Evaluation of hybrid optimization methods for the optimal design of heat integrated distillation sequences

被引:18
|
作者
Fraga, ES
Zilinskas, A
机构
[1] UCL, Ctr Proc Syst Engn, Dept Chem Engn, London WC1E 7JE, England
[2] Vytautas Magnus Univ, Inst Math & Informat, Vilnius, Lithuania
关键词
hybrid optimization; direct search; evolutionary; stochastic optimization; heat integration; process optimization;
D O I
10.1016/S0965-9978(02)00125-4
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Optimal process design often requires the solution of mixed integer non-linear programming problems. Optimization procedures must be robust and efficient if they are to be incorporated in automated design systems. For heat integrated separation process design, a natural hybrid evolutionary/local search method with these properties is possible. The method is based on the use of local search methods for the continuous design parameters for the units in the process and the use of an evolutionary optimization procedure for the design of the heat exchanger network. The use of a stochastic method for the heat exchanger network aspect introduces noise in the evaluation of the objective function used by the local search methods. A smoothing procedure has been designed and implemented to improve the efficacy of the hybrid approach. This paper presents the evaluation of a variety of local search methods. It is shown that the Hooke and Jeeves algorithm, combined with a simple genetic algorithm, provides a robust, efficient and effective solution procedure for optimizing heat integrated distillation sequences. (C) 2003 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:73 / 86
页数:14
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