Estimation of flash point and autoignition temperature of organic sulfur chemicals

The combustible nature of organic sulfur containing chemicals demands an accurate hazardous knowledge for their safe handling and application in industries and researches. In this work, a quantitative structure–property relationship (QSPR) study was performed to thoroughly investigate such crucial hazardous properties i.e., flash point (FP) and autoignition temperature (AIT) of the organic sulfur chemicals which are comprising a wide range of mercaptans, sulfides/thiophenes, polyfunctional C,H,O,S material classes. Based on multivariate linear regression (MLR) the multivariate model was gained using a robust binary particle swarm optimization (PSO) for the feature selection step, the three molecular descriptors were realized as the most responsible descriptors for the flammability behaviors of such chemicals. Next, a three-layer feed-forward neural network model (ANN model) was utilized. The implemented multivariate linear regression and three-layer feed-forward neural network models were practically able to predict the flammability characteristics of a diverse range organic sulfur containing chemicals with high accuracy. sults for PSO-MLR model illustrated that the squared correlation coefficient (R2) between predicted and experimental values were 0.9286 and 0.9259 for FP and AIT, respectively. The results for ANN model showed that the squared correlation coefficients (R2) were 0.9858 and 0.9889 for FP and AIT, respectively. The ANN model of FP and AIT is more accurate than the multivariate model, and the PSO-MLR model is more simple and touchable.