Gene expression programming strategy for estimation of flash point temperature of non-electrolyte organic compounds

The accuracy and predictability of correlations and models to determine the flammability characteristics of chemical compounds are of drastic significance in various chemical industries. In the present study, the main focus is on introducing and applying the gene expression programming (GEP) mathematical strategy to develop a comprehensive empirical method for this purpose. This work deals with presenting an empirical correlation to predict the flash point temperature of 1471 (non-electrolyte) organic compounds from 77 different chemical families. The parameters of the correlation include the molecular weight, critical temperature, critical pressure, acentric factor, and normal boiling point of the compounds. The obtained statistical parameters including root mean square of error of the results from DIPPR 801 data (8.8, 8.9, 8.9 K for training, optimization and prediction sets, respectively) demonstrate improved accuracy of the results of the presented correlation with respect to previously-proposed methods available in open literature.

► The novel gene expression programming strategy as a powerful modeling tool is introduced.
► A corresponding state model for flash point temperature is presented using the strategy.
► Statistical parameters demonstrate that the model results are in good agreement with experimental data.