Abstract: The auto- ignition temperature (AIT) were predicted by Quantitative Structure- Pharmacokinetics Relationship (QSPR). Thirty-nine kinds of hydrocarbons in the International Electrotechnical Commission (IEC) database were selected as sample sets, 34 kinds were randomly selected as training sets and 5 kinds as test sets. Genetic algorithm (GA) was used to screen variables, multiple linear regression (MLR) model and support vector machine (SVM) model were established by combining linear and nonlinear methods respectively, and the spontaneous ignition points of 5 hydrocarbon substances were predicted theoretically. Finally, the performance and application fields of the model were evaluated. The results showed that the two prediction models are stable and have strong prediction ability and generalization performance. The theoretical predicted values are consistent with the experimental values, and the predicted results of GA-SVM model are closer to the experimental values than GA-MLR model, which indicates that the relationship between auto- ignition temperature and its molecular structure is more nonlinear.  

Key words: hydrocarbon substances, prediction of auto-ignition temperature, QSPR