Selection of Best Suitable Eco-Friendly Refrigerants for HVAC Sector and Renewable Energy Devices

Abstract

This paper investigates the selection of the best suitable eco-friendly organic and in-organic refrigerants for heating, ventilation, and air conditioning (HVAC) and renewable energy devices. Inorganic and organic refrigerants are used to cool renewable energy devices, such as solar cells, photovoltaics, and electronic devices. Owing to the renewable energy community’s importance, development in this area has rapidly improved over the past few years. R134a, R404, and R717 have become the most used refrigerants in the HVAC sector and supermarkets across the globe for both cooling and frozen food refrigeration. R134a and R404A have two significant drawbacks: (a) they do not attain optimal energy efficiency in many applications, and (b) they have a large global warming potential (GWP). Hence, optimization experiments were performed to select low global potential refrigerants for replacing R134a and R404A from the HVAC sector and supermarkets using multi-criteria making (MCDN) tools. The techniques used for investigation involved (i) the technique for order of preference by similarity to ideal solution (TOPSIS), (ii) evaluation based on distance from average solution (EDAS), and (iii) multi-objective optimization based on ratio analysis (MOORA). The assessment criteria of optimization involved (i) thermo–physical properties of refrigerants, (ii) environmental aspects of refrigerants, and (iii) economic status of refrigerants. Out of 27 refrigerants chosen for the study, R290 (PROPANE) aced the selection by all the three techniques, i.e., TOPSIS, EDAS, and MOORA, with assessment scores of 0.6056, 0.6761, and 0.466, respectively. R41 (FLUOROMETHANE) is the least preferred refrigerant by EDAS (assessment score—0.3967) and MOORA, while R407C is the least preferred by TOPSIS (assessment value—0.5123). The likelihood of making a bad refrigerant decision may be reduced by the effective evaluation of the MCDM analysis. In conclusion, the suggested MCDM technique provides a practical tool and systematic way for reducing the number of options and may be utilized to identify the ideal refrigerant.

1. Introduction

R404A is the most widely used refrigerant in supermarkets across the globe. It was first used as a substitute for ozone-depleting refrigerants such as CFCs (such as R12 and R502) in the mid-1990s, and more recently as a replacement for HCFCs (such as R22). It has become the most used refrigerant in supermarkets across the globe for both chilled and frozen food refrigeration. R404A has two major drawbacks: (a) it does not attain optimal energy efficiency in many applications, and (b) it has a large global warming potential (GWP). R404A has the highest GWP of all the regularly used refrigerants, at 3922. Switching away from R404A can assist the environment while also lowering operating expenses.

The highest permitted GWP is 150 degrees Celsius, according to European Union rules. Hence, appropriate refrigerant selection is an important task and plays a vital role in maintaining the performance of refrigeration units while maintaining a low GWP. To achieve a maximum coefficient of performance, a good refrigerant candidate must have the right thermodynamic characteristics, such as increased latent heat of fusion, thermal conductivity, specific heat, and reduced dynamic viscosity. Due to the aforementioned limits, selecting a refrigerant is more difficult and required for exact usage. Currently, refrigerants are classified using theoretical modelling and experimental approaches, a process that takes a long time and costs a lot of money. Modern developments in multi-criteria decision-making methodology (MCDM) techniques that offer optimal answers even in the presence of thermodynamic, environmental, and economic considerations should be used to solve these difficulties [1,2,3,4,5].

