The Selection of Amine Solvent in Gas Treating Process Considering Physical and Process Criteria Using Multiple Criteria Decision-making Techniques: A Case Study of Ilam Gas Treating Company

Document Type: Research Paper

Authors

1 Assistant Professor, School of Engineering, Ilam University, Ilam, P.O. Box 69315/516, Iran

2 Ph.D. Candidate, Department of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

3 Assistant Professor, Department of Chemical Engineering, Ilam University, Ilam, P.O. Box 69315/516, Iran

4 Assistant Professor, Department of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

10.22050/ijogst.2017.93209.1396

Abstract

In the current work, a framework is presented for amine solvent selection in gas treating process. Since the appropriate decision making in this field affects the capital and operational costs, multi attribute decision making (MADM) techniques were used to rank alternatives. The determination of criteria and alternatives is the most important aspect in the MADM. Criteria were divided into two categories, namely physical and process, and twelve physical indexes and nine process indexes were detected. Mono-ethanol amine (MEA), di-glycol amine (DGA), di-ethanol amine (DEA), di-isopropanol amine (DIPA), and methyl di-ethanol amine (MDEA) are intended as alternatives. The importance of the criteria was expressed by weights, and the weights were determined by the analytic hierarchy process (AHP) method. The traditional Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method was applied to the physical criteria with crisp data. The modified interval TOPSIS technique was used to study the process criteria with interval data. The data of the criteria and alternatives were collected from Ilam Gas Treating Company, and the solution for sour gas sweetening was ranked by the proposed approach. Based on our computations, MDEA was defined as the best amine solvent with an average ranking of 1.5.

Keywords

Main Subjects


1. Introduction

In the coming decades, the energy demand is expected to grow rapidly (Langè et al., 2015). Natural gas is a prime, clean, safe, and most useful form of energy (Kazemi et al., 2016), which has been widely used as an industrial and indoor fuel. There are impurities in natural gas which cause serious problems such as fouling and corrosion in pipelines, freezing, plugging, and erosion (Al-Lagtah et al., 2015; Gutierrez et al., 2016; Muhammad and GadelHak, 2015; Qiu et al., 2014). H2S and CO2 are the major impurities in natural gas which must be removed to bring natural gas to the market (Alhseinat et al., 2015; Cho et al., 2015). Chemical absorption by alkanol amines, bio scrubber, adsorption, membrane, and the oxidation of H2S in an iron-chelate process have been used for the removal of these contaminates over years (Ameli Forough, 2012; Tavan et al., 2016). Amine absorption process is the most commonly used acid gas removal technology (Niu and Rangaiah, 2014). This process allows acidic gases to be dissolved in a solvent and released by regeneration at a later stage (Devold, 2013; Jassim, 2016). Amines are some compounds of ammonia (NH3) which have replaced one or more of their hydrogen atoms with hydrocarbon groups (Arthur Kidnay, 2006). Depending on the composition and operation conditions of the raw gas streams to be treated, aqueous amines are used to meet the sweet gas specifications (Vahid Abkhiz, 2014).

Furthermore, the amines capacity for the absorption of acidic gases is an important characteristic (Momeni and Riahi, 2014). Mono-ethanol amine (MEA), di-glycol amine (DGA), di-ethanol amine (DEA), di-isopropanol amine (DIPA), and methyl di-ethanol amine (MDEA) are commonly used in the natural gas sweetening process (Shokouhi et al., 2015).

The selection of a suitable amine solution for sweetening depends on the feed condition and process objectives (Zahid et al., 2017). It can reduce capital and operating costs while providing more flexibility in achieving specific purity requirements. The specification of the required purity with respect to acidic gases (CO2 and H2S), equipment sizing, and operating costs are the primary concern in the gas sweetening. Amine circulation rate, reboiler/condenser size and duty, and corrosion problems are important factors to be considered in the selection of the proper amine. In the amine sweetening unit, 50 to 70% of the initial investment is related to the magnitude of the solvent circulation rate, and 10 to 20% of the initial investment is dependent on the regeneration energy requirement. In addition, about 70% of the operating costs, excluding labor, result from regeneration. Since the selection of the proper amine can greatly reduce both the regeneration energy requirement and solution circulation rate, choosing the amine or a combination of amines best suited to the conditions can have a dramatic impact on the overall costs associated with a sweetening unit (Astarita, 1983; John Polasek, 2006).

