| Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization | |
| Alternative Title | Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization |
| Yang Qiangda1; Gao Hongbo2; Zhang Weijun1; Li Huimin1 | |
| 2016 | |
| Source Publication | CHINESE JOURNAL OF CHEMICAL ENGINEERING
 |
| ISSN | 1004-9541 |
| Volume | 24Issue:11Pages:1631-1639 |
| Abstract | 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. |
| Other Abstract | 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. |
| Keyword | FED-BATCH FERMENTATION NEURAL-NETWORKS ALGORITHM Bioprocess Dynamic modeling Neural networks Optimization |
| Indexed By | CSCD |
| Language | 英语 |
| Funding Project | [Specialized Research Fund for the Doctoral Program of Higher Education] |
| CSCD ID | CSCD:5871164 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imr.ac.cn/handle/321006/145294 |
| Collection | 中国科学院金属研究所 |
| Affiliation | 1.东北大学 2.中国科学院金属研究所 |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment