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Constrained nonlinear estimation for industrial process fouling / Benjamin J. Spivey in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7824–7831 Titre : Constrained nonlinear estimation for industrial process fouling Type de document : texte imprimé Auteurs : Benjamin J. Spivey, Auteur ; John D. Hedengren, Auteur ; Thomas F. Edgar, Auteur Année de publication : 2010 Article en page(s) : pp 7824–7831 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Nonlinear  estimation  Industrial  process. Résumé : Industrial process monitoring tools require robust and efficient estimation techniques that maintain a high service factor by remaining online during abnormal operating conditions, such as during loss of measurements, changes in control status, or maintenance. Constraints incorporate additional process knowledge into estimation by bounding estimated disturbances within feasibility limits thereby providing robustness to faulty measurements or conditions that violate process models. Moving horizon estimation (MHE) and unscented Kalman filtering (UKF) are two estimation techniques that permit incorporation of constraints prior to evaluating the a priori estimate. This paper evaluates both constrained nonlinear estimators versus the extended Kalman filter (EKF) using industrial process data provided by ExxonMobil Chemical Company. Results provide short-term insight into the fouling process, and parameter estimates produced by UKF and MHE are shown to be more accurate than EKF. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9018116 [article] Constrained nonlinear estimation for industrial process fouling [texte imprimé] / Benjamin J. Spivey, Auteur ; John D. Hedengren, Auteur ; Thomas F. Edgar, Auteur . - 2010 . - pp 7824–7831. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7824–7831 Mots-clés : Nonlinear  estimation  Industrial  process. Résumé : Industrial process monitoring tools require robust and efficient estimation techniques that maintain a high service factor by remaining online during abnormal operating conditions, such as during loss of measurements, changes in control status, or maintenance. Constraints incorporate additional process knowledge into estimation by bounding estimated disturbances within feasibility limits thereby providing robustness to faulty measurements or conditions that violate process models. Moving horizon estimation (MHE) and unscented Kalman filtering (UKF) are two estimation techniques that permit incorporation of constraints prior to evaluating the a priori estimate. This paper evaluates both constrained nonlinear estimators versus the extended Kalman filter (EKF) using industrial process data provided by ExxonMobil Chemical Company. Results provide short-term insight into the fouling process, and parameter estimates produced by UKF and MHE are shown to be more accurate than EKF. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9018116