Documents disponibles écrits par cet auteur

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 vaporization enthalpy of pure compounds using a group contribution-based method / Farhad Gharagheizi in Industrial & engineering chemistry research, Vol. 50 N° 10 (Mai 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 10 (Mai 2011) . - pp. 6503-6507 Titre : Prediction of vaporization enthalpy of pure compounds using a group contribution-based method Type de document : texte imprimé Auteurs : Farhad Gharagheizi, Auteur ; Omid Babaie, Auteur ; Sahar Mazdeyasna, Auteur Année de publication : 2011 Article en page(s) : pp. 6503-6507 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Thermodynamic  properties  Enthalpy  Vaporization  Prediction Résumé : In this work, the artificial neural network-group contribution (ANN-GC) method is applied to estimate the vaporization enthalpy of pure chemical compounds at their normal boiling point. A group of 4907 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coeffident (R2) of 0.993, root mean square error of 1.1 kJ/mol, and average absolute deviation lower than 1.5% for the estimated properties from existing experimental values. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24158948 [article] Prediction of vaporization enthalpy of pure compounds using a group contribution-based method [texte imprimé] / Farhad Gharagheizi, Auteur ; Omid Babaie, Auteur ; Sahar Mazdeyasna, Auteur . - 2011 . - pp. 6503-6507. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 10 (Mai 2011) . - pp. 6503-6507 Mots-clés : Thermodynamic  properties  Enthalpy  Vaporization  Prediction Résumé : In this work, the artificial neural network-group contribution (ANN-GC) method is applied to estimate the vaporization enthalpy of pure chemical compounds at their normal boiling point. A group of 4907 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coeffident (R2) of 0.993, root mean square error of 1.1 kJ/mol, and average absolute deviation lower than 1.5% for the estimated properties from existing experimental values. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24158948

Solubility parameters of nonelectrolyte organic compounds / Farhad Gharagheizi in Industrial & engineering chemistry research, Vol. 50 N° 19 (Octobre 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 11382-11395 Titre : Solubility parameters of nonelectrolyte organic compounds : determination using quantitative structure — property relationship strategy Type de document : texte imprimé Auteurs : Farhad Gharagheizi, Auteur ; Ali Eslamimanesh, Auteur ; Farhad Farjood, Auteur Année de publication : 2011 Article en page(s) : pp. 11382-11395 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Property  structure  relationship  Solubility Résumé : The solubility parameter is considered to be a significant parameter for the chemical industry. In this study, the quantitative structure―property relationship (QSPR) method is applied to develop three models for determination of the solubility parameters of pure nonelectrolyte organic compounds at 298.15 K and atmospheric pressure. To propose comprehensive, reliable, and predictive models, about 1400 data belonging to experimental solubility parameter values of various nonelectrolyte organic compounds are studied The genetic function approximation (GFA) mathematical approach is applied for selection ofproper model parameters (molecular descriptors) and to develop a linear QSPR model. To study the nonlinear relations between the selected molecular descriptors and the solubility parameter, two approaches are pursued: the three-layer feed forward artificial neural networks (3FFANN) and the least square support vector machine (LSSVM). Furthermore, the Levenberg―Marquardt (LM) and genetic algorithm (GA) optimization methods are respectively implemented to optimize the 3FFANN and LSSVM models. Consequently, we obtain three predictive models with satisfactory results quantified by the following statistical parameters: absolute average relative deviation (AARD) of the represented/predicted properties from existing experimental values by the GFA linear equation of 4.6% and squared correlation coefficient of 0.896; AARD of the QSPR-ANN model of 3.4% and squared correlation coefficient of 0.941; and AARD of 3.1% and squared correlation coefficient of 0.947 evaluated by the QSPR-LSSVM modeL. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573335 [article] Solubility parameters of nonelectrolyte organic compounds : determination using quantitative structure — property relationship strategy [texte imprimé] / Farhad Gharagheizi, Auteur ; Ali Eslamimanesh, Auteur ; Farhad Farjood, Auteur . - 2011 . - pp. 11382-11395. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 11382-11395 Mots-clés : Property  structure  relationship  Solubility Résumé : The solubility parameter is considered to be a significant parameter for the chemical industry. In this study, the quantitative structure―property relationship (QSPR) method is applied to develop three models for determination of the solubility parameters of pure nonelectrolyte organic compounds at 298.15 K and atmospheric pressure. To propose comprehensive, reliable, and predictive models, about 1400 data belonging to experimental solubility parameter values of various nonelectrolyte organic compounds are studied The genetic function approximation (GFA) mathematical approach is applied for selection ofproper model parameters (molecular descriptors) and to develop a linear QSPR model. To study the nonlinear relations between the selected molecular descriptors and the solubility parameter, two approaches are pursued: the three-layer feed forward artificial neural networks (3FFANN) and the least square support vector machine (LSSVM). Furthermore, the Levenberg―Marquardt (LM) and genetic algorithm (GA) optimization methods are respectively implemented to optimize the 3FFANN and LSSVM models. Consequently, we obtain three predictive models with satisfactory results quantified by the following statistical parameters: absolute average relative deviation (AARD) of the represented/predicted properties from existing experimental values by the GFA linear equation of 4.6% and squared correlation coefficient of 0.896; AARD of the QSPR-ANN model of 3.4% and squared correlation coefficient of 0.941; and AARD of 3.1% and squared correlation coefficient of 0.947 evaluated by the QSPR-LSSVM modeL. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573335