01

Timely estimation of deviations from optimal performance in complex systems and the ability to identify corrective measures in response to the estimated parameter deviations has been the subject of extensive research over the past four decades. The implications in terms of lost revenue from costly industrial processes, operation of large-scale public works projects and the volume of the published literature on this topic clearly indicates the significance of the problem. Applications range from manufacturing industries (integrated circuits, automotive, etc.), to large-scale chemical plants, pharmaceutical production, power distribution grids, and avionics. In this project we investigated a new framework for building parsimonious models that are suited for diagnosis and fault estimation of complex technical systems. We used Support Vector Machines (SVMs) to model potentially time-varying parameters of a First-Principles (FP) description of the process. The combined SVM and FP model was built (i.e. model parameters were trained) using constrained optimization techniques. We used the trained models to estimate faults affecting simulated beam lifetime. In the case where a large number of process inputs are required for model-based fault estimation, the proposed framework performs an optimal nonlinear principal component analysis of the large-scale input space, and creates a lower dimension feature space in which fault estimation results can be effectively presented to the operation personnel. To fulfill the main technical objectives of the Phase I research, our Phase I efforts have focused on: (1) SVM Training in a Combined Model Structure - We developed the software for the constrained training of the SVMs in a combined model structure, and successfully modeled the parameters of a first-principles model for beam lifetime with support vectors. (2) Higher-order Fidelity of the Combined Model - We used constrained training to ensure that the output of the SVM (i.e. the

Optimal Model-Based Fault Estimation and Correction for Particle Accelerators and Industrial Plants Using Combined Support Vector Machines and First Principles Models

Energy Technology Data Exchange (ETDEWEB)

Sayyar-Rodsari, Bijan; Schweiger, Carl; /SLAC /Pavilion Technologies, Inc., Austin, TX

2010-08-25

Timely estimation of deviations from optimal performance in complex systems and the ability to identify corrective measures in response to the estimated parameter deviations has been the subject of extensive research over the past four decades. The implications in terms of lost revenue from costly industrial processes, operation of large-scale public works projects and the volume of the published literature on this topic clearly indicates the significance of the problem. Applications range from manufacturing industries (integrated circuits, automotive, etc.), to large-scale chemical plants, pharmaceutical production, power distribution grids, and avionics. In this project we investigated a new framework for building parsimonious models that are suited for diagnosis and fault estimation of complex technical systems. We used Support Vector Machines (SVMs) to model potentially time-varying parameters of a First-Principles (FP) description of the process. The combined SVM & FP model was built (i.e. model parameters were trained) using constrained optimization techniques. We used the trained models to estimate faults affecting simulated beam lifetime. In the case where a large number of process inputs are required for model-based fault estimation, the proposed framework performs an optimal nonlinear principal component analysis of the large-scale input space, and creates a lower dimension feature space in which fault estimation results can be effectively presented to the operation personnel. To fulfill the main technical objectives of the Phase I research, our Phase I efforts have focused on: (1) SVM Training in a Combined Model Structure - We developed the software for the constrained training of the SVMs in a combined model structure, and successfully modeled the parameters of a first-principles model for beam lifetime with support vectors. (2) Higher-order Fidelity of the Combined Model - We used constrained training to ensure that the output of the SVM (i.e. the

DEVELOPMENT OF THE ALGORITHM FOR CHOOSING THE OPTIMAL SCENARIO FOR THE DEVELOPMENT OF THE REGION'S ECONOMY

Directory of Open Access Journals (Sweden)

I. S. Borisova

2018-01-01

Full Text Available Purpose: the article deals with the development of an algorithm for choosing the optimal scenario for the development of the regional economy. Since the "Strategy for socio-economic development of the Lipetsk region for the period until 2020" does not contain scenarios for the development of the region, the algorithm for choosing the optimal scenario for the development of the regional economy is formalized. The scenarios for the development of the economy of the Lipetsk region according to the indicators of the Program of social and economic development are calculated: "Quality of life index", "Average monthly nominal wage", "Level of registered unemployment", "Growth rate of gross regional product", "The share of innovative products in the total volume of goods shipped, works performed and services rendered by industrial organizations", "Total volume of atmospheric pollution per unit GRP" and "Satisfaction of the population with the activity of executive bodies of state power of the region". Based on the calculation of development scenarios, the dynamics of the values of these indicators was developed in the implementation of scenarios for the development of the economy of the Lipetsk region in 2016–2020. Discounted financial costs of economic participants for realization of scenarios of development of economy of the Lipetsk region are estimated. It is shown that the current situation in the economy of the Russian Federation assumes the choice of a paradigm for the innovative development of territories and requires all participants in economic relations at the regional level to concentrate their resources on the creation of new science-intensive products. An assessment of the effects of the implementation of reasonable scenarios for the development of the economy of the Lipetsk region was carried out. It is shown that the most acceptable is the "base" scenario, which assumes a consistent change in the main indicators. The specific economic

Mathematical modelling and optimization of a large-scale combined cooling, heat, and power system that incorporates unit changeover and time-of-use electricity price

International Nuclear Information System (INIS)

Zhu, Qiannan; Luo, Xianglong; Zhang, Bingjian; Chen, Ying

2017-01-01

Highlights: • We propose a novel superstructure for the design and optimization of LSCCHP. • A multi-objective multi-period MINLP model is formulated. • The unit start-up cost and time-of-use electricity prices are involved. • Unit size discretization strategy is proposed to linearize the original MINLP model. • A case study is elaborated to demonstrate the effectiveness of the proposed method. - Abstract: Building energy systems, particularly large public ones, are major energy consumers and pollutant emission contributors. In this study, a superstructure of large-scale combined cooling, heat, and power system is constructed. The off-design unit, economic cost, and CO_2 emission models are also formulated. Moreover, a multi-objective mixed integer nonlinear programming model is formulated for the simultaneous system synthesis, technology selection, unit sizing, and operation optimization of large-scale combined cooling, heat, and power system. Time-of-use electricity price and unit changeover cost are incorporated into the problem model. The economic objective is to minimize the total annual cost, which comprises the operation and investment costs of large-scale combined cooling, heat, and power system. The environmental objective is to minimize the annual global CO_2 emission of large-scale combined cooling, heat, and power system. The augmented ε–constraint method is applied to achieve the Pareto frontier of the design configuration, thereby reflecting the set of solutions that represent optimal trade-offs between the economic and environmental objectives. Sensitivity analysis is conducted to reflect the impact of natural gas price on the combined cooling, heat, and power system. The synthesis and design of combined cooling, heat, and power system for an airport in China is studied to test the proposed synthesis and design methodology. The Pareto curve of multi-objective optimization shows that the total annual cost varies from 102.53 to 94.59 M

Combined heat and power economic dispatch by harmony search algorithm

Energy Technology Data Exchange (ETDEWEB)

Vasebi, A.; Bathaee, S.M.T. [Power System Research Laboratory, Department of Electrical and Electronic Engineering, K.N.Toosi University of Technology, 322-Mirdamad Avenue West, 19697 Tehran (Iran); Fesanghary, M. [Department of Mechanical Engineering, Amirkabir University of Technology, 424-Hafez Avenue, Tehran (Iran)

2007-12-15

The optimal utilization of multiple combined heat and power (CHP) systems is a complicated problem that needs powerful methods to solve. This paper presents a harmony search (HS) algorithm to solve the combined heat and power economic dispatch (CHPED) problem. The HS algorithm is a recently developed meta-heuristic algorithm, and has been very successful in a wide variety of optimization problems. The method is illustrated using a test case taken from the literature as well as a new one proposed by authors. Numerical results reveal that the proposed algorithm can find better solutions when compared to conventional methods and is an efficient search algorithm for CHPED problem. (author)

[Optimization of process of icraiin be hydrolyzed to Baohuoside I by cellulase based on Plackett-Burman design combined with CCD response surface methodology].

Science.gov (United States)

Song, Chuan-xia; Chen, Hong-mei; Dai, Yu; Kang, Min; Hu, Jia; Deng, Yun

2014-11-01

To optimize the process of Icraiin be hydrolyzed to Baohuoside I by cellulase by Plackett-Burman design combined with Central Composite Design (CCD) response surface methodology. To select the main influencing factors by Plackett-Burman design, using CCD response surface methodology to optimize the process of Icraiin be hydrolyzed to Baohuoside I by cellulase. Taking substrate concentration, the pH of buffer and reaction time as independent variables, with conversion rate of icariin as dependent variable,using regression fitting of completely quadratic response surface between independent variable and dependent variable,the optimum process of Icraiin be hydrolyzed to Baohuoside I by cellulase was intuitively analyzed by 3D surface chart, and taking verification tests and predictive analysis. The best enzymatic hydrolytic process was as following: substrate concentration 8. 23 mg/mL, pH 5. 12 of buffer,reaction time 35. 34 h. The optimum process of Icraiin be hydrolyzed to Baohuoside I by cellulase is determined by Plackett-Burman design combined with CCD response surface methodology. The optimized enzymatic hydrolytic process is simple, convenient, accurate, reproducible and predictable.