The selection of amine solvent in the process of gas treating is considered to be an important decision in gas treating companies. To this end, in the current work, MADM techniques were used for amine solvent selection for the first time. In the next section, a summary of MADM relevant literature is described.

2. Relevant literature

Multi-criteria decision-making (MCDM) is a discipline aimed at supporting decision makers which are faced with numerous and conflicting alternatives to make an optimal decision. To achieve this purpose, two critical questions should be unlocked: preference structure and weights. MCDM methods have been considered in many fields, some of which include economic, social, medical, and technical issues (Nădăban et al., 2016). To facilitate systematic research in the field of MCDM, Hwang and Yoon suggested that MCDM problems can be classified into two main categories, namely multiple attribute decision making (MADM) and multiple objective decision making (MODM), based on the different purposes and different data types (Gwo-Hshiung Tzeng and Huang, 2011). Different methods of decision analysis are illustrated in Figure 1, among which TOPSIS and AHP methods are highlighted.

 

Figure 1

Decision analysis methods.

The TOPSIS method is currently one of the most frequently used methods for MCDM. Its original version was intended for dealing with real-valued data. The Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method was proposed by Hwang and Yoon in 1981. The main idea came from the concept of the compromise solution to choose the best alternative nearest to the positive ideal solution (optimal solution) and the farthest alternative from the negative ideal solution (inferior solution); then, the best one of sorting is chosen, which will be the best alternative (Hwang and Yoon, 1981). As a practical application of this method, we can address the works of Ansarifar et al.(2015), Barros and Wanke (2015), and Chen et al. (2015).

A new interval type-2 fuzzy multiple-attribute decision making model is developed using TOPSIS and decision making trial and evaluation laboratory (DEMATEL) by Baykasoglu et al. (Baykasolu and Glck, 2017), Hatami et al. (Hatami-Marbini and Kangi, 2017), and Runik et al. (Rudnik and Kacprzak, 2017). They have presented a general overview about the development of fuzzy TOPSIS methods. Nadaban et al. have also mentioned several works presenting some applications of fuzzy TOPSIS such as location problem, supplier selection, and sustainable and renewable energy (Nădăban et al., 2016).

In this study, for deciding on the amine solvent selection, a systematic decision-making approach was proposed. The proposed approach will be explained in the next section.

3. The proposed approach

Hierarchical structure creation, calculation of criteria weights by AHP, and selection by TOPSIS are three phases of the selection of the amine solvent. The different phases of the proposed approach are presented in Figure 2.

In the first phase, criteria and alternatives are identified using technical information gathered from Ilam Gas Treating Company; criteria are divided into two categories: physical and process, and twelve physical indexes and nine process indexes are detected. The physical criteria include 1-molecular weight, 2-boiling point, 3-freezing point, 4-critical constants, 5-density, 6-weight, 7-relative density, 8-specific heat, 9-thermal conductivity, 10-latent heat of vaporization, 11-flash point, and 12-toxicity. The process criteria are 1-acid gas pickup (m3(GPA)/L at 38 °C), 2-acid gas pickup (mol/mol amine), 3-lean solution residual acid gas, 4-rich solution acid gas loading, 5-solution concentration, 6-approximate reboiler heat duty, 7-reboiler temperature, normal operating, 8-heats of reaction, and 9-absorption. MEA, DEA, DIPA, DGA, and MDEA are intended as alternatives. The decision hierarchical structure is depicted in Figure 3.

 

 

Figure 2

The proposed approach.

 

Figure 3

Decision hierarchical structure.

In the second phase, the weights of the criteria are calculated by the analytic hierarchy process (AHP), developed by Thomas L. Saaty in 1980. An advantage of the AHP is that it is designed to handle situations in which the subjective judgments of individuals constitute an important part of the decision process. Basically, the AHP is a method of (1) breaking down a complex, unstructured situation into its component parts; (2) arranging these parts, or variables into a hierarchic order; (3) assigning numerical values to subjective judgments on the relative importance of each variable; and (4) synthesizing the judgments to determine which variables have the highest priority and should be acted upon to influence the outcome of the situation. The major steps of AHP include:

  1. developing a graphical representation of the problem in terms of the overall goal, the criteria, and the decision alternatives;
  2. specifying his/her judgments about the relative importance of each criterion in terms of its contribution to the achievement of the overall goal;
  3. indicating a preference or priority for each decision alternative in terms of how it contributes to each criterion;
  4. using a mathematical process to synthesize the information (including consistency checking) and provide a priority ranking of all the alternatives and criteria, given the information on relative importance and preferences.

