PSO Based Surrogate Model Steady State Optimization with Its application

Author

Li, Xiugai ; Huang, Dexian

Author_Institution

Dept. of Autom., Tsinghua Univ., Beijing

Volume

2

fYear

0

fDate

0-0 0

Firstpage

6578

Lastpage

6582

Abstract

RBF neural network based surrogate model is constructed from process simulator and particle swarm optimization (PSO) strategy is discussed to solve this nonlinear programming problem with some output variables are unmeasured. Performance of maximum yield rate for a hydrolysis of the propylene oxide reaction is developed and the constrained PSO can give the best value of the steady state with different inflow rate and temperature. Comparison results with the evolutionary strategy (ES) show the efficiency of this new intelligent optimization method, it provides a practically method for industry process optimization

Keywords

nonlinear programming; particle swarm optimisation; radial basis function networks; PSO based surrogate model; RBF neural network; constrained PSO; evolutionary strategy; hydrolysis; intelligent optimization method; nonlinear programming problem; particle swarm optimization; propylene oxide reaction; radial basis function; steady state optimization; Automatic programming; Automation; Electronic mail; Intelligent control; Minimax techniques; Neural networks; Optimization methods; Particle swarm optimization; Steady-state; Temperature; Evolutionary Strategy; Particle Swarm Optimization; Radial Basis Function; Surrogate Model;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714354

Filename

1714354