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

Abstract

Keywords: AHP; Energy efficiency; Fuzzy Logic; Refrigerant; Sustainability; Type-2 fuzzy numbers

1. Introduction

The global energy consumption of refrigeration and air conditioning is approximately 20% [1]. For this reason, refrigeration systems, including heat pumps, pulsating heat pipes [24, 25], refrigerators, HVAC (heating, ventilation, and air conditioning), etc., have vital importance in our daily and business lives. They help cool dwelling places, business offices, and meat, vegetables, and fruit products [2, 3]. The main component of refrigeration systems is refrigerants, which are working fluids that change phase from gas to liquid regularly. In the literature, there are two different classifications of refrigerants. The first is related

to chemical compositions [4], and the second is about the progression of refrigerants from the past to now [5]. Refrigerants can be divided into five groups that are natural refrigerants, chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), refrigerant blends, and hydrofluorocarbons (HFCs) [3, 4] in terms of chemical compositions. The evolution of refrigerants from their invention to the future is shown in Figure 1 by dividing into four generations [5]. These are the first generation from 1830 to the 1930s, the second generation from 1931 to 1990s, the third generation from 1991 to 2010s, and the fourth generation from 2010 to now in terms of development of refrigerants in the long run. The first generation of refrigerants

*Corresponding author. *E-mail address: ertgrulayyildiz@ktu.edu.tr Published by Yıldız Technical University Press, İstanbul, Türkiye This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

J Sustain Const Mater Technol, Vol. 9, Issue. 1, pp. 45−59, March 2024

was used regardless of flammability, toxicity, and environmental impacts [1, 5]. In the second generation of refrigerants, the producer was taken into account the safety and durability of the refrigerants. HCFCs and CFCs are the refrigerant types commonly used in this generation. Thirdgeneration refrigerants were developed to protect the ozone layer due to the requirement of the Montreal Protocol [6]. This protocol restricts the HCFCs and CFCs to achieve low ozone depletion potential (ODP). The fourth-generation refrigerants evolved because of concerns about the growing global warming after the Kyoto Protocol. According to this Protocol, countries must restrict or prohibit using the HFCs and PFCs types of refrigerants [3, 5]. In developing fourth-generation refrigerants, refrigerants with low/ zero ODP, low global warming potential (GWP), and high efficiency have been developed and produced due to the increasing global warming concern. The European Union (EU) Commission has restricted using HFC refrigerants due to their high global warming potential [7]. Hydrofluoroolefin refrigerants (HFO) present low/zero ODP and extremely low GWP due to the absence of no chlorine and a brief atmospheric period [8]. Hence, HFOs have great potential to be used as the fourth-generation refrigerant [9]. Along with developing or selecting the more eco-friendly refrigerant, other essential properties, such as sustainability, thermodynamic properties, flammability, etc., play an essential role in choosing a new refrigerant [10, 11]. Thermodynamic properties must be a high coefficient of performance (COP), thermal conductivity, vapor density, latent vaporization heat, low liquid viscosity, critical pressure, and liquid density to determine the suitable refrigerants in refrigeration systems. In selecting the appropriate refrigerant, it must have low/zero ODP [12], [13], low GWP [2, 3, 6, 10–12, 14–19], flammability [2, 3, 6, 10–12, 14, 15, 17–19], low secondary environmental impacts [2] and toxicity [2, 3, 10–12, 14, 15, 17, 19] in terms of environmental impacts. McLinden et al. [1] and Kaseeian et al. [2] prepared the required criteria