Finally, in the third phase, alternative ranking is performed. Due to the different quantities of criteria (crisp and interval data), ranking procedure is performed twice. At first, amine solvents are ranked by the traditional TOPSIS method with the crisp data, and then the alternatives are ranked by the modified TOPSIS method with the interval data; ranking average is considered as the final ranking. In the following, the traditional and modified TOPSIS methods are presented.

3.1. The traditional TOPSIS method with the crisp data

Suppose that there are n criteria (C1, C2, …, Cn) which are taken into consideration among m alternatives (A1, A2, …, An) in an MCDM problem. Let  be the performance of the alternative i at the criterion j. The basic principle of the TOPSIS method is that the best alternative should have the shortest distance from the ideal solution and the farthest distance from the negative-ideal solution. Its procedure is given below:

  1. Calculate the normalized value .
 

(1)

  1. Calculate the weighted normalized value .
 

(2)

where,  is the weight of the criterion.

  1. Determine the positive-ideal solution  and the negative-ideal solution .
 

(3)

 

(4)

where, SB and SC denote the set of benefit criteria and the set of cost criteria respectively.

  1. Calculate Euclidean distance.
 

(5)

 

(6)

  1. Calculate the relative closeness to the ideal solution.
 

(7)

  1. Rank the preference order.

For ranking, we can choose the best alternative with the maximum value of the relative closeness. According to the other works (Khezerloo et al., 2011; Levin, 2004; Sevastianov, 2007), the modified TOPSIS method with the interval data is described.

3.2. Modified TOPSIS method with interval data

  1. Calculate the normalized interval value as given below:
 

(8)

 

(9)

  1. Calculate the weighted normalized interval data  defined by:
 

(10)

 

(11)

  1. Identify the positive ideal solution and the negative ideal solution as follows:
 

(12)

 

(13)

  1. Calculate the Euclidean distance.
 

(14)

 

(15)

 

(16)

 

(17)

  1. Calculate the relative closeness Cli as reads:
 

(18)

4. Case Study: Ilam Gas Treating Company

The amine solvent selection in Ilam Gas Treating Company (IGTC) is performed using the proposed approach. The flow sheet of the plant is presented in Figure 4, and the composition and feed condition of the plant is tabulated in Table 1

 

Figure 4

Amine gas sweetening flow sheet.

Table 1

Amine gas sweetening feed composition and conditions.

Feed composition

Component

Molar fraction

Component

Molar fraction

H2O

0

i-Pentane

2.09×10-3

Nitrogen

1.29×10-3

n-Butane

6.16×10-3

H2S

3.25×10-2

i-Butane

4.37×10-3

CO2

3.74×10-2

Propane

2.30×10-2

MDEA

0

Ethane

5.51×10-2

Piperazine

0

Methane

0.831679

n-Octane

9.94×10-4

diM-Sulfide

1.26×10-4

n-Heptane

5.96×10-4

E-Mercaptan

2.78×10-5

n-Hexane

1.29×10-3

M-Mercaptan

1.30×10-3

n-Pentane

2.09×10-3

COS

2.29×10-5

Feed flow rate and condition

Stream name

1

Vapor / Phase Fraction

1

Temperature (°C)

18.3

Pressure (barg)

76

Molar flow (kg mol/hr.)

6157.338

Mass flow (kg/hr.)

123593.3

Standard ideal LiqVol flow (m3/hr.)

351.4239

Molar enthalpy (kJ/kg mol)

-89327.4

Molar entropy (kJ/kg mol.°C)

-124.918

Heat flow (kJ/hr.)

-5.5×10+8

LiqVol flow in standard conditions (m3/hr.)

921.7805

The crisp and interval data about the physical and process criteria are listed in Tables 2 and 3.

Table 2

Physical criteria of the crisp data.

