Mass flow rate prediction of direct-expansion solar-assisted heat pump using R290 based on ANN model

Abstract

A direct-expansion solar-assisted heat pump (DX-SAHP) system using R290 (propane) is studied experimentally. A Coriolis mass flow meter is employed to measure the R290 mass flow rate, and the artificial neural network (ANN) model is used to predict it. Based on numerous experiments, the 5 independent variables are chosen as the input parameters of the ANN model, including the ambient temperature, solar radiation intensity, electronic expansion valve (EEV) opening, compressor frequency and water temperature. The appropriate number of the hidden layer neurons is obtained. The results show that the Mean Relative Error (MRE), Root Mean Square Error (RMSE), and Standard Deviation (SD) of the ANN model are 0.0012, 0.4139 kg h−1 and 0.0447, respectively. Above 97% of prediction results agree well with experimental data within a maximum error of 10%. Furthermore, as the ambient temperature increases, the refrigerant mass flow rate increases. As the solar radiation intensity increases, the refrigerant mass flow rate increases on the whole, and the effect of solar radiation intensity is weakened with the increment of ambient temperature. At a higher level of compressor frequency, the variation in the refrigerant mass flow rate is approximately linear with the ambient temperature, solar radiation intensity, EEV opening or water temperature. With the proposed ANN prediction model, the refrigerant mass flow rate of the DX-SAHP system can be quickly got, which will be very useful in system efficient operation.