An adaptive immune optimization algorithm with dynamic lattice searching operation for fast optimization of atomic clusters

International Nuclear Information System (INIS)

Wu, Xia; Wu, Genhua

2014-01-01

Highlights: • A high efficient method for optimization of atomic clusters is developed. • Its performance is studied by optimizing Lennard-Jones clusters and Ag clusters. • The method is proved to be quite efficient. • A new Ag 61 cluster with stacking-fault face-centered cubic motif is found. - Abstract: Geometrical optimization of atomic clusters is performed by a development of adaptive immune optimization algorithm (AIOA) with dynamic lattice searching (DLS) operation (AIOA-DLS method). By a cycle of construction and searching of the dynamic lattice (DL), DLS algorithm rapidly makes the clusters more regular and greatly reduces the potential energy. DLS can thus be used as an operation acting on the new individuals after mutation operation in AIOA to improve the performance of the AIOA. The AIOA-DLS method combines the merit of evolutionary algorithm and idea of dynamic lattice. The performance of the proposed method is investigated in the optimization of Lennard-Jones clusters within 250 atoms and silver clusters described by many-body Gupta potential within 150 atoms. Results reported in the literature are reproduced, and the motif of Ag 61 cluster is found to be stacking-fault face-centered cubic, whose energy is lower than that of previously obtained icosahedron

PROBLEM OF OPTIMIZATION OF ENTERPRISE FINANCIAL STREAMS: URGENCY UNDER ECONOMIC CRISIS CONDITIONS

Directory of Open Access Journals (Sweden)

J. E. Gorbach

2011-01-01

Full Text Available The paper considers a problem of structural optimization of financial streams in the economic activities of the enterprises. The authors describe a general process of enterprise capital structure optimization while breaking it in stages and consider the most interesting financial stream theories. The paper presents for the first time «Combined optimization model». In order to develop the model the most commonly applied methods have been used, namely: an optimization  method on the basis of an average capital price, an optimization method on the basis of financial leverage effect and an optimization method on the basis of the average managing subject price. Alternative calculations of optimum structure of financial stream sources on the basis of the proposed «combined model» have been presented in corresponding tables. The authors also use for the first time such concepts as «a break-even point» and «a safety zone» in respect of enterprise financial streams while using a graphic method.

Optimization of microwave-induced chemical etching for rapid development of neutron-induced recoil tracks in CR-39 detectors

International Nuclear Information System (INIS)

Sahoo, G.S.; Tripathy, S.P.; Bandyopadhyay, T.

2014-01-01

A systematic investigation is carried out to optimize the recently established microwave-induced chemical etching (MICE) parameters for rapid development of neutron-induced recoil tracks in CR-39 detectors. Several combinations of all available microwave powers with different etching durations were analysed to determine the most suitable etching condition. The etching duration was found to reduce with increasing microwave power and the tracks were observed at about 18, 15, 12, and 6 min for 300, 450, 600 and 900 W of microwave powers respectively compared to a few hours in chemical etching (CE) method. However, for complete development of tracks the etching duration of 30, 40, 50 and 60 min were found to be suitable for the microwave powers of 900, 600, 450 and 300 W, respectively. Temperature profiles of the etchant for all the available microwave powers at different etching durations were generated to regulate the etching process in a controlled manner. The bulk etch rates at different microwave powers were determined by 2 methods, viz., gravimetric and removed thickness methods. A logarithmic expression was used to fit the variation of bulk etch rate with microwave power. Neutron detection efficiencies were obtained for all the cases and the results on track parameters obtained with MICE technique were compared with those obtained from another detector processed with chemical etching. - Highlights: • Microwave-induced chemical etching method is optimized for rapid development of recoil tracks due to neutrons in CR-39 detector. • Several combinations of microwave powers and etching durations are investigated to standardize the suitable etching condition. • Bulk-etch rates are determined for all microwave powers by two different methods, viz. gravimetric and removed thickness method. • The method is found to be simple, effective and much faster compared to conventional chemical etching

Coating optimization for the ATHENA+ mission

DEFF Research Database (Denmark)

Ferreira, Desiree Della Monica; Christensen, Finn Erland; Jakobsen, Anders Clemen

2013-01-01

The ATHENA mission concept, now called ATHENA+, continues to be refined to address important questions in modern astrophysics. Previous studies have established that the requirement for effective area can be achieved using a combination of bi-layer coatings and/or simple graded multilayers. We find...... that further coating developments can improve on the baseline specifications and present here preliminary results on the optimization of coating design based on the new specifications of the ATHENA+ mission. The performances of several material combinations are investigated with the goal of maximizing...

Application of optimal design methodologies in retrofitting natural gas combined cycle power plants with CO_2 capture

International Nuclear Information System (INIS)

Pan, Ming; Aziz, Farah; Li, Baohong; Perry, Simon; Zhang, Nan; Bulatov, Igor; Smith, Robin

2016-01-01

Highlights: • A new approach is proposed for retrofitting NGCC power plants with CO2 capture. • HTI techniques are developed for improving heat recovery in NGCC power plants. • EGR techniques are developed to increase the process overall energy efficiency. • The proposed methods are efficient for practical application. - Abstract: Around 21% of the world’s power production is based on natural gas. Energy production is considered to be the significant sources of carbon dioxide (CO_2) emissions. This has a significant effect on the global warming. Improving power plant efficiency and adding a CO_2 capture unit into power plants, have been suggested to be a promising countermeasure against global warming. This paper presents a new insight to the application of energy efficient technologies in retrofitting natural gas combined cycle (NGCC) power plants with CO_2 capture. High fidelity models of a 420 MW NGCC power plant and a CO_2 capture plant with CO_2 compression train have been built and integrated for 90% capture level. These models have been then validated by comparisons with practical operating data and literature results. The novelty of the paper is to propose optimal retrofitting strategies to minimize the efficiency penalty caused by integrating carbon capture units into the power plant, including (1) implementing heat transfer intensification techniques to increase energy saving in the heat recovery steam generator (HRSG) of the power plant; (2) extracting suitable steam from the HRSG to supply the heat required by the capture process, thus on external heat is purchased; (3) employing exhaust gas recirculation (EGR) to increase the overall energy efficiency of the integrated process, which can benefit both power plant (e.g. increasing power plant efficiency) and capture process (e.g. reducing heat demands). Compared with the base case without using any integrating and retrofitting strategies, the optimal solution based on the proposed approaches

Optimization of high pressure machine decocting process for Dachengqi Tang using HPLC fingerprints combined with the Box-Behnken experimental design.

Science.gov (United States)

Xie, Rui-Fang; Shi, Zhi-Na; Li, Zhi-Cheng; Chen, Pei-Pei; Li, Yi-Min; Zhou, Xin

2015-04-01

Using Dachengqi Tang (DCQT) as a model, high performance liquid chromatography (HPLC) fingerprints were applied to optimize machine extracting process with the Box-Behnken experimental design. HPLC fingerprints were carried out to investigate the chemical ingredients of DCQT; synthetic weighing method based on analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) was performed to calculate synthetic scores of fingerprints; using the mark ingredients contents and synthetic scores as indicators, the Box-Behnken design was carried out to optimize the process parameters of machine decocting process under high pressure for DCQT. Results of optimal process showed that the herb materials were soaked for 45 min and extracted with 9 folds volume of water in the decocting machine under the temperature of 140 °C till the pressure arrived at 0.25 MPa; then hot decoction was excreted to soak Dahuang and Mangxiao for 5 min. Finally, obtained solutions were mixed, filtrated and packed. It concluded that HPLC fingerprints combined with the Box-Behnken experimental design could be used to optimize extracting process of traditional Chinese medicine (TCM).

Developments in model-based optimization and control distributed control and industrial applications

CERN Document Server

Grancharova, Alexandra; Pereira, Fernando

2015-01-01

This book deals with optimization methods as tools for decision making and control in the presence of model uncertainty. It is oriented to the use of these tools in engineering, specifically in automatic control design with all its components: analysis of dynamical systems, identification problems, and feedback control design. Developments in Model-Based Optimization and Control takes advantage of optimization-based formulations for such classical feedback design objectives as stability, performance and feasibility, afforded by the established body of results and methodologies constituting optimal control theory. It makes particular use of the popular formulation known as predictive control or receding-horizon optimization. The individual contributions in this volume are wide-ranging in subject matter but coordinated within a five-part structure covering material on: · complexity and structure in model predictive control (MPC); · collaborative MPC; · distributed MPC; · optimization-based analysis and desi...

X-ray backscatter imaging for radiography by selective detection and snapshot: Evolution, development, and optimization

Science.gov (United States)

Shedlock, Daniel

Compton backscatter imaging (CBI) is a single-sided imaging technique that uses the penetrating power of radiation and unique interaction properties of radiation with matter to image subsurface features. CBI has a variety of applications that include non-destructive interrogation, medical imaging, security and military applications. Radiography by selective detection (RSD), lateral migration radiography (LMR) and shadow aperture backscatter radiography (SABR) are different CBI techniques that are being optimized and developed. Radiography by selective detection (RSD) is a pencil beam Compton backscatter imaging technique that falls between highly collimated and uncollimated techniques. Radiography by selective detection uses a combination of single- and multiple-scatter photons from a projected area below a collimation plane to generate an image. As a result, the image has a combination of first- and multiple-scatter components. RSD techniques offer greater subsurface resolution than uncollimated techniques, at speeds at least an order of magnitude faster than highly collimated techniques. RSD scanning systems have evolved from a prototype into near market-ready scanning devices for use in a variety of single-sided imaging applications. The design has changed to incorporate state-of-the-art detectors and electronics optimized for backscatter imaging with an emphasis on versatility, efficiency and speed. The RSD system has become more stable, about 4 times faster, and 60% lighter while maintaining or improving image quality and contrast over the past 3 years. A new snapshot backscatter radiography (SBR) CBI technique, shadow aperture backscatter radiography (SABR), has been developed from concept and proof-of-principle to a functional laboratory prototype. SABR radiography uses digital detection media and shaded aperture configurations to generate near-surface Compton backscatter images without scanning, similar to how transmission radiographs are taken. Finally, a

Techno-economic optimization of a scaled-up solar concentrator combined with CSPonD thermal energy storage

Science.gov (United States)

Musi, Richard; Grange, Benjamin; Diago, Miguel; Topel, Monika; Armstrong, Peter; Slocum, Alexander; Calvet, Nicolas

2017-06-01

A molten salt direct absorption receiver, CSPonD, used to simultaneously collect and store thermal energy is being tested by Masdar Institute and MIT in Abu Dhabi, UAE. Whilst a research-scale prototype has been combined with a beam-down tower in Abu Dhabi, the original design coupled the receiver with a hillside heliostat field. With respect to a conventional power-tower setup, a hillside solar field presents the advantages of eliminating tower costs, heat tracing equipment, and high-pressure pumps. This analysis considers the industrial viability of the CSPonD concept by modeling a 10 MWe up-scaled version of a molten salt direct absorption receiver combined with a hillside heliostat field. Five different slope angles are initially simulated to determine the optimum choice using a combination of lowest LCOE and highest IRR, and sensitivity analyses are carried out based on thermal energy storage duration, power output, and feed-in tariff price. Finally, multi-objective optimization is undertaken to determine a Pareto front representing optimum cases. The study indicates that a 40° slope and a combination of 14 h thermal energy storage with a 40-50 MWe power output provide the best techno-economic results. By selecting one simulated result and using a feed-in tariff of 0.25 /kWh, a competitive IRR of 15.01 % can be achieved.

