Modeling of Glumatic Acid Fermentation Process Based on PSO-SVM
In a fermentation process several variables,such as biomass concentration are conventionally determined by off-line laboratory analysis,i.e.,the process control is unavailable to industrial production in time just because of time delay that often makes the analysis results inefficient.Utilizing the ability simple in application and quick in convergence of Particle Swarm Optimization (PSO) algorithm and the high generalization ability of Support Vector Machine (SVM),selecting the appropriate state variables,a dynamic time-varying model has been built.Using the model and algorithm to per-estimate some biochemical state variables which can not be measured on-line,and to optimize some operational variables.It is proved that the method is efficiency through the practical application of Glumatic Acid fermentation process.
Particle Swarm Optimization Support Vector Machine State variables fermentation process Modeling
Xianfang Wang Zhiyong Du Hua Wen Feng Pan
Henan Institute of Science and Technology,Xinxiang,Henan 453003,China;School of Information & Contro Henan Mechanical and Electrical Engineering College,Xinxiang,Henan 453002,China Department of Electronic Science & Engineering,Huanghuai University,Zhumadian,Henan 463000,China School of Information & Control Engineering,Jiangnan University,Wuxi,Jiangsu 214122,China
国际会议
大连
英文
1311-1316
2008-07-27（万方平台首次上网日期，不代表论文的发表时间）