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Dynamic modeling and nonlinear predictive control based on partitioned model and nonlinear optimization / Ridong Zhang in Industrial & engineering chemistry research, Vol. 50 N° 13 (Juillet 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 13 (Juillet 2011) . - pp. 8110-8121 Titre : Dynamic modeling and nonlinear predictive control based on partitioned model and nonlinear optimization Type de document : texte imprimé Auteurs : Ridong Zhang, Auteur ; Anke Xue, Auteur ; Shuqing Wang, Auteur Année de publication : 2011 Article en page(s) : pp. 8110-8121 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Optimization  Predictive  control  Modeling  Dynamic  model Résumé : The paper presents a combination modeling procedure and the implementation of a nonlinear predictive control scheme for the optimization of industrial chemical processes. The model structure is first based on a simple step response method. This provides a way to use prior knowledge about the dynamics, which has a general validity, while additional information about the process behavior is derived from measured plant data-model error. This data error driven model framework is applicable for a wide range of chemical operating units under a certain control policy. The same idea is also used to solve the online optimization problem in the predictive controller. The efficiency and effectiveness of the modeling training algorithm and the nonlinear predictive control approach are demonstrated through a coke furnace case study. A good model fitting for the nonlinear plant is obtained by using the new method. A comparison with traditional approaches shows that the new algorithm can considerably reduce modeling error and improve control accuracy. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24332137 [article] Dynamic modeling and nonlinear predictive control based on partitioned model and nonlinear optimization [texte imprimé] / Ridong Zhang, Auteur ; Anke Xue, Auteur ; Shuqing Wang, Auteur . - 2011 . - pp. 8110-8121. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 13 (Juillet 2011) . - pp. 8110-8121 Mots-clés : Optimization  Predictive  control  Modeling  Dynamic  model Résumé : The paper presents a combination modeling procedure and the implementation of a nonlinear predictive control scheme for the optimization of industrial chemical processes. The model structure is first based on a simple step response method. This provides a way to use prior knowledge about the dynamics, which has a general validity, while additional information about the process behavior is derived from measured plant data-model error. This data error driven model framework is applicable for a wide range of chemical operating units under a certain control policy. The same idea is also used to solve the online optimization problem in the predictive controller. The efficiency and effectiveness of the modeling training algorithm and the nonlinear predictive control approach are demonstrated through a coke furnace case study. A good model fitting for the nonlinear plant is obtained by using the new method. A comparison with traditional approaches shows that the new algorithm can considerably reduce modeling error and improve control accuracy. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24332137