High-Precision Modeling of Gas-Phase Density for the DME/HFC-134a Binary System Using Machine Learning
This study demonstrates that a Support Vector Regression model with a radial basis function kernel outperforms both other machine learning algorithms and classical cubic equations of state in accurately predicting the high-precision gas-phase density of the DME/HFC-134a binary system across a wide range of thermodynamic conditions.