A novel machine-learning schemes to predict heat transfer coefficient during condensation of CO2 in porous media

Accurate heat transfer predictions during CO2 condensation in porous media are needed to improve two-phase flow devices like refrigeration and air-conditioning systems. However, phase-change condensation system behavior makes predicting of the heat transfer coefficient very difficult. A reference dataset is used by gathering information from previous experimental investigations on heat transfer during condensation of CO2 as refrigerant in porous media at subcritical pressures. This database comprises four key factors: porosities of 39.8–44.5