prediction of true critical temperature and pressure of binary hydrocarbon mixtures: a comparison between the artificial neural networks and the support vector machine

Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. in order to make a fair comparison and to achieve the highest efficiency, a comprehensive search method is used in neural network modeling and a particle swarm optimization algorithm is applied to svm modeling. to compare the accuracy of the models, various criteria such as ard, mae, mse, rae, and r2 are used. the simulation results show that the ard for the prediction of the true critical temperature and pressure of the binary hydrocarbon mixtures for the final optimized ann-based model are equal to 0.0161 and 0.0387, respectively. the corresponding ard values for the svm-based model are equal to 0.0086 and 0.0091 for critical temperature and pressure, respectively. simulation results show that although both models have very high predictive accuracy, the svm has higher learning speed and accuracy than ann.