Just - in - time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes / Yi Liu in Industrial & engineering chemistry research, Vol. 51 N° 11 (Mars 2012)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 51 N° 11 (Mars 2012) . - pp. 4313–4327 Titre : Just - in - time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes Type de document : texte imprimé Auteurs : Yi Liu, Auteur ; Zengliang Gao, Auteur ; Ping Li, Auteur Année de publication : 2012 Article en page(s) : pp. 4313–4327 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Batch processes Résumé : An efficient nonlinear just-in-time learning (JITL) soft sensor method for online modeling of batch processes with uneven operating durations is proposed. A recursive least-squares support vector regression (RLSSVR) approach is combined with the JITL manner to model the nonlinearity of batch processes. The similarity between the query sample and the most relevant samples, including the weight of similarity and the size of the relevant set, can be chosen using a presented cumulative similarity factor. Then, the kernel parameters of the developed JITL-RLSSVR model structure can be determined adaptively using an efficient cross-validation strategy with low computational load. The soft sensor implement algorithm for batch processes is also developed. Both the batch-to-batch similarity and variation characteristics are taken into consideration to make the modeling procedure more practical. The superiority of the proposed soft sensor approach is demonstrated by predicting the concentrations of the active biomass and recombinant protein in the streptokinase fed-batch fermentation process, compared with other existing JITL-based and global soft sensors. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie201650u [article] Just - in - time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes [texte imprimé] / Yi Liu, Auteur ; Zengliang Gao, Auteur ; Ping Li, Auteur . - 2012 . - pp. 4313–4327. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 11 (Mars 2012) . - pp. 4313–4327 Mots-clés : Batch processes Résumé : An efficient nonlinear just-in-time learning (JITL) soft sensor method for online modeling of batch processes with uneven operating durations is proposed. A recursive least-squares support vector regression (RLSSVR) approach is combined with the JITL manner to model the nonlinearity of batch processes. The similarity between the query sample and the most relevant samples, including the weight of similarity and the size of the relevant set, can be chosen using a presented cumulative similarity factor. Then, the kernel parameters of the developed JITL-RLSSVR model structure can be determined adaptively using an efficient cross-validation strategy with low computational load. The soft sensor implement algorithm for batch processes is also developed. Both the batch-to-batch similarity and variation characteristics are taken into consideration to make the modeling procedure more practical. The superiority of the proposed soft sensor approach is demonstrated by predicting the concentrations of the active biomass and recombinant protein in the streptokinase fed-batch fermentation process, compared with other existing JITL-based and global soft sensors. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie201650u

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Simple nonlinear predictive control strategy for chemical processes using sparse kernel learning with polynomial form / Yi Liu in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 8209–8218 Titre : Simple nonlinear predictive control strategy for chemical processes using sparse kernel learning with polynomial form Type de document : texte imprimé Auteurs : Yi Liu, Auteur ; Yanchen Gao, Auteur ; Zengliang Gao, Auteur Année de publication : 2010 Article en page(s) : pp 8209–8218 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Nonlinear predictive control Polynomial form. Résumé : A simple nonlinear control strategy using sparse kernel learning (SKL) with a polynomial kernel form is presented and applied to chemical processes. The nonlinear process is first identified by SKL with a polynomial kernel, and then a predictive control performance index is formulated. This index is characterized as an even-degree polynomial function of the manipulated input and has the benefit that the input can be separated from the index because of its special structure. Consequently, the optimal manipulated input can be efficiently obtained by solving a simple root problem of an odd-degree polynomial equation. Moreover, the control parameter directly relates to its performance and can be tuned in a guided manner. All these attributes result in a practicable solution for real-time process control. The novel controller is applied to two chemical processes to evaluate its performance. The obtained results show the superiority of the proposed method compared to a well-tuned proportional−integral−derivative controller in different situations. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901548u [article] Simple nonlinear predictive control strategy for chemical processes using sparse kernel learning with polynomial form [texte imprimé] / Yi Liu, Auteur ; Yanchen Gao, Auteur ; Zengliang Gao, Auteur . - 2010 . - pp 8209–8218. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 8209–8218 Mots-clés : Nonlinear predictive control Polynomial form. Résumé : A simple nonlinear control strategy using sparse kernel learning (SKL) with a polynomial kernel form is presented and applied to chemical processes. The nonlinear process is first identified by SKL with a polynomial kernel, and then a predictive control performance index is formulated. This index is characterized as an even-degree polynomial function of the manipulated input and has the benefit that the input can be separated from the index because of its special structure. Consequently, the optimal manipulated input can be efficiently obtained by solving a simple root problem of an odd-degree polynomial equation. Moreover, the control parameter directly relates to its performance and can be tuned in a guided manner. All these attributes result in a practicable solution for real-time process control. The novel controller is applied to two chemical processes to evaluate its performance. The obtained results show the superiority of the proposed method compared to a well-tuned proportional−integral−derivative controller in different situations. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901548u

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