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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