文献类型：期刊文献

中文题名：Novel Control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of Chemical Processes

英文题名：基于差分进化算法的控制变量参数化方法及其在化工过程动态优化中的应用(英文)

作者：孙帆[1];钟伟民[2];程辉[1];钱锋[1]

机构：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes (Ministry of Education), East China University of Science and Technology;[2]Department of Automation, East China University of Science and Technology

年份：2013

卷号：21

期号：1

起止页码：64

中文期刊名：Chinese Journal of Chemical Engineering

外文期刊名：中国化学工程学报（英文版）

收录：CSTPCD;;Scopus;CSCD:【CSCD2013_2014】；

基金：Supported by the Major State Basic Research Development Program of China(2012CB720500);the National Natural Science Foundation of China(Key Program:U1162202);the National Science Fund for Outstanding Young Scholars(61222303);the National Natural Science Foundation of China(61174118,21206037);Shanghai Leading Academic Discipline Project(B504)

语种：中文

中文关键词：control vector pararneterization; differential evolution algorithm; dynamic optimization; chemical processes

外文关键词：差分进化算法;参数化方法;动态优化;化工过程;控制向量;应用;数值方法;时间间隔

摘要：Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution （DE） algorithm and control vector parameteri- zation （CVP） is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the oroposed methods.
Two general approaches are adopted in solving dynamic optimization problems in chemical processes, namely, the analytical and numerical methods. The numerical method, which is based on heuristic algorithms, has been widely used. An approach that combines differential evolution (DE) algorithm and control vector parameterization (CVP) is proposed in this paper. In the proposed CVP, control variables are approximated with polynomials based on state variables and time in the entire time interval. Region reduction strategy is used in DE to reduce the width of the search region, which improves the computing efficiency. The results of the case studies demonstrate the feasibility and efficiency of the proposed methods.