Estimation of vapour liquid equilibria for the Sulfur Dioxide–difluoromethane system using adaptive neuro-fuzzy inference system

Intelligent computing tools such as artificial neural network (ANN) and fuzzy logic approaches are proven to be efficient when applied individually to a variety of problems. Recently there has been a growing interest in combining both these approaches, and as a result, neuro-fuzzy computing techniques have evolved. This paper presents the application of an adaptive neuro fuzzy interface system (ANFIS) as an alternative tool for the purpose of estimating vapor liquid equilibria (VLE) for the binary system, sulfur dioxide–difluoromethane, which is an attractive alternative to chlorofluorocarbons and hydrochlorofluorocarbons, normally used as refrigerants. To evaluate the effectiveness of our proposal (ANFIS), a computer simulation is developed on MATLAB environment. The statistical methods, such as the root-mean squared errors (RMSE), the coefficient of multiple determinations (R2) and the mean absolute errors (MAE), are given to compare the predicted and actual values for model validation. Furthermore, the comparison in terms of statistical values between the predicted results for the whole temperature range and literature results predicted by the Peng–Robinson equation of state using the Mathias Copeman alpha function and the Wong–Sandler mixing rules involving the NRTL model. The results obtained in this work indicate that ANFIS is effective method for estimate the vapor liquid equilibrium pressure and mole fraction sulfur dioxide in vapor phase in the temperature range 288.07– 403.16 K and in the pressure range 0.417–7.31 MPa.

Keywords:

ANFIS , VLE , Estimation , Refrigerants , Binary mixture