Abstract :
[en] Reliable data of the properties of lubricant + refrigerant mixtures are essential in many applications to assess the behavior of refrigeration and heat pump systems. The accurate modeling of all required thermophysical properties (including density, viscosity, thermal conductivity, enthalpy, entropy, and phase equilibria) remains a key challenge today.
In this work, thermophysical property measurements of a "pure" POE lubricant and its mixture with the refrigerant R1233zd(E) were carried out in the temperature range from 283.15 to 373.15 K with pressures up to 1.2 MPa, using experimental facitilies from the Technische Universitat Dresden. Based on the modelling approach developed by Yang et al. (Ind. Eng. Chem. Res. 2023, 44, 18736-18749), the thermophysical properties of the "pure" POE and its mixture with R1233zd(E) are modeled in much larger temperature and pressure ranges. This model is a semi-empirical approach making use of a small amount of experimental data ( density, viscosity, thermal conductivity and heat capacity) of the pure lubricant to obtain the lubricant's fluid constants ( e.g., critical temperature). Subsequently, some experimental bubble point pressure data of the mixture were used to fit the binary interaction parameters in mixing rules, which enable mixture predictions. The predictions of this model are compared with those of the classical empirical models employed for lubricant + refrigerant mixtures, specifically the Henderson equations for density and viscosity, and the Cavestri equation for the vapor pressure. The results show a better agreement with the experimental data for the empirical modeling approach for both the density and viscosity, while the vapor pressure data prediction accuracies are even for both approaches. In particular, the viscosity prediction of the thermodynamic approach are not good for the mixture, with a root mean square relative error of 40%. Finally, the two modelling approaches are compared on other modeling aspects than accuracy, for instance, the number of experimental data required, the ability to predict other properties and the physical sense of the calibrated parameters, making the approach developed by Yang et al. a convenient option in many applications.
