Evaluating the factors influencing the sustainable refrigerant selection by fuzzy decision making approach

Abstract

Considering that cooling in cooling systems is more costly than heating, the importance of refrigerant selection in cooling systems is even more obvious. Due to the complexity of the refrigerant selection problem, a multi-criteria decision approach must be used to implement a thorough and organized evaluation of the factors. The purpose of this study is to evaluate the criteria to be considered when choosing refrigerants using the interval type-2 trapezoidal fuzzy Analytic Hierarchy Process (AHP). As a result, the most important and least crucial refrigerant selection criteria are determined by calculating the weights and obtaining the ranking of the requirements. In this way, the refrigerant selection criteria are prioritized, and the most crucial factor in refrigerant selection has emerged as energy efficiency. In light of the results, it has become clear that it is now essential for everyone in the world to use environ- mentally friendly, highly effective refrigerants.

Keywords

• AHP
• Energy efficiency
• Fuzzy Logic
• Refrigerant
• Sustainability
• Type-2 fuzzy numbers

References

1. McLinden, M. O., & Huber, M. L. (2020). (R)Evolution of refrigerants. J Chem Eng Data, 65(9), 4176–4193. [CrossRef]
2. Han, X., Wang, X., Zheng, H.,Xu, X., & Chen, G. (2016). Review of the development of pulsating heat pipe for heat dissipation. Renew Sustain Energy Rev, 59, 692–709. [CrossRef]
3. Der, O., Alqahtani, A. A., Marengo, M., & Bertola, V. (2021). Characterization of polypropylene pulsating heat stripes: Effects of orientation, heat transfer fluid, and loop geometry. Appl Therm Eng, 184, 116304. [CrossRef]
4. McLinden, M. O., Seeton, C. J., & Pearson, A. (2020). New refrigerants and system configurations for vapor-compression refrigeration. Science, 370(6518), 791–796. [CrossRef]
5. Vuppaladadiyam, A. K., Antunes, E., Vuppaladadiyam, S. S. V., Baig, Z. T., Subiantoro, A., Lei, G., Leu, S. Y., Sarmah, A. K., Duan, H. (2022). Progress in the development and use of refrigerants and unintended environmental consequences. Sci Total Environ, 823, 153670. [CrossRef]
6. Sánchez, D., Cabello, R., Llopis, R., Arauzo, I., Catalán-Gil, J., & Torrella, E. (2017). Energy performance evaluation of R1234yf, R1234ze(E), R600a, R290 and R152a as low-GWP R134a alternatives. Int J Refrig, 74, 267–280. [CrossRef]
7. Calm, J. M. (2008). The next generation of refrigerants - historical review, considerations, and outlook. Int J Refrig, 31(7), 1123–1133. [CrossRef]
8. Abas, N., Nawaz, R., & Khan, N. (2015). Parametric quantification of low GWP refrigerant for thermosyphon driven solar water heating system. Procedia Comput Sci, 52(1), 804–811. [CrossRef]

