Accurate modeling of vapor–liquid equilibria of binary mixtures of refrigerants using intelligent models
Abstract Developing simple, accurate and general models for prediction of different properties of hydrofluorocarbons (HFCs) and hydrocarbons (HCs) with hydrofluoro-olefins (HFOs) mixtures is of crucial importance in the design of new refrigeration system. In this communication, four computer based models namely Radial Basis Function Neural Network, Multilayer Perceptron Neural Network, Least Square Support Vector Machine optimized by Coupled Simulated Annealing and Adaptive Neuro Fuzzy Inference System trained by Hybrid method were used for prediction of vapor–liquid equilibrium (VLE) for binary mixtures of different HFC and HC compounds with HFO refrigerants. Results reveal that the developed models are accurate and effective for prediction of experimental VLE data for different systems. However, the RBF-NN model provides better predictions compared to other models. Moreover, the predictions of the developed models were better than the Peng-Robinson (PR) and Soave–Redlich–Kwong (SRK) equations of state (EoSs).