Alternatives

(Amine solvent)

Physical criteria

Molecular weight

Boiling point

Freezing point (°C)

Critical constants

Density

Weight (kg/m3)

Relative density

Specific heat

Thermal conductivity

Latent heat of vaporization (kJ/kg)

Flash point

Toxicity

760 mm Hg (°C)

Pressure (kPa) (abs)

LC50

MEA

61.08

170.5

10.5

5985

1.018

1016

1.0179

2.55

0.256

826

93

206

DEA

105.14

269

28

3273

1.095

1089

1.0919

2.51

0.22

670

138

837

DIPA

133.19

248.7

42

3770

0.999

1080

0.989

2.89

0.002

430

124

580

DGA

105.14

221

-12.5

3772

1.058

1057

1.0572

2.39

0.209

510

127

460

MDEA

119.16

247

-23

2761.36

1.04

1040

1.0418

2.24

0.275

476

127

250

Table 3

Process criteria of the interval data.

Alternatives

(Amine solvent)

Process criteria

C1

C2

C3

C4

C5

C6

C7

C8

C9

                                   

MEA

0.023

0.032

0.33

0.4

0.12

0.12

0.45

0.52

15

25

280

335

107

127

1280

1560

1445

1630

DEA

0.0285

0.0375

0.35

0.65

0.08

0.08

0.43

0.73

25

35

245

280

110

121

1160

1400

1350

1515

DIPA

0.05

0.0585

0.72

1.02

0.08

0.08

0.8

1.1

25

30

245

280

110

121

1190

1190

1520

1520

DGA

0.035

0.0495

0.25

0.3

0.1

0.1

0.35

0.4

50

70

300

360

121

127

1570

1570

2000

2000

MDEA

0.022

0.056

0.2

0.55

0.005

0.01

0.4

0.55

40

50

220

335

121

127

1040

1210

1325

1390

The normalized values of the data are calculated in Tables 4 and 5.

Table 4

Normalized value of the crisp data.

Alternatives

(Amine solvent)

Physical criteria

Molecular weight

Boiling point

Freezing point (°C)

Critical constants

Density

Weight (kg/m3)

Relative density

Specific heat

Thermal conductivity

Latent heat of vaporization (kJ/kg)

Flash point

Toxicity

760 mm Hg (°C)

Pressure (kPa) (abs)

LC50

MEA

0.254

0.326

0.182

0.659

0.437

0.430

0.438

0.452

0.530

0.615

0.339

0.177

DEA

0.437

0.515

0.484

0.360

0.470

0.461

0.469

0.445

0.456

0.499

0.503

0.719

DIPA

0.554

0.476

0.726

0.415

0.429

0.457

0.425

0.512

0.004

0.320

0.452

0.499

DGA

0.437

0.423

-0.216

0.415

0.454

0.447

0.455

0.423

0.433

0.380

0.463

0.395

MDEA

0.496

0.473

-0.398

0.304

0.446

0.440

0.448

0.397

0.569

0.355

0.463

0.215

Table 5

Normalized value of the interval data.

Alternatives

(Amine solvent)

Process criteria

 

C1

C2

C3

C4

C5

C6

C7

C8

C9

                                   

MEA

0.2496

0.3472

0.2756

0.3340

0.6216

0.6216

0.3271

0.3780

0.1692

0.2819

0.4301

0.5146

0.4007

0.4756

0.4304

0.5246

0.4073

0.4595

DEA

0.3092

0.4069

0.2923

0.5428

0.4144

0.4144

0.3125

0.5306

0.2819

0.3947

0.3763

0.4301

0.4120

0.4531

0.3901

0.4708

0.3805

0.4270

DIPA

0.5425

0.6347

0.6013

0.8518

0.4144

0.4144

0.5815

0.7995

0.2819

0.3383

0.3763

0.4301

0.4120

0.4531

0.4002

0.4002

0.4285

0.4285

DGA

0.3798

0.5371

0.2088

0.2505

0.5180

0.5180

0.2544

0.2907

0.5639

0.7894

0.4608

0.5530

0.4531

0.4756

0.5280

0.5280

0.5638

0.5638

MDEA

0.2387

0.6076

0.1670

0.4593

0.0259

0.0518

0.2907

0.3998

0.4511

0.5639

0.3379

0.5146

0.4531

0.4756

0.3497

0.4069

0.3735

0.3918

                                       

Physical index weights are determined by using the AHP method as follows:

 

(19)

Experts proposed process index weights as follows:

 

(20)

The positive-ideal solution A+ and the negative-ideal solution A- are tabulated in Tables 6 and 7.

Table 6

A+ and A- of the physical criteria.