This section of the present article reviews and presents studies on the optimization of various thermal, photovoltaic, and industrial applications utilizing various MCDM methods [5,6,7,8,9,10,11,12,13,14,15]. Sivalingam et al. [16] evaluated and optimized automobile radiator performance by changing temperature, volume concentration and mass flow rate of MWCNT nanofluid for highest Nusselt number and lowest friction factor using the additive ratio assessment technique of MCDM methodology. Poongavanam et al. [17] employed TOPSIS, EDAS and MOORA techniques of optimization for replacing the R134a refrigerant with low GWP refrigerant and reported that R430A is the best refrigerant for automobile air conditioning. Vats et al. [18] optimize the process parameters and quantity of lubricating oil for maximum machining performance for turning operation of AISI 1040 steel. Junankar et al. [19] also carried out similar investigation for optimizing the quantity of nanofluid as the lubrication and the machining parameter for turning operation. Subasi and Erdem [20] combines multi-objective and MCDM techniques of optimization for heat transfer enhancement using nanofluids in tubes fitted with inserts. It was revealed that wire coils and hybrid nanofluids could be used for higher heat transfer performance with a penalty of the friction factor. Yang et al. [21] ranked the PCMs using the TOPSIS technique and reported that Ba(OH)₂·8H₂O was the best alternative for a given operating condition. Deepa et al. [22] carried out an optimization analysis for the best material selection for micro-fins in electronic components for higher heat transfer performance. The optimum materials for micro-fin fabrication were determined to be aluminum with a paint finish and aluminum. Jajimoggala et al. [23] employed MCDM for optimizing the hot extrusion parameters for AA6061 using the Analytic Hierarchy Process (AHP) and TOPSIS techniques. Vishwakarma et al. [24] optimized the parameters of Al6082 for thermal properties and generated mathematical models for predicting the thermal conductivity and thermal expansion coefficient. Ilangkumaran et al. [25] employed MCDM for risk analysis and health warnings for the foundry industry. Simsek et al. [26] reviewed the various articles related to solar power and classified them based on aim, motivation, and contribution by using MCDM methodology. Mukhametzyanov [27] compared the various techniques of MCDM to find out the weight of criteria and reveals that all MCDM tools are not able to give the results correctly. On the basis of this analysis, they proposed an EWM-Corr method which help in relocate the weight for each criterion. Kizielewicz et al. [28] also employed MCDM methods to determine the weight for the criteria and compared the results obtained by COMET, TOPSIS, and SPOTIS methods. El-Araby et al. [29] employed MCDM for facility location problems and compared the results obtained by four different methods of MCDM.

The decision-makers can use the versatile tools provided by MCDM techniques to help them map out the issue. Moreover, these tools can handle a variety of factors that are evaluated in various ways. Based on the aforesaid analysis, the current research provides an integrated MCDM technique to evaluate the optimal refrigerant for retail food refrigeration applications. The following are the main objectives of the present investigation:

I.

To identify the best refrigerant with low GWP and high performance out of the 27 refrigerants for retail food refrigeration in supermarkets based on their thermal properties using TOPSIS, EDAS, and MOORA techniques of MCDM methodology.

II.

To integrate technical, economic, social, environmental, and resource aspects in the assessment index method to select novel refrigerants in the retail food refrigeration industry.

Various methods of MCDM techniques exist in the literature. However, the authors of the present investigation opted for TOPSIS, EDAS, and MOORA techniques as they are simple, intuitive, and have a comprehensible concept. The approaches used here can quantify the relative performance for each option in a straightforward mathematical manner and disclose a scalar number that accounts for both the best and worst alternative capabilities. Furthermore, these approaches are simple, require relatively few mathematical computations, and have exceptional stability.

The present research work employed MCDM techniques for selecting the best refrigerant for refrigeration techniques in the food market. Previously, several articles were published where authors used MCDM for decision making. However, to the best of the authors’ knowledge, no articles are present in the open literature which employed MCDM for the selection of refrigerant for food market techniques.

The rest of this article is organized as follows. Section 2 and Section 3 presents research and optimization methodology, which describes the properties of selected refrigerants and explains our proposed methods of MCDM. Section 4 reports our results and discussion. Finally, Section 5 summarizes the paper in the conclusion and identifies future work.

2. Research Methodology

2.1. Features of Refrigerants

The thermophysical properties of new refrigerants played an important part in retrofitting/selection processes, as they determined the performance parameters of individual components and whole systems, such as cooling capacity, COP, power consumption, and heat transfer rates. The performance of a commercial refrigeration system is influenced by critical thermophysical parameters such as vapor pressure, vapor density, liquid density, liquid viscosity, vapor viscosity, and enthalpy of evaporation and condensation. When it comes to picking an alternative refrigerant, the safe working environment for humans is far more crucial than system performance. The key issues that must be addressed in the refrigerant selection are flammability, toxicity, asphyxiation, and physical risks. Finally, the new refrigerant’s cost will encourage system manufacturers and the general public to use it. In general, the thermodynamic characteristics, environmental circumstances (GWP, flammability, and toxicity), and economic factors all play a role in selecting the optimal refrigerant.