list, including minimal flammability, low toxicity, GWP and secondary environmental impacts, zero ODP, long operational life, material compatibility, maximized recyclable content, maximum energy efficiency, reasonable cost, and stable for the life of the system for requirements for new refrigerants. In the study of Vuppaladadiyam et al. [3], they reviewed the development of refrigerants and their undesired environmental impacts. They mentioned ideal fourth-generation refrigerant properties such as zero ODP, low GWP, high efficiency, no toxicity, and no flammability. Abas et al. [4] researched the optimal refrigerant for low GWP and no ODP for the solar water heating system. Mohanraj and Abraham’s review study [5] analyzed eco-friendly refrigerants for automobile air conditioners regarding thermophysical, thermodynamic, and chemical characteristics. Another review study by Bolaji and Huan [6] suggested natural refrigerants reduce the environmental impacts of the HFC, CFC, and HCFC refrigerants. Meng et al. [7] investigated the conditioning performance of a suggested R1234yf/R134a refrigerant having low GWP, no ODP, and no flammability for automobile air conditioner systems. Their suggested refrigerant helps to reduce the GWP impacts and eliminate the ODP and flammability. Direk et al. [8] experimentally investigated the performance of alternative refrigerants, R444A and R152a, in automobile air conditioner systems to reduce environmental impacts such as GWP and ODP. They found that R152a refrigerant significantly enhances the refrigeration performance compared to the R134a and R444A. Table 1 summarizes the literature on refrigerant selection using different types of MCDM and machine learning methods. In the study of Poongavanam et al. [11], they found the optimum refrigerant for the automobile refrigeration system with the help of TOPSIS, MOORA, EDAS, and sensitivity analysis. Souayeh et al. [12] used MCDM methods like TOPSIS, EDAS, and MOORA to select environmentally friendly refrigerants in applying HVAC and renewable energy devices. Similarly, Ustaoğlu

J Sustain Const Mater Technol, Vol. 9, Issue. 1, pp. 45−59, March 2024

Table 1. Summary of literature related to refrigerant selection Author(s)

Methods

Refrigerant selection in the application of refrigeration systems for automobile

Eco-friend refrigerant selection in the application of HVAC and Renewable energy devices

Topsis, EDAS, Moora

Optimization of future refrigerants for domestic refrigerant system

Selection of best refrigerant in terms of Environmental, Exergy, Economic, and Energy for heat pump

Topsis

Refrigerant selection for vapor compression refrigeration cycle in terms of environmental, economic, safety, and cost

et al. [20], for the vapor compression refrigeration cycle, and Koundinya and Seshadri [21] for the heat pump, selected the best refrigerant by using Taguchi, TOPSIS, and ANOVA and TOPSIS, respectively. On the other hand, Devotta et al. [22] performed a classification study for refrigerants regarding flammability with the help of ANN and the Random forest model. Considering the sub-criteria mentioned above under main criteria such as thermodynamics, sustainability, and environmental, selecting the most suitable refrigerant for refrigeration systems is very challenging. At the same time, suitable refrigerants are determined using theoretical, simulation, and experimental methods, which take a long time and cost a lot of money [11]. Multi-criteria decision-making (MCDM) methods present a comprehensive selection and evaluation process considering different criteria for selecting eco-friendly, more efficient, and sustainable refrigerants. The need to consider many factors in the selection of refrigerants has made it possible to use the MCDM approach. Furthermore, it would be reasonable to use fuzzy logic to express the values that cannot be obtained numerically in the process and to model the uncertainty best. Based on all these important considerations, the problem of refrigerant selection is discussed in this paper, and a fuzzy MCDM approach is proposed to solve this problem. Besides, type-2 fuzzy sets, an extended version of fuzzy sets, handle uncertainty best and obtain results closest to the real world. As a result of detailed literature research, elements that should be considered in selecting the best refrigerant are determined, and then a prioritization analysis is performed to reveal the relative importance of these factors. For this purpose, the Analytic Hierarchy Process (AHP) method is utilized, which is an MCDM approach based on the pairwise comparison principle and is the most frequently used in the literature. As a result of this proposed fuzzy-based MCDM analysis, factors that should be considered first in selecting a refrigerant system have been successfully determined. This study presents an innovative approach to addressing the complicated problem of refrigerant selection in cooling systems. This study introduces the interval type-2

trapezoidal fuzzy AHP for evaluating and ranking these criteria and factors. Previous research has emphasized the significance of considering various criteria and characteristics during selection. This study provides a more accurate representation of the decision-making process by employing type-2 fuzzy sets, which can effectively deal with uncertainty and model real-world scenarios. In addition, using the AHP method in conjunction with fuzzy logic is a novel combination that provides a systematic and exhaustive evaluation of refrigerant selection criteria. The results of this study not only list the factors that influence the selection of a sustainable refrigerant but also emphasize the importance of energy efficiency in this decision-making process. This research contributes to the field by introducing a novel methodology that improves the comprehension and application of sustainable refrigerant selection in cooling systems. Section 2 presents the adopted methodology in detail. Section 3 includes the application for the refrigerant selection problem. While discussing the study’s findings in Section 4 in Section 5, the study concludes with an evaluation of the results.

