Artificial neural network (ANN) technique has been applied for estimation of vapor-liquid equilibria (VLE) for eight binary\r\nrefrigerant systems. The refrigerants include difluoromethane (R32), propane (R290), 1,1-difluoroethane (R152a), hexafluoroethane\r\n(R116), decafluorobutane (R610), 2,2-dichloro-1,1,1-trifluoroethane (R123), 1-chloro-1,2,2,2-tetrafluoroethane (R124), and 1,1,1,2-\r\ntetrafluoroethane (R134a). The related experimental data of open literature have been used to construct the model. Furthermore,\r\nsome new experimental data (not applied in ANN training) have been used to examine the reliability of the model. The results\r\nconfirm that there is a reasonable conformity between the predicted values and the experimental data. Additionally, the ability\r\nof the ANN model is examined by comparison with the conventional thermodynamic models. Moreover, the presented model is\r\ncapable of predicting the azeotropic condition.
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