A new approach using adaptive neuro-fuzzy inference system for estimation of vapour liquid equilibria for the system Carbon Dioxide–Difluoromethane

(Vapour + liquid) equilibrium (VLE) data on environmentally acceptable refrigerant fluids are of the utmost interest for the refrigeration industry and, in particular, for designing and optimizing refrigeration equipment. Since it is not always possible to carry out experiments at all possible temperatures and pressures, generally thermodynamic models based on equations of state are used for estimation of VLE. New models are then highly required. Therefore, an effort has been made to develop an alternative to a classical equation of state. This paper a new approach using neural fuzzy model based on adaptive network-based fuzzy inference system (ANFIS) was proposed to high-pressure VLE related literature data to develop and validate a model capable of predicting VLE for the binary system, carbon dioxide–difluoromethane, which is an attractive alternative to chlorofluorocarbons and hydrochlorofluorocarbons, normally used as refrigerants. Furthermore, the comparison in terms o statistical values between the predicted results for each binary for the whole temperature range and literature results predicted by Peng–Robinson equation of state using the Mathias Copeman alpha function and the Wong–Sandler mixing rules involving the NRTL model shows that the ANFIS model gives far better results.

کلیدواژه ها:

VLE ، ANFIS ، Estimation ، Binary mixture ، Refrigerants