Table 1. Properties of refrigerants.

s. No	Refrigerants	Critical Temperature, °C	Critical Pressure, Bar	Saturated Pressure, Bar	Liquid Density, kg/m3	Vapor Density, kg/m3	Thermal Conductivity of Refrigerant, W/m-K	Viscosity of Liquid, µPa.s	Pressure at 60 Deg C	ODP	GWP	Toxicity and Flammability	Cost Per kg, USD
1	R134a	102	41	3.49	1278.J	17.1	0.089	250	16.8	0	1430	I	5
2	R152a	113.4	45.1	3.14	947.7	9.89	0.106	206	15	0	124	2	5.5
3	R1234yf	95	34	3.73	1160.4	20.7	0.074	196	16.4	1	1	1	12.33
4	R1234ze (E)	109.4	36.3	2.59	1111.5	40.6	0.078	269	12.7	1	1	1	53
5	R1233zd (E)	166.6	36.2	0.59	1319.8	35.6	0.081	470	38.7	1	1	1	39.5
6	R290	96.74	42.5	5.51	521.75	11.9	0.103	119	21.2	0	3	4	5.98
7	R600a	134.6	36.3	1.87	574.8	5.01	0.097	187	8.69	0	4	4	10
8	R744	30.98	73.8	39.7	896.03	114	0.104	90.8		0	1	1	10.91
9	R1270	91.06	45.5	6.76	538.6	14.2	0.093	115	25.2	0	1	4	4.5
10	R744 + R290	57	67.9	29.9	644.9	78.3	0.083	87.7	8.69	0	3	4	9
11	R430A	106.9	40.8	3.5	802.l	10.7	0.984	180	15.7	2	2208	4	7
12	R436A	115.9	42.7	3.85	548.01	8.66	0.1	146	15.1	2	1805	4	8
13	R444A	101.2	42.3	4.47	1199.l	28.5	0.9	225	19.8	1	2506	1	50
14	R445A	104.7	44.9	4.67	1190	29.4	0.95	220	19.4	1	2472	1	46
15	R22	96.3	49.9	5.8	1030.5	5.6	0.092	206.3	2.42	0.06	1810	1	7.97
16	R41	44.28	59	9.4	1038.5	9.4	0.142	147.3	39.3	0	2	1	12
17	R124	22.43	36.2	1.9	1424.2	16.4	0.0743	328.1	9.96	1	609	1	25.33
18	R245fa	154.2	36.5	0.69	1390.9	10.7	0.094	540.8	4.63	1	1370	1	10.67
19	R717(Ammonia)	132.4	113.3	4.9	633.1	0.7	0.547	162.9	26.1	0	0	2	3
20	R718(Water)	373.99	220.6	0.0087	999.7	0.068	0.57	1518.3	0.1995	1	0	1	0.01
21	R170(Ethane)	32.18	48.7	26.88	387.7	0.7	0.086	54.3		0	6	4	5.8
22	R290(Propane)	96.68	42.5	5.35	523.1	15.1	0.103	120.5	21.1	0	3	4	13.4
23	R600(Butane)	151.98	38	1.19	596.8	9.2	0.113	194.4	6.34	0	4	4	12.3
24	R601(Pentane)	196.55	337	1.84	576	8.4	0.097	189.1	8.69	0	4	4	15.6
25	R404A	72.05	37.29	7.1	1132.9	16.3	0.075	167.2	29.1	0	180	1	23.1
26	R410A	71.36	49.03	9.5	1147.6	15.7	0.111	158.7	38.5	0	120	1	22.5
27	R407C	86.03	46.3	6.75	1215.7	14.8	0.102	197.2	28.1	0	230	1	17.8
	Objective	High	Low	Low	Low	High	High	Low	Low	Low	Low	Low	Low

summarizes the various properties of refrigerants.

