New structure-based models for the prediction of flash point of multi-component organic mixtures

Abstract

In this study, new models based on quantitative structure–property relationship (QSPR) have been developed to estimate the flash point of multi-component mixtures. For the model development, a large data set including 921 experimental data on the flash point of 93 different binary mixtures including different chemical families was applied. Enhanced replacement method (ERM) was employed for subset variable selection. Based on the ERM selected molecular descriptors of mixture, two new models including linear model, and artificial neural network (ANN) based model have been proposed. The prediction capability of the developed models was evaluated using different statistical criteria. Besides, the estimation capability of the developed models was evaluated by estimating the flash point of 4 different ternary mixtures containing 221 experimental data as an extra external validation data set based on “mixture out” external validation strategy. All statistical analysis confirmed that the proposed models are reliable and accurate.