Modelling enthalpy and entropy of pure and mixed refrigerants with an innovative corresponding states method

In this work an original improvement of the Corresponding States technique is developed and a new model, based on a three parameters CS format, is proposed to predict the enthalpy and the entropy of the new generation halogenated alkanes fluids together with some alkanes. Limiting the analysis of the selected fluids to a specific thermodynamic property behaviour, an appropriate conformality approach can be deduced, which allows to set up a predictive model of high accuracy level on a wide range of the enthalpy and entropy surfaces. The fundamentals of the model are innovative scaling parameters deduced from the enthalpy of vaporization and from two dedicated equations, belonging to the selected family of fluids. This allows to set up innovative models following a CS format. Through the introduction of advanced mixing rules, the models can be simply extended to calculate the corresponding properties for mixtures. The proposed models allow also the calculation of VLE for systems of rather regular behaviour. The required inputs for a pure target fluid are an ideal gas isobaric heat capacity correlation, a single value of saturated liquid density and of vaporization enthalpy; if the last one is lacking, a single value of vapor pressure can be alternatively supplied. For non azeotropic mixtures the enthalpy and entropy models are predictive, whereas in case of azeotropy VLE calculations are possibly only applying regressed interaction coefficients. Due to the lack of accurate experimental enthalpy data and to the particular nature of the entropy function, the validation of the models is proposed against fundamental dedicated EoS available, both for pure and mixtures, for a significant number of the studied family of fluids. The predictive character of the proposed approach as well as the high performances reached, make these models particularly suitable for the new families of fluids regarding advanced technological applications. (C) 2003 Elsevier Ltd and IIR. All rights reserved.