2. The Proposed Methodology

MCDM approaches are valuable for handling comparison problems with diverse measurement units [24]. They enable the evaluation of qualitative and quantitative factors simultaneously, aiding in real-life problem-solving [25]. MCDM-based methodologies are widely used in different areas [26]. Concrete and abstract criteria pose challenges in decision-making, mainly when more abstract criteria exist. Fuzzy sets help represent data accurately and handle uncertainties and ambiguities [25]. The utilization of MCDM approaches in decision-making offers various benefits. Firstly, MCDM enables a structured and systematic evaluation and comparison of options based on multiple criteria. This approach provides a comprehensive framework for considering various factors and facilitates a thorough analysis of available alternatives. Secondly, MCDM allows for the explicit

J Sustain Const Mater Technol, Vol. 9, Issue. 1, pp. 45−59, March 2024

consideration and weighing of the relative importance of criteria. Decision makers can assign weights to each criterion, ensuring the final decision reflects their priorities and values. Thirdly, MCDM approaches provide a clear and transparent decision rationale, fostering stakeholder support and consensus. The systematic nature of MCDM allows decision-makers to justify their choices based on a well-defined evaluation process. Furthermore, MCDM facilitates the identification and evaluation of trade-offs between different criteria. In situations where it is impossible to optimize all criteria simultaneously, MCDM assists in understanding the compromises and trade-offs associated with other alternatives. Lastly, MCDM approaches enable the integration and synthesizing information from multiple sources, which is particularly beneficial in complex decision-making situations. By considering diverse perspectives and combining information from various stakeholders, MCDM enhances the quality and effectiveness of the decision-making process. Fuzzy MCDM methods utilize fuzzy logic to handle uncertain or imprecise information [27]. They are beneficial when evaluating multiple options with conflicting criteria and uncertain or inaccurate information. Fuzzy sets and membership functions allow flexible representation of criteria uncertainty and subjectivity. They are valuable in decision-making where classical methods fall short [28]. Fuzzy logic, suggested by Zadeh [29, 30] deals with approximate reasoning, representing uncertainty and fuzziness in real-world situations flexibly [31, 32]. It utilizes fuzzy sets with membership degrees from 0 to 1 [33]. Fuzzy logic finds applications in control systems, pattern recognition, and natural language processing, effectively handling uncertain or imprecise information. Fuzzy set theory quantifies linguistic variables to compare alternatives. Type-2 fuzzy sets take more uncertainty and are suitable for decision-making with subjective judgments [34]. They provide a more realistic conversion of information from decision-makers into numerical values [35]. Type-2 fuzzy sets offer more flexibility and expressiveness than type-1 fuzzy sets [36], thus enhancing the decision-making process in several ways. Firstly, type-2 fuzzy sets allow for the modeling of higher levels of uncertainty by accommodating varying degrees of fuzziness within the membership function [37]. This capability is particularly relevant in complex decision-making scenarios, such as refrigerant selection, where multiple factors and criteria may exhibit different levels of uncertainty. Secondly, type-2 fuzzy sets represent uncertainty in the membership function and the uncertainty bounds associated with the membership grades [38]. This additional layer of uncertainty modeling provides decision-makers with more nuanced information about each criterion’s assessment’s reliability and confidence level, contributing to a more robust decision-making process. Furthermore, type-2 fuzzy sets offer flexibility in handling conflicting and ambiguous information, which is common in real-world decision-making contexts [39].

Type-2 fuzzy sets are used in this study based on their ability to address these specific challenges inherent in refrigerant selection [40]. Adopting type-2 fuzzy sets in this study facilitated a more comprehensive analysis of refrigerant selection criteria, enhancing the decision-making process compared to traditional type-1 fuzzy sets [41]. AHP is used to model MCDM problems with tangible and intangible criteria in a hierarchical structure [42] and developed by Saaty [43]. It calculates criteria weights through pairwise comparisons based on expert judgments. AHP is chosen due to its ability to handle qualitative and quantitative criteria, ensure consistency of judgments, and create a hierarchical structure. For the sustainable refrigerant selection problem, AHP is suitable because of its hierarchical structure [44] and linguistic evaluation of decision-makers. Type-2 fuzzy AHP is a promising approach that addresses high uncertainty from experts’ subjective assessment of membership degrees. MCDM approaches, AHP based on type-2 fuzzy sets, offer valuable tools to improve decision-making in complex situations, enhancing the quality and effectiveness of the process [45]. The method adopted to solve the refrigerant selection problem in the paper is expressed in detail in the following sub-sections.

