Machine learning integration in thermodynamics: Predicting CO2 mixture saturation properties for sustainable refrigeration applications

Abstract

The need for sustainable alternatives in refrigeration has grown as Europe enforces mandates on avoiding high global warming potential (GWP) refrigerants. CO₂-based refrigerants have emerged as a promising choice in response, distinguished by its low GWP and reduced flammability, compared to formulated hydrofluoroolefins, thus offering a safer and sustainable solution in the context of next generation drop-in refrigerants. This study presents a machine-learning-based methodology to estimate the saturation properties of CO₂-based mixtures, allowing for the precise tuning of molecular-based models like the polar soft-SAFT, used for technical evaluation, without relying on experimental data, often unavailable for such systems. The approach departs from the thermodynamic characterization of several pure-components, including novel fluorine-based refrigerants. The parametrization allows an excellent description of the vapor pressure, saturated densities, and latent heat. Next, a constant, temperature-independent binary parameter is used to estimate the solubility profiles of CO₂-derived mixtures in selected refrigerants. The model effectively captures azeotropic and zeotropic behaviors, demonstrating its strength in fine-tuning solubility with minimal corrections. Subsequently, data from the molecular characterization via polar soft-SAFT is used as output targets to train a machine learning algorithm based on artificial neural networks, enabling the prediction of mixture saturation properties out of the training dataset's scope. Using COSMO σ-profiles, the developed ANN demonstrates high efficiency in predicting saturation bubble and dew temperatures, achieving R² > 0.9999, RMSE< 0.0959, AARD < 0.0220 %, and NMAD of 0.00044. Statistical analysis confirms minimal mean deviations, with outliers limited to 2.63 % for bubble and 2.44% for dew phase predictions, respectively.