2.2. Decision Tree Model

Figure 1 shows the decision-making criteria for selecting suitable refrigerants. Figure 2 presents the four-level ladder method used to determine an appropriate refrigerant in the present work. Level 1 contains the aim of the study (to choose the best refrigerant out of 27 refrigerants), level 2 contains the sub-criteria (thermodynamic properties, environment affability, and economic status) followed by level 3 which contains the nine sub-criteria (refer Figure 2) and at level 4 contains 27 refrigerants involved in the investigation. The main objective of this research is to obtain the best refrigerant out of 27 refrigerants based on nine sub-criteria. The selected refrigerants are sub-categorized on the basis of their thermos-physical properties, environmental impact, economic impact on the society. Th–physical properties include latent heat of vaporization, thermal conductivity, density, specific heat, viscosity, and vapor pressure, while environmental factors include ozone depletion potential, global warming potential, flammability, and toxicity of the refrigerant. Last is the cost of the refrigerant in USD. The above mentioned factors were selected to include all the possible scenarios which affect the selection of refrigerant. Each of these factors helps to determine the best possible solution for best refrigerant in our problem.

3. Optimization Methodology

3.1. Entropy Technique

In 1948, Shannon created entropy as an objective weighting method. The entropy approach is used when decision-makers have competing viewpoints on the value of criterion weightiness. The following is a step-by-step technique for using the entropy weight method.

STEP I: Obtain a project outcome by normalizing the arrays of a decision matrix [30].

$$P_{ij}=\frac{x_{ij}}{{{\displaystyle \sum }}_{i=1}^m{x}_{ij}}$$

(1)

STEP II: Compute the entropy measure of project outcomes [30].

$$E_j=-k{\displaystyle \sum }_{i=1}^m{P}_{ij}\times \ln P_{ij}$$

(2)

$$k=\frac{1}{\ln \left(m\right)}$$

(3)

STEP III: Define the objective weight based on the entropy concept; $w_j$—weighted values [30].

$$W_j=\frac{1-E_j}{{{\displaystyle \sum }}_{j=1}^n\left(1-E_j\right)}$$

(4)

$$i=1,2,3,\dots ..,9;j=1,2,3,\dots ,5$$

3.2. Multi-Criteria Decision Making

Multi-criteria decision making (MCDM) is an operations research sub-discipline that assesses multiple competing criteria in decision making. MCDM is involved with constructing and addressing multi-criteria decision and planning issues. The goal is to assist decision-makers who are dealing with such issues. In most cases, there is no one best answer to such challenges. Hence, decision-makers’ preferences must be used to distinguish between options.

The decision space or the criteria space can be used to depict the MCDM problem. It is also feasible to describe the problem in the weight space if a weighted linear function merges several criteria.

3.3. TOPSIS Technique

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision-making process that was developed in the 1980s. TOPSIS opts for the smallest Euclidean distance to the ideal solution and the largest distance to the negative ideal solution. TOPSIS is a method for allocating rankings based on the weights and impact of the various elements.

✓

The term “weights” refers to how much a certain aspect should be considered.

✓

The term “impact” refers to whether a particular item has a positive or negative effect.

Procedure:

STEP I: Normalized Matrix and Weighted Normalize Matrix Calculation [30].

$$r_{ij}=\frac{x_{ij}}{\sqrt{{{\displaystyle \sum }}_{i=1}^m{x}_{ij}^2}}$$

(5)

$$i=1,2,\dots .,m;j=1,2\dots ,n$$

STEP II: Calculate the ideal best and worst values, as well as the Euclidean distance between them, for each row [30].

$$d_{iw}=\sqrt{{\displaystyle \sum }_{j=1}^m{\left[t_{ij}-t_{wj}\right]}^2}$$

(6)

STEP III: TOPSIS Score and Ranking Calculation [30].

$$TOPSIS SCROE=\frac{d_{iw}}{d_{ib}+d_{iw}}$$

(7)

Now, rank according to the TOPSIS score, with a higher score equaling a higher rank.

3.4. EDAS Technique

The EDAS approach (evaluation based on distance from average answer) plays an important part in decision-making difficulties, especially when there are several competing criteria in multi-criteria decision making (MCDM).

