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On the introduction of a qualitative variable to the neural network for reactor modeling / Maryam Ghadrdan in Industrial & engineering chemistry research, Vol. 48 N° 8 (Avril 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 8 (Avril 2009) . - pp. 3820–3824 Titre : On the introduction of a qualitative variable to the neural network for reactor modeling : feed type Type de document : texte imprimé Auteurs : Maryam Ghadrdan, Auteur ; Hamid Mehdizadeh, Auteur ; Ramin Bozorgmehry Boozarjomehry, Auteur Année de publication : 2009 Article en page(s) : pp. 3820–3824 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Reactor  feed  type  Hydrocarbons  Neural  networks Résumé : Thermal cracking of hydrocarbons converts them into valuable materials in the petrochemical industries. Multiplicity of the reaction routes and complexity of the mathematical approach has led us use a kind of black-box modeling—artificial neural networks. Reactor feed type plays an essential role on the product qualities. Feed type is a qualitative character. In this paper, a method is presented to introduce a range of petroleum fractions to the neural network. To introduce petroleum cuts with final boiling points of 865 °F maximum to the neural network, a real component substitute mixture is made from the original mixture. Such substitute mixture is fully defined, it has a chemical character, and physical properties can be simply retrieved from databases. The mixture compositions are defined with the aid of an optimization algorithm−interval method. The obtained TBP curves of substitute mixture are in good agreement with the experimentally obtained curves. Nine single carbon structural increments will be the representative of 93 real component compositions in order to make the topology of the neural network smaller and hence to have a less complex model. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800794n [article] On the introduction of a qualitative variable to the neural network for reactor modeling : feed type [texte imprimé] / Maryam Ghadrdan, Auteur ; Hamid Mehdizadeh, Auteur ; Ramin Bozorgmehry Boozarjomehry, Auteur . - 2009 . - pp. 3820–3824. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 8 (Avril 2009) . - pp. 3820–3824 Mots-clés : Reactor  feed  type  Hydrocarbons  Neural  networks Résumé : Thermal cracking of hydrocarbons converts them into valuable materials in the petrochemical industries. Multiplicity of the reaction routes and complexity of the mathematical approach has led us use a kind of black-box modeling—artificial neural networks. Reactor feed type plays an essential role on the product qualities. Feed type is a qualitative character. In this paper, a method is presented to introduce a range of petroleum fractions to the neural network. To introduce petroleum cuts with final boiling points of 865 °F maximum to the neural network, a real component substitute mixture is made from the original mixture. Such substitute mixture is fully defined, it has a chemical character, and physical properties can be simply retrieved from databases. The mixture compositions are defined with the aid of an optimization algorithm−interval method. The obtained TBP curves of substitute mixture are in good agreement with the experimentally obtained curves. Nine single carbon structural increments will be the representative of 93 real component compositions in order to make the topology of the neural network smaller and hence to have a less complex model. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800794n