3. Application

Applications for air conditioning and refrigeration have become more crucial in recent years due to global warming and high energy consumption. The significance of refrigerant selection in refrigeration systems is even more apparent when considering that cooling costs more than heating. Incorrect material choice and improper application result in issues that are difficult to resolve after the system is

operational and impose significant financial expenses on the user. In this situation, it is crucial to identify the criteria that affect the choice of the best refrigerant and to evaluate their significance methodically. This study aims to identify the requirements that should be considered for refrigeration and the most essential criteria in the evaluation process. For this aim, AHP, a well-known MCDM technique, is used by consulting experts in an interval type-2 trapezoidal fuzzy environment. Table 2 indicates expert proficiencies as the decision makers in refrigerant selection. The criteria are determined and weighted using expert opinions and available literature about the problem. When selecting experts, their skills and expertise are considered in this field. In choosing the experts to be consulted for this study on sustainable refrigerant selection, several key aspects were crucial to ensure the credibility and relevance of their contributions. First, priority is given to experts with deep expertise in refrigerant selection, sustainable practices, environmental management, and multi-criteria decision making methodologies. Their reputation, experience, and publication record are thoroughly evaluated to ensure they have a track record of high-quality research and contributions to the field. It is critical to select experts who are available and accessible for consultation and have strong communication skills to facilitate fruitful collaboration. Careful consideration is also given to identifying and managing potential conflicts of interest to protect the integrity of the study. By carefully considering these factors, a panel of experts is assembled, ready to provide valuable insight and expertise and to validate the findings and conclusions of the study with the expert group. E-1, E-2, E-3, E-4, E-5, E-6, and E-7 are the abbreviations for seven decision-makers with a deep knowledge of the HVAC sector. Because each expert has a different experience level, as shown in Table 3, their weights (reputations) also differ. Although all experts are mechanical engineers, five have academic careers, and the remaining two work as engineers in the HVAC sector.

2. Table 5 shows the matrices for the main criteria created

by experts. The consistency of the experts’ opinions is examined; if the pairwise comparisons are inconsistent, the experts are asked to reassess. In response to inconsistencies identified by the CR, the revision process involves reviewing

expert judgments and seeking consensus among experts. Re-evaluation of the relevance of the criteria further resolves inconsistencies and ensures robust decision outcomes. The weight calculation phase is initiated when the CR is less than 0.1 [57], indicating that the relevant matrix is consistent. The CR values for the pairwise comparisons of the main criteria for each expert are shown in Table 6. All matrices are determined to be consistent, as shown in Table 6.

J Sustain Const Mater Technol, Vol. 9, Issue. 1, pp. 45−59, March 2024

Table 8. Pairwise comparison of Expert-1 for sub-criteria of Environmental Sub-Criteria

Table 9. Pairwise comparison of Expert-3 for sub-criteria of Thermodynamic Sub-Criteria

Table 10. Consistency ratios for sub-criteria evaluations Expert

The weights of the main criteria are computed by using the interval type-2 trapezoidal fuzzy AHP steps once all pairwise comparison matrices have been consistently obtained. Table 7 presents the main criteria weights based on expert opinions. It can be noticed that Environmental (C1) is the most essential criterion for three experts (E-2, E-4, and E-6), and Thermodynamic (C2) is the most critical criterion for two experts (E-3 and E-5). Lastly, Sustainability (C3) is evaluated as the most crucial main criterion by two experts (E-1 and E-7). From this point of view, it can be said that experts have different opinions according to their knowledge and experience. Thus, making a more inclusive evaluation is possible by taking experts’ opinions with other ideas. The same experts are consulted to assess the second level of the criteria. For this reason, expert opinions are

