Modeling and Experimental Validation of the Thermophysical Properties of a POE+R1233zd(E) Mixture

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.