Determination with data mining approach of thermodynamic properties of R471A as new HFO refrigerant.

The use of refrigerants used in most refrigeration systems in use today has been or will be banned soon. Therefore, it is extremely important to search for alternative refrigerants. In this study, the thermodynamic properties of R471A, a very new refrigerant with low global warming potential, were estimated using a data mining approach. The results obtained from the data mining were compared with the actual data obtained from the REFROP program. The correlation coefficients (R2) for the saturation liquid enthalpy (hf), saturation vapor enthalpy (hg), saturation fluid entropy (sf), saturation vapor entropy (sg), superheated vapor enthalpy (hsh), superheated vapor entropy (ssh) have been found 0.9999, 0.9997, 1, 0.9884, 0.9992, and 0.9601, respectively. In addition, compared the hf, hg, sf, sg, hsh, and ssh values with the values calculated using the data mining and the actual values, and the percent error values. It was seen that the values calculated from the obtained formulations and the actual values are in good agreement. This study shows that the data mining approach can be successfully applied to determine enthalpy and entropy values for any temperature and pressure of R471A refrigerant. Thus, the determination of the thermodynamic properties of refrigerants and the simulation of vapor compression refrigeration systems become quite easy. [ABSTRACT FROM AUTHOR]