used to build pairwise comparison matrices for the sub-criteria. For instance, Table 8 displays the pairwise comparison matrix constructed using Expert-1’s assessments of the sub-criteria under the Environmental main criterion. Additionally, Table 9 shows the pairwise comparison matrix constructed using Expert-3’s judgments of the sub-criteria for the Thermodynamic main criterion. The pairwise comparison matrices for the sub-criteria are first examined for consistency, and each is consistent. Table 10 displays the CR value for each matrix. The criteria weights for the second level are computed by reapplying the interval type-2 trapezoidal fuzzy AHP steps after determining the consistency of all matrices. Table 11 lists the sub-criteria weights considering each expert. The priority scores for each sub-criteria are determined, as shown in Figure 4, by multiplying the aggregated

J Sustain Const Mater Technol, Vol. 9, Issue. 1, pp. 45−59, March 2024

Table 11. The weights of sub-criteria for each expert Sub-Criteria/Expert

Figure 4. Sub-criteria weights. sub-criteria weights by the corresponding main criterion weights. The criteria weights of 7 experts are compiled, considering the weights (reputations) of experts. Energy efficiency (C34) is the most significant criterion among all sub-criteria. This outcome is expected because sustainability (C3) is the main criterion that matters the most to the two experts with the highest

reputations. With final weights of 0.092 and 0.081, respectively, Toxicity (C15) and Global warming potential (C12) rank second and third. This rating illustrates that choosing a refrigerant is influenced by the Sustainability factor and the sub-criteria stated below, and decision-makers should consider this. The fourth ranking, Operation Life (C31), shows the significance of choosing a refrigerant with a

J Sustain Const Mater Technol, Vol. 9, Issue. 1, pp. 45−59, March 2024

Figure 5. The weight changes of sub-criteria show that the proposed method is sensitive to small changes. long lifetime. Secondary environmental effect (C13), one of the sub-criteria of the Environmental main criteria, is the least significant criterion. It implies that focusing on more essential criteria in the selection process would be more acceptable and that this criterion is not very important when choosing refrigerants.

4. Discussion

This study initially identified criteria based on a comprehensive literature review and consultation with experts to ensure relevance to the decision-making context. Subsequently, the interval type-2 trapezoidal fuzzy AHP methodology is employed to assign weights to these criteria. Furthermore, to ensure the accuracy and validity of the results, the obtained criteria weights are verified through interviews with experts in the field. This approach ensured that the selected criteria accurately reflected the decision-making context. To reduce the impact of individual biases and limited perspectives, this study uses a variety of strategies, including diverse expert panels, rigorous validation processes, sensitivity analyses, and transparency in decision-making, ensuring that the final decision reflects a consensus-based, comprehensive assessment of all relevant factors. As mentioned in the introduction, the energy consumption of refrigeration systems in domestic and industrial usage is approximately 20% worldwide [1]. Refrigeration and air conditioning are no longer considered luxuries because they are indispensable for food, health, and financial services but also for human comfort [58]. As a result of the ever-increasing number of refrigerators in all areas of our lives, there is a dramatic increase in energy demand and emissions, a growing contribution to global warming. Therefore, the energy efficiency of refrigerator systems is critical to postpone the global warming potential. Energy efficiency refers to the energy cost required to achieve a specific goal. The review study of McLinden et al. [2], it is indicated that the main aim is to achieve refrigeration

J Sustain Const Mater Technol, Vol. 9, Issue. 1, pp. 45−59, March 2024

with new refrigerants while maximizing energy efficiency by minimizing environmental impact. In the study of Poongavanam et al. [9], they selected the best refrigerant by considering thermal properties, ecological impacts, and cost. When examined their selected R430A, which is the best refrigerant according to their criteria, it is seen that the thermodynamic properties of the refrigerant, i.e., its efficiency, are prioritized over environmental impacts such as GWP and ODP in the selection of refrigerant. This situation in Poongavanam et al. [9] study supports energy efficiency as the most influential criterion identified in the present study. Prabakaran et al. [10] selected the refrigerant among 14 alternatives by considering many criteria such as power construction, coefficient of performance, total environmental impacts, thermodynamic properties, cost, and lifetime. While R444B refrigerant was the best alternative regarding coefficient of performance, total environmental impacts, lifetime, and cost, R290 refrigerant was the best alternative regarding refrigerant charge and discharge pressure. While the selection of R444B refrigerant supports the results of the present study, while that of R290 refrigerant does not. The Natural Resources Defense Council (NRDC) proposed strategies for enhancing air conditioners (ACs) in India to rapidly expand the use of energy-efficient and low-GWP refrigerants [59]. As the studies above address, energy efficiency is the most valuable criterion in the selection of refrigerants. Studies on increasing the refrigerant energy efficiency of refrigerants also show the importance of energy efficiency in the selection of refrigerants, which is also emphasized in this study.