STEP I: Create the preliminary decision matrix (x_ij)_m×n

STEP II: Average solution [31].

$$A{V}_j=\frac{{{\displaystyle \sum }}_{i=1}^n{x}_{ij}}{n}$$

(8)

STEP III: Positive distance from average PDA [31].

$$PD{A}_{ij}=\left(\begin{array}{ll}\frac{m \left(0,\left(x_{ij}-A{V}_j\right)\right)}{A{V}_j}\hfill & \mathrm{if}j\in \mathrm{Beneficial}\hfill \\ \frac{m \left(0,\left(A{V}_j-x_{ij}\right)\right)}{A{V}_j}\hfill & \mathrm{if}j\in \mathrm{NonBeneficial}\hfill \end{array}\right)$$

(9)

Step IV: Negative distance from average NDA [31].

$$ND{A}_{ij}=\left(\begin{array}{ll}\frac{m \left(0,\left(A{V}_j-x_{ij}\right)\right)}{A{V}_j}\hfill & \mathrm{if}j\in \mathrm{Beneficial}\hfill \\ \frac{m \left(0,\left(x_{ij}-A{V}_j\right)\right)}{A{V}_j}\hfill & \mathrm{if}j\in \mathrm{NonBeneficial}\hfill \end{array}\right)$$

(10)

Step V: Normalized values of SP and SN [31].

$$NS{P}_i=\frac{S{P}_i}{\underset{i}{\max }(S{P}_i)}{\displaystyle \sum }_{j=1}^m{w}_j^{*}PD{A}_{ij}$$

(11)

$$NS{N}_i=1-\frac{S{N}_i}{\underset{i}{\max }(S{N}_i)}{\displaystyle \sum }_{j=1}^m{w}_j^{*}PD{A}_{ij}$$

(12)

Step VI: Appraisal score $\left(A{S}_i\right)$ [31].

$$A{S}_i=\left[\frac{NS{P}_i+NS{N}_i}{2}\right]$$

(13)

3.5. MOORA Technique

Brauers (2004) initially proposed the MOORA approach, a multi-objective optimization strategy that may be used to tackle various complicated decision-making situations.

The MOORA method’s steps to completion are as follow:

STEP I: Create a decision matrix to help make decisions [32].

$$x=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \cdot & x_{1n}\\ x_{21}& x_{22}& \cdot & x_{2n}\\ \cdot & \cdot & \cdot & \cdot \\ x_{m1}& x_{m2}& \cdot & x_{mn}\end{array}\right]$$

(14)

STEP II: Normalize the decision matrix [32].

$$x_{ij}^{*}=x_{ij}/\sqrt{\left[{\displaystyle \sum }_{i=1}^m{x}_{ij}^2\right]\left(j=1,2,\dots ,n\right)}$$

(15)

STEP III: Optimize attributes [32].

$$y_i={\displaystyle \sum }_{j=1}^g{x}_{ij}^{*}-{\displaystyle \sum }_{j=g+1}^n{x}_{ij}^{*}$$

(16)

$$y_i={\displaystyle \sum }_{j=1}^g{w}_j{x}_{ij}^{*}-{\displaystyle \sum }_{j=g+1}^n{w}_j{x}_{ij}^{*}\left(j=1,2,\dots ,n\right)$$

(17)

STEP IV: Depending on the maximal and minimal values in the decision matrix, the value of y_i might be positive or negative.

The greatest option has the highest Y_i value, while the poorest option has the lowest.

4. Results and Discussion

In this work, multi-objective decision-making systems such as TOPSIS, EDAS, and MOORA were used to choose an ideal refrigerant among the 27 refrigerants mentioned in Table 1 for retail food cooling and refrigeration applications. Equations discussed in the above section (optimization methodology) were used to produce the normalized matrix values for various refrigerants for TOPSIS and EDAS investigations.

Table 2. Assessment score of refrigerants by EDAS technique.

