An accurate model for predictions of vaporization enthalpies of hydrocarbons and petroleum fractions

Abstract Vaporization enthalpy is an essential property in various areas of science and engineering applications such as optimizing the transportation processes, designing the oil and gas production and processing facilities, and heat flux calculations. This work aims to develop an intelligent technique, namely Radial Basis Function (RBF) approach to predict the vaporization enthalpy of petroleum fractions and pure hydrocarbons. The model was coupled with an optimization algorithm namely Genetic Algorithm (GA) to determine the tuning parameters of RBF model. The model performance was evaluated through various graphical and statistical approaches. Results of the developed GA-RBF model were also compared with other literature correlations and intelligent models. It was found that the proposed GA-RBF model exhibits reliable results with acceptable accuracy for the prediction of experimental vaporization enthalpy data. In addition, results show that the model outperforms other literature models and correlations and exhibits better performance for prediction of experimental data.

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