Performance analysis and enhancement of cascade refrigeration systems through multi-objective grey wolf optimization algorithm

• Various optimization algorithms for cascade refrigeration systems are compared • Thermodynamic performance is optimized using a multi-objective grey wolf optimizer. • Optimal evaporation temperature of −70℃ minimizes exergy destruction by 58.83% • Exergy destruction is reduced by 50.83% in the optimized system. This study employs multi-objective optimization algorithms to optimize cascade refrigeration systems (CRS) operating at ultra-low temperatures. By comparing the multi-objective grey wolf optimizer (MOGWO), the multi-objective heat exchange algorithm, and the non-dominated sorting genetic algorithm II algorithm, and evaluating them based on various algorithmic metrics, MOGWO is identified as the optimal choice. Subsequently, the study systematically evaluates the capital costs of system components and their impact on the CRS. The objective is to minimize the total economic cost, optimize the inter-stage capacity ratio, and reduce the total exergy destruction while considering the lowest single design parameter and overall system unity. The results reveal that evaporation temperature and intermediate temperature significantly influence system component costs, total exergy destruction, inter-stage capacity ratio, and overall economic expenditure, in contrast to condensing temperature, the cascade temperature difference of the cascade heat exchanger, and the superheat degree of the high-temperature stage. Ultimately, the MOGWO algorithm achieves a 50.83% reduction in exergy destruction (65.26 W to 32.08 W), an inter-stage capacity ratio of 0.23, and annual capital costs of $4015.9. The findings of this study not only enhance the efficiency and economic performance of CRS but also provide valuable references for future design and optimization efforts.