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05AIChE: 2005 AIChE Annual Meeting and Fall Showcase

作者： Bhutani, Naveen Ray, Ajay K. Rangaiah, Gade P.

Hydrocracking is a complex catalytic cracking process that involves the cracking of relatively heavy oil fractions such as heavy gas oil and vacuum gas oil into lighter products: naphtha, kerosene and diesel, in the presence of hydrogen. Since the feed is a complex mixture of olefins, paraffins, aromatics and napthenic compounds, the rate constant for individual reaction steps are impossible to obtain. Hence, many researchers have followed pseudo-component approach which is based on classifying hydrocarbons on the basis of either structural classes (Martens and Marin, 2001) or common properties like boiling point range and specific gravity to reduce the complexity of problem. These first principle models are not satisfactory for industrial applications due to common process variations such as change in feed stock composition, operating conditions and catalyst deactivation. The main reason is the complex reaction kinetics which necessitates regular update of model internal parameters for accurate prediction. Hybrid models which combine first principle and data-based approaches have significant advantages over the first principle model. They allow the integration of a-priori knowledge with the valuable information contained in the industrial operating data. Hence, the hybrid model structure captures underlying system characteristics better. Several hybrid model structures have been proposed in the literature (Duarte et al., 2004) These structures generally follow first principles approach for macroscopic balances (i.e. mass, energy and momentum balances) and employ artificial neural networks to model nonlinear reaction kinetics of complex chemical/biological kinetics. Application of such approaches to modeling of chemical (Hugget et al., 1999), biochemical (Psichogios and Ungar, 1992;Thompson and Kramer, 1994;Oliveira, 2004) and petrochemical processes (Bellos et al., 2005) has been studied using simulated, laboratory or plant data. However, application of hybrid mode

关键词： Hydrocracking

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计及负荷静态电压特性的多目标交易研究

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中国电机工程学报 2005年第9期25卷 1-5,113页

作者：马瑞贺仁睦王鹏杨华春张进胡国强电力系统保护与动态安全监控教育部重点实验室(华北电力大学) 长沙理工大学电气与信息工程学院湖南省长沙市410077

提出了一种基于计及负荷静态电压特性最优潮流的多目标优化交易计划新模型,并讨论了负荷模型对交易的影响约束,能够反映节点电压变化对交易的影响,从而使交易模型更加符合电力系统运行的特点;采用模糊集理论并结合内点法对模型求解;基于... 详细信息

提出了一种基于计及负荷静态电压特性最优潮流的多目标优化交易计划新模型,并讨论了负荷模型对交易的影响约束,能够反映节点电压变化对交易的影响,从而使交易模型更加符合电力系统运行的特点;采用模糊集理论并结合内点法对模型求解;基于Kuhn-Tucker 一阶最优性必要条件分析了多目标竞标机理并定义了节点广义实时价格和节点电压对节点需求灵敏度等指标,从而可定量评估发电节点经济.环保等综合指标和需求节点电压对系统交易的影响.算例结果表明了该模型、算法及所定义指标的有效性。

关键词：电压特性电力系统运行节点电压多目标优化模糊集理论交易计划最优潮流负荷模型电压变化交易模型模型求解条件分析定量评估综合指标新模型内点法灵敏度最优性需求定义发电

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practical SOLUTIONS OF multi-objective SYSTEM RELIABILITY DESIGN PROBLEMS USING GENETIC ALGORITHMS

PRACTICAL SOLUTIONS OF MULTI-OBJECTIVE SYSTEM RELIABILITY DE...

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第四届质量与可靠性国际会议

作者： HEIDI A.TABOADA FATEMA BAHERANWALA DAVID W.COIT NARUEMON WATTANAPONOSAKORN Department of Industrial and Systems Engineering Rutgers University Piscataway NJ 08854 USA Department of Industrial and Systems Engineering Rutgers University Piscataway NJ 08854 USA Department of Industrial and Systems Engineering Rutgers University Piscataway NJ 08854 USA King Mongkut's University of Technology Thonburi Bangkok Thailand

Two methods are presented as practical approaches to reduce the size of the Pareto optimal set of multiple-objective system reliability design problems. The first method is a pseudo-ranking scheme that helps the decision maker select solutions that reflect his/her preferences. In the second approach we used clustering techniques used in data mining, to group the data by using the k-means algorithm to find clusters of similar solutions, which allows the decision maker to have just k solutions to choose from without using any objective function preference information. Under this second method, from the clustered Pareto optimal set, we attempted to find solutions which are likely to be more relevant to the decision maker, which are solutions where a small improvement in one objective would lead to a large deterioration in at least one other objective. To show how these methods work, the well-known Redundancy Allocation Problem was solved as a multiple objective problem by using the NSGA genetic algorithm to initially find the Pareto optimal solutions, and then, the two proposed methods are applied to prune the Pareto set.