Alternatives

(Amine solvent)

Physical criteria

Molecular weight

Boiling point

Freezing point (°C)

Critical constants

Density

Weight, (kg/m3)

Relative density

Specific heat

Thermal conductivity

Latent heat of vaporization (kJ/kg)

Flash point

Toxicity

760 mm Hg (°C)

Pressure (kPa) (abs)

LC50

MEA

0.011

0.043

0.024

0.05

0.019

0.019

0.019

0.033

0.055

0.063

0.025

0.023

DEA

0.019

0.068

0.064

0.03

0.021

0.020

0.021

0.033

0.047

0.051

0.037

0.095

DIPA

0.024

0.063

0.096

0.03

0.019

0.020

0.019

0.038

0.000

0.033

0.033

0.066

DGA

0.019

0.056

-0.029

0.03

0.020

0.020

0.020

0.031

0.045

0.039

0.034

0.052

MDEA

0.022

0.063

-0.053

0.02

0.020

0.019

0.020

0.029

0.059

0.036

0.034

0.028

 

0.024

0.068

-0.053

0.048

0.021

0.020

0.021

0.038

0.059

0.033

0.037

0.023

 

0.019

0.056

0.096

0.022

0.019

0.019

0.019

0.029

0.000

0.051

0.033

0.095

Table 7

A+ and A- of the process criteria.

Alternatives

(Amine solvent)

Process criteria

C1

C2

C3

C4

C5

C6

C7

C8

C9

                                   

MEA

0.1872

0.3472

0.2067

0.3340

0.6216

0.7771

0.2453

0.3780

0.1269

0.2819

0.3226

0.5146

0.3005

0.4756

0.3228

0.5246

0.1018

0.2297

DEA

0.2319

0.4069

0.2192

0.5428

0.4144

0.5180

0.2344

0.5306

0.2115

0.3947

0.2822

0.4301

0.3090

0.4531

0.2926

0.4708

0.0951

0.2135

DIPA

0.4069

0.6347

0.4510

0.8518

0.4144

0.5180

0.4361

0.7995

0.2115

0.3383

0.2822

0.4301

0.3090

0.4531

0.3001

0.4002

0.1071

0.2142

DGA

0.2848

0.5371

0.1566

0.2505

0.5180

0.6476

0.1908

0.2907

0.4229

0.7894

0.3456

0.5530

0.3399

0.4756

0.3960

0.5280

0.1409

0.2819

MDEA

0.1790

0.6076

0.1253

0.4593

0.0259

0.0648

0.2181

0.3998

0.3383

0.5639

0.2534

0.5146

0.3399

0.4756

0.2623

0.4069

0.0934

0.1959

 

                                   

A+

0.407

0.635

0.451

0.852

0.026

0.065

0.436

0.800

0.423

0.789

0.253

0.430

0.301

0.453

0.262

0.400

0.093

0.196

 

                                   

A-

0.179

0.347

0.125

0.251

0.622

0.777

0.191

0.291

0.127

0.282

0.346

0.553

0.340

0.476

0.396

0.528

0.141

0.282

The Euclidean distance, the relative closeness to the ideal solution, and the rank of the preference order for the physical criteria are presented in Table 8.

Table 8

Rank the preference order of physical criteria.

 

     

Rank

MEA

0.088

0.120

0.57644

3

DEA

0.141

0.058

0.29259

4

DIPA

0.166

0.038

0.18397

5

DGA

0.047

0.140

0.74912

2

MDEA

0.029

0.174

0.85728

1

 

The Euclidean distance and the relative closeness to the ideal solution for the process criteria are summarized in Tables 9 and 10.

Table 9

Euclidean distance .

 

 

 

j=1

j=2

j=3

j=4

j=5

j=6

j=7

j=8

j=9

 

i=1

0.254373907

0.389116937

0.654891537

0.313347055

0.406378007

0.07692837

0.012973163

0.094300094

0.022373479

2.22468255

i=2

0.201981728

0.271287702

0.421323081

0.236115158

0.307670014

0.016627791

0.004864936

0.051770571

0.010716282

1.52235726

i=3

0

0

0.421323081

0

0.338428207

0.016627791

0.004864936

0.021842416

0.016086326

0.81917276

i=4

0.110082402

0.456511857

0.538104151

0.384646074

0

0.10

Al-Lagtah, N.M.A., Al-Habsi, S., and Onaizi, S.A., Optimization and Performance Improvement of Lekhwair Natural Gas Sweetening Plant Using Aspen HYSYS, Journal of Natural Gas Science and Engineering, Vol. 26, p. 367-381, 2015.

