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In this work, a feed-forward artificial neural network (ANN) was developed for the calculation of isobaric heat capacity of pure HFC and HFO refrigerants in liquid phase. First of all, a total of 1142 available experimental data points for different pure HFC or HFO refrigerants were collected and evaluated before being used to train and test the network. By a trial-and-error method, optimum structural of the network was found out to be an input layer using four dimensionless input parameters, one hidden layer with 34 neurons, and an output layer with reduced residual heat capacity as output. The ANN was applied for 12 different refrigerants, and the predicted isobaric heat capacity showed satisfactory agreement with experimental data. The overall average absolute deviation (AAD %) and maximum absolute deviation (MAD %) were 0.383% and 5.92% respectively.

In this work, isobaric heat capacity of liquid refrigerants mixtures was investigated experimentally and theoretically. First of all, experimental isobaric heat capacity data of a representative HFC-32+HFO-1234ze(E) binary mixture was obtained by a modified Calvet calorimeter at different temperatures and concentrations. After that, a previously proposed corresponding state equation, which was used for estimation of isobaric heat capacity of pure liquid HFCs and HFOs refrigerants, was extended to mixtures. Different mixing rules for pseudo-critical parameters were tested and a comparison between predicted values and experimental data was carried out in search of a best mixing rule. In the end, the corresponding state equation coupled with the selected mixing rule was applied to other binary mixtures R-410A, R-507A and ternary mixtures R-404A, R-407C. It was found that the calculated results agreed well with their fundamental Helmholtz equations of state. The average absolute deviation was 0.88% and maximum absolute deviations were no more than 1.90%.

Accurate prediction models for the viscosity and thermal conductivity of refrigerants are of great importance and have drawn wide attention from scholars. Considering the great advantage of artificial neural network (ANN) models in solving non-linear problems, two fully connected feed-forward ANN models were proposed to predict the viscosity and thermal conductivity of the HFC/HFO refrigerants in this paper. The reduced pressure (pr), reduced temperature (Tr), mole mass (M) and acentric factor (ω) of the refrigerants were selected as the input variables for both ANN models. Regarding the ANN model for viscosity, the neural number of the hidden layer was optimized to be 9 by trial-and-error method. The prediction results coincided with the experimental data very well. The correlation coefficient and the average absolute deviation (AAD) of regression were 0.9998 and 1.21 %, respectively. The prediction of thermal conductivity also showed a good agreement with the experimental data, and the AAD of the model was 1.00 %. The paper is expected to provide valuable methods to predict the viscosity and thermal conductivity of HFC/HFO refrigerants.

In this context, a two-stage absorption-transcritical hybrid refrigeration system is proposed. R744 is chosen as a refrigerant for the transcritical heat pump subsystem and LiBr-H 2 O working pair for the two-stage absorption refrigeration subsystem. Based on the mathematical and physical models, theoretical investigation is carried out on its performance. The main effects are discussed on COP net (the ratio of cooling capacity powered by low-grade heat to the low-grade heat consumption for the hybrid system) and COP mt (the ratio of cooling capacity powered by mechanical work to the mechanical work consumption for the hybrid system). Comparing with the normal two-stage absorption refrigeration system, theoretical results show that COP net could be improved up to about 55% when the refrigeration temperature is 7 °C. In addition, COP mt are more than 50% higher than that of the conventional transcritical refrigeration system. It is also found that both 45–55 °C low-grade heat and condensing heat could be used as actuating heat of the two-stage absorption refrigeration subsystem.