| Titre : | Estimation of aniline point temperature of pure hydrocarbons : a quantitative structure-property relationship approach (2009) |
| Auteurs : | Farhad Gharagheizi, Auteur ; Behnam Tirandazi, Auteur ; Reza Barzin, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Industrial & engineering chemistry research (Vol. 48 N°3, Février 2009) |
| Article en page(s) : | p. 1678–1682 |
| Note générale : | Chemical engineering |
| Langues : | Anglais |
| Tags : | Aniline Hydrocarbons Structure -- quantitative study Material physicochemical properties |
| Résumé : |
In the present work, a quantitative structure-property relationship (QSPR) study is performed to predict the aniline point temperature of pure hydrocarbon components. As a powerful tool, genetic algorithm-based multivariate linear regression (GA-MLR) is applied to select most statistically effective molecular descriptors on the aniline point temperature of pure hydrocarbon components. Also, a three-layer feed forward neural network (FFNN) is constructed to consider the nonlinear behavior of appearing molecular descriptors in GA-MLR result. The obtained results show that the constructed FFNN can accurately predict the aniline point temperature of pure hydrocarbon components. |
| En ligne : | http://pubs.acs.org/doi/abs/10.1021/ie801212a |
