A new group contribution-based method for estimation of flash point temperature of alkanes

Abstract

Flash point is a primary property used to determine the fire and explosion hazards of a liquid. New group contribution-based models were presented for estimation of the flash point of alkanes by the use of multiple linear regression (MLR) and artificial neural network (ANN). This simple linear model shows a low average relative deviation (AARD) of 2.8% for a data set including 50 (40 for training set and 10 for validation set) flash points. Furthermore, the predictive ability of the model was evaluated using LOO cross validation. The results demonstrate ANN model is clearly superior both in fitness and in prediction performance. ANN model has only the average absolute deviation of 2.9 K and the average relative deviation of 0.72%.

Keywords

flash point / alkane / group contribution / artificial neural network (ANN) / quantitative structure-property relationship (QSPR)

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