title: A new group contribution-based model for estimation of lower flammability limit of pure compounds.
abstract: In the present study, a new method is presented for estimation of lower flammability limit (LFL) of pure compounds. This method is based on a combination of a group contribution method and neural networks. The parameters of the model are the occurrences of a new collection of 105 functional groups. Basing on these 105 functional groups, a feed forward neural network is presented to estimate the LFL of pure compounds. The average absolute deviation error obtained over 1057 pure compounds is 4.62%. Therefore, the model is an accurate model and can be used to predict the LFL of a wide range of pure compounds.
authors.affiliation: Department of Chemical Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11365-4563, Tehran, Iran. fghara@ut.ac.ir
authors.forename: Farhad
authors.initials: F
authors.lastname: Gharagheizi
mesh_terms: D000465:Algorithms; D002617:Chemical Industry / Q000295:instrumentation*; D016208:Databases, Factual; D004581:Electronics; D005390:Fires / Q000517:prevention & control*; D015386:Hazardous Substances / Q000032:analysis*; D008956:Models, Chemical; D016571:Neural Networks, Computer; D012449:Safety
chemical_list: D015386:Hazardous Substances