Transition process modeling and monitoring based on dynamic ensemble clustering and multiclass support vector data description / Zhibo Zhu in Industrial & engineering chemistry research, Vol. 50 N° 24 (Décembre 2011)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 50 N° 24 (Décembre 2011) . - pp. 13969-13983 Titre : Transition process modeling and monitoring based on dynamic ensemble clustering and multiclass support vector data description Type de document : texte imprimé Auteurs : Zhibo Zhu, Auteur ; Zhihuan Song, Auteur ; Palazoglu Ahmet, Auteur Année de publication : 2012 Article en page(s) : pp. 13969-13983 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Surveillance Modeling Résumé : Monitoring and management of process transitions is a critical activity in chemical plants due to increased potential for abnormal operations. This activity is often hampered by the lack of a proper approach to label the transition states. In this paper, we present a systematic framework that constructs process transition states thus facilitating their monitoring for faulty operations. To address the nonstationary and non-Gaussian characteristics of the time series data collected during the transition process, an ensemble clustering method based on dynamic k-principal component analysis-independent component analysis (k-ICA-PCA) models is proposed to enable labeling of transitions. Next, we combine a PCA-based dimension reduction with a pattern classification strategy based on multiclass support vector data description (SVDD) to achieve transition process monitoring. The Tennessee Eastman (TE) benchmark process is used as a case study to evaluate the performance of the proposed framework. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25299865 [article] Transition process modeling and monitoring based on dynamic ensemble clustering and multiclass support vector data description [texte imprimé] / Zhibo Zhu, Auteur ; Zhihuan Song, Auteur ; Palazoglu Ahmet, Auteur . - 2012 . - pp. 13969-13983. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 24 (Décembre 2011) . - pp. 13969-13983 Mots-clés : Surveillance Modeling Résumé : Monitoring and management of process transitions is a critical activity in chemical plants due to increased potential for abnormal operations. This activity is often hampered by the lack of a proper approach to label the transition states. In this paper, we present a systematic framework that constructs process transition states thus facilitating their monitoring for faulty operations. To address the nonstationary and non-Gaussian characteristics of the time series data collected during the transition process, an ensemble clustering method based on dynamic k-principal component analysis-independent component analysis (k-ICA-PCA) models is proposed to enable labeling of transitions. Next, we combine a PCA-based dimension reduction with a pattern classification strategy based on multiclass support vector data description (SVDD) to achieve transition process monitoring. The Tennessee Eastman (TE) benchmark process is used as a case study to evaluate the performance of the proposed framework. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25299865

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