[article] 
inIndustrial & engineering chemistry research > Vol. 48 N° 15 (Août 2009) . - pp. 7406–7416

Titre :	New neural network group contribution model for estimation of lower flammability limit temperature of pure compounds
Type de document :	texte imprimé
Auteurs :	Farhad Gharagheizi, Auteur
Année de publication :	2009
Article en page(s) :	pp. 7406–7416
Note générale :	Chemical engineering
Langues :	Anglais (eng)
Mots-clés :	Neural network method Pure compounds Lower flammability limit temperature
Résumé :	In the present study, a group contribution based neural network method is developed to predict the lower flammability limit temperature (LFLT) of pure compounds. The needed parameters of the model are the occurrences of 125 functional groups in every molecule. The average absolute deviation error obtained over 1429 pure compounds used in this study is 2.35%. Therefore, the model is an accurate model and can be used to predict the LFLT of a wide range of pure compounds.
En ligne :	http://pubs.acs.org/doi/abs/10.1021/ie9003738

[article] New neural network group contribution model for estimation of lower flammability limit temperature of pure compounds [texte imprimé] / Farhad Gharagheizi, Auteur . - 2009 . - pp. 7406–7416.
Chemical engineering
Langues : Anglais (eng)
in Industrial & engineering chemistry research > Vol. 48 N° 15 (Août 2009) . - pp. 7406–7416

Mots-clés :	Neural network method Pure compounds Lower flammability limit temperature
Résumé :	In the present study, a group contribution based neural network method is developed to predict the lower flammability limit temperature (LFLT) of pure compounds. The needed parameters of the model are the occurrences of 125 functional groups in every molecule. The average absolute deviation error obtained over 1429 pure compounds used in this study is 2.35%. Therefore, the model is an accurate model and can be used to predict the LFLT of a wide range of pure compounds.
En ligne :	http://pubs.acs.org/doi/abs/10.1021/ie9003738