Pharmaceutical development and optimization of azithromycin suppository for paediatric use

Science.gov (United States)

Kauss, Tina; Gaubert, Alexandra; Boyer, Chantal; Ba, Boubakar B.; Manse, Muriel; Massip, Stephane; Léger, Jean-Michel; Fawaz, Fawaz; Lembege, Martine; Boiron, Jean-Michel; Lafarge, Xavier; Lindegardh, Niklas; White, Nicholas J.; Olliaro, Piero; Millet, Pascal; Gaudin, Karen

2013-01-01

Pharmaceutical development and manufacturing process optimization work was undertaken in order to propose a potential paediatric rectal formulation of azithromycin as an alternative to existing oral or injectable formulations. The target product profile was to be easy-to-use, cheap and stable in tropical conditions, with bioavailability comparable to oral forms, rapidly achieving and maintaining bactericidal concentrations. PEG solid solution suppositories were characterized in vitro using visual, HPLC, DSC, FTIR and XRD analyses. In vitro drug release and in vivo bioavailability were assessed; a study in rabbits compared the bioavailability of the optimized solid solution suppository to rectal solution and intra-venous product (as reference) and to the previous, non-optimized formulation (suspended azithromycin suppository). The bioavailability of azithromycin administered as solid solution suppositories relative to intra-venous was 43%, which compared well to the target of 38% (oral product in humans). The results of 3-month preliminary stability and feasibility studies were consistent with industrial production scale-up. This product has potential both as a classical antibiotic and as a product for use in severely ill children in rural areas. Industrial partners for further development are being sought. PMID:23220079

Pharmaceutical development and optimization of azithromycin suppository for paediatric use.

Science.gov (United States)

Kauss, Tina; Gaubert, Alexandra; Boyer, Chantal; Ba, Boubakar B; Manse, Muriel; Massip, Stephane; Léger, Jean-Michel; Fawaz, Fawaz; Lembege, Martine; Boiron, Jean-Michel; Lafarge, Xavier; Lindegardh, Niklas; White, Nicholas J; Olliaro, Piero; Millet, Pascal; Gaudin, Karen

2013-01-30

Pharmaceutical development and manufacturing process optimization work was undertaken in order to propose a potential paediatric rectal formulation of azithromycin as an alternative to existing oral or injectable formulations. The target product profile was to be easy-to-use, cheap and stable in tropical conditions, with bioavailability comparable to oral forms, rapidly achieving and maintaining bactericidal concentrations. PEG solid solution suppositories were characterized in vitro using visual, HPLC, DSC, FTIR and XRD analyses. In vitro drug release and in vivo bioavailability were assessed; a study in rabbits compared the bioavailability of the optimized solid solution suppository to rectal solution and intra-venous product (as reference) and to the previous, non-optimized formulation (suspended azithromycin suppository). The bioavailability of azithromycin administered as solid solution suppositories relative to intra-venous was 43%, which compared well to the target of 38% (oral product in humans). The results of 3-month preliminary stability and feasibility studies were consistent with industrial production scale-up. This product has potential both as a classical antibiotic and as a product for use in severely ill children in rural areas. Industrial partners for further development are being sought. Copyright © 2012 Elsevier B.V. All rights reserved.

Optimization Analysis Model of Self-Anchored Suspension Bridge

Directory of Open Access Journals (Sweden)

Pengzhen Lu

2014-01-01

Full Text Available The hangers of self-anchored suspension bridge need to be tensioned suitably during construction. In view of this point, a simplified optimization calculation method of cable force for self-anchored suspension bridge has been developed based on optimization theories, such as minimum bending energy method, and internal force balanced method, influence matrix method. Meanwhile, combined with the weak coherence of main cable and the adjacently interaction of hanger forces, a simplified analysis method is developed using MATLAB, which is then compared with the optimization method that consider the main cable's geometric nonlinearity with software ANSYS in an actual example bridge calculation. This contrast proves the weak coherence of main cable displacement and the limitation of the adjacent cable force influence. Furthermore, a tension program that is of great reference value has been developed; some important conclusions, advices, and attention points have been summarized.

Improving LED CCT uniformity using micropatterned films optimized by combining ray tracing and FDTD methods.

Science.gov (United States)

Ding, Xinrui; Li, Jiasheng; Chen, Qiu; Tang, Yong; Li, Zongtao; Yu, Binhai

2015-02-09

Although the light-emitting diode (LED) has revolutionized lighting, the non-uniformity of its correlated color temperature (CCT) still remains a major concern. In this context, to improve the light distribution performance of remote phosphor LED lamps, we employ a micropatterned array (MPA) optical film fabricated using a low-cost molding process. The parameters of the MPA, including different installation configurations, positioning, and diameters, are optimized by combining the finite-difference time-domain and ray-tracing methods. Results show that the sample with the upward-facing convex-cone MPA film that has a diameter of half of that of the remote phosphor glass, and is tightly affixed to the inward surface of the remote phosphor glass renders a superior light distribution performance. When compared with the case in which no MPA film is used, the deviation of the CCT distribution decreases from 1033 K to 223 K, and the corresponding output power of the sample is an acceptable level of 85.6%. We perform experiments to verify our simulation results, and the two sets of results exhibit a close agreement. We believe that our approach can be used to optimize MPA films for various lighting applications.

Boosting antibody developability through rational sequence optimization.

Science.gov (United States)

Seeliger, Daniel; Schulz, Patrick; Litzenburger, Tobias; Spitz, Julia; Hoerer, Stefan; Blech, Michaela; Enenkel, Barbara; Studts, Joey M; Garidel, Patrick; Karow, Anne R

2015-01-01

The application of monoclonal antibodies as commercial therapeutics poses substantial demands on stability and properties of an antibody. Therapeutic molecules that exhibit favorable properties increase the success rate in development. However, it is not yet fully understood how the protein sequences of an antibody translates into favorable in vitro molecule properties. In this work, computational design strategies based on heuristic sequence analysis were used to systematically modify an antibody that exhibited a tendency to precipitation in vitro. The resulting series of closely related antibodies showed improved stability as assessed by biophysical methods and long-term stability experiments. As a notable observation, expression levels also improved in comparison with the wild-type candidate. The methods employed to optimize the protein sequences, as well as the biophysical data used to determine the effect on stability under conditions commonly used in the formulation of therapeutic proteins, are described. Together, the experimental and computational data led to consistent conclusions regarding the effect of the introduced mutations. Our approach exemplifies how computational methods can be used to guide antibody optimization for increased stability.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

Science.gov (United States)

Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

2017-03-01

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

Utilization of special computerized tomography and nuclear medicine techniques for quality control and for the optimization of combined precision chemotherapy and precision radiation therapy

International Nuclear Information System (INIS)

Wiley, A.L. Jr.; Wirtanen, G.W.; Chien, I.-C.

1984-01-01

A combination of precision (selective, intra-arterial) chemotherapy and precision radiotherapy can be used for advanced pancreatic, biliary tract, and sarcomatous malignancies. There were some remarkable responses, but also a few poor responses and even some morbidity. Accordingly, methods are developed of pre-selecting those patients whose tumors are likely to respond to such therapy, as well as methods for improving the therapeutic ratio by the rational optimization of combined therapy. Specifically, clinical tumor blood flow characteristics (monitored with nuclear medicine techniques) may provide useful criteria for such selection. The authors also evaluate qualitatively the drug distribution or exposure space with specialized color-coded computerized tomography images, which demonstrate spatially dependent enhancement of intra-arterial contrast in tumor and in adjacent normal tissues. Such clinical data can improve the quality control aspects of intra-arterial chemotherapy administration, as well as the possibility of achievement of a significant therapeutic ratio by the integration of precision chemotherapy and precision radiation therapy. (Auth.)

Tracing the transition path between optimal strategies combinations within a competitive market of innovative industrial products

Science.gov (United States)

Batzias, Dimitris F.; Pollalis, Yannis A.

2012-12-01

In several cases, a competitive market can be simulated by a game, where each company/opponent is referred to as a player. In order to accommodate the fact that each player (alone or with alliances) is working against some others' interest, the rather conservative maximin criterion is frequently used for selecting the strategy or the combination of strategies that yield the best of the worst possible outcomes for each one of the players. Under this criterion, an optimal solution is obtained when neither player finds it beneficial to alter his strategy, which means that an equilibrium has been achieved, giving also the value of the game. If conditions change as regards a player, e.g., because of either achieving an unexpected successful result in developing an innovative industrial product or obtaining higher liquidity permitting him to increase advertisement in order to acquire a larger market share, then a new equilibrium is reached. The identification of the path between the old and the new equilibrium points may prove to be valuable for investigating the robustness of the solution by means of sensitivity analysis, since uncertainty plays a critical role in this situation, where evaluation of the payoff matrix is usually based on experts' estimates. In this work, the development of a standard methodology (including 16 activity stages and 7 decision nodes) for tracing this path is presented while a numerical implementation follows to prove its functionality.

