[article]
| 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 : |
Farhad Gharagheizi, 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 |
in Industrial & engineering chemistry research > Vol. 49 N° 20 (Octobre 2010) . - pp. 10149-10152
[article] Prediction of henry’s law constant of organic compounds in water from a new group - contribution - based model [texte imprimé] / Farhad Gharagheizi, 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 |
|
Ajouter le résultat dans votre panier Faire une suggestion Affiner la rechercheA new Neural network group contribution method for estimation of upper flash point of pure chemicals in Industrial & engineering chemistry research, Vol. 49 N° 24 (Décembre 2010)
