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Détail de l'auteur
Auteur Yuming Liu
Documents disponibles écrits par cet auteur
Affiner la rechercheOnline 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)
in Industrial & 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
in Industrial & 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
