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)  

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