关键词： System Reliability multi-objective optimization Pareto optimal set cluster analysis.

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A novel response surface method for design optimization of electronic packages

A novel response surface method for design optimization of e...

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Thermal, Mechanical and multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems EuroSimE

作者： Bo Ping Wang Zhen Xue Han Leon Xu T. Reinikainen University of Texas Arlington USA Nokia Inc.

The response surface method has been widely used in practical engineering design optimization problems, where the optimal searches are based on the response surfaces mimicking the physical processes or models. Sometimes the response surface method is the only practical option to be able to perform design optimization, such as simulation-based design optimization when the simulations are usually computationally expensive. In conventional approaches, the response surfaces are usually built up using a group of sampling points based on certain design of experiment schemes. The accuracy of the response surface depends largely on the number of sampling points and their distributions in the design space, as well as the approximation functions for the response surface. Currently there is no general method that could achieve the necessary accuracy of a response surface with the minimal expensive (or the number of sampling points). In this paper, a novel sequential response surface updating approach is proposed to improve the efficiency and the accuracy of simulation-based optimizations for electronic packages. It is a dynamic and adaptive approach, which starts with a small number of initial sampling points based on Halton sequence (also called quasi Monte Carlo method), and then refines the response surface by adding more sampling points during the optimization process. This method is demonstrated with a design optimization problem of the thermo-mechanical analyses of a ceramic chip carrier assembly. A simplified thermo-mechanical model is used to perform the interfacial stress analysis of the solder bounding. The objective of is to minimize the interfacial stress by changing the bonding compliance in the specified design space. The case study indicates that, comparing with the conventional direct methods, this approach could greatly improve the computational efficiency of optimization processes with the needed accuracy for simulation-based design optimization.

关键词： Response surface methodology Design optimization Electronics packaging Sampling methods Computational modeling Thermomechanical processes Thermal stresses Design engineering optimization methods Ceramics

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optimization of injection moulding conditions with user-definable objective functions based on a genetic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2004年第7期42卷 1365-1390页

作者： Deng, YM Lam, YC Britton, GA Nanyang Technol Univ Sch Mech & Prod Engn Singapore 639798 Singapore Nanyang Technol Univ Singapore MIT Alliance Singapore 639798 Singapore

This paper applies a Genetic Algorithm (GA) method to optimize injection moulding conditions, such as melt temperature, mould temperature and injection time. A GA is very suitable for moulding conditions optimization where complex patterns of local minima are possible. Existing work in the literature has limited versatility because the optimization algorithm is hard-wired with specific objective function. However, for most of the practical applications, the appropriateness of optimization objective functions depends on each specific moulding problem. The paper develops a multi-objective GA optimization strategy, where the objective functions may be defined by the designers, including using different criteria and/or weights. For parts with general quality requirements, an objective function is also recommended with some quality measuring criteria, which are either more accurately represented or cover more moulding defects than those from existing simulation-based optimization approaches. The paper also elaborates on the effective GA attributes suited to moulding conditions optimization, such as population size, crossover rate and mutation rate. A case study demonstrates the effectiveness of the proposed approach and algorithm. The optimization results are compared with those from an exhaustive search method to determine the algorithm's accuracy in finding global optimum. It is found to be favourable.

关键词： Search methods quality requirements Injection molding Gallium objective function global optimum Mutation Rate optimization algorithms Designers Genetic Algorithm (GA)

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Leader identification and leader selection: its effect on a swarm's performance for multi-objective design optimization problems

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STRUCTURAL AND multiDISCIPLINARY optimization 2004年第2-3期28卷 156-169页

作者： Liew, KM Tan, PK Ray, T Nanyang Technol Univ Nanyang Ctr Supercomp & Visualisat Singapore 639798 Singapore Nanyang Technol Univ Sch Mech & Prod Engn Singapore 639798 Singapore Natl Univ Singapore Singapore MIT Alliance High Performance Computat Engineered Syst Program Singapore 117548 Singapore Natl Univ Singapore Temasek Labs Singapore 117508 Singapore

In this paper, three distinct swarm strategies for the optimization of engineering design problems with multiple objectives are presented. These strategies build upon the swarm algorithm of Ray et al. by incorporating new processes which improve the performance of their predecessor. The constraint-matching strategy calls for the mating of solutions based on constraint satisfaction characteristics. Local search entails the thorough exploration of regions in parametric space where good solutions potentially reside. Migrating leaders prescribes the exchange of information between the best performing members of the swarm. As proof of their utility, the strategies were used to solve two well-studied optimal engineering design problems. Solutions obtained by the strategies are comparable with those of other optimization approaches documented in the literature.

