| Titre : | Transition process modeling and monitoring based on dynamic ensemble clustering and multiclass support vector data description (2012) |
| Auteurs : | Zhibo Zhu, Auteur ; Zhihuan Song, Auteur ; Palazoglu Ahmet, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Industrial & engineering chemistry research (Vol. 50 N° 24, Décembre 2011) |
| Article en page(s) : | pp. 13969-13983 |
| Note générale : | Chimie industrielle |
| Langues : | Anglais |
| Tags : | 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 |
