Enhancing CO2 Water-to-Water Heat Pump Performance through the Application of a Multi-Objective Evolutionary Algorithm

Due to growing concerns about the environmental impact of refrigerants, carbon dioxide (CO2) heat pumps have been increasingly evaluated as efficient alternatives for conventional heat pumps. Performance analyses of CO2 heat pump water heaters (HPWHs) have been the subject of many studies, but these are typically limited to parametric analyses of air-source HPWHs. The interrelated behavior of the supercritical and subcritical thermodynamic properties, component operation, and efficiency means that a parametric study cannot adequately capture the inherent nonlinearity. Therefore, this paper, for the first time, aims to perform a multi-objective optimization on CO2 water-sourced HPWH performance in order to minimize the total component costs, maximize gas cooler (GC) heating capacity, and maximize the coefficient of performance (COP) using two different optimization scenarios. The decision variables are defined as GC pressure (75 to 140 bar), evaporator temperature (−19.5 to 0.2°C), and GC outlet temperature for CO2 (16 to 36°C). The model performance is constrained by the practical ranges of the GC and evaporator inlet and outlet temperatures for water. A coupled simulation-optimization model through Python is developed using Engineering Equation Solver (EES) software and the non-dominated sorting genetic algorithm II (NSGA-II). The result of the optimal Pareto front showed that the optimal GC heating capacity changes from 19.2 to 56.7 kW, with a lowest cost of 7, 771 to a highest cost of 9,742, respectively. When the lower bound of the GC outlet temperature was set to 32°C, the Pareto front showed a maximum COP of 3.23, with a corresponding GC heating capacity of 44.36 kW.