Documents disponibles écrits par cet auteur

QSPR prediction of surface tension of refrigerants from their molecular structures / Aboozar Khajeh in International journal of refrigeration, Vol. 35 N° 1 (Janvier 2012) 

Public ISBD [article]  in International journal of refrigeration > Vol. 35 N° 1 (Janvier 2012) . - pp. 150–159 Titre : QSPR prediction of surface tension of refrigerants from their molecular structures Titre original : Prévision QSPR (fondée sur la relation quantitative entre la structure et les propriétés) de la tension superficielle des frigorigènes à partir de leurs structures moléculaires Type de document : texte imprimé Auteurs : Aboozar Khajeh, Auteur ; Hamid Modarress, Auteur Année de publication : 2012 Article en page(s) : pp. 150–159 Note générale : Génie mécanique Langues : Anglais (eng) Mots-clés : Surface  tension  Refrigerant  Structure  Property  Relationship  Neuro-fuzzy  inference  system Résumé : In this work, quantitative structure-property relationship (QSPR) models for prediction of surface tension of 224 refrigerant compounds on the basis of their molecular structures were developed by using genetic function approximation (GFA) and adaptive neuro-fuzzy inference system (ANFIS) methods. GFA was used to select the most important molecular descriptors and develop the linear model. To develop a nonlinear model, the four descriptors selected by GFA were used as the inputs for ANFIS method. The predictive ability of the developed model was evaluated by predicting the surface tension of a number of compounds as a test set. The squared correlation coefficients of surface tension predicted by the GFA and ANFIS methods were 0.985 and 0.996, respectively. The final results suggest that the obtained QSPR model can be applied for predicting the surface tension of refrigerant compounds with high accuracy and simplicity. ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700711001964 [article] QSPR prediction of surface tension of refrigerants from their molecular structures = Prévision QSPR (fondée sur la relation quantitative entre la structure et les propriétés) de la tension superficielle des frigorigènes à partir de leurs structures moléculaires [texte imprimé] / Aboozar Khajeh, Auteur ; Hamid Modarress, Auteur . - 2012 . - pp. 150–159. Génie mécanique Langues : Anglais (eng) in International journal of refrigeration > Vol. 35 N° 1 (Janvier 2012) . - pp. 150–159 Mots-clés : Surface  tension  Refrigerant  Structure  Property  Relationship  Neuro-fuzzy  inference  system Résumé : In this work, quantitative structure-property relationship (QSPR) models for prediction of surface tension of 224 refrigerant compounds on the basis of their molecular structures were developed by using genetic function approximation (GFA) and adaptive neuro-fuzzy inference system (ANFIS) methods. GFA was used to select the most important molecular descriptors and develop the linear model. To develop a nonlinear model, the four descriptors selected by GFA were used as the inputs for ANFIS method. The predictive ability of the developed model was evaluated by predicting the surface tension of a number of compounds as a test set. The squared correlation coefficients of surface tension predicted by the GFA and ANFIS methods were 0.985 and 0.996, respectively. The final results suggest that the obtained QSPR model can be applied for predicting the surface tension of refrigerant compounds with high accuracy and simplicity. ISSN : 0140-7007 En ligne : http://www.sciencedirect.com/science/article/pii/S0140700711001964

Quantitative structure - property relationship for flash points of alcohols / Aboozar Khajeh 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. 11337-11342 Titre : Quantitative structure - property relationship for flash points of alcohols Type de document : texte imprimé Auteurs : Aboozar Khajeh, Auteur ; Hamid Modarress, Auteur Année de publication : 2011 Article en page(s) : pp. 11337-11342 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Property  structure  relationship Résumé : In this paper, quantitative structure―property relationship (QSPR) models have been developed to predict flash points for some common alcohols based on a data set of 151 components. With the use of the genetic function approximation (GFA) approach, four descriptors have been selected from a set of more than 1000 molecular descriptors. These selected descriptors were used as inputs for the adaptive neuro-fuzzy inference system (ANFIS) model The GFA model resulted in squared correlation coefficient values of 0.935 and 0.91 respectively for the training and test sets, whereas ANFIS resulted in the values of 0.959 and 0.951 for the training and test sets, respectively. However, the linear and nonlinear models can give satisfactory prediction results, but the ANFIS model is somewhat superior. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573330 [article] Quantitative structure - property relationship for flash points of alcohols [texte imprimé] / Aboozar Khajeh, Auteur ; Hamid Modarress, Auteur . - 2011 . - pp. 11337-11342. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 19 (Octobre 2011) . - pp. 11337-11342 Mots-clés : Property  structure  relationship Résumé : In this paper, quantitative structure―property relationship (QSPR) models have been developed to predict flash points for some common alcohols based on a data set of 151 components. With the use of the genetic function approximation (GFA) approach, four descriptors have been selected from a set of more than 1000 molecular descriptors. These selected descriptors were used as inputs for the adaptive neuro-fuzzy inference system (ANFIS) model The GFA model resulted in squared correlation coefficient values of 0.935 and 0.91 respectively for the training and test sets, whereas ANFIS resulted in the values of 0.959 and 0.951 for the training and test sets, respectively. However, the linear and nonlinear models can give satisfactory prediction results, but the ANFIS model is somewhat superior. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24573330