Euler's fluid equations: Optimal control vs optimization

International Nuclear Information System (INIS)

Holm, Darryl D.

2009-01-01

An optimization method used in image-processing (metamorphosis) is found to imply Euler's equations for incompressible flow of an inviscid fluid, without requiring that the Lagrangian particle labels exactly follow the flow lines of the Eulerian velocity vector field. Thus, an optimal control problem and an optimization problem for incompressible ideal fluid flow both yield the same Euler fluid equations, although their Lagrangian parcel dynamics are different. This is a result of the gauge freedom in the definition of the fluid pressure for an incompressible flow, in combination with the symmetry of fluid dynamics under relabeling of their Lagrangian coordinates. Similar ideas are also illustrated for SO(N) rigid body motion.

Selecting statistical models and variable combinations for optimal classification using otolith microchemistry.

Science.gov (United States)

Mercier, Lény; Darnaude, Audrey M; Bruguier, Olivier; Vasconcelos, Rita P; Cabral, Henrique N; Costa, Maria J; Lara, Monica; Jones, David L; Mouillot, David

2011-06-01

Reliable assessment of fish origin is of critical importance for exploited species, since nursery areas must be identified and protected to maintain recruitment to the adult stock. During the last two decades, otolith chemical signatures (or "fingerprints") have been increasingly used as tools to discriminate between coastal habitats. However, correct assessment of fish origin from otolith fingerprints depends on various environmental and methodological parameters, including the choice of the statistical method used to assign fish to unknown origin. Among the available methods of classification, Linear Discriminant Analysis (LDA) is the most frequently used, although it assumes data are multivariate normal with homogeneous within-group dispersions, conditions that are not always met by otolith chemical data, even after transformation. Other less constrained classification methods are available, but there is a current lack of comparative analysis in applications to otolith microchemistry. Here, we assessed stock identification accuracy for four classification methods (LDA, Quadratic Discriminant Analysis [QDA], Random Forests [RF], and Artificial Neural Networks [ANN]), through the use of three distinct data sets. In each case, all possible combinations of chemical elements were examined to identify the elements to be used for optimal accuracy in fish assignment to their actual origin. Our study shows that accuracy differs according to the model and the number of elements considered. Best combinations did not include all the elements measured, and it was not possible to define an ad hoc multielement combination for accurate site discrimination. Among all the models tested, RF and ANN performed best, especially for complex data sets (e.g., with numerous fish species and/or chemical elements involved). However, for these data, RF was less time-consuming and more interpretable than ANN, and far more efficient and less demanding in terms of assumptions than LDA or QDA

Developing an optimal valve closing rule curve for real-time pressure control in pipes

Energy Technology Data Exchange (ETDEWEB)

Bazarganlari, Mohammad Reza; Afshar, Hossein [Islamic Azad University, Tehran (Iran, Islamic Republic of); Kerachian, Reza [University of Tehran, Tehran (Iran, Islamic Republic of); Bashiazghadi, Seyyed Nasser [Iran University of Science and Technology, Tehran (Iran, Islamic Republic of)

2013-01-15

Sudden valve closure in pipeline systems can cause high pressures that may lead to serious damages. Using an optimal valve closing rule can play an important role in managing extreme pressures in sudden valve closure. In this paper, an optimal closing rule curve is developed using a multi-objective optimization model and Bayesian networks (BNs) for controlling water pressure in valve closure instead of traditional step functions or single linear functions. The method of characteristics is used to simulate transient flow caused by valve closure. Non-dominated sorting genetic algorithms-II is also used to develop a Pareto front among three objectives related to maximum and minimum water pressures, and the amount of water passes through the valve during the valve-closing process. Simulation and optimization processes are usually time-consuming, thus results of the optimization model are used for training the BN. The trained BN is capable of determining optimal real-time closing rules without running costly simulation and optimization models. To demonstrate its efficiency, the proposed methodology is applied to a reservoir-pipe-valve system and the optimal closing rule curve is calculated for the valve. The results of the linear and BN-based valve closure rules show that the latter can significantly reduce the range of variations in water hammer pressures.

Spatial Optimization of Future Urban Development with Regards to Climate Risk and Sustainability Objectives.

Science.gov (United States)

Caparros-Midwood, Daniel; Barr, Stuart; Dawson, Richard

2017-11-01

Future development in cities needs to manage increasing populations, climate-related risks, and sustainable development objectives such as reducing greenhouse gas emissions. Planners therefore face a challenge of multidimensional, spatial optimization in order to balance potential tradeoffs and maximize synergies between risks and other objectives. To address this, a spatial optimization framework has been developed. This uses a spatially implemented genetic algorithm to generate a set of Pareto-optimal results that provide planners with the best set of trade-off spatial plans for six risk and sustainability objectives: (i) minimize heat risks, (ii) minimize flooding risks, (iii) minimize transport travel costs to minimize associated emissions, (iv) maximize brownfield development, (v) minimize urban sprawl, and (vi) prevent development of greenspace. The framework is applied to Greater London (U.K.) and shown to generate spatial development strategies that are optimal for specific objectives and differ significantly from the existing development strategies. In addition, the analysis reveals tradeoffs between different risks as well as between risk and sustainability objectives. While increases in heat or flood risk can be avoided, there are no strategies that do not increase at least one of these. Tradeoffs between risk and other sustainability objectives can be more severe, for example, minimizing heat risk is only possible if future development is allowed to sprawl significantly. The results highlight the importance of spatial structure in modulating risks and other sustainability objectives. However, not all planning objectives are suited to quantified optimization and so the results should form part of an evidence base to improve the delivery of risk and sustainability management in future urban development. © 2017 The Authors Risk Analysis published by Wiley Periodicals, Inc. on behalf of Society for Risk Analysis.

Development of Optimal Stressor Scenarios for New Operational Energy Systems

Science.gov (United States)

2017-12-01

OPTIMAL STRESSOR SCENARIOS FOR NEW OPERATIONAL ENERGY SYSTEMS by Geoffrey E. Fastabend December 2017 Thesis Advisor: Alejandro S... ENERGY SYSTEMS 5. FUNDING NUMBERS 6. AUTHOR(S) Geoffrey E. Fastabend 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate School...developed and tested simulation model for operational energy related systems in order to develop better stressor scenarios for acceptance testing

Optimal Control Problems for Partial Differential Equations on Reticulated Domains

CERN Document Server

Kogut, Peter I

2011-01-01

In the development of optimal control, the complexity of the systems to which it is applied has increased significantly, becoming an issue in scientific computing. In order to carry out model-reduction on these systems, the authors of this work have developed a method based on asymptotic analysis. Moving from abstract explanations to examples and applications with a focus on structural network problems, they aim at combining techniques of homogenization and approximation. Optimal Control Problems for Partial Differential Equations on Reticulated Domains is an excellent reference tool for gradu

Simultaneous Learning and Filtering without Delusions: A Bayes-Optimal Derivation of Combining Predictive Inference and AdaptiveFiltering

Directory of Open Access Journals (Sweden)

Jan eKneissler

2015-04-01

Full Text Available Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF. PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than ten-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

Penumbra modifier for optimal electron fields combination

International Nuclear Information System (INIS)

ElSherbini, N.; Hejazy, M.A.; Khalil, W.

2003-01-01

Abutment of two or more electron fields to irradiate extended areas may lead to significant dose inhomogeneities in the junction region. This study describes the geometric and dosimetric characteristics of a device developed to modify the penumbra of an electron beam and therapy improve of dose uniformity in the over lap region when fields are abutted. The device is lipowitz metal block placed on top of the insertion plate of the electron applicator and positioned to stop part of he electron beam on side of field abutment. The air-scattered electrons beyond the block increase the penumbra width from about 1,4 to 2-7-43.4 cm at SSD 100 cm, the modified penumbra is broad and almost linear at all depths for the 6.8, and 15 MeV electron beams used. Film dosimetry was used to obtain profiles, iso-dose distributions, single modified beams and matched fields of 6, 10, and 15 MeV. Wellhofer dosimetry system was used to obtain beam profiles and iso-dose distributions of single modified beams needed for CADPLAN treatment planning system, which used to optimize and compare the skin gap to be used and to quantify the dose uniformity in a junction of the field separation for both modified and non-modified beams. Results are presented for various field configurations without the penumbra modifier; lateral setup error of 2-3 mm may introduce dose variations of 20% or more in the junction region. Similar setup error cause less than 5% dose variations when the penumbra modifier is used to match the field

LinkMind: Link Optimization in Swarming Mobile Sensor Networks

DEFF Research Database (Denmark)

Ngo, Trung Dung

2012-01-01

of the most advantageous properties of the swarming wireless sensor network is that mobile nodes can work cooperatively to organize an ad-hoc network and optimize the network link capacity to maximize the transmission of gathered data from a source to a target. This paper describes a new method of link...... optimization of swarming mobile sensor networks. The new method is based on combination of the artificial potential force guaranteeing connectivities of the mobile sensor nodes and the max-flow min-cut theorem of graph theory ensuring optimization of the network link capacity. The developed algorithm...

