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Estimation of aniline point temperature of pure hydrocarbons / Gharagheizi, Farhad in Industrial & engineering chemistry research, Vol. 48 N°3 (Février 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N°3 (Février 2009) . - p. 1678–1682 Titre : Estimation of aniline point temperature of pure hydrocarbons : a quantitative structure-property relationship approach Type de document : texte imprimé Auteurs : Gharagheizi, Farhad, Auteur ; Behnam Tirandazi, Auteur ; Reza Barzin, Auteur Année de publication : 2009 Article en page(s) : p. 1678–1682 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Aniline  Hydrocarbons  Structure  --  quantitative  study  Material  --  physicochemical  properties Résumé : In the present work, a quantitative structure-property relationship (QSPR) study is performed to predict the aniline point temperature of pure hydrocarbon components. As a powerful tool, genetic algorithm-based multivariate linear regression (GA-MLR) is applied to select most statistically effective molecular descriptors on the aniline point temperature of pure hydrocarbon components. Also, a three-layer feed forward neural network (FFNN) is constructed to consider the nonlinear behavior of appearing molecular descriptors in GA-MLR result. The obtained results show that the constructed FFNN can accurately predict the aniline point temperature of pure hydrocarbon components. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801212a [article] Estimation of aniline point temperature of pure hydrocarbons : a quantitative structure-property relationship approach [texte imprimé] / Gharagheizi, Farhad, Auteur ; Behnam Tirandazi, Auteur ; Reza Barzin, Auteur . - 2009 . - p. 1678–1682. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N°3 (Février 2009) . - p. 1678–1682 Mots-clés : Aniline  Hydrocarbons  Structure  --  quantitative  study  Material  --  physicochemical  properties Résumé : In the present work, a quantitative structure-property relationship (QSPR) study is performed to predict the aniline point temperature of pure hydrocarbon components. As a powerful tool, genetic algorithm-based multivariate linear regression (GA-MLR) is applied to select most statistically effective molecular descriptors on the aniline point temperature of pure hydrocarbon components. Also, a three-layer feed forward neural network (FFNN) is constructed to consider the nonlinear behavior of appearing molecular descriptors in GA-MLR result. The obtained results show that the constructed FFNN can accurately predict the aniline point temperature of pure hydrocarbon components. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie801212a