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A new Neural network group contribution method for estimation of upper flash point of pure chemicals / Farhad Gharagheizi in Industrial & engineering chemistry research, Vol. 49 N° 24 (Décembre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 24 (Décembre 2010) . - pp. 12685-12695 Titre : A new Neural network group contribution method for estimation of upper flash point of pure chemicals Type de document : texte imprimé Auteurs : Farhad Gharagheizi, Auteur ; Reza Abbasi, Auteur Année de publication : 2011 Article en page(s) : pp. 12685-12695 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Group  contribution  method  Neural  network Résumé : In this study, a new group contribution-based model is presented for the prediction of the upper flash point temperature of pure compounds based on a large data set containing 1294 pure compounds. The model is a neural network using a number of occurrences of 122 chemical groups in a pure compound to predict its related UFLT (Upper Flash Point Limit). The squared correlation coefficient, average percent error, mean average error, and root-mean-square error of the model over the main data set containing 1294 pure compounds are 0.99, 1.7%, 6, and 8.5, respectively. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=23692025 [article] A new Neural network group contribution method for estimation of upper flash point of pure chemicals [texte imprimé] / Farhad Gharagheizi, Auteur ; Reza Abbasi, Auteur . - 2011 . - pp. 12685-12695. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 24 (Décembre 2010) . - pp. 12685-12695 Mots-clés : Group  contribution  method  Neural  network Résumé : In this study, a new group contribution-based model is presented for the prediction of the upper flash point temperature of pure compounds based on a large data set containing 1294 pure compounds. The model is a neural network using a number of occurrences of 122 chemical groups in a pure compound to predict its related UFLT (Upper Flash Point Limit). The squared correlation coefficient, average percent error, mean average error, and root-mean-square error of the model over the main data set containing 1294 pure compounds are 0.99, 1.7%, 6, and 8.5, respectively. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=23692025

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