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Application of balanced truncation to nonlinear systems / Ivan Dones in Industrial & engineering chemistry research, Vol. 50 N° 17 (Septembre 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 17 (Septembre 2011) . - pp. 10093-10101 Titre : Application of balanced truncation to nonlinear systems Type de document : texte imprimé Auteurs : Ivan Dones, Auteur ; Sigurd Skogestad, Auteur ; Heinz A. Preisig, Auteur Année de publication : 2011 Article en page(s) : pp. 10093-10101 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Non  linear  system Résumé : The balanced truncation method for reducing the size of a model was originally developed for linear systems. When extended to nonlinear systems, some considerations must be faced. First of all, the calculation of the balancing transformation matrix is not unique. This may results in nonphysical values for the reconstructed states, which may lead to failure, for example, in thermodynamic routines. To reduce this problem, it is recommended to include all the states in the balancing outputs. To further reduce the effect of nonlinearties in the original model, it is recommended to use a linearizing static transformation of the states, if available. In this paper, distillation column models are used as a case study, and, in this case, a logarithmic transformation of the compositions is beneficial. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24483654 [article] Application of balanced truncation to nonlinear systems [texte imprimé] / Ivan Dones, Auteur ; Sigurd Skogestad, Auteur ; Heinz A. Preisig, Auteur . - 2011 . - pp. 10093-10101. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 17 (Septembre 2011) . - pp. 10093-10101 Mots-clés : Non  linear  system Résumé : The balanced truncation method for reducing the size of a model was originally developed for linear systems. When extended to nonlinear systems, some considerations must be faced. First of all, the calculation of the balancing transformation matrix is not unique. This may results in nonphysical values for the reconstructed states, which may lead to failure, for example, in thermodynamic routines. To reduce this problem, it is recommended to include all the states in the balancing outputs. To further reduce the effect of nonlinearties in the original model, it is recommended to use a linearizing static transformation of the states, if available. In this paper, distillation column models are used as a case study, and, in this case, a logarithmic transformation of the compositions is beneficial. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24483654

Nonlinear model predictive control / Ivan Dones in Industrial & engineering chemistry research, Vol. 49 N° 10 (Mai 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 10 (Mai 2010) . - pp. 4782–4791 Titre : Nonlinear model predictive control : a self - adaptive approach Type de document : texte imprimé Auteurs : Ivan Dones, Auteur ; Flavio Manenti, Auteur ; Heinz A. Preisig, Auteur Année de publication : 2010 Article en page(s) : pp. 4782–4791 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Dynamic  models  Predictive  Control Résumé : Model predictive control (MPC) is an online application based on dynamic models. Its application faces two major obstacles: (i) computational constraints and (ii) the need to accurately simulate the process by a model that properly predicts how the plant will behave in the future. Implementation of MPC is not always possible in large-scale or industrial applications due to the computational complexity of MPC and to the dimensionality of the models. To facilitate MPC implementations, this paper proposes a self-adaptive approach based on simplified (or reduced-order) nonlinear models. The proposed methodology yields an MPC that adjusts the dimension of the model according to both the current process conditions and the control objectives. The self-adaptive approach is described and validated on an industrial case study, a C4-splitter. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901693w [article] Nonlinear model predictive control : a self - adaptive approach [texte imprimé] / Ivan Dones, Auteur ; Flavio Manenti, Auteur ; Heinz A. Preisig, Auteur . - 2010 . - pp. 4782–4791. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 10 (Mai 2010) . - pp. 4782–4791 Mots-clés : Dynamic  models  Predictive  Control Résumé : Model predictive control (MPC) is an online application based on dynamic models. Its application faces two major obstacles: (i) computational constraints and (ii) the need to accurately simulate the process by a model that properly predicts how the plant will behave in the future. Implementation of MPC is not always possible in large-scale or industrial applications due to the computational complexity of MPC and to the dimensionality of the models. To facilitate MPC implementations, this paper proposes a self-adaptive approach based on simplified (or reduced-order) nonlinear models. The proposed methodology yields an MPC that adjusts the dimension of the model according to both the current process conditions and the control objectives. The self-adaptive approach is described and validated on an industrial case study, a C4-splitter. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901693w