Prediction of vapor–liquid equilibria using reconstruction—learning neural network method

This paper deals with the proposal of a predictive method for Margules parameters using reconstruction-learning neural network (NN). The input layers in the NN method are critical volume, acentric factor, dipole moment, entropy of vaporization and electronegativity of components 1 and 2. The number of Margules parameters used for evaluating the weight matrix in the NN method is 872, and the obtained correlation coefficient is R2 = 0.8537. The Margules parameters not used as learning data were predicted for 17 binary systems. The vapor–liquid equilibria were then predicted for 17 binary systems using these Margules parameters in combination with Riedel vapor pressure constants predicted by the NN method proposed previously. We observed a high degree of similarity between experimental and predicted vapor compositions.