Alhseinat, E., Pal, P., Ganesan, A., and Banat, F., Effect of MDEA Degradation Products on Foaming Behavior and Physical Properties of Aqueous MDEA Solutions, International Journal of Greenhouse Gas Control, Vol. 37, p. 280-286, 2015.

Ameli Forough , D.B. and Farzi Farbod, Modeling of Acid Gas Absorption in MDEA Solution in Liquid Ring Compressors Extending Film Theory, Journal of Purity, Utility Reaction and Environment, Vol. 1, No. 10, p. 496-516, 2012.

Ansarifar, E., Shahidi, F., Mohebbi, M., Razavi, S.M., and Ansarifar, J., A New Technique to Evaluate the Effect of Chitosan on Properties of Deep-fried Kurdish Cheese Nuggets by TOPSIS, LWT-food Science and Technology, Vol. 62, p. 1211-1219, 2015.

Arthur Kidnay, W.R.P., Fundamentals of Natural Gas Processing, Taylor and Francis, 2006.

Astarita, G., Gas Treating with Chemical Solvents, John Wiley, New York. 1983.

Barros, C.P. and Wanke, P., An Analysis of African Airlines Efficiency with Two-stage TOPSIS and Neural Networks, Journal of Air Transport Management, Vol. 44–45, p. 90-102, 2015.

Baykasolu, A. and Glck, L., Development of an Interval Type-2 Fuzzy Sets based Hierarchical MADM Model by Combining DEMATEL and TOPSIS, Expert Systems with Applications, Vol. 70, p. 37-51, 2017.

Chen, F., Wang, J., and Deng, Y., Road Safety Risk Evaluation by Means of Improved Entropy TOPSIS–RSR, Safety Science, Vol. 79, p. 39-54, 2015.

Cho, H., Binns, M., Min, K.-J., and Kim, J.K., Automated Process Design of Acid Gas Removal Units in Natural Gas Processing, Computers & Chemical Engineering, Vol. 83, p. 97-109, 2015.

Devold, H., Oil and Gas Production Handbook an Introduction to Oil and Gas Production, Transport, Refining and Petrochemical Industry, ABB Oil and Gas, 2013.

Gutierrez, J.P., Benitez, L.A., Ale Ruiz, E.L., and Erdmann, E., A Sensitivity Analysis and a Comparison of Two Simulators Performance for the Process of Natural Gas Sweetening, Journal of Natural Gas Science and Engineering, Vol. 31, p. 800-807, 2016.

Gwo-Hshiung, T. and Huang, J.J., Multiple Attribute Decision Making: Methods and Applications, CRC Press Taylor & Francis Group, LLC, 2011.

Hatami-Marbini, A. and Kangi, F., An Extension of Fuzzy TOPSIS for a Group Decision Making with an Application to Tehran Stock Exchange, Applied Soft Computing, Vol. 52, p. 1084-1097, 2017.

Hwang, C.L. and Yoon, K., Multiple Attribute Decision Making: Methods and Applications: A State-of-the-art Survey, Springer, Berlin; New York, 1981.

Jassim, M.S., Sensitivity Analyses and Optimization of aGas Sweetening Plant for Hydrogen Sulfide and Carbon Dioxide Capture Using Methyldiethanolamine Solutions, Journal of Natural Gas Science and Engineering, Vol. 36, Part A, p. 175-183, 2016.

Kazemi, A., Kazemi Joujili, A., Mehrabani-Zeinabad, A., Hajian, Z., and Salehi, R., Influence of CO2 Residual of Regenerated Amine on the Performance of Natural Gas Sweetening Processes Using Alkanolamine Solutions, Energy & Fuels, Vol. 30, p. 4263-4273, 2016.

Khezerloo, S., Allahviranloo, T., and Khezerloo, M., Ranking of Fuzzy Numbers Based on Alpha-distance, EUSFLAT Conference, Atlantis Press, p. 770-777, 2011.

Langè, S., Pellegrini, L.A., Vergani, P., and Lo Savio, M., Energy and Economic Analysis of a New Low-temperature Distillation Process for the Upgrading of High-CO2 Content Natural Gas Streams, Industrial & Engineering Chemistry Research, Vol. 54, p. 9770-9782, 2015.

Levin, V.I., Comparison of Interval Numbers and