Prediction of Autoignition Temperatures of Organic Compounds from Molecular Structure

Abstract

A quantitative structure−property relationship study is performed to develop mathematical models that relate the structures of a heterogeneous group of organic compounds to their autoignition temperature values. The molecular structures of the compounds are represented by calculated numerical descriptors which encode their topological, electronic, and geometric features. These descriptors are used to develop several multiple linear regression and computational neural network models to predict the autoignition temperatures of a data set consisting of hydrocarbons, halohydrocarbons, and compounds containing oxygen, sulfur, and nitrogen. Both genetic algorithm and simulated annealing routines are used to select subsets of descriptors based on multiple linear regression and computational neural networks. The models that are developed have predictive ability in the range of the experimental error of autoignition temperature measurements.