A method of online safety assessment for industrial process operations based on hopf bifurcation analysis / Lubin Ye in Industrial & engineering chemistry research, Vol. 50 N° 6 (Mars 2011)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 50 N° 6 (Mars 2011) . - pp. 3403–3414 Titre : A method of online safety assessment for industrial process operations based on hopf bifurcation analysis Type de document : texte imprimé Auteurs : Lubin Ye, Auteur ; Zhengshun Fei, Auteur ; Jun Liang, Auteur Année de publication : 2011 Article en page(s) : pp. 3403–3414 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Industrial process Bifurcation analysis Résumé : This paper proposes an approach of online safety assessment for industrial process operations. The core idea is to apply the Hopf bifurcation analysis of nonlinear systems in safety assessment. Herein, the Hopf bifurcation equations are used to characterize the critical stability boundary or manifold of the system, which is taken as the safety limit. Then, a safety index (SI) is constructed to denote the integrated exponential distances between each parameter and their bifurcation points. In the online implementation, the parameters are estimated by the extended Kalman filter (EKF), and the Hopf bifurcation points are generated by solving the nonlinear bifurcation equations. Afterward, the value of the SI can be online calculated. The introduced approach was then applied to a simulated gas phase polyethylene reactor process, in which the efficiency of the proposed method was verified in indicating the distance to the potential unsafe oscillation and in the early identification of potential threats. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie1018854 [article] A method of online safety assessment for industrial process operations based on hopf bifurcation analysis [texte imprimé] / Lubin Ye, Auteur ; Zhengshun Fei, Auteur ; Jun Liang, Auteur . - 2011 . - pp. 3403–3414. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 6 (Mars 2011) . - pp. 3403–3414 Mots-clés : Industrial process Bifurcation analysis Résumé : This paper proposes an approach of online safety assessment for industrial process operations. The core idea is to apply the Hopf bifurcation analysis of nonlinear systems in safety assessment. Herein, the Hopf bifurcation equations are used to characterize the critical stability boundary or manifold of the system, which is taken as the safety limit. Then, a safety index (SI) is constructed to denote the integrated exponential distances between each parameter and their bifurcation points. In the online implementation, the parameters are estimated by the extended Kalman filter (EKF), and the Hopf bifurcation points are generated by solving the nonlinear bifurcation equations. Afterward, the value of the SI can be online calculated. The introduced approach was then applied to a simulated gas phase polyethylene reactor process, in which the efficiency of the proposed method was verified in indicating the distance to the potential unsafe oscillation and in the early identification of potential threats. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie1018854

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Online probabilistic assessment of operating performance based on safety and optimality indices for multimode industrial processes / Lubin Ye 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. 10912–10923 Titre : Online probabilistic assessment of operating performance based on safety and optimality indices for multimode industrial processes Type de document : texte imprimé Auteurs : Lubin Ye, Auteur ; Yuming Liu, Auteur ; Zhengshun Fei, Auteur Année de publication : 2010 Article en page(s) : pp. 10912–10923 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Online--Probabilistic--Assessment--Operating--Performance--Based--Safety--Optimality--Indices--Multimode--Industrial--Processes Résumé : Operating performance of industrial process on safety and optimality may deteriorate with time due to process characteristic variation, and it is crucial to develop strategies for online operating performance assessment. Although there have been some studies and applications on process safety assessment, optimality assessment has not yet been paid sufficient attention. This paper proposes a probabilistic framework of online operating assessment for industrial processes. First, a Gaussian mixture model (GMM) is used to characterize multiple operating modes. Considering the distribution of process variables, safety and optimality indices (SI and OI) are defined and calculated by two successive nonlinear mappings. A hierarchical-level classification method is then presented to divide these indices into different performance levels, and margin analysis on each level is introduced. Finally, performance prediction and preliminary suggestions for improvement are provided. The proposed assessment strategy is then applied in two examples: Tennessee Eastman Process (TEP) and polypropylene (PP) production process, which indicate the efficiency of the proposed approach. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801870g [article] Online probabilistic assessment of operating performance based on safety and optimality indices for multimode industrial processes [texte imprimé] / Lubin Ye, Auteur ; Yuming Liu, Auteur ; Zhengshun Fei, Auteur . - 2010 . - pp. 10912–10923. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 10912–10923 Mots-clés : Online--Probabilistic--Assessment--Operating--Performance--Based--Safety--Optimality--Indices--Multimode--Industrial--Processes Résumé : Operating performance of industrial process on safety and optimality may deteriorate with time due to process characteristic variation, and it is crucial to develop strategies for online operating performance assessment. Although there have been some studies and applications on process safety assessment, optimality assessment has not yet been paid sufficient attention. This paper proposes a probabilistic framework of online operating assessment for industrial processes. First, a Gaussian mixture model (GMM) is used to characterize multiple operating modes. Considering the distribution of process variables, safety and optimality indices (SI and OI) are defined and calculated by two successive nonlinear mappings. A hierarchical-level classification method is then presented to divide these indices into different performance levels, and margin analysis on each level is introduced. Finally, performance prediction and preliminary suggestions for improvement are provided. The proposed assessment strategy is then applied in two examples: Tennessee Eastman Process (TEP) and polypropylene (PP) production process, which indicate the efficiency of the proposed approach. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801870g