Methodology for Designing and Developing a New Ultra-Wideband Antenna Based on Bio-Inspired Optimization Techniques

Science.gov (United States)

2017-11-01

on Bio -Inspired Optimization Techniques by Canh Ly, Nghia Tran, and Ozlem Kilic Approved for public release; distribution is...Research Laboratory Methodology for Designing and Developing a New Ultra-Wideband Antenna Based on Bio -Inspired Optimization Techniques by...SUBTITLE Methodology for Designing and Developing a New Ultra-Wideband Antenna Based on Bio -Inspired Optimization Techniques 5a. CONTRACT NUMBER

OPTIMAL MODEL OF FUNCTIONING OF OLERICULTURE: VERTICAL INTEGRATION, AGRICULTURAL FILIERES, CLUSTERS

Directory of Open Access Journals (Sweden)

Y. B. Mindlin

2016-01-01

Full Text Available The goal of the present paper is to identify the optimal strategy of development of the Russian olericulture in order to substitute imported products and to build up logistic and transport infrastructure. Existing problems of the Russian olericulture are described. It is demonstrated that these problems can be solved on the basis of big integrated structures. Formation of these structures can be based on hierarchical (vertical  integration or networking (agricultural filieres or clusters models. A comparative analysis of these models of development of olericulture is made. Advantages and inconveniences of each model are described. It is demonstrated that sustainable development of the Russian olericulture can be insured only by a combination of hierarchical and networking tools. Vertical integration will help to reach quick increase of production, while networking models are necessary for inclusion of small producers into production chains, development of product range and development of supporting industries. Networking models are also necessary for social tasks. It means that the optimal strategy of development of the  Russian olericulture should be based on a combination of networking and hierarchical tools. This combination is necessary for agricultural corporation as well as for the Russian olericulture in general.

Urban Saturated Power Load Analysis Based on a Novel Combined Forecasting Model

Directory of Open Access Journals (Sweden)

Huiru Zhao

2015-03-01

Full Text Available Analysis of urban saturated power loads is helpful to coordinate urban power grid construction and economic social development. There are two different kinds of forecasting models: the logistic curve model focuses on the growth law of the data itself, while the multi-dimensional forecasting model considers several influencing factors as the input variables. To improve forecasting performance, a novel combined forecasting model for saturated power load analysis was proposed in this paper, which combined the above two models. Meanwhile, the weights of these two models in the combined forecasting model were optimized by employing a fruit fly optimization algorithm. Using Hubei Province as the example, the effectiveness of the proposed combined forecasting model was verified, demonstrating a higher forecasting accuracy. The analysis result shows that the power load of Hubei Province will reach saturation in 2039, and the annual maximum power load will reach about 78,630 MW. The results obtained from this proposed hybrid urban saturated power load analysis model can serve as a reference for sustainable development for urban power grids, regional economies, and society at large.

On combining multi-normalization and ancillary measures for the optimal score level fusion of fingerprint and voice biometrics

Science.gov (United States)

Mohammed Anzar, Sharafudeen Thaha; Sathidevi, Puthumangalathu Savithri

2014-12-01

In this paper, we have considered the utility of multi-normalization and ancillary measures, for the optimal score level fusion of fingerprint and voice biometrics. An efficient matching score preprocessing technique based on multi-normalization is employed for improving the performance of the multimodal system, under various noise conditions. Ancillary measures derived from the feature space and the score space are used in addition to the matching score vectors, for weighing the modalities, based on their relative degradation. Reliability (dispersion) and the separability (inter-/intra-class distance and d-prime statistics) measures under various noise conditions are estimated from the individual modalities, during the training/validation stage. The `best integration weights' are then computed by algebraically combining these measures using the weighted sum rule. The computed integration weights are then optimized against the recognition accuracy using techniques such as grid search, genetic algorithm and particle swarm optimization. The experimental results show that, the proposed biometric solution leads to considerable improvement in the recognition performance even under low signal-to-noise ratio (SNR) conditions and reduces the false acceptance rate (FAR) and false rejection rate (FRR), making the system useful for security as well as forensic applications.

A combined experimental and mathematical approach for molecular-based optimization of irinotecan circadian delivery.

Directory of Open Access Journals (Sweden)

Annabelle Ballesta

2011-09-01

Full Text Available Circadian timing largely modifies efficacy and toxicity of many anticancer drugs. Recent findings suggest that optimal circadian delivery patterns depend on the patient genetic background. We present here a combined experimental and mathematical approach for the design of chronomodulated administration schedules tailored to the patient molecular profile. As a proof of concept we optimized exposure of Caco-2 colon cancer cells to irinotecan (CPT11, a cytotoxic drug approved for the treatment of colorectal cancer. CPT11 was bioactivated into SN38 and its efflux was mediated by ATP-Binding-Cassette (ABC transporters in Caco-2 cells. After cell synchronization with a serum shock defining Circadian Time (CT 0, circadian rhythms with a period of 26 h 50 (SD 63 min were observed in the mRNA expression of clock genes REV-ERBα, PER2, BMAL1, the drug target topoisomerase 1 (TOP1, the activation enzyme carboxylesterase 2 (CES2, the deactivation enzyme UDP-glucuronosyltransferase 1, polypeptide A1 (UGT1A1, and efflux transporters ABCB1, ABCC1, ABCC2 and ABCG2. DNA-bound TOP1 protein amount in presence of CPT11, a marker of the drug PD, also displayed circadian variations. A mathematical model of CPT11 molecular pharmacokinetics-pharmacodynamics (PK-PD was designed and fitted to experimental data. It predicted that CPT11 bioactivation was the main determinant of CPT11 PD circadian rhythm. We then adopted the therapeutics strategy of maximizing efficacy in non-synchronized cells, considered as cancer cells, under a constraint of maximum toxicity in synchronized cells, representing healthy ones. We considered exposure schemes in the form of an initial concentration of CPT11 given at a particular CT, over a duration ranging from 1 to 27 h. For any dose of CPT11, optimal exposure durations varied from 3h40 to 7h10. Optimal schemes started between CT2h10 and CT2h30, a time interval corresponding to 1h30 to 1h50 before the nadir of CPT11 bioactivation rhythm in

A short numerical study on the optimization methods influence on topology optimization

DEFF Research Database (Denmark)

Rojas Labanda, Susana; Sigmund, Ole; Stolpe, Mathias

2017-01-01

Structural topology optimization problems are commonly defined using continuous design variables combined with material interpolation schemes. One of the challenges for density based topology optimization observed in the review article (Sigmund and Maute Struct Multidiscip Optim 48(6):1031–1055...... 2013) is the slow convergence that is often encountered in practice, when an almost solid-and-void design is found. The purpose of this forum article is to present some preliminary observations on how designs evolves during the optimization process for different choices of optimization methods...

[Multi-mathematical modelings for compatibility optimization of Jiangzhi granules].

Science.gov (United States)

Yang, Ming; Zhang, Li; Ge, Yingli; Lu, Yanliu; Ji, Guang

2011-12-01

To investigate into the method of "multi activity index evaluation and combination optimized of mult-component" for Chinese herbal formulas. According to the scheme of uniform experimental design, efficacy experiment, multi index evaluation, least absolute shrinkage, selection operator (LASSO) modeling, evolutionary optimization algorithm, validation experiment, we optimized the combination of Jiangzhi granules based on the activity indexes of blood serum ALT, ALT, AST, TG, TC, HDL, LDL and TG level of liver tissues, ratio of liver tissue to body. Analytic hierarchy process (AHP) combining with criteria importance through intercriteria correlation (CRITIC) for multi activity index evaluation was more reasonable and objective, it reflected the information of activity index's order and objective sample data. LASSO algorithm modeling could accurately reflect the relationship between different combination of Jiangzhi granule and the activity comprehensive indexes. The optimized combination of Jiangzhi granule showed better values of the activity comprehensive indexed than the original formula after the validation experiment. AHP combining with CRITIC can be used for multi activity index evaluation and LASSO algorithm, it is suitable for combination optimized of Chinese herbal formulas.

Risky play and children's safety: balancing priorities for optimal child development.

Science.gov (United States)

Brussoni, Mariana; Olsen, Lise L; Pike, Ian; Sleet, David A

2012-08-30

Injury prevention plays a key role in keeping children safe, but emerging research suggests that imposing too many restrictions on children's outdoor risky play hinders their development. We explore the relationship between child development, play, and conceptions of risk taking with the aim of informing child injury prevention. Generational trends indicate children's diminishing engagement in outdoor play is influenced by parental and societal concerns. We outline the importance of play as a necessary ingredient for healthy child development and review the evidence for arguments supporting the need for outdoor risky play, including: (1) children have a natural propensity towards risky play; and, (2) keeping children safe involves letting them take and manage risks. Literature from many disciplines supports the notion that safety efforts should be balanced with opportunities for child development through outdoor risky play. New avenues for investigation and action are emerging seeking optimal strategies for keeping children "as safe as necessary," not "as safe as possible." This paradigm shift represents a potential for epistemological growth as well as cross-disciplinary collaboration to foster optimal child development while preserving children's safety.

A theoretical cost optimization model of reused flowback distribution network of regional shale gas development

International Nuclear Information System (INIS)

Li, Huajiao; An, Haizhong; Fang, Wei; Jiang, Meng

2017-01-01

The logistical issues surrounding the timing and transport of flowback generated by each shale gas well to the next is a big challenge. Due to more and more flowback being stored temporarily near the shale gas well and reused in the shale gas development, both transportation cost and storage cost are the heavy burden for the developers. This research proposed a theoretical cost optimization model to get the optimal flowback distribution solution for regional multi shale gas wells in a holistic perspective. Then, we used some empirical data of Marcellus Shale to do the empirical study. In addition, we compared the optimal flowback distribution solution by considering both the transportation cost and storage cost with the flowback distribution solution which only minimized the transportation cost or only minimized the storage cost. - Highlights: • A theoretical cost optimization model to get optimal flowback distribution solution. • An empirical study using the shale gas data in Bradford County of Marcellus Shale. • Visualization of optimal flowback distribution solutions under different scenarios. • Transportation cost is a more important factor for reducing the cost. • Help the developers to cut the storage and transportation cost of reusing flowback.

