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

[Display omitted] •A Thermally regenerative electrochemical refrigerator is modeled by Python software.•Sensitivity analysis was performed to investigate the effect of key parameters on the system performance.•Four temperature ranges were defined for the system and its thermodynamics was examined in...

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Bibliographic Details
Published in:Energy conversion and management Vol. 252; p. 115060
Main Authors: Kamali, Hamed, Mehrpooya, Mehdi, Mousavi, Seyed Hamed, Ganjali, Mohammad Reza
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 15.01.2022
Elsevier Science Ltd
Subjects:
Algorithms
Coefficient of performance
cold
computer software
Cooling
Cooling capacity
Decision making
Electrochemistry
energy conversion
Genetic algorithms
Heat exchangers
Materials selection
Multi-Criteria decision-making
Multi-objective genetic algorithm
Multiple objective analysis
Optimization
Performance analysis
Python
Refrigerators
Sensitivity analysis
Specific heat
temperature
Thermally regenerative electrochemical refrigerator
Multi-objective genetic algorithm
Multi-Criteria decision-making
Thermally regenerative electrochemical refrigerator
Cooling capacity
Performance analysis
Coefficient of performance
ISSN:0196-8904, 1879-2227
Online Access:Get full text
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Summary:[Display omitted] •A Thermally regenerative electrochemical refrigerator is modeled by Python software.•Sensitivity analysis was performed to investigate the effect of key parameters on the system performance.•Four temperature ranges were defined for the system and its thermodynamics was examined in detail.•The system is optimized using genetic algorithm by MATLAB software.•The optimal points are prioritized by the WASPAS method. 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.
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ISSN:0196-8904
1879-2227
DOI:10.1016/j.enconman.2021.115060