Documents disponibles écrits par cet auteur

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

Prediction of henry’s law constant of organic compounds in water from a new group - contribution - based model / Gharagheizi, Farhad in Industrial & engineering chemistry research, Vol. 49 N° 20 (Octobre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 20 (Octobre 2010) . - pp. 10149-10152 Titre : Prediction of henry’s law constant of organic compounds in water from a new group - contribution - based model Type de document : texte imprimé Auteurs : Gharagheizi, Farhad, Auteur ; Reza Abbasi, Auteur ; Behnam Tirandazi, Auteur Année de publication : 2011 Article en page(s) : pp. 10149-10152 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Modeling  Prediction Résumé : In this work, a new model is presented for estimation of Henry's law constant of pure compounds in water at 25 °C (H). This model is based on a combination between a group contribution method and neural networks. The needed parameters of the model are the occurrences of a new collection of 107 functional groups. On the basis of these 107 functional groups, a feed forward neural network is presented to estimate the H of pure compounds. The squared correlation coefficient, absolute percent error, standard deviation error, and root-mean-square error of the model over a diverse set of 1940 pure compounds used are, respectively, 0.9981, 2.84%, 2.4, and 0.1 (all the values obtained using log H based data). Therefore, the model is a comprehensive and an accurate model and can be used to predict the H of a wide range of chemical families of pure compounds in water better than previously presented models. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=23325840 [article] Prediction of henry’s law constant of organic compounds in water from a new group - contribution - based model [texte imprimé] / Gharagheizi, Farhad, Auteur ; Reza Abbasi, Auteur ; Behnam Tirandazi, Auteur . - 2011 . - pp. 10149-10152. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 20 (Octobre 2010) . - pp. 10149-10152 Mots-clés : Modeling  Prediction Résumé : In this work, a new model is presented for estimation of Henry's law constant of pure compounds in water at 25 °C (H). This model is based on a combination between a group contribution method and neural networks. The needed parameters of the model are the occurrences of a new collection of 107 functional groups. On the basis of these 107 functional groups, a feed forward neural network is presented to estimate the H of pure compounds. The squared correlation coefficient, absolute percent error, standard deviation error, and root-mean-square error of the model over a diverse set of 1940 pure compounds used are, respectively, 0.9981, 2.84%, 2.4, and 0.1 (all the values obtained using log H based data). Therefore, the model is a comprehensive and an accurate model and can be used to predict the H of a wide range of chemical families of pure compounds in water better than previously presented models. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=23325840