Evaluating Optimism: Developing Children’s Version of Optimistic Attributional Style Questionnaire

Directory of Open Access Journals (Sweden)

Gordeeva T.O.,

2017-08-01

Full Text Available People differ significantly in how they usually explain to themselves the reasons of events, both positive and negative, that happen in their lives. Psychological research shows that children who tend to think optimistically have certain advantages as compared to their pessimistically thinking peers: they are less likely to suffer from depression, establish more positive relationships with peers, and demonstrate higher academic achievements. This paper describes the process of creating the children’s version of the Optimistic Attributional Style Questionnaire (OASQ-C. This technique is based on the theory of learned hopelessness and optimism developed by M. Seligman, L. Abramson and J. Teas dale and is an efficient (compact tool for measuring optimism as an explanatory style in children and adolescents (9-14 years. Confirmatory factor analysis revealed that this technique is a two-factor structure with acceptable reliability. Validity is supported by the presence of expected correlations between explanatory style and rates of psychological well-being, dispositional optimism, positive attitude to life and its aspects, depression, and academic performance. The outcomes of this technique are not affected by social desirability. The developed questionnaire may be recommended to researchers and school counsellors for evaluating optimism (optimistic thinking as one of the major factors in psychological well-being of children; it may also be used in assessing the effectiveness of cognitive oriented training for adolescents.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems

Directory of Open Access Journals (Sweden)

Weixing Su

2017-03-01

Full Text Available There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

Reservoir Operating Rule Optimization for California's Sacramento Valley

Directory of Open Access Journals (Sweden)

Timothy Nelson

2016-03-01

Full Text Available doi: http://dx.doi.org/10.15447/sfews.2016v14iss1art6Reservoir operating rules for water resource systems are typically developed by combining intuition, professional discussion, and simulation modeling. This paper describes a joint optimization–simulation approach to develop preliminary economically-based operating rules for major reservoirs in California’s Sacramento Valley, based on optimized results from CALVIN, a hydro-economic optimization model. We infer strategic operating rules from the optimization model results, including storage allocation rules to balance storage among multiple reservoirs, and reservoir release rules to determine monthly release for individual reservoirs. Results show the potential utility of considering previous year type on water availability and various system and sub-system storage conditions, in addition to normal consideration of local reservoir storage, season, and current inflows. We create a simple simulation to further refine and test the derived operating rules. Optimization model results show particular insights for balancing the allocation of water storage among Shasta, Trinity, and Oroville reservoirs over drawdown and refill seasons, as well as some insights for release rules at major reservoirs in the Sacramento Valley. We also discuss the applicability and limitations of developing reservoir operation rules from optimization model results.

MODELLING, SIMULATING AND OPTIMIZING BOILERS

DEFF Research Database (Denmark)

Sørensen, Kim; Condra, Thomas Joseph; Houbak, Niels

2004-01-01

In the present work a framework for optimizing the design of boilers for dynamic operation has been developed. A cost function to be minimized during the optimization has been formulated and for the present design variables related to the Boiler Volume and the Boiler load Gradient (i.e. ring rate...... on the boiler) have been dened. Furthermore a number of constraints related to: minimum and maximum boiler load gradient, minimum boiler size, Shrinking and Swelling and Steam Space Load have been dened. For dening the constraints related to the required boiler volume a dynamic model for simulating the boiler...... performance has been developed. Outputs from the simulations are shrinking and swelling of water level in the drum during for example a start-up of the boiler, these gures combined with the requirements with respect to allowable water level uctuations in the drum denes the requirements with respect to drum...

Optimization of Multipurpose Reservoir Operation with Application Particle Swarm Optimization Algorithm

Directory of Open Access Journals (Sweden)

Elahe Fallah Mehdipour

2012-12-01

Full Text Available Optimal operation of multipurpose reservoirs is one of the complex and sometimes nonlinear problems in the field of multi-objective optimization. Evolutionary algorithms are optimization tools that search decision space using simulation of natural biological evolution and present a set of points as the optimum solutions of problem. In this research, application of multi-objective particle swarm optimization (MOPSO in optimal operation of Bazoft reservoir with different objectives, including generating hydropower energy, supplying downstream demands (drinking, industry and agriculture, recreation and flood control have been considered. In this regard, solution sets of the MOPSO algorithm in bi-combination of objectives and compromise programming (CP using different weighting and power coefficients have been first compared that the MOPSO algorithm in all combinations of objectives is more capable than the CP to find solution with appropriate distribution and these solutions have dominated the CP solutions. Then, ending points of solution set from the MOPSO algorithm and nonlinear programming (NLP results have been compared. Results showed that the MOPSO algorithm with 0.3 percent difference from the NLP results has more capability to present optimum solutions in the ending points of solution set.

Synthesizing optimal waste blends

International Nuclear Information System (INIS)

Narayan, V.; Diwekar, W.M.; Hoza, M.

1996-01-01

Vitrification of tank wastes to form glass is a technique that will be used for the disposal of high-level waste at Hanford. Process and storage economics show that minimizing the total number of glass logs produced is the key to keeping cost as low as possible. The amount of glass produced can be reduced by blending of the wastes. The optimal way to combine the tanks to minimize the vole of glass can be determined from a discrete blend calculation. However, this problem results in a combinatorial explosion as the number of tanks increases. Moreover, the property constraints make this problem highly nonconvex where many algorithms get trapped in local minima. In this paper the authors examine the use of different combinatorial optimization approaches to solve this problem. A two-stage approach using a combination of simulated annealing and nonlinear programming (NLP) is developed. The results of different methods such as the heuristics approach based on human knowledge and judgment, the mixed integer nonlinear programming (MINLP) approach with GAMS, and branch and bound with lower bound derived from the structure of the given blending problem are compared with this coupled simulated annealing and NLP approach

Development of sterilized porridge for patients by combined treatment of food technology with radiation technology

International Nuclear Information System (INIS)

Kim, Jaehun; Choi, Jongil; Song, Beomseok

2010-09-01

This research was conducted to develop patient foods of high quality using a radiation fusion technology with food processing. Radiation technique to increase calorie of porridge was established, and it was investigated that radiation effects on functional materials, which can could be added to increase functionality of patient foods. Moreover, sterilized semi-fluid meal (milk porridge) for patients with higher calorie was developed by a sterilization process by gamma irradiation, combined treatments to improve the sensory qualities, and fortification with various nutrients. Also, sensory survey on irradiated commercial patient foods was performed to find the problems and improvement points of the developed products. Optimal packaging material was selected by evaluation of effect of irradiation in packaging materials and a convenient package for consuming by patients was decided. Safety of the irradiated milk porridge was confirmed by in-vivo genotoxicological test, and its nutritional composition for patients was evaluated by nutritional analysis. Finally, the milk porridge was developed as liquid, dried, powdered, and pellet type products. This research may contribute to improve life quality of patients by supplement of various foods with high quality to immuno-compromised patients. Furthermore, economic profits and technological advances are expected by commercialization of the patient foods

Development of sterilized porridge for patients by combined treatment of food technology with radiation technology

Energy Technology Data Exchange (ETDEWEB)

Kim, Jaehun; Choi, Jongil; Song, Beomseok; and others

2010-09-15

This research was conducted to develop patient foods of high quality using a radiation fusion technology with food processing. Radiation technique to increase calorie of porridge was established, and it was investigated that radiation effects on functional materials, which can could be added to increase functionality of patient foods. Moreover, sterilized semi-fluid meal (milk porridge) for patients with higher calorie was developed by a sterilization process by gamma irradiation, combined treatments to improve the sensory qualities, and fortification with various nutrients. Also, sensory survey on irradiated commercial patient foods was performed to find the problems and improvement points of the developed products. Optimal packaging material was selected by evaluation of effect of irradiation in packaging materials and a convenient package for consuming by patients was decided. Safety of the irradiated milk porridge was confirmed by in-vivo genotoxicological test, and its nutritional composition for patients was evaluated by nutritional analysis. Finally, the milk porridge was developed as liquid, dried, powdered, and pellet type products. This research may contribute to improve life quality of patients by supplement of various foods with high quality to immuno-compromised patients. Furthermore, economic profits and technological advances are expected by commercialization of the patient foods.

An algorithm for online optimization of accelerators

Energy Technology Data Exchange (ETDEWEB)

Huang, Xiaobiao [SLAC National Accelerator Lab., Menlo Park, CA (United States); Corbett, Jeff [SLAC National Accelerator Lab., Menlo Park, CA (United States); Safranek, James [SLAC National Accelerator Lab., Menlo Park, CA (United States); Wu, Juhao [SLAC National Accelerator Lab., Menlo Park, CA (United States)

2013-10-01

We developed a general algorithm for online optimization of accelerator performance, i.e., online tuning, using the performance measure as the objective function. This method, named robust conjugate direction search (RCDS), combines the conjugate direction set approach of Powell's method with a robust line optimizer which considers the random noise in bracketing the minimum and uses parabolic fit of data points that uniformly sample the bracketed zone. Moreover, it is much more robust against noise than traditional algorithms and is therefore suitable for online application. Simulation and experimental studies have been carried out to demonstrate the strength of the new algorithm.

