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Data-based, fault-tolerant model predictive control of a complex industrial dearomatization process / Markus Kettunen in Industrial & engineering chemistry research, Vol. 50 N° 11 (Juin 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 11 (Juin 2011) . - pp. 6755–6768 Titre : Data-based, fault-tolerant model predictive control of a complex industrial dearomatization process Type de document : texte imprimé Auteurs : Markus Kettunen, Auteur ; Sirkka-Liisa Jamsa-Jounela, Auteur Année de publication : 2011 Article en page(s) : pp. 6755–6768 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Industrial  dearomatization  process Résumé : The main focus of this paper is on the development of an active data-based fault-tolerant model predictive controller (FTMPC) for an industrial dearomatization process. Three different fault-tolerant control (FTC) strategies are presented; these comprise data-based fault detection and diagnosis methods and fault accommodation- and controller reconfiguration-based FTC methods. These three strategies are tested with the simulated industrial dearomatization process. According to the validation and performance testing, the FTMPC performs efficiently and detects and prevents the effects of the most common faults in the analyzer, flow, and temperature measurements as well as the controller actuators. The reliability of the model predictive controller is increased and the profitability is enhanced owing to the lower off-spec production. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie102312g [article] Data-based, fault-tolerant model predictive control of a complex industrial dearomatization process [texte imprimé] / Markus Kettunen, Auteur ; Sirkka-Liisa Jamsa-Jounela, Auteur . - 2011 . - pp. 6755–6768. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 11 (Juin 2011) . - pp. 6755–6768 Mots-clés : Industrial  dearomatization  process Résumé : The main focus of this paper is on the development of an active data-based fault-tolerant model predictive controller (FTMPC) for an industrial dearomatization process. Three different fault-tolerant control (FTC) strategies are presented; these comprise data-based fault detection and diagnosis methods and fault accommodation- and controller reconfiguration-based FTC methods. These three strategies are tested with the simulated industrial dearomatization process. According to the validation and performance testing, the FTMPC performs efficiently and detects and prevents the effects of the most common faults in the analyzer, flow, and temperature measurements as well as the controller actuators. The reliability of the model predictive controller is increased and the profitability is enhanced owing to the lower off-spec production. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie102312g