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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