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Dynamic modeling and optimal control of batch reactors, based on structure approaching hybrid neural networks / J. Wang in Industrial & engineering chemistry research, Vol. 50 N° 10 (Mai 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 10 (Mai 2011) . - pp. 6174-6186 Titre : Dynamic modeling and optimal control of batch reactors, based on structure approaching hybrid neural networks Type de document : texte imprimé Auteurs : J. Wang, Auteur ; L. Lin Cao, Auteur ; H. Yan Wu, Auteur Année de publication : 2011 Article en page(s) : pp. 6174-6186 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Neural  network  Reactor  Batchwise  Optimal  control  Modeling  Dynamic  model Résumé : A novel Structure Approaching Hybrid Neural Network (SAHNN) approach to model batch reactors is presented. The Virtual Supervisor-Artificial Immune Algorithm method is utilized for the training of SAHNN, especially for the batch processes with partial unmeasurable state variables. SAHNN involves the use of approximate mechanistic equations to characterize unmeasured state variables. Since the main interest in batch process operation is on the end-of-batch product quality, an extended integral square error control index based on the SAHNN model is applied to track the desired temperature profile of a batch process. This approach introduces model mismatches and unmeasured disturbances into the optimal control strategy and provides a feedback channel for control. The performance of robustness and antidisturbances of the control system are then enhanced. The simulation result indicates that the SAHNN model and model-based optimal control strategy of the batch process are effective. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24158916 [article] Dynamic modeling and optimal control of batch reactors, based on structure approaching hybrid neural networks [texte imprimé] / J. Wang, Auteur ; L. Lin Cao, Auteur ; H. Yan Wu, Auteur . - 2011 . - pp. 6174-6186. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 10 (Mai 2011) . - pp. 6174-6186 Mots-clés : Neural  network  Reactor  Batchwise  Optimal  control  Modeling  Dynamic  model Résumé : A novel Structure Approaching Hybrid Neural Network (SAHNN) approach to model batch reactors is presented. The Virtual Supervisor-Artificial Immune Algorithm method is utilized for the training of SAHNN, especially for the batch processes with partial unmeasurable state variables. SAHNN involves the use of approximate mechanistic equations to characterize unmeasured state variables. Since the main interest in batch process operation is on the end-of-batch product quality, an extended integral square error control index based on the SAHNN model is applied to track the desired temperature profile of a batch process. This approach introduces model mismatches and unmeasured disturbances into the optimal control strategy and provides a feedback channel for control. The performance of robustness and antidisturbances of the control system are then enhanced. The simulation result indicates that the SAHNN model and model-based optimal control strategy of the batch process are effective. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24158916