Performance evaluation of an auto-cascade refrigeration system using grey correlation theory and response surface methodology

Abstract

This article focuses on evaluating and analyzing the performance of an autocascade refrigeration system with the zeotropic mixture of R600a/R1150/R14 refrigerants using grey correlation theory and response surface methodology (RSM). The correlation degree among various factors is analyzed using grey correlation theory. The interactive influences that have significant impacts on the six output parameters are illustrated, and a regression model is further validated by five confirmation parameters. The results show that the vapor quality of the evaporator outlet is the greatest factor that affecting the refrigerating capacity and coefficient of performance (COP). However, the evaporation temperature has the greatest effect on compressor’s discharge temperature, pressure ratio, exergy destruction rates, and exergy efficiency. Lower vapor quality of the condenser outlet and condensation temperature can improve the performances of refrigerating capacity, COP, compressor’s discharge temperature, compression ratio, total exergy destruction rate, and exergy efficiency based on the RSM results. The random five groups of validation parameters indicate that the errors between the predicted modeling and calculation results are all less than 5%, demonstrating that the RSM model has good predictability. The results achieved in this work will assist in the optimizing analysis and the application of cryogenic refrigerators.