Application of artificial neural network method for performance prediction of a gas cooler in a CO₂ heat pump

The objective of this work is to train an artificial neural network (ANN) to predict the performance of gas cooler in carbon dioxide transcritical air-conditioning system. The designed ANN was trained by performance test data under varying conditions. The deviations between the ANN predicted and measured data are basically less than ±5%. The well-trained ANN is then used to predict the effects of the five input parameters individually. The predicted results show that for the heat transfer and CO₂ pressure drop the most effective factor is the inlet air velocity, then come the inlet CO₂ pressure and temperature. The inlet mass flow rate can enhance heat transfer with a much larger CO₂ pressure drop penalty. The most unfavorable factor is the increase in the inlet air temperature, leading to the deterioration of heat transfer and severely increase in CO₂ pressure drop.