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Data-derived analysis and inference for an industrial deethanizer / Francesco Corona in Industrial & engineering chemistry research, Vol. 51 N° 42 (Octobre 2012)

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 42 (Octobre 2012) . - pp. 13732–13742 Titre : Data-derived analysis and inference for an industrial deethanizer Type de document : texte imprimé Auteurs : Francesco Corona, Auteur ; Michela Mulas, Auteur ; Roberto Baratti, Auteur Année de publication : 2012 Article en page(s) : pp. 13732–13742 Note générale : Industrial chemistry Langues : Anglais (eng) Résumé : This paper presents an application of data-derived approaches for analyzing and monitoring industrial processes. The discussed methods are used in visualizing process measurements, extracting operational information, and designing estimation models for primary process variables otherwise difficult to measure in real-time. Emphasis is given to the modeling of the data with two classical machine learning paradigms; the self-organizing map (SOM) and the multi-layer perceptron (MLP). The effectiveness of the proposed approach is validated on an industrial deethanizer, where the goal is to identify operational modes and most sensitive variables for this full-scale unit, as well as design an inferential model for a critical process variable, the bottom ethane concentration. The study led to the definition of a fully automated monitoring tool to be implemented online in the plant’s distributed control system. The results confirmed the potential of the data-derived approach, and based on the analysis, the existing control configuration of the unit could be redefined toward more consistent operations. Because it is general and modular by design, the tool can be easily used for other processes. [article] Data-derived analysis and inference for an industrial deethanizer [texte imprimé] / Francesco Corona, Auteur ; Michela Mulas, Auteur ; Roberto Baratti, Auteur . - 2012 . - pp. 13732–13742. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 42 (Octobre 2012) . - pp. 13732–13742 Résumé : This paper presents an application of data-derived approaches for analyzing and monitoring industrial processes. The discussed methods are used in visualizing process measurements, extracting operational information, and designing estimation models for primary process variables otherwise difficult to measure in real-time. Emphasis is given to the modeling of the data with two classical machine learning paradigms; the self-organizing map (SOM) and the multi-layer perceptron (MLP). The effectiveness of the proposed approach is validated on an industrial deethanizer, where the goal is to identify operational modes and most sensitive variables for this full-scale unit, as well as design an inferential model for a critical process variable, the bottom ethane concentration. The study led to the definition of a fully automated monitoring tool to be implemented online in the plant’s distributed control system. The results confirmed the potential of the data-derived approach, and based on the analysis, the existing control configuration of the unit could be redefined toward more consistent operations. Because it is general and modular by design, the tool can be easily used for other processes.

Data-derived analysis and inference for an industrial deethanizer / Francesco Corona in Industrial & engineering chemistry research, Vol. 51 N° 42 (Octobre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 42 (Octobre 2012) . - pp. 13732–13742 Titre : Data-derived analysis and inference for an industrial deethanizer Type de document : texte imprimé Auteurs : Francesco Corona, Auteur ; Michela Mulas, Auteur ; Roberto Baratti, Auteur Année de publication : 2012 Article en page(s) : pp. 13732–13742 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Industrial  processes Résumé : This paper presents an application of data-derived approaches for analyzing and monitoring industrial processes. The discussed methods are used in visualizing process measurements, extracting operational information, and designing estimation models for primary process variables otherwise difficult to measure in real-time. Emphasis is given to the modeling of the data with two classical machine learning paradigms; the self-organizing map (SOM) and the multi-layer perceptron (MLP). The effectiveness of the proposed approach is validated on an industrial deethanizer, where the goal is to identify operational modes and most sensitive variables for this full-scale unit, as well as design an inferential model for a critical process variable, the bottom ethane concentration. The study led to the definition of a fully automated monitoring tool to be implemented online in the plant’s distributed control system. The results confirmed the potential of the data-derived approach, and based on the analysis, the existing control configuration of the unit could be redefined toward more consistent operations. Because it is general and modular by design, the tool can be easily used for other processes. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie202854b [article] Data-derived analysis and inference for an industrial deethanizer [texte imprimé] / Francesco Corona, Auteur ; Michela Mulas, Auteur ; Roberto Baratti, Auteur . - 2012 . - pp. 13732–13742. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 42 (Octobre 2012) . - pp. 13732–13742 Mots-clés : Industrial  processes Résumé : This paper presents an application of data-derived approaches for analyzing and monitoring industrial processes. The discussed methods are used in visualizing process measurements, extracting operational information, and designing estimation models for primary process variables otherwise difficult to measure in real-time. Emphasis is given to the modeling of the data with two classical machine learning paradigms; the self-organizing map (SOM) and the multi-layer perceptron (MLP). The effectiveness of the proposed approach is validated on an industrial deethanizer, where the goal is to identify operational modes and most sensitive variables for this full-scale unit, as well as design an inferential model for a critical process variable, the bottom ethane concentration. The study led to the definition of a fully automated monitoring tool to be implemented online in the plant’s distributed control system. The results confirmed the potential of the data-derived approach, and based on the analysis, the existing control configuration of the unit could be redefined toward more consistent operations. Because it is general and modular by design, the tool can be easily used for other processes. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie202854b