Multi-objective optimization analysis of combined heating and cooling transcritical CO2 system integrated with mechanical subcooling utilizing hydrocarbon mixture based on machine learning

To meet the simultaneous needs of high temperature disinfection and freezing in the field of food processing, a new concept of combined heating and cooling transcritical CO2 system integrated with dedicated mechanical subcooling utilizing hydrocarbon mixture is proposed. The system performance in terms of thermodynamics, economy and environment is studied and compared with the baseline combined heating and cooling transcritical CO2 system and four traditional combined heating and cooling solutions, considering the influence of temperature glide and the heat transfer deterioration. The new proposed system is further optimized by using the machine learning method of artificial neural network and non-dominated sorting genetic algorithm. Multi-objective optimization is conducted considering the objective function of energy efficiency, initial capital cost and life cycle carbon emissions of the new system, to obtain the optimum components and concentration ratio of the hydrocarbon mixture. The results indicate the thermodynamic performance and environmental benefits of subcooling subsystem with hydrocarbon mixture are better than those of the pure system. In contrast to that using pure R290 and R601, the coefficient of performance is enhanced by 8.20 % and 8.13 % and the life cycle carbon emission is reduced by 8.54 % and 9.31 %, respectively, when R290/R601 (50/50) is used. However, the initial capital cost is 9.25 % and 10.23 % higher than that of pure R290 and R601, respectively. Finally, the hydrocarbon mixture corresponding to the optimal design point is R1270/R601a (53/47), the corresponding discharge pressure is 12.86 MPa, and the subcooling degree is 37.50 °C. This study can provide a theoretical reference for the application of CO2 refrigeration and heat pump technology.