Thermally regenerative electrochemical refrigerators decision-making process and multi-objective optimization

Thermally regenerative electrochemical refrigerators have drawn tremendous attention due to their reliability, quietness, eco-friendliness, non-emission of CFC gases, and potential for various applications. This work was conducted to model a thermally regenerative electrochemical refrigerator based on finite-time analysis. The proposed system is analyzed in four diverse temperature ranges, and all losses are considered for more accurate modeling by Python. Moreover, sensitivity analysis was performed for these temperature ranges. The thermodynamic analysis of these states has been demonstrated in detail for the first time. A multi-objective genetic algorithm in MATLAB software was used to achieve the maximum cooling capacity and COP and minimum input power. The optimal values, including system temperature, cell materials, and parameters related to heat exchangers and output results of the genetic algorithm, were prioritized using the weighted aggregated sum product assessment method. The results revealed that in the temperature ranges, 263K < TL < 283K, 297K < TH < 301K, which are the temperature ranges of cold and hot cells, respectively, the system indicated better performance. Meanwhile, selecting materials with higher specific charging/discharging capacity, isothermal coefficient, and smaller specific heat and internal resistance improves the system's performance. The optimum values of cooling capacity and system coefficient of performance were acquired as 367.01 W and 0.7301. This paper is expected to pave the way for the lab-scale design of thermally regenerative electrochemical refrigerators.