Quantitative structure–property relationship prediction of gas heat capacity for organic compounds / Aboozar Khajeh in Industrial & engineering chemistry research, Vol. 51 N° 41 (Octobre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 41 (Octobre 2012) . - pp. 13490–13495 Titre : Quantitative structure–property relationship prediction of gas heat capacity for organic compounds Type de document : texte imprimé Auteurs : Aboozar Khajeh, Auteur ; Hamid Modarress, Auteur Année de publication : 2012 Article en page(s) : pp. 13490–13495 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Gas  heat  Organic  compounds Résumé : In the present work, a quantitative structure–property relationship study is performed to predict gas heat capacity for a structurally wide variety of organic compounds using the genetic function approximation (GFA) and the adaptive neuro-fuzzy inference system (ANFIS) methods. The simple proposed models contain only three descriptors calculated solely from the molecular structure of compounds which are 3D-independent descriptors. The models were validated by an external prediction set. Good results were obtained from both models which get the squared correlation coefficients of 0.996 and 0.997 for GFA and ANFIS, respectively. This study discloses enhanced correlations of the heat capacity of gases with their molecular structures, wherein the influence of the size of molecules is found to predominate. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie301317f [article] Quantitative structure–property relationship prediction of gas heat capacity for organic compounds [texte imprimé] / Aboozar Khajeh, Auteur ; Hamid Modarress, Auteur . - 2012 . - pp. 13490–13495. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 41 (Octobre 2012) . - pp. 13490–13495 Mots-clés : Gas  heat  Organic  compounds Résumé : In the present work, a quantitative structure–property relationship study is performed to predict gas heat capacity for a structurally wide variety of organic compounds using the genetic function approximation (GFA) and the adaptive neuro-fuzzy inference system (ANFIS) methods. The simple proposed models contain only three descriptors calculated solely from the molecular structure of compounds which are 3D-independent descriptors. The models were validated by an external prediction set. Good results were obtained from both models which get the squared correlation coefficients of 0.996 and 0.997 for GFA and ANFIS, respectively. This study discloses enhanced correlations of the heat capacity of gases with their molecular structures, wherein the influence of the size of molecules is found to predominate. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie301317f

Quantitative structure - property relationship prediction of liquid heat capacity at 298.15 K for organic compounds / Aboozar Khajeh in Industrial & engineering chemistry research, Vol. 51 N° 17 (Mai 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 17 (Mai 2012) . - pp. 6251-6255 Titre : Quantitative structure - property relationship prediction of liquid heat capacity at 298.15 K for organic compounds Type de document : texte imprimé Auteurs : Aboozar Khajeh, Auteur ; Hamid Modarress, Auteur Année de publication : 2012 Article en page(s) : pp. 6251-6255 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Heat  capacity  Prediction  Property  structure  relationship Résumé : Novel QSPR models were developed and evaluated for the prediction of heat capacity of liquids at 298.15 K with only three descriptors. Two linear and nonlinear models were produced using genetic function approximation (GFA) and adaptive neurofuzzy inference system (ANFIS) methods based on a data set of 706 compounds with a wide variety of functional groups. The results showed that both GFA and ANFIS methods could model the relationship between the liquid heat capacity of organic compounds and their structures with high accuracy. The predictive quality of the QSPR models were tested for an external test set, where the squared correlation coefficients of prediction for the GFA and ANFIS methods were 0.970 and 0.973, respectively. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25862984 [article] Quantitative structure - property relationship prediction of liquid heat capacity at 298.15 K for organic compounds [texte imprimé] / Aboozar Khajeh, Auteur ; Hamid Modarress, Auteur . - 2012 . - pp. 6251-6255. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 17 (Mai 2012) . - pp. 6251-6255 Mots-clés : Heat  capacity  Prediction  Property  structure  relationship Résumé : Novel QSPR models were developed and evaluated for the prediction of heat capacity of liquids at 298.15 K with only three descriptors. Two linear and nonlinear models were produced using genetic function approximation (GFA) and adaptive neurofuzzy inference system (ANFIS) methods based on a data set of 706 compounds with a wide variety of functional groups. The results showed that both GFA and ANFIS methods could model the relationship between the liquid heat capacity of organic compounds and their structures with high accuracy. The predictive quality of the QSPR models were tested for an external test set, where the squared correlation coefficients of prediction for the GFA and ANFIS methods were 0.970 and 0.973, respectively. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25862984