Modeling and multi-objective optimization of an R450A vapor compression refrigeration system

The main objective of this work is to comprehensively investigate R450A behavior in refrigeration systems and subsequently optimize the main operating variables for the first time to reach the maximum performance. For this purpose, a hybrid multi-objective optimization model coupling response surface method and non-dominated sorted genetic algorithm II is established. The regression analysis results reveal a good agreement of experimental data samples with the quadratic polynomial models with a coefficient of determination exceeding 0.97. The optimum results for the first scenario indicate that the reduction in the motor-compressor electrical power consumption and discharge temperature is 18.39% and 53.51%, respectively, and percentage of growth in the refrigerant mass flow rate is 215.57% when the middle evaporator temperature, middle condenser temperature, superheating degree, and subcooling degree change from -14.95 degrees C to 8.71 degrees C, 31.28 degrees C to 24.50 degrees C, 13.12 K to 10.49K, and 15.65K to 15.66 K, respectively. (C) 2019 Elsevier Ltd and IIR. All rights reserved.