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

Public ISBD [article]  inIndustrial & 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 : Farhad Gharagheizi, 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] / Farhad Gharagheizi, 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

Exemplaires

Code-barres	Cote	Support	Localisation	Section	Disponibilité
aucun exemplaire