In this article, a three-objective optimization technique constructed on Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to increase the coefficient of performance (COP) and exergy efficiency (η) of a two-stage vapor compression cascade refrigeration system (CCRS) and decrease its total cost rate (C ̇_t). The model was built in MATLAB and the refrigerants thermo-physical properties were probed by using REFPROP. Since refrigerant mixtures with zeotropic behavior can improve the energy efficiency of the system, the binary refrigerant mixtures with variable mass fractions are used on both sides of CCRS. The cycle optimization is done based on 10 independent design variables. The design variables are refrigerant type and their mass fractions on the LTC and HTC of cycle, and four temperatures. While the cooling capacity and evaporator and condenser secondary flow conditions are kept fixed during the optimization process. The comparison of optimization results with the base case shows a 15.55% and 17.74% increase in COP and η, respectively. while the total cost rate (C ̇_t) shows a 6.07% reduction. Finally, the optimization process leads to a 24.47% and 13.1% reduction in the CCRS cycle total exergy destruction and maximum cycle temperature.

In this article, a three-objective optimization technique constructed on Non-dominated Sorting Genetic Algorithm (NSGA-II) is used to increase the coefficient of performance (COP) and exergy efficiency (η) of a two-stage vapor compression cascade refrigeration system (CCRS) and decrease its total cost rate (C ̇_t). The model was built in MATLAB and the refrigerants thermo-physical properties were probed by using REFPROP. Since refrigerant mixtures with zeotropic behavior can improve the energy efficiency of the system, the binary refrigerant mixtures with variable mass fractions are used on both sides of CCRS. The cycle optimization is done based on 10 independent design variables. The design variables are refrigerant type and their mass fractions on the LTC and HTC of cycle, and four temperatures. While the cooling capacity and evaporator and condenser secondary flow conditions are kept fixed during the optimization process. The comparison of optimization results with the base case shows a 15.55% and 17.74% increase in COP and η, respectively. while the total cost rate (C ̇_t) shows a 6.07% reduction. Finally, the optimization process leads to a 24.47% and 13.1% reduction in the CCRS cycle total exergy destruction and maximum cycle temperature.

Cascade Refrigeration Coefficient of Performance Exergy Efficiency Cost Analysis Three-Objective Optimization Mixed Refrigerant