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Online probabilistic assessment of operating performance based on safety and optimality indices for multimode industrial processes / Lubin Ye 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. 10912–10923 Titre : Online probabilistic assessment of operating performance based on safety and optimality indices for multimode industrial processes Type de document : texte imprimé Auteurs : Lubin Ye, Auteur ; Yuming Liu, Auteur ; Zhengshun Fei, Auteur Année de publication : 2010 Article en page(s) : pp. 10912–10923 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Online operating assessment Probabilistic framework Gaussian mixture model Résumé : Operating performance of industrial process on safety and optimality may deteriorate with time due to process characteristic variation, and it is crucial to develop strategies for online operating performance assessment. Although there have been some studies and applications on process safety assessment, optimality assessment has not yet been paid sufficient attention. This paper proposes a probabilistic framework of online operating assessment for industrial processes. First, a Gaussian mixture model (GMM) is used to characterize multiple operating modes. Considering the distribution of process variables, safety and optimality indices (SI and OI) are defined and calculated by two successive nonlinear mappings. A hierarchical-level classification method is then presented to divide these indices into different performance levels, and margin analysis on each level is introduced. Finally, performance prediction and preliminary suggestions for improvement are provided. The proposed assessment strategy is then applied in two examples: Tennessee Eastman Process (TEP) and polypropylene (PP) production process, which indicate the efficiency of the proposed approach. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801870g [article] Online probabilistic assessment of operating performance based on safety and optimality indices for multimode industrial processes [texte imprimé] / Lubin Ye, Auteur ; Yuming Liu, Auteur ; Zhengshun Fei, Auteur . - 2010 . - pp. 10912–10923. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 10912–10923 Mots-clés : Online operating assessment Probabilistic framework Gaussian mixture model Résumé : Operating performance of industrial process on safety and optimality may deteriorate with time due to process characteristic variation, and it is crucial to develop strategies for online operating performance assessment. Although there have been some studies and applications on process safety assessment, optimality assessment has not yet been paid sufficient attention. This paper proposes a probabilistic framework of online operating assessment for industrial processes. First, a Gaussian mixture model (GMM) is used to characterize multiple operating modes. Considering the distribution of process variables, safety and optimality indices (SI and OI) are defined and calculated by two successive nonlinear mappings. A hierarchical-level classification method is then presented to divide these indices into different performance levels, and margin analysis on each level is introduced. Finally, performance prediction and preliminary suggestions for improvement are provided. The proposed assessment strategy is then applied in two examples: Tennessee Eastman Process (TEP) and polypropylene (PP) production process, which indicate the efficiency of the proposed approach. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801870g

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