5. Conclusion

Refrigeration systems are crucial for home and commercial applications, including ice production, air conditioning, and air separation. Due to the wide applications of refrigeration systems in daily life, refrigerants are not a significant threat inside the system, but their leakage and discharge to the environment pose a significant threat to the environment by causing global warming and ozone depletion. On the other hand, the refrigeration system’s performance is primarily based on the refrigerant performance, so developing highly efficient and environmentally friendly refrigerant is indispensable. The refrigerant choice for the refrigeration system is critical because it affects operating conditions and cycle performance. This study analyzes the refrigerant selection criteria by considering sustainability, environmental, and thermodynamics dimensions under ambiguous conditions. The interval type-2 trapezoidal fuzzy AHP approach, which allows multiple criteria to be evaluated simultaneously, solves the factors of the refrigerant selection problem. Three main criteria and 17 sub-criteria are determined in this study. The adoption of interval type-2 trapezoidal fuzzy AHP approach in the refrigerant selection is presented as a methodology to reveal the more suitable criteria. In this way, MCDM analysis is then

carried out. The most significant criterion in the proposed multi-criteria analysis is “energy efficiency,” the least considerable criterion is “secondary environmental impact.” Although “vapor pressure,” “latent heat of vaporization,” and “material compatibility” are determined as other significant criteria, energy efficiency is almost twice as substantial as the second-most important criterion. Refrigerant and refrigeration systems developers can use the proposed methodology to improve their system performance by considering determined sub-criteria such as energy efficiency, GWP, vapor pressure, etc. The main contributions of this study are as follows: (1) The Refrigerant selection problem is handled as an MCDM problem; (2) The most significant refrigerant selection criteria are identified and categorized in a hierarchical structure; (3) These main criteria and their sub-criteria are evaluated under uncertain conditions, and the weight of each criterion is determined; and (4) To the best of authors’ knowledge, this study presents the first decision-making model for refrigerant selection problem. These contributions demonstrate how this fuzzy decision-making model incorporates several innovative elements in method and application domains, ensuring the study has novel characteristics for the pertinent literature. Limitations of this study include potential biases in expert opinions, the complexity of integrating interval type-2 trapezoidal fuzzy AHP into decision-making processes, and the need for further validation of results in different contexts. Future research could use larger and more diverse expert panels to address these limitations, increase transparency in the decision-making process, and conduct comparative analyses to assess the robustness of this model. Additional criteria can be added, or the fuzzy AHP methodology can be developed better to capture the uncertainties and complexities in the decision-making process. The proposed interval type-2 trapezoidal fuzzy AHP approach can efficiently handle larger and more complex decision problems by establishing hierarchical structures. Such problems can be decomposed into smaller, more manageable sub-problems, allowing faster and more scalable solutions. This hierarchical decomposition strategy makes it possible to effectively navigate the complexities of decision-making while optimizing the use of computational resources. Future recommendations include using various MCDM methods to verify the findings or probabilistic approaches to look at the issue from a probabilistic viewpoint. Different MCDM methods can extend this framework with different fuzzy environments, and heating and refrigeration materials can be evaluated for various cases. Integrating interval type-2 trapezoidal fuzzy AHP with other decision-making tools or software provides numerous advantages. For example, it combines different methodologies, enabling the selection of the most suitable refrigerant among alternatives. Furthermore, the integration allows the creation of various scenarios to validate results while taking advantage of advanced visualization and reporting tools for advanced analysis.

J Sustain Const Mater Technol, Vol. 9, Issue. 1, pp. 45−59, March 2024

Conflict Of Interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript.

Consent To Publish

The author confirms that the final version of the manuscript has been reviewed, approved, and consented to for publication.

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

Share and Cite