Prediction of evaporation temperature in air-water heat source heat pump based on artificial neural network

Abstract

To improve the low-temperature heating stability and performance of the air-source heat pump (ASHP), a heat transfer model was established for an air-water heat source heat pump (A-WSHP) in this study. Key factors affecting A-WSHP evaporating temperature were analyzed through simulation. An artificial neural network (ANN) prediction model for refrigerant evaporating temperature was developed using fin pitch, air velocity, air temperature, spray flow rate, spray water temperature, and droplet diameter as inputs. In the winter climate of Xiangtan City, this model predicts the refrigerant evaporating temperature and regulates it by adjusting various input parameters. Results indicate that fin pitch and droplet size significantly impact the refrigerant evaporating temperature, more so than fin thickness. The air temperature (34.78 %) and spray water temperature (38.02 %) have the highest weights, while fin thickness has the least influence, with a weight of 0.75 %. The R2 value of the ANN refrigerant evaporating temperature prediction model is 0.974, indicating a good fit. By properly adjusting the heat pump fan and spray valve, changes in air velocity and spray flow rate can enhance the refrigerant evaporating temperature, leading to stable and efficient heat pump operation. This research not only provides a theoretical basis for the operational strategy of air-water heat pumps but also offers guidance for frost-free operation in low-temperature environments.