Inferential model for industrial polypropylene melt index prediction with embedded priori knowledge and delay estimation / Haichuan Lou in Industrial & engineering chemistry research, Vol. 51 N° 25 (Juin 2012)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 51 N° 25 (Juin 2012) . - pp. 8510-8525 Titre : Inferential model for industrial polypropylene melt index prediction with embedded priori knowledge and delay estimation Type de document : texte imprimé Auteurs : Haichuan Lou, Auteur ; Hongye Su, Auteur ; Lei Xie, Auteur Année de publication : 2012 Article en page(s) : pp. 8510-8525 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Prediction Modeling Résumé : Melt index inferential model plays an important role in the control and optimization of polypropylene production. This study proposed a novel multiple-priori-knowledge based neural network (MPKNN) inferential model for melt index prediction. The prior knowledge from the industrial propylene polymerization process is fully exploited and embedded into the construction of multilayer perceptron neural network in the form of nonlinear constraints. Meanwhile, an adaptive PSO-SQP (particle swarm optimization-sequential quadratics programming) is proposed to optimize the network weights. The proposed MPKNN model has good fitting and prediction ability. Meanwhile, it can avoid unwanted zero value and wrong signal of the model gains. By embedding priori knowledge, the model ensures the safety in the quality control of melt index. In addition, a hybrid model combining the MPKNN model with a simplified mechanism model is proposed to enhance the extrapolation capability. A normalized mutual information method is employed to estimate the delay between independent variables and dependent variables. The proposed hybrid inferential model is validated on recorded data from an industrial double-loop propylene-polymerization reaction process. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26066778 [article] Inferential model for industrial polypropylene melt index prediction with embedded priori knowledge and delay estimation [texte imprimé] / Haichuan Lou, Auteur ; Hongye Su, Auteur ; Lei Xie, Auteur . - 2012 . - pp. 8510-8525. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 25 (Juin 2012) . - pp. 8510-8525 Mots-clés : Prediction Modeling Résumé : Melt index inferential model plays an important role in the control and optimization of polypropylene production. This study proposed a novel multiple-priori-knowledge based neural network (MPKNN) inferential model for melt index prediction. The prior knowledge from the industrial propylene polymerization process is fully exploited and embedded into the construction of multilayer perceptron neural network in the form of nonlinear constraints. Meanwhile, an adaptive PSO-SQP (particle swarm optimization-sequential quadratics programming) is proposed to optimize the network weights. The proposed MPKNN model has good fitting and prediction ability. Meanwhile, it can avoid unwanted zero value and wrong signal of the model gains. By embedding priori knowledge, the model ensures the safety in the quality control of melt index. In addition, a hybrid model combining the MPKNN model with a simplified mechanism model is proposed to enhance the extrapolation capability. A normalized mutual information method is employed to estimate the delay between independent variables and dependent variables. The proposed hybrid inferential model is validated on recorded data from an industrial double-loop propylene-polymerization reaction process. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26066778

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A novel statistical-based monitoring approach for complex multivariate processes / Zhiqiang Ge in Industrial & engineering chemistry research, Vol. 48 N° 10 (Mai 2009)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 48 N° 10 (Mai 2009) . - pp. 4892–4898 Titre : A novel statistical-based monitoring approach for complex multivariate processes Type de document : texte imprimé Auteurs : Zhiqiang Ge, Auteur ; Lei Xie, Auteur Année de publication : 2009 Article en page(s) : pp. 4892–4898 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Non-Gaussian variables Gaussian essential Independent component analysis Factor Résumé : Conventional methods are under the assumption that a process is driven by either non-Gaussian or Gaussian essential variables. However, many complex processes may be simultaneously driven by these two types of essential source. This paper proposes a novel independent component analysis and factor analysis (ICA-FA) method to capture the non-Gaussian and Gaussian essential variables. The non-Gaussian part is first extracted by ICA and support vector data description is utilized to obtain tight confidence limit. A probabilistic approach is subsequently incorporated to separate the residual Gaussian part into latent influential factors and unmodeled uncertainty. By retrieving the underlying process data generating structure, ICA-FA facilitates the diagnosis of process faults that occur in different sources. A further contribution of this paper is the definition of a new similarity factor based on the ICA-FA for fault identification. The efficiency of the proposed method is shown by a case study on the TE benchmark process. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800935e [article] A novel statistical-based monitoring approach for complex multivariate processes [texte imprimé] / Zhiqiang Ge, Auteur ; Lei Xie, Auteur . - 2009 . - pp. 4892–4898. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 10 (Mai 2009) . - pp. 4892–4898 Mots-clés : Non-Gaussian variables Gaussian essential Independent component analysis Factor Résumé : Conventional methods are under the assumption that a process is driven by either non-Gaussian or Gaussian essential variables. However, many complex processes may be simultaneously driven by these two types of essential source. This paper proposes a novel independent component analysis and factor analysis (ICA-FA) method to capture the non-Gaussian and Gaussian essential variables. The non-Gaussian part is first extracted by ICA and support vector data description is utilized to obtain tight confidence limit. A probabilistic approach is subsequently incorporated to separate the residual Gaussian part into latent influential factors and unmodeled uncertainty. By retrieving the underlying process data generating structure, ICA-FA facilitates the diagnosis of process faults that occur in different sources. A further contribution of this paper is the definition of a new similarity factor based on the ICA-FA for fault identification. The efficiency of the proposed method is shown by a case study on the TE benchmark process. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800935e

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