Approximate predictive versus self-tuning adaptive control strategies of biodiesel reactors / Farouq S. Mjalli in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11034–11047 Titre : Approximate predictive versus self-tuning adaptive control strategies of biodiesel reactors Type de document : texte imprimé Auteurs : Farouq S. Mjalli, Auteur ; Mohamed Azlan Hussain, Auteur Année de publication : 2010 Article en page(s) : pp. 11034–11047 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Palm oil Transesterification reactions Reactor nonlinearity Résumé : Producing biodiesel from palm oil as a raw material involves complex transesterification reactions which add up to the process nonlinearity. In this work, more emphasis will be focused on the reactor nonlinearity and ways of solving its control problem. The reactor nonlinearity is addressed via the application of an instantaneous linearization technique to control the reactor temperature and the triglyceride product concentration. A feed-forward neural network with delayed inputs and outputs was trained and validated to capture the dynamics of the biodiesel process. The generated nonlinear model was then utilized in an instantaneous linearization algorithm using two control algorithms adopting the self-tuning adaptive control and an approximate model predictive framework. The two algorithms were compared in terms of set-point tracking capability, efficiency, and stability. The minimum variance control algorithm attained poor performance compared to the pole-placement self-tuning adaptive algorithm. However, the approximate model predictive control strategy was superior to the self-tuning control in terms of its ability for forcing the output to follow the set-point trajectory efficiently with smooth controller moves. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900930k [article] Approximate predictive versus self-tuning adaptive control strategies of biodiesel reactors [texte imprimé] / Farouq S. Mjalli, Auteur ; Mohamed Azlan Hussain, Auteur . - 2010 . - pp. 11034–11047. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11034–11047 Mots-clés : Palm oil Transesterification reactions Reactor nonlinearity Résumé : Producing biodiesel from palm oil as a raw material involves complex transesterification reactions which add up to the process nonlinearity. In this work, more emphasis will be focused on the reactor nonlinearity and ways of solving its control problem. The reactor nonlinearity is addressed via the application of an instantaneous linearization technique to control the reactor temperature and the triglyceride product concentration. A feed-forward neural network with delayed inputs and outputs was trained and validated to capture the dynamics of the biodiesel process. The generated nonlinear model was then utilized in an instantaneous linearization algorithm using two control algorithms adopting the self-tuning adaptive control and an approximate model predictive framework. The two algorithms were compared in terms of set-point tracking capability, efficiency, and stability. The minimum variance control algorithm attained poor performance compared to the pole-placement self-tuning adaptive algorithm. However, the approximate model predictive control strategy was superior to the self-tuning control in terms of its ability for forcing the output to follow the set-point trajectory efficiently with smooth controller moves. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900930k

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité
aucun exemplaire

Approximate predictive versus self-tuning adaptive control strategies of biodiesel reactors / Farouq S. Mjalli in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11034–11047 Titre : Approximate predictive versus self-tuning adaptive control strategies of biodiesel reactors Type de document : texte imprimé Auteurs : Farouq S. Mjalli, Auteur ; Mohamed Azlan Hussain, Auteur Année de publication : 2010 Article en page(s) : pp. 11034–11047 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Approximate--Predictive--versus--Self-Tuning--Adaptive--Control--Strategies--Biodiesel--Reactors Résumé : Producing biodiesel from palm oil as a raw material involves complex transesterification reactions which add up to the process nonlinearity. In this work, more emphasis will be focused on the reactor nonlinearity and ways of solving its control problem. The reactor nonlinearity is addressed via the application of an instantaneous linearization technique to control the reactor temperature and the triglyceride product concentration. A feed-forward neural network with delayed inputs and outputs was trained and validated to capture the dynamics of the biodiesel process. The generated nonlinear model was then utilized in an instantaneous linearization algorithm using two control algorithms adopting the self-tuning adaptive control and an approximate model predictive framework. The two algorithms were compared in terms of set-point tracking capability, efficiency, and stability. The minimum variance control algorithm attained poor performance compared to the pole-placement self-tuning adaptive algorithm. However, the approximate model predictive control strategy was superior to the self-tuning control in terms of its ability for forcing the output to follow the set-point trajectory efficiently with smooth controller moves. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900930k [article] Approximate predictive versus self-tuning adaptive control strategies of biodiesel reactors [texte imprimé] / Farouq S. Mjalli, Auteur ; Mohamed Azlan Hussain, Auteur . - 2010 . - pp. 11034–11047. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11034–11047 Mots-clés : Approximate--Predictive--versus--Self-Tuning--Adaptive--Control--Strategies--Biodiesel--Reactors Résumé : Producing biodiesel from palm oil as a raw material involves complex transesterification reactions which add up to the process nonlinearity. In this work, more emphasis will be focused on the reactor nonlinearity and ways of solving its control problem. The reactor nonlinearity is addressed via the application of an instantaneous linearization technique to control the reactor temperature and the triglyceride product concentration. A feed-forward neural network with delayed inputs and outputs was trained and validated to capture the dynamics of the biodiesel process. The generated nonlinear model was then utilized in an instantaneous linearization algorithm using two control algorithms adopting the self-tuning adaptive control and an approximate model predictive framework. The two algorithms were compared in terms of set-point tracking capability, efficiency, and stability. The minimum variance control algorithm attained poor performance compared to the pole-placement self-tuning adaptive algorithm. However, the approximate model predictive control strategy was superior to the self-tuning control in terms of its ability for forcing the output to follow the set-point trajectory efficiently with smooth controller moves. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie900930k

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité
aucun exemplaire