Refrigerants	Assessment Score	Ranking
R134a	0.661595	5
R152a	0.560129	10
R1234yf	0.503422	14
R1234ze (E)	0.443894	20
R1233zd (E)	0.632693	6
R290	0.676133	4
R600a	0.701347	3
R744	0.614293	9
R1270	0.630613	7
R744 + R290	0.398386	23
R430A	0.236577	26
R436A	0.384311	25
R444A	0.416013	21
R445A	0.487884	18
R22	0.526079	13
R41	0.396785	27
R124	0.408803	22
R245fa	0.705378	2
R717 (Ammonia)	0.48903	17
R718 (Water)	0.491104	16
R170 (Ethane)	0.61595	8
R290 (Propane)	0.718788	1
R600 (Butane)	0.457986	19
R601 (Pentane)	0.543101	12
R404A	0.502021	15
R410A	0.547391	11
R407C	0.51489	24

provides the assessment score and ranking for different refrigerants obtained using the EDAS technique, while

Table 3. Assessment score of refrigerants by TOPSIS technique.

Refrigerants	Assessment Score	Ranking
R134a	0.605788	5
R152a	0.588928	10
R1234yf	0.574792	13
R1234ze (E)	0.56048	18
R1233zd (E)	0.598414	7
R290	0.605625	4
R600a	0.600674	3
R744	0.594666	9
R1270	0.603746	6
R744 + R290	0.557436	19
R430A	0.526175	25
R436A	0.553233	21
R444A	0.561435	17
R445A	0.568823	14
R22	0.564821	16
R41	0.546647	23
R124	0.554006	20
R245fa	0.609451	2
R717 (Ammonia)	0.516533	26
R718 (Water)	0.549363	22
R170 (Ethane)	0.597454	8
R290 (Propane)	0.614269	1
R600 (Butane)	0.539931	24
R601 (Pentane)	0.577198	12
R404A	0.565808	15
R410A	0.580136	11
R407C	0.512399	27

provides the assessment based on TOPSIS technique.

Table 4. Assessment score of refrigerants by MOORA technique.

Refrigerants	Assessment Score	Ranking
R134a	0.56482	5
R152a	0.54665	10
R1234yf	0.60067	16
R1234ze (E)	0.60563	23
R1233zd (E)	0.60679	6
R290	0.59467	4
R600a	0.59941	3
R744	0.54936	9
R1270	0.51853	7
R744 + R290	0.52618	22
R430A	0.54973	26
R436A	0.58014	25
R444A	0.56581	21
R445A	0.5144	11
R22	0.55744	15
R41	0.61945	27
R124	0.56882	19
R245fa	0.56144	2
R717 (Ammonia)	0.59845	14
R718 (Water)	0.62427	17
R170 (Ethane)	0.55501	8
R290 (Propane)	0.57479	1
R600 (Butane)	0.56048	20
R601 (Pentane)	0.5772	13
R404A	0.53993	18
R410A	0.52109	12
R407C	0.52074	24

provides the assessment score and ranking based on the MOORA technique.

Table 5. Ranking of refrigerants.

Refrigerants	EDAS	TOPSIS	MOORA
R134a	5	5	5
R152a	10	10	10
R1234yf	14	13	16
R1234ze (E)	20	18	23
R1233zd (E)	6	7	6
R290	4	4	4
R600a	3	3	3
R744	9	9	9
R1270	7	6	7
R744 + R290	23	19	22
R430A	26	25	26
R436A	25	21	25
R444A	21	17	21
R445A	18	14	11
R22	13	16	15
R41	27	23	27
R124	22	20	19
R245fa	2	2	2
R717 (Ammonia)	17	26	14
R718 (Water)	16	22	17
R170 (Ethane)	8	8	8
R290 (Propane)	1	1	1
R600 (Butane)	19	24	20
R601 (Pentane)	12	12	13
R404A	15	15	18
R410A	11	11	12
R407C	24	27	24

compares the ranking of various refrigerants on the basis of TOPSIS, EDAS, and MOORA technique of MCDM methodology. It is revealed from the investigation that R290 (Propane) is the best refrigerant with a GWP of 3 by all the techniques utilized in the study. R 41 is the worst refrigerant per the EDAS and MOORA technique, while R407C is the worst refrigerant per the TOPSIS MCDM methodology technique. The GWP of refrigerant R404A is the highest at 3922 of all refrigerants. Other potential refrigerants which hold the highest ranking as per the analysis are R245fa with GWP of 1, and R600a with GWP of 4.