Engineering application of in-core fuel management optimization code with CSA algorithm

Energy Technology Data Exchange (ETDEWEB)

Liu, Zhihong; Hu, Yongming [INET, Tsinghua university, Beijing 100084 (China)

2009-06-15

PWR in-core loading (reloading) pattern optimization is a complex combined problem. An excellent fuel management optimization code can greatly improve the efficiency of core reloading design, and bring economic and safety benefits. Today many optimization codes with experiences or searching algorithms (such as SA, GA, ANN, ACO) have been developed, while how to improve their searching efficiency and engineering usability still needs further research. CSA (Characteristic Statistic Algorithm) is a global optimization algorithm with high efficiency developed by our team. The performance of CSA has been proved on many problems (such as Traveling Salesman Problems). The idea of CSA is to induce searching direction by the statistic distribution of characteristic values. This algorithm is quite suitable for fuel management optimization. Optimization code with CSA has been developed and was used on many core models. The research in this paper is to improve the engineering usability of CSA code according to all the actual engineering requirements. Many new improvements have been completed in this code, such as: 1. Considering the asymmetry of burn-up in one assembly, the rotation of each assembly is considered as new optimization variables in this code. 2. Worth of control rods must satisfy the given constraint, so some relative modifications are added into optimization code. 3. To deal with the combination of alternate cycles, multi-cycle optimization is considered in this code. 4. To confirm the accuracy of optimization results, many identifications of the physics calculation module in this code have been done, and the parameters of optimization schemes are checked by SCIENCE code. The improved optimization code with CSA has been used on Qinshan nuclear plant of China. The reloading of cycle 7, 8, 9 (12 months, no burnable poisons) and the 18 months equilibrium cycle (with burnable poisons) reloading are optimized. At last, many optimized schemes are found by CSA code

E-Learning Optimization: The Relative and Combined Effects of Mental Practice and Modeling on Enhanced Podcast-Based Learning--A Randomized Controlled Trial

Science.gov (United States)

Alam, Fahad; Boet, Sylvain; Piquette, Dominique; Lai, Anita; Perkes, Christopher P.; LeBlanc, Vicki R.

2016-01-01

Enhanced podcasts increase learning, but evidence is lacking on how they should be designed to optimize their effectiveness. This study assessed the impact two learning instructional design methods (mental practice and modeling), either on their own or in combination, for teaching complex cognitive medical content when incorporated into enhanced…

Optimization and development of antidiabetic phytosomes by the Box-Behnken design.

Science.gov (United States)

Rathee, Sushila; Kamboj, Anjoo

2018-06-01

Researchers have extensively reviewed on herbs and natural products for their marked clinical efficacy in some recent years, however, maximum of the newly discovered bioactive constituents offer poor bioavailability due to their large size molecules or to their poor miscibility with oils and lipids, thereby limiting their ability to pass across the lipid-rich outer membranes of the enterocytes of the small intestine. Phytosomes are more bioavailable as compared to herbal extracts owing to their enhanced capacity to cross the bio-membranes and thus reaching the systemic circulation. This study was aimed to investigate the development and optimization of antidiabetic phytosomes using a three-factor, three-level the Box-Behnken design (17 batches). The fruits of Citrullus colocynthis (L.) Momordica balsamina and Momordica dioica were extracted using Soxhlet's apparatus. The phytochemical fingerprint profile of the combined methanolic extracts was done by using high-performance thin layer chromatography (HPTLC). The polynomial quadratic equation analysis was designed to study the response (entrapment efficiency (EE), % yield) of independent significant factors at different levels. Phytosomes were characterized in terms of drug content, particle size, EE, zeta potential and in vitro dissolution. TEM analysis revealed good stability and a spherical, self-closed structure of phytosomes in complex formulations. Average particle size was found to 450 nm. Total flavonoid content was found to be 10.0 ± 0.002 μg/g. Optimized formulation was selected and was prepared using A (1:3), B (60 °C) and C (2.5 h) to give maximum yield and entrapment efficiencies (72% and 92.1 ± 5.1%). Phytosomes were found to have antidiabetic activity comparable to metformin in low dose. HPTLC showed the presence of the phyto-constituent quercetin.

Families of optimal thermodynamic solutions for combined cycle gas turbine (CCGT) power plants

International Nuclear Information System (INIS)

Godoy, E.; Scenna, N.J.; Benz, S.J.

2010-01-01

Optimal designs of a CCGT power plant characterized by maximum second law efficiency values are determined for a wide range of power demands and different values of the available heat transfer area. These thermodynamic optimal solutions are found within a feasible operation region by means of a non-linear mathematical programming (NLP) model, where decision variables (i.e. transfer areas, power production, mass flow rates, temperatures and pressures) can vary freely. Technical relationships among them are used to systematize optimal values of design and operative variables of a CCGT power plant into optimal solution sets, named here as optimal solution families. From an operative and design point of view, the families of optimal solutions let knowing in advance optimal values of the CCGT variables when facing changes of power demand or adjusting the design to an available heat transfer area.

A Hybrid Optimization Framework with POD-based Order Reduction and Design-Space Evolution Scheme

Science.gov (United States)

Ghoman, Satyajit S.

The main objective of this research is to develop an innovative multi-fidelity multi-disciplinary design, analysis and optimization suite that integrates certain solution generation codes and newly developed innovative tools to improve the overall optimization process. The research performed herein is divided into two parts: (1) the development of an MDAO framework by integration of variable fidelity physics-based computational codes, and (2) enhancements to such a framework by incorporating innovative features extending its robustness. The first part of this dissertation describes the development of a conceptual Multi-Fidelity Multi-Strategy and Multi-Disciplinary Design Optimization Environment (M3 DOE), in context of aircraft wing optimization. M 3 DOE provides the user a capability to optimize configurations with a choice of (i) the level of fidelity desired, (ii) the use of a single-step or multi-step optimization strategy, and (iii) combination of a series of structural and aerodynamic analyses. The modularity of M3 DOE allows it to be a part of other inclusive optimization frameworks. The M 3 DOE is demonstrated within the context of shape and sizing optimization of the wing of a Generic Business Jet aircraft. Two different optimization objectives, viz. dry weight minimization, and cruise range maximization are studied by conducting one low-fidelity and two high-fidelity optimization runs to demonstrate the application scope of M3 DOE. The second part of this dissertation describes the development of an innovative hybrid optimization framework that extends the robustness of M 3 DOE by employing a proper orthogonal decomposition-based design-space order reduction scheme combined with the evolutionary algorithm technique. The POD method of extracting dominant modes from an ensemble of candidate configurations is used for the design-space order reduction. The snapshot of candidate population is updated iteratively using evolutionary algorithm technique of

Ant colony optimization algorithm for interpretable Bayesian classifiers combination: application to medical predictions.

Directory of Open Access Journals (Sweden)

Salah Bouktif

Full Text Available Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models' interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions.The datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm.

Simultaneous Optimization of Decisions Using a Linear Utility Function.

Science.gov (United States)

Vos, Hans J.

1990-01-01

An approach is presented to simultaneously optimize decision rules for combinations of elementary decisions through a framework derived from Bayesian decision theory. The developed linear utility model for selection-mastery decisions was applied to a sample of 43 first year medical students to illustrate the procedure. (SLD)

Development of an innovative two-stage process, a combination of acidogenic hydrogenesis and methanogenesis

Energy Technology Data Exchange (ETDEWEB)

Han, S.K.; Shin, H.S. [Korea Advanced Inst. of Science and Technology, Daejeon (Korea, Republic of). Dept. of Civil and Enviromental Engineering

2004-07-01

Hydrogen produced from waste by means of fermentative bacteria is an attractive way to produce this fuel as an alternative to fossil fuels. It also helps treat the associated waste. The authors have undertaken to optimize acidogenic hydrogenesis and methanogenesis. Building on this, they then developed a two-stage process that produces both hydrogen and methane. Acidogenic hydrogenesis of food waste was investigated using a leaching bed reactor. The dilution rate was varied in order to maximize efficiency which was as high as 70.8 per cent. Further to this, an upflow anaerobic sludge blanket reactor converted the wastewater from acidogenic hydrogenesis into methane. Chemical oxygen demand (COD) removal rates exceeded 96 per cent up to a COD loading of 12.9 COD/l/d. After this, the authors devised a new two-stage process based on a combination of acidogenic hydrogenesis and methanogenesis. The authors report on results for this process using food waste as feedstock. 5 refs., 5 figs.

Combining of Direct Search and Signal-to-Noise Ratio for economic dispatch optimization

International Nuclear Information System (INIS)

Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

2011-01-01

This paper integrated the ideas of Direct Search and Signal-to-Noise Ratio (SNR) to develop a Novel Direct Search (NDS) method for solving the non-convex economic dispatch problems. NDS consists of three stages: Direct Search (DS), Global SNR (GSNR) and Marginal Compensation (MC) stages. DS provides a basic solution. GSNR searches the point with optimization strategy. MC fulfills the power balance requirement. With NDS, the infinite solution space becomes finite. Furthermore, a same optimum solution can be repeatedly reached. Effectiveness of NDS is demonstrated with three examples and the solutions were compared with previously published results. Test results show that the proposed method is simple, robust, and more effective than many other previously developed algorithms.

Optimal Load-Tracking Operation of Grid-Connected Solid Oxide Fuel Cells through Set Point Scheduling and Combined L1-MPC Control

Directory of Open Access Journals (Sweden)

Siwei Han

2018-03-01

Full Text Available An optimal load-tracking operation strategy for a grid-connected tubular solid oxide fuel cell (SOFC is studied based on the steady-state analysis of the system thermodynamics and electrochemistry. Control of the SOFC is achieved by a two-level hierarchical control system. In the upper level, optimal setpoints of output voltage and the current corresponding to unit load demand is obtained through a nonlinear optimization by minimizing the SOFC’s internal power waste. In the lower level, a combined L1-MPC control strategy is designed to achieve fast set point tracking under system nonlinearities, while maintaining a constant fuel utilization factor. To prevent fuel starvation during the transient state resulting from the output power surging, a fuel flow constraint is imposed on the MPC with direct electron balance calculation. The proposed control schemes are testified on the grid-connected SOFC model.

