Comparative Evaluation of MCDM Methods and Weighting Techniques for Refrigerant Selection in Air-Source Heat Pump Systems

Abstract

In this study, alternative refrigerants are evaluated under a total of sixteen different operating conditions covering both heating and cooling modes by using multi-criteria decision-making methods. Seven refrigerants are compared based on the coefficient of performance as the thermodynamic performance criterion, global warming potential as the environmental impact, and flammability classification as the safety criterion. Criterion weights are determined using the Shannon Entropy, CRITIC, and equal weighting methods, and the ranking procedures are carried out using the TOPSIS, VIKOR, and EDAS techniques. Based on the sixteen operating scenarios and the three evaluation criteria considered (COP, GWP, and flammability), R1234ze(E) is the most suitable alternative under all methods and operating conditions, achieving an average COP of approximately 3.28 together with a global warming potential of only 1 compared with 1430 for R134a, corresponding to a GWP reduction of more than 99%; R134a is consequently ranked among the least favorable options in most scenarios due to its high environmental impact, despite its competitive average COP of approximately 3.22. Despite its high global warming potential, R134a ranked among the top alternatives in some evaluation scenarios when entropy-based weighting and the TOPSIS method were applied. However, the EDAS method penalized R134a regardless of the weighting approach, indicating a higher sensitivity to unfavorable environmental indicators. The EDAS method reflects this trade-off more strongly than the other methods by effectively discriminating against alternatives with high GWP values, making it more suitable for identifying balanced working fluids within a candidate pool.

Keywords

• Multi-criteria decision making
• TOPSIS
• VIKOR
• EDAS
• Weighting methods
• Refrigerant selection
• Heat pump

Thanks

The license support for the software used in this study was obtained within the scope of a previously conducted project funded by the Kırıkkale University Scientific Research Projects Coordination Unit (BAP), Project No. 24-025. AI tools were used only as supporting tools for language refinement.

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