Development of a hybrid model for industrial ethylene oxide reactor / Na Luo in Industrial & engineering chemistry research, Vol. 51 N° 19 (Mai 2012)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 51 N° 19 (Mai 2012) . - pp. 6926-6932 Titre : Development of a hybrid model for industrial ethylene oxide reactor Type de document : texte imprimé Auteurs : Na Luo, Auteur ; Wenli Du, Auteur ; Zhencheng Ye, Auteur Année de publication : 2012 Article en page(s) : pp. 6926-6932 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Reactor Modeling Résumé : A hybrid modeling approach for an ethylene oxidation reactor with silver catalyst is proposed. Under the form of mechanistic model, support vector regression is used to construct the catalyst deactivation model with the operating data coming from real plant. Prior knowledge is extracted to enhance the generalization of the deactivation model. With the hybrid model, the prediction error is less than 5% for the prediction of industrial reactor. The approach is shown to predict the production more accurately and have more reliable extrapolation. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25900246 [article] Development of a hybrid model for industrial ethylene oxide reactor [texte imprimé] / Na Luo, Auteur ; Wenli Du, Auteur ; Zhencheng Ye, Auteur . - 2012 . - pp. 6926-6932. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 19 (Mai 2012) . - pp. 6926-6932 Mots-clés : Reactor Modeling Résumé : A hybrid modeling approach for an ethylene oxidation reactor with silver catalyst is proposed. Under the form of mechanistic model, support vector regression is used to construct the catalyst deactivation model with the operating data coming from real plant. Prior knowledge is extracted to enhance the generalization of the deactivation model. With the hybrid model, the prediction error is less than 5% for the prediction of industrial reactor. The approach is shown to predict the production more accurately and have more reliable extrapolation. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25900246

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