Kernel reconstruction methods for Doppler broadening — Temperature interpolation by linear combination of reference cross sections at optimally chosen temperatures

International Nuclear Information System (INIS)

Ducru, Pablo; Josey, Colin; Dibert, Karia; Sobes, Vladimir; Forget, Benoit; Smith, Kord

2017-01-01

This paper establishes a new family of methods to perform temperature interpolation of nuclear interactions cross sections, reaction rates, or cross sections times the energy. One of these quantities at temperature T is approximated as a linear combination of quantities at reference temperatures (T_j). The problem is formalized in a cross section independent fashion by considering the kernels of the different operators that convert cross section related quantities from a temperature T_0 to a higher temperature T — namely the Doppler broadening operation. Doppler broadening interpolation of nuclear cross sections is thus here performed by reconstructing the kernel of the operation at a given temperature T by means of linear combination of kernels at reference temperatures (T_j). The choice of the L_2 metric yields optimal linear interpolation coefficients in the form of the solutions of a linear algebraic system inversion. The optimization of the choice of reference temperatures (T_j) is then undertaken so as to best reconstruct, in the L∞ sense, the kernels over a given temperature range [T_m_i_n,T_m_a_x]. The performance of these kernel reconstruction methods is then assessed in light of previous temperature interpolation methods by testing them upon isotope "2"3"8U. Temperature-optimized free Doppler kernel reconstruction significantly outperforms all previous interpolation-based methods, achieving 0.1% relative error on temperature interpolation of "2"3"8U total cross section over the temperature range [300 K,3000 K] with only 9 reference temperatures.

Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization

Science.gov (United States)

Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard

2002-01-01

The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.

Optimal design of water supply networks for enhancing seismic reliability

International Nuclear Information System (INIS)

Yoo, Do Guen; Kang, Doosun; Kim, Joong Hoon

2016-01-01

The goal of the present study is to construct a reliability evaluation model of a water supply system taking seismic hazards and present techniques to enhance hydraulic reliability of the design into consideration. To maximize seismic reliability with limited budgets, an optimal design model is developed using an optimization technique called harmony search (HS). The model is applied to actual water supply systems to determine pipe diameters that can maximize seismic reliability. The reliabilities between the optimal design and existing designs were compared and analyzed. The optimal design would both enhance reliability by approximately 8.9% and have a construction cost of approximately 1.3% less than current pipe construction cost. In addition, the reinforcement of the durability of individual pipes without considering the system produced ineffective results in terms of both cost and reliability. Therefore, to increase the supply ability of the entire system, optimized pipe diameter combinations should be derived. Systems in which normal status hydraulic stability and abnormal status available demand could be maximally secured if configured through the optimal design. - Highlights: • We construct a seismic reliability evaluation model of water supply system. • We present technique to enhance hydraulic reliability in the aspect of design. • Harmony search algorithm is applied in optimal designs process. • The effects of the proposed optimal design are improved reliability about by 9%. • Optimized pipe diameter combinations should be derived indispensably.

Housing Development Building Management System (HDBMS For Optimized Electricity Bills

Directory of Open Access Journals (Sweden)

Weixian Li

2017-08-01

Full Text Available Smart Buildings is a modern building that allows residents to have sustainable comfort with high efficiency of electricity usage. These objectives could be achieved by applying appropriate, capable optimization algorithms and techniques. This paper presents a Housing Development Building Management System (HDBMS strategy inspired by Building Energy Management System (BEMS concept that will integrate with smart buildings using Supply Side Management (SSM and Demand Side Management (DSM System. HDBMS is a Multi-Agent System (MAS based decentralized decision making system proposed by various authors. MAS based HDBMS was created using JAVA on a IEEE FIPA compliant multi-agent platform named JADE. It allows agents to communicate, interact and negotiate with energy supply and demand of the smart buildings to provide the optimal energy usage and minimal electricity costs.  This results in reducing the load of the power distribution system in smart buildings which simulation studies has shown the potential of proposed HDBMS strategy to provide the optimal solution for smart building energy management.

A Theoretical and Empirical Integrated Method to Select the Optimal Combined Signals for Geometry-Free and Geometry-Based Three-Carrier Ambiguity Resolution.

Science.gov (United States)

Zhao, Dongsheng; Roberts, Gethin Wyn; Lau, Lawrence; Hancock, Craig M; Bai, Ruibin

2016-11-16

Twelve GPS Block IIF satellites, out of the current constellation, can transmit on three-frequency signals (L1, L2, L5). Taking advantages of these signals, Three-Carrier Ambiguity Resolution (TCAR) is expected to bring much benefit for ambiguity resolution. One of the research areas is to find the optimal combined signals for a better ambiguity resolution in geometry-free (GF) and geometry-based (GB) mode. However, the existing researches select the signals through either pure theoretical analysis or testing with simulated data, which might be biased as the real observation condition could be different from theoretical prediction or simulation. In this paper, we propose a theoretical and empirical integrated method, which first selects the possible optimal combined signals in theory and then refines these signals with real triple-frequency GPS data, observed at eleven baselines of different lengths. An interpolation technique is also adopted in order to show changes of the AR performance with the increase in baseline length. The results show that the AR success rate can be improved by 3% in GF mode and 8% in GB mode at certain intervals of the baseline length. Therefore, the TCAR can perform better by adopting the combined signals proposed in this paper when the baseline meets the length condition.

Triple-Frequency Code-Phase Combination Determination: A Comparison with the Hatch-Melbourne-Wübbena Combination Using BDS Signals

Directory of Open Access Journals (Sweden)

Chenlong Deng

2018-02-01

Full Text Available Considering the influence of the ionosphere, troposphere, and other systematic errors on double-differenced ambiguity resolution (AR, we present an optimal triple-frequency code-phase combination determination method driven by both the model and the real data. The new method makes full use of triple-frequency code measurements (especially the low-noise of the code on the B3 signal to minimize the total noise level and achieve the largest AR success rate (model-driven under different ionosphere residual situations (data-driven, thus speeding up the AR by directly rounding. With the triple-frequency Beidou Navigation Satellite System (BDS data collected at five stations from a continuously-operating reference station network in Guangdong Province of China, different testing scenarios are defined (a medium baseline, whose distance is between 20 km and 50 km; a medium-long baseline, whose distance is between 50 km and 100 km; and a long baseline, whose distance is larger than 100 km. The efficiency of the optimal code-phase combination on the AR success rate was compared with that of the geometry-free and ionosphere-free (GIF combination and the Hatch-Melbourne-Wübbena (HMW combination. Results show that the optimal combinations can always achieve better results than the HMW combination with B2 and B3 signals, especially when the satellite elevation angle is larger than 45°. For the wide-lane AR which aims to obtain decimeter-level kinematic positioning service, the standard deviation (STD of ambiguity residuals for the suboptimal combination are only about 0.2 cycles, and the AR success rate by directly rounding can be up to 99%. Compared with the HMW combinations using B1 and B2 signals and using B1 and B3 signals, the suboptimal combination achieves the best results in all baselines, with an overall improvement of about 40% and 20%, respectively. Additionally, the STD difference between the optimal and the GIF code-phase combinations decreases

ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization

DEFF Research Database (Denmark)

Pappalardo, F.; Halling-Brown, M. D.; Rapin, Nicolas

2009-01-01

conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common conceptual models and ontologies......Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines...

Optimizing the multimodal approach to pancreatic cyst fluid diagnosis: developing a volume-based triage protocol.

Science.gov (United States)

Chai, Siaw Ming; Herba, Karl; Kumarasinghe, M Priyanthi; de Boer, W Bastiaan; Amanuel, Benhur; Grieu-Iacopetta, Fabienne; Lim, Ee Mun; Segarajasingam, Dev; Yusoff, Ian; Choo, Chris; Frost, Felicity

2013-02-01

The objective of this study was to develop a triage algorithm to optimize diagnostic yield from cytology, carcinoembryonic antigen (CEA), and v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) testing on different components of a single pancreatic cyst fluid specimen. The authors also sought to determine whether cell block supernatant was suitable for CEA and KRAS testing. Fifty-four pancreatic cysts were triaged according to a volume-dependent protocol to generate fluid (neat and supernatant) and cell block specimens for cytology, comparative CEA, and KRAS testing. Follow-up histology, diagnostic cytology, or a combined clinicopathologic interpretation was recorded as the final diagnosis. There were 26 mucinous cystic lesions and 28 nonmucinous cystic lesions with volumes ranging from 0.3 mL to 55 mL. Testing different components of the specimens (cell block, neat, and/or supernatant) enabled all laboratory investigations to be performed on 50 of 54 cyst fluids (92.6%). Interpretive concordance was observed in 17 of 17 cases (100%) and in 35 of 40 cases (87.5%) that had multiple components tested for CEA and KRAS mutations, respectively. An elevated CEA level (>192 ng/mL) was the most sensitive test for the detection of a mucinous cystic lesion (62.5%) versus KRAS mutation (56%) and "positive" cytology (61.5%). KRAS mutations were identified in 2 of 25 mucinous cystic lesions (8%) in which cytology and CEA levels were not contributory. A volume-based protocol using different components of the specimen was able to optimize diagnostic yield in pancreatic cyst fluids. KRAS mutation testing increased diagnostic yield when combined with cytology and CEA analysis. The current results demonstrated that supernatant is comparable to neat fluid and cell block material for CEA and KRAS testing. Copyright © 2012 American Cancer Society.

Optimal Repair And Replacement Policy For A System With Multiple Components

Science.gov (United States)

2016-06-17

increases. A future research direction is to develop efficient heuristic methods that can produce near-optimal policy with much less computational...decision variables represent the long-run fraction of time for each state- action pair. The objective function is the linear combination of long-run...exists an optimal policy. To find this policy we solve the linear program. The solution shows that for each state only one state-action pair, represented