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Modeling and simulation of an industrial ethylene oxide (EO) reactor using artificial neural networks (ANN) / M. R. Rahimpour in Industrial & engineering chemistry research, Vol. 50 N° 10 (Mai 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 10 (Mai 2011) . - pp. 6044–6052 Titre : Modeling and simulation of an industrial ethylene oxide (EO) reactor using artificial neural networks (ANN) Type de document : texte imprimé Auteurs : M. R. Rahimpour, Auteur ; M. Shayanmehr, Auteur ; M. Nazari, Auteur Année de publication : 2011 Article en page(s) : pp. 6044–6052 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Ethylene  oxide  Artificial  neural  networks Résumé : In the present work, a one-dimensional heterogeneous model was used for dynamic simulation of an industrial fixed-bed catalytic ethylene oxide (EO) reactor in the presence of long-term catalyst deactivation. In order to determine the level of optimum ethylene dichloride (EDC), a multilayer perceptron (MLP) neural network was used. In addition, the effect of inlet gas velocity on EO mole fraction of gas and solid phases was investigated. The model validation was carried out by comparison of model results with corresponding industrial conditions and over a period of three operating years. A good agreement was found between the simulation results of the dynamic model and historical process data. The error of simulation was found to be less than 5%. The results of the artificial neural network (ANN) modeling showed that the maximum selectivity occurs in the range of 0.37–0.42 ppm of EDC. Also, it was observed that with decrease of gas velocity the difference between the EO mole fraction of gas phase and solid phase increases. This behavior was attributed to the distinct resistances of kinetically controlled and the mass transfer resistance of gas film around the catalyst pellets. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie101319d [article] Modeling and simulation of an industrial ethylene oxide (EO) reactor using artificial neural networks (ANN) [texte imprimé] / M. R. Rahimpour, Auteur ; M. Shayanmehr, Auteur ; M. Nazari, Auteur . - 2011 . - pp. 6044–6052. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 10 (Mai 2011) . - pp. 6044–6052 Mots-clés : Ethylene  oxide  Artificial  neural  networks Résumé : In the present work, a one-dimensional heterogeneous model was used for dynamic simulation of an industrial fixed-bed catalytic ethylene oxide (EO) reactor in the presence of long-term catalyst deactivation. In order to determine the level of optimum ethylene dichloride (EDC), a multilayer perceptron (MLP) neural network was used. In addition, the effect of inlet gas velocity on EO mole fraction of gas and solid phases was investigated. The model validation was carried out by comparison of model results with corresponding industrial conditions and over a period of three operating years. A good agreement was found between the simulation results of the dynamic model and historical process data. The error of simulation was found to be less than 5%. The results of the artificial neural network (ANN) modeling showed that the maximum selectivity occurs in the range of 0.37–0.42 ppm of EDC. Also, it was observed that with decrease of gas velocity the difference between the EO mole fraction of gas phase and solid phase increases. This behavior was attributed to the distinct resistances of kinetically controlled and the mass transfer resistance of gas film around the catalyst pellets. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie101319d