9. European Commission. (2016). Progress of the HFC Phase Down. http://ec.europa.eu/clima/policies/f-gas/docs/phase-down_progress_en.pdf
10. Wu, X., Dang, C., Xu, S., & Hihara, E. (2019). State of the art on the flammability of hydrofluoroolefin (HFO) refrigerants. Int J Refrig, 108, 209–223. [CrossRef]
11. Mota-Babiloni, A., Makhnatch, P., & Khodabandeh, R. (2017). Recent investigations in HFCs substitution with lower GWP synthetic alternatives: Focus on energetic performance and environmental impact. Int J Refrig, 82, 288–301. [CrossRef]
12. Mohanraj, M., & Abraham, J. D. A. P. (2022). Environment friendly refrigerant options for automobile air conditioners: a review. J Therm Anal Calorim, 147(1), 47–72. [CrossRef]
13. Poongavanam, G., Sivalingam, V., Prabakaran, R., Salman, M., & Kim, S. C. (2021). Selection of the best refrigerant for replacing R134a in automobile air conditioning system using different MCDM methods: A comparative study. Case Stud Therm Eng, 27, 101344. [CrossRef]
14. Souayeh, B., Bhattacharyya, S., Hdhiri, N., & Alam, M. W. (2022). Selection of best suitable eco-friendly refrigerants for HVAC sector and renewable energy devices. Sustain, 14(18), 11663. [CrossRef]
15. Tripathi, A. K., Dubey, S., Pandey, V. K., & Tiwari, S. K. (2015, November 26-28). Selection of refrigerant for air conditioning system using MCDM-TOPSIS approach. Proc of 3rd International Conference on Industrial Engineering (ICIE-2015). SVNIT, Surat, India.
16. Devecioğlu, A. G., & Oruç, V. (2015). Characteristics of some new generation refrigerants with low GWP. Energy Procedia, 75, 1452–1457. [CrossRef]
17. Direk, M., Mert, M. S., Soylu, E., & Yüksel, F. (2019). Experimental investigation of an automotive air conditioning system using R444A and R152a refrigerants as alternatives of R134a. J Mech Eng, 65(4), 212–218. [CrossRef]
18. Meng, Z., Zhang, H., Lei, M., Qin, Y., & Qiu, J. (2018). Performance of low GWP R1234yf/R134a mixture as a replacement for R134a in automotive air conditioning systems. Int J Heat Mass Transf, 116, 362–370. [CrossRef]
19. de Paula, C. H., Duarte, W. M., Rocha, T. T. M., de Oliveira, R. N., & Maia, A. A. T. (2020). Optimal design and environmental, energy and exergy analysis of a vapor compression refrigeration system using R290, R1234yf, and R744 as alternatives to replace R134a. Int J Refrig, 113, 10–20. [CrossRef]
20. Bolaji, B. O., & Huan, Z. (2013). Ozone depletion and global warming: Case for the use of natural refrigerant - A review. Renew Sustain Energy Rev, 18, 49–54. [CrossRef]
21. Kasaeian, A., Hosseini, S. M., Sheikhpour, M., Mahian, O., Yan, W. M., & Wongwises, S. (2018). Applications of eco-friendly refrigerants and nano refrigerants: A review. Renew Sustain Energy Rev, 96(C), 91–99. [CrossRef]
22. Ustaoglu, A., Kursuncu, B., Kaya, A. M., & Caliskan, H. (2022). Analysis of vapor compression refrigeration cycle using advanced exergetic approach with Taguchi and ANOVA optimization and refrigerant selection with enviroeconomic concerns by TOPSIS analysis. Sustain Energy Technol Assessments, 52, 102182. [CrossRef]
23. Koundinya, S., & Seshadri, S. (2022). Energy, exergy, environmental, and economic (4E) analysis and selection of best refrigerant using TOPSIS method for industrial heat pumps. Therm Sci Eng Prog, 36, 101491. [CrossRef]
24. Devotta, S., Chelani, A., & Vonsild, A. (2021). Prediction of flammability classifications of refrigerants by artificial neural network and random forest model. Int J Refrig, 131, 947–955. [CrossRef]
25. Prabakaran, R., Sivalingam, V., Kim, S. C., Ganesh Kumar, P., & Praveen Kumar, G. (2022). Future refrigerants with low global warming potential for residential air conditioning system: A thermodynamic analysis and MCDM tool optimization. Environ Sci Pollut Res 2022;29(52):7841478428. [CrossRef]
26. Çalış Boyacı, A., Şişman, A., & Sarıcaoğlu, K. (2021). Site selection for waste vegetable oil and waste battery collection boxes: A GIS-based hybrid hesitant fuzzy decision-making approach. Environ Sci Pollut Res, 28(14), 17431–17444. [CrossRef]
27. Alkan, N., & Kahraman, C. (2022). An intuitionistic fuzzy multi-distance based evaluation for aggregated dynamic decision analysis (IF-DEVADA): Its application to waste disposal location selection. Eng Appl Artif Intell, 111, 104809. [CrossRef]
28. Thakkar, N., & Paliwal, P. (2022). Quad-level MCDM framework to analyse technology combinations for sustainable micro-grid planning in uncertainty domain. Arab J Sci Eng, 48(5), 5829–5858. [CrossRef]
29. Ayyildiz, E. (2022). A novel pythagorean fuzzy multi-criteria decision-making methodology for e-scooter charging station location-selection. Transp Res Part D Transp Environ, 111, 103459. [CrossRef]
30. Kiliҫ, M., & Kaya, I. (2015). Investment project evaluation by a decision-making methodology based on type-2 fuzzy sets. Appl Soft Comput, 27, 399–410. [CrossRef]
