Neural computing approach for predicting vaporization enthalpy of pure hydrocarbons and petroleum fractions

Abstract

ABSTRACTThe current collaboration was aimed to approximate the heat of vaporization for petroleum fractions and pure hydrocarbons through using the multi-layer perceptron artificial neural network (MLP-ANN) based on the specific gravity, molecular weight, and boiling point temperature. Furthermore, Levenberg Marquardt algorithm was utilized to train the ANN structure and optimize its tuning parameters. Another comparison was carried out between the outcomes of suggested MLP-ANN model and six well-known correlations. Better results were observed for predicting heat of vaporization by the MLP-ANN model with the obtained value of mean relative error (MRE) and R-squared (R2) equal to 1.31% and 0.9962%, respectively. This computational approach can be applied in the petroleum engineering for a precise determination of heat of vaporization.