Modeling and optimization of R-717 and R-134a ice thermal energy storage air conditioning systems using NSGA-II and MOPSO algorithms

In this study, an Ice Thermal Energy Storage (ITES) is integrated to an office building air-conditioning system as a full load storage system. The R-134a and R-717 refrigeration systems by exergy, economic and environmental considerations are modeled and investigated separately. Two multi-objective optimization algorithms: Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO), are engaged to obtain the optimal design parameters which lead to the optimal objective functions, exergy efficiency and total cost rate. The optimum point from Pareto frontier of each optimization algorithm is selected for both refrigerants by using TOPSIS decision making method, and energy demand of the new system is compared to the conventional one. The results indicated that by using NSGA-II and MOPSO algorithms for the R-717 refrigerant based system, the optimum design parameters lead to electricity consumption decrease by 11% and 8% more than R-134a refrigerant based system, respectively. Furthermore, the results showed that NSGA-II is more capable to achieve more effective solutions. At the optimum point of NSGA-II for R-717 based system, the exergy efficiency and total cost rate are 49% and 255USDh−1 respectively. While the annual CO2 emission is 11% lower than the R-134a based system.