Quantitative structure-property relationship study for predicting heat of combustion of liquid hydrocarbon

Both Xu index based atom-type AI indices and electrotopological state indices were used to describe the structures of 80 liquid hydrocarbon molecules, and quantitative structure-property relationship(QSPR) models were developed to predict the heat of combustion of those 80 liquid hydrocarbon by using the artificial neural network and the multilinear regression approach, respectively. The results show that the characteristics of liquid hydrocarbon molecular structures can be better described by Xu index based atom-type AI indices. Furthermore, the linear relationship between the heat of combustion of liquid hydrocarbon and molecular structure is more obvious than the nonlinear relationship. The optimal model was obtained by combining of atom-type AI indices and multi-linear regression, whose correlation coefficient and average relative errors for the testing set were 0.999 and 0.637% respectively. The predicted values of the models are in good agreement with the experimental data.