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Artificial neural approach for modeling the heat and mass transfer characteristics in three-phase fluidized beds / Farouq S. Mjalli in Industrial & engineering chemistry research, Vol. 47 N° 13 (Juillet 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N° 13 (Juillet 2008) . - p. 4542–4552 Titre : Artificial neural approach for modeling the heat and mass transfer characteristics in three-phase fluidized beds Type de document : texte imprimé Auteurs : Farouq S. Mjalli, Auteur ; A. Al-Mfargi, Auteur Année de publication : 2008 Article en page(s) : p. 4542–4552 Note générale : Bibliogr. p. 4551-4552 Langues : Anglais (eng) Mots-clés : Fluidized  bed  three-phase  reactors;  Heat  transfer;  Mass  transfer;  Artificial  neural  network Résumé : The study of reactor design and modeling is conducted frequently both at the initial stage of equipment design as well as during further stages of equipment operation. Fluidized bed three-phase reactors have very complex behavior which relies to a high extent on the mass and heat transfer characteristics of the reaction constituents. Numerous previous experimental and theoretical based studies for modeling heat and mass transfer coefficients have the common shortcoming of low prediction efficiency compared to experimental data. In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The newly developed heat and mass transfer coefficients models proved to be of high prediction quality compared to experimental data and previous correlations. The new correlations will be used in a further study for the hybrid steady state and dynamic modeling of fluidized bed catalytic reactors. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0715714 [article] Artificial neural approach for modeling the heat and mass transfer characteristics in three-phase fluidized beds [texte imprimé] / Farouq S. Mjalli, Auteur ; A. Al-Mfargi, Auteur . - 2008 . - p. 4542–4552. Bibliogr. p. 4551-4552 Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N° 13 (Juillet 2008) . - p. 4542–4552 Mots-clés : Fluidized  bed  three-phase  reactors;  Heat  transfer;  Mass  transfer;  Artificial  neural  network Résumé : The study of reactor design and modeling is conducted frequently both at the initial stage of equipment design as well as during further stages of equipment operation. Fluidized bed three-phase reactors have very complex behavior which relies to a high extent on the mass and heat transfer characteristics of the reaction constituents. Numerous previous experimental and theoretical based studies for modeling heat and mass transfer coefficients have the common shortcoming of low prediction efficiency compared to experimental data. In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The newly developed heat and mass transfer coefficients models proved to be of high prediction quality compared to experimental data and previous correlations. The new correlations will be used in a further study for the hybrid steady state and dynamic modeling of fluidized bed catalytic reactors. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0715714