Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization

IMR OpenIR

	Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization
其他题名	Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization
	Yang Qiangda 1; Gao Hongbo 2; Zhang Weijun 1; Li Huimin 1
	2016
发表期刊	CHINESE JOURNAL OF CHEMICAL ENGINEERING
ISSN	1004-9541
卷号	24 期号:11 页码:1631-1639
摘要	Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybrid model. Therefore, a simultaneous hybrid modeling approach is presented in this paper. It transforms the training of the empirical model part into a dynamic system parameter identification problem, and thus allows training the empirical model part with only measured data. An adaptive escaping particle swarm optimization (AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated. (C) 2016 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.
其他摘要	Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybridmodel. Therefore, a simultaneous hybridmodeling approach is presented in this paper. It transforms the training of the empiricalmodel part into a dynamic system parameter identification problem, and thus allows training the empiricalmodel partwith only measured data. An adaptive escaping particle swarm optimization (AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.
关键词	FED-BATCH FERMENTATION NEURAL-NETWORKS ALGORITHM Bioprocess Dynamic modeling Neural networks Optimization
收录类别	CSCD
语种	英语
资助项目	[Specialized Research Fund for the Doctoral Program of Higher Education]
CSCD记录号	CSCD:5871164
引用统计
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/145294
专题	中国科学院金属研究所
作者单位	1.东北大学 2.中国科学院金属研究所
推荐引用方式 GB/T 7714	Yang Qiangda,Gao Hongbo,Zhang Weijun,et al. Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization[J]. CHINESE JOURNAL OF CHEMICAL ENGINEERING,2016,24(11):1631-1639.
APA	Yang Qiangda,Gao Hongbo,Zhang Weijun,&Li Huimin.(2016).Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization.CHINESE JOURNAL OF CHEMICAL ENGINEERING,24(11),1631-1639.
MLA	Yang Qiangda,et al."Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization".CHINESE JOURNAL OF CHEMICAL ENGINEERING 24.11(2016):1631-1639.