关键词： engineering design multiple-objective optimization swarm strategy

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Differential evolution for solving multiobjective optimization problems

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ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH 2004年第2期21卷 225-240页

作者： Sarker, R Abbass, HA Univ New S Wales Sch Informat Technol & Elect Engn Canberra ACT 2600 Australia

The use of evolutionary strategies (ESs) to solve problems with multiple objectives [known as vector optimization problems (VOPs)] has attracted much attention recently. Being population-based approaches, ESs offer a means to find a set of Pareto-optimal solutions in a single run. Differential evolution (DE) is an ES that was developed to handle optimization problems over continuous domains. The objective of this paper is to introduce a novel Pareto-frontier differential evolution (PDE) algorithm to solve VOPs. The solutions provided by the proposed algorithm for two standard test problems, outperform the "strength Pareto evolutionary algorithm", one of the state-of-the-art evolutionary algorithm for solving VOPs.

关键词： multi-objective optimization vector optimization evolutionary strategies differential evolution Pareto frontier population-based approach

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multi-objective decisions on capacity planning and production - Inventory control under uncertainty

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2004年第9期43卷 2192-2208页

作者： Cheng, LF Subrahmanian, E Westerberg, AW Carnegie Mellon Univ Dept Chem Engn Inst Complex Engineered Syst Pittsburgh PA 15213 USA

In the presence of a changing and stochastic environment, firms must appropriately choose the timing and sizes of capacity acquisition as well as the production decisions to replenish inventory, such that various conflicting goals are satisfied. This paper considers multicriteria decision making on joint capacity planning and inventory control under uncertainty. We formulate this class of problems into a multi-objective Markov decision process, which simultaneously searches for both capacity and inventory policies that optimize multiple objectives. In a previous work, we developed a multi-objective dynamic programming algorithm to solve small problems by propagating Pareto optimal solutions recursively backward in time. The numerical intractability of the rigorous algorithm for large problems necessitates approximation approaches. However, and importantly, on the basis of the optimality equations constructed in the dynamic programming framework, we are able to obtain analytical insights into the structure of optimal solutions for certain simplified problems. For example, through convexity analysis, we show that such structural policies, as the target interval capacity policy and base-stock inventory policy, are optimal for a certain class of problems. We propose using these structural results to form a basis for parametric representation of suboptimal policies for more general problems. This approach greatly simplifies the optimization to that of finding a parameter vector that defines these optimal or suboptimal policies. We propose a simulation-based optimization framework to find the parameters that optimize multiple performance measures. Next we show how to exploit a multi-objective evolutionary algorithm to find a diverse set of Pareto optimal solutions in one single run, and finally we present numerical results from an example problem that we carry from formulation to solution.

关键词： Chemical structure Algorithms optimization Approximation Mathematical methods

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A multi-objective optimum design of general 3R manipulators for prescribed workspace limits

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MECHANISM AND MACHINE THEORY 2004年第2期39卷 119-132页

作者： Ceccarelli, M Lanni, C Univ Cassino DIMSAT Lab Robot & Mechatron I-03043 Cassino FR Italy

Manipulator design can be conveniently expressed as a function of workspace requirements, since a fundamental feature of manipulators is recognized in workspace capabilities. In this paper a suitable formulation for the workspace has been used for the manipulator design, which has been formulated as multiobjective optimization problem by using the workspace volume and robot dimensions as objective functions, and given workspace limits as constraints. Additional constraints have been included to obtain manipulator sizes within practical values. The optimum design has been successfully tested by numerical examples, which have also proved the efficiency of using an algebraic formulation for the workspace of 3R manipulators. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： MANIPULATORS (Machinery) WORK environment STRUCTURAL optimization SPACE robotics

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An efficient pareto set identification approach for multi-objective optimization on black-box functions

An efficient pareto set identification approach for multi-ob...

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2004 ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference

作者： Wang, G. Gary Shan, Songqing Dept. of Mechanical Engineering University of Manitoba Winnipeg Man. R3T 5V6 Canada

Both multiple objectives and computation-intensive black-box functions often exist simultaneously in engineering design problems. Few of existing multi-objective optimization approaches addresses problems with expensive black-box functions. In this paper, a new method called the Pareto set pursuing (PSP) method is developed. By developing sampling guidance functions, this approach progressively provides a designer with a rich and evenly distributed Pareto optimal points. This work describes PSP in detail with analysis of its properties. From testing and design application, PSP demonstrates considerable efficiency, accuracy, and robustness. Theoretical proof of convergence of PSP is also given. It is believed that PSP has a great potential to be a practical tool for multi-objective optimization problems.

关键词： Pareto principle

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