Conference Agenda

In our previous work (Ind. Eng. Chem. Res. 2023, 62, 18736-18749), a modeling approach was developed to calculate all the essential thermophysical properties, including density, phase behavior, heat capacity, entropy, enthalpy, viscosity, and thermal conductivity, of lubricant oil. This approach treats oil as a quasi-pure fluid, sets up a simple set of equations for the essential properties, and develops a parameter-fitting procedure using a minimal set of experimental data (fewer than 20 and at least 12 data points). This approach can be easily extended for mixture (e.g., oil + refrigerant) property prediction. Calculations using this approach generally agree with experimental data within the same level of experimental uncertainty, except for up to 3% of quasi-pure oil density, 5% of the mixture’s density, and several hundred percent of the mixture’s viscosity. In this work, a new cubic equation of state (EoS) recently developed by us was adopted to replace the initially used Patel-Teja-Valderrama (PTV) EoS. As a result, for density, relative deviations were down to approximately 1% for quasi-pure oil and generally 3% for mixtures. A van der Waals (vdW) type mixing rule containing one adjustable parameter, which could be fitted to experimental data, is applied to the mixture’s viscosity prediction using the residual entropy scaling approach. The relative deviations could be significantly reduced; however, they are still at the level of a few tens up to a hundred percent. Careful evaluations of the mixture’s viscosity data revealed that the uncertainty of the experimental data could be higher than expected, and there is an apparent lack of high-quality viscosity data of oil + refrigerant mixtures. A software package called OilProp 1.0, with a simple graphical user interface (GUI), was developed in Matlab to fit oil parameters and calculate the thermophysical properties of oils and their mixtures with other fluids, particularly refrigerants.

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 Universität 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 was 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.

Decarbonization efforts to reduce the use of fossil fuels for both comfort and process heating has challenged traditional vapor compression heat pump systems to be run at temperatures above traditional comfort cooling and refrigeration applications. These elevated temperatures introduce new challenges for the refrigerant and lubricant system, specifically long-term chemical stability of the working fluids. In some cases, application temperatures being proposed are at or near the traditional ASHRAE 97 highly accelerated test temperature of 175°C(347°F) for comfort applications. Because of this, new standard testing conditions using Arrhenius theory may need to be applied to select proper test temperatures to understand the long-term chemical stability and equipment reliability risks of these systems.

AHRTI (Air-Conditioning Research Technology Institute), with funding from the US Department of Energy Building Technology Office, and NYSERDA (New York State Energy Research & Development Authority) sponsored the second phase of the AHRTI Project 9016 to continue the study of Low GWP (global warming potential) refrigerants. Phase 2 of this project expanded upon the chemical stability testing with more system materials of construction and included material compatibility of common non-metallic materials used in refrigerant containing systems. This expanded testing included understanding the impact on stereoisomerization of R-1336mzz(E&Z) when subjected to highly accelerated life tests (HALT) with and without various materials. The authors tested R-1233zd(E) in conjunction with this testing through funding from their employer.

This paper summarizes stereoisomerization results of R-1336mzz(Z), R-1336mzz(E), and R-1233zd(E) tested at very highly accelerated life test (VHALT) conditions at multiple test temperatures with various catalysts.

Lubricants for compressor oil play a significant role in enhancing the energy efficiency and performance of air con- ditioning (AC) systems. The current study compares prognostic machine learning (ML) models designed to forecast the thermal conductivity and viscosity of nanolubricants used in AC compressors. Nanodiamond (ND) nanoparticles are distributed at quantities ranging from 0.05 to 0.5 vol.% in Polyolester (POE) oil. The thermophysical properties of ND/POE based nanolubricants are determined experimentally in the temperatures range of 10 to 100°C. The data col- lected throughout the experimental research is used to build prognostic models using modern supervised ML techniques like gaussian process regression (GPR), and a modern ensemble machine learning method named boosted regression tree (BRT). These sophisticated ML models, which have been trained on precise experimental data, can provide precise and on-time predictions for a wide range of ND/POE nanolubricants compositions and operating conditions. The de- veloped machine learning models are likely to aid in the design and optimization of ND/POE nanolubricants, allowing them to achieve desired performance parameters despite remaining economically viable and reducing time-consuming laboratory-based testing.