4.1. TOPSIS Technique

TOPSIS selects the option with the largest distance from the negative ideal solution and the lowest Euclidean distance from the ideal solution. TOPSIS is a method for allocating rankings based on the importance and weights of the supplied elements. The ranking of the chosen 27 refrigerant on the basis of TOPSIS analysis is as follows: R290 (Propane) > R245fa > R600a > R290 > R134a > R1270 > R1233zd > R170 > R744 > R152a > R410A > R601 > R1234yf > R445A > R404A > R22 > R444A > R1234ze > R744 + R290 > R124 > R436A > R718 > R41 > R600 > R430A > R717 > R407C. Refrigerant R290 with assessment score of 0.614269 is the best performer based on TOPSIS, while R407C with assessment score of 0.512399 is the worst among all the refrigerants considered for the investigation. Other refrigerants such as R245fa, R600a, and R290 with ranks 2, 3, and 4 can also be considered. R290 has a GWP of 3 and toxicity and flammability level of 4, while its cost is 13.4 USD per kg.

4.2. EDAS Technique

To choose the optimal option, this method should simply take the distance from the average answer into account. The computations are congruent with the findings, which are significantly simplified. As per the EDAS technique, the ranks of refrigerants are as follow: R290 (Propane) > R245fa > R600a > R290 > R134a > R1233zd > R1270 > R170 > R744 > R152a > R410A > R601 > R22 > R1234yf > R404A > R718 > R445A > R600 > R1234ze > R444A > R124 > R744 + R290 > R407C > R436A > R430A > R41. As per the EDAS, R290 is the best refrigerant with assessment score of 0.718788 while R41 is the worst refrigerant with assessment score of 0.396785.

4.3. MOORA Technique

This approach may be used in various complicated and conflictual supply chain environments. A decision matrix serves as the method’s foundation. The decision matrix shows how well options are performed according to specific criteria. The ranks of refrigerants is as follow: R290 (Propane) > R245fa > R600a > R290 > R134a > R1233zd > R1270 > R170 > R744 > R152a > R445A > R410A > R601 > R717 > R22 > R1234yf > R718 > R404A > R124 > R600 > R444A > R744 + R290 > R1234ze > R407C > R436A > R430A > R41. Once again, as per the EDAS, R290 is the best refrigerant while R41 is the worst.

Figure 3 and Figure 4 show the graphical comparison of various MCDM techniques used in the present investigation. After the suggested comparison research is successfully completed, the following conclusions can be made:

• R290 i.e., Propane, R245fa, R600a, R290, and R134a were observed to have the same rank (Rank 1, 2, 3, 4, and 5, respectively) with all the proposed MCDM techniques.
• R290 followed by R245fa can be considered the best choice of refrigerant for the supermarket application.
• A strong correlation was observed between EDAS, TOPSIS and MOORA, owing to the fact that, they exhibited identical results in ranking the selected alternatives.
• All of the above procedures are effective illustrations of quantitative methods. Each technique; however, has advantages and disadvantages.

5. Conclusions

The article optimization studies were performed for selecting low global warming potential refrigerants for replacing R-404A from supermarkets by using multi-criteria making (MCDM) tools. The technique for order of preference by similarity to ideal solution (TOPSIS), evaluation based on distance from average solution (EDAS), and multi-objective optimization based on ratio analysis (MOORA) techniques of MCDM methodology are used to estimate the best refrigerant for replacing the R404A for low GWP. Nine criteria, namely, latent heat of vaporization, thermal conductivity, vapor pressure, saturated fluid density, the heat capacity of the liquid, dynamic viscosity, GWP, flammability and toxicity, ozone depletion potential, and refrigerant cost, were used for the evaluation of refrigerants. The following conclusions can be made from the investigation:

• EDAS, TOPSIS, and MOORA techniques can be successfully employed for the selection of the best refrigerant out of 27 chosen refrigerants.
• R290, i.e., propane aces the ranking by all the three techniques of evaluation, followed by R245fa and R600a.
• R41 is not the promising refrigerant per the EDAS and MOORA technique while R407C is not the favorable refrigerant per the TOPSIS technique of MCDM methodology.
• A strong correlation was observed between EDAS, TOPSIS, and MOORA, owing to the fact that they exhibited identical results in ranking the selected alternatives.
• All of the above procedures are effective illustrations of quantitative methods. Each technique, however, has advantages and disadvantages.