31. Zadeh, L. A. (1965). Fuzzy sets. Inf Control, 8(3), 338–353. [CrossRef]
32. Ayyildiz, E., & Taskin Gumus, A. (2021). A novel distance learning ergonomics checklist and risk evaluation methodology: A case of Covid-19 pandemic. Hum Factors Ergon Manuf, 31(4), 397–411. [CrossRef]
33. Sinha, A. K., Narang, H. K., & Bhattacharya, S. (2020). A fuzzy logic approach for modelling and prediction of mechanical properties of hybrid abaca-reinforced polymer composite. J Brazilian Soc Mech Sci Eng, 42(6), 1–11. [CrossRef]
34. Ayyildiz, E. (2022). Fermatean fuzzy step-wise Weight Assessment Ratio Analysis (SWARA) and its application to prioritizing indicators to achieve sustainable development goal-7. Renew Energy, 193, 136–148. [CrossRef]
35. Görener, A., Ayvaz, B., Kuşakcı, A. O., & Altınok, E. (2017). A hybrid type-2 fuzzy based supplier performance evaluation methodology: The Turkish Airlines technic case. Appl Soft Comput J, 56, 436–445. [CrossRef]
36. Ecer, F. (2022). Multi-criteria decision making for green supplier selection using interval type-2 fuzzy AHP: A case study of a home appliance manufacturer. Oper Res, 22(1), 199–233. [CrossRef]
37. Coupland, S., & John, R. (2008). A fast geometric method for defuzzification of type-2 fuzzy sets. IEEE Trans Fuzzy Syst, 16(4), 929–941. [CrossRef]
38. Paik, B., & Mondal, S. K. (2021). Representation and application of fuzzy soft sets in type-2 environment. Complex Intell Syst, 7(3), 15971617. [CrossRef]
39. Mo, H., Wang, F. Y., Zhou, M., Li, R., & Xiao, Z. (2014). Footprint of uncertainty for type-2 fuzzy sets. Inf Sci Ny, 272, 96110. [CrossRef]
40. Yildiz, A., Ayyildiz, E., Taskin Gumus, A., & Ozkan, C. (2021). A framework to prioritize the public expectations from water treatment plants based on trapezoidal type-2 fuzzy Ahp Method. Environ Manage, 67(3), 439–448. [CrossRef]
41. Karasan, A., Ilbahar, E., & Kahraman, C. (2019). A novel pythagorean fuzzy AHP and its application to landfill site selection problem. Soft Comput, 23(21), 10953–10968. [CrossRef]
42. Ayyildiz, E., & Erdogan, M. (2023). A decision support mechanism in the determination of organic waste collection and recycling center location: A sample application for Turkiye. Appl Soft Comput, 147, 110752. [CrossRef]
43. Saaty, T. L. T. L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill. [CrossRef]
44. Li, L., & Tang, Y. (2023). A new method of human reliability analysis based on the correlation coefficient in the evidence theory and analytic hierarchy process method. Arab J Sci Eng, 48, 1071310726. [CrossRef]
45. Kusakci, S., Yilmaz, M. K., Kusakci, A. O., Sowe, S., & Nantembelele, F. A. (2022). Towards sustainable cities: A sustainability assessment study for metropolitan cities in Türkiye via a hybridized IT2F-AHP and COPRAS approach. Sustain Cities Soc, 78, 103655. [CrossRef]
46. Mendel, J. M., John, R. I., & Liu, F. (2006). Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst, 14(6), 808–821. [CrossRef]
47. Lee, L. W., & Chen, S. M. (2008). Fuzzy multiple attributes group decision-making based on the extension of TOPSIS method and interval type-2 fuzzy sets. Proceedings of the 7th International Conference on Machine Learning and Cybernetics. ICMLC, vol. 6, pp. 3260–3265. [CrossRef]
48. Chen, S. M., & Lee, L. W. (2010). Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Syst Appl, 37(4), 2790–2798. [CrossRef]
49. Ordu, M., & Der, O. (2023). Polymeric materials selection for flexible pulsating heat pipe manufacturing using a comparative hybrid MCDM approach. Polymers Basel, 15(13), 2933. [CrossRef]
50. Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew Sustain Energy Rev, 69, 596609. [CrossRef]
51. van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst, 11(1–3), 229–241. [CrossRef]
52. Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets Syst, 17(3), 233–247. [CrossRef]
53. Erdogan, M., & Kaya, I. (2019). Prioritizing failures by using hybrid multi criteria decision making methodology with a real case application. Sustain Cities Soc, 45, 117–130. [CrossRef]
54. Singh, V., Chandrasekaran, M., Samanta, S., Devarasiddappa, D., & Arunachalam, R. (2021). Sustainability assessment of gas metal arc welding process of AISI 201LN using AHP-TLBO integrated optimization methodology. J Brazilian Soc Mech Sci Eng, 43(2), 1–20. [CrossRef]
55. Ayyildiz, E., Erdogan, M., & Taskin Gumus, A. (2021). A pythagorean fuzzy number-based integration of AHP and WASPAS methods for refugee camp location selection problem: A real case study for İstanbul, Turkey. Neural Comput Appl, 33(22), 1575115768. [CrossRef]
56. Devotta, S. (2014). Refrigerant Selection – Global Environment, Thermodynamics, Safety and Efficiency. Indian Chem Eng, 56(3), 294–312. [CrossRef]
57. NRDC International. (2018). Cooling with less warming: Improving air conditioners in India. https://www.nrdc.org/sites/default/files/cooling-india-air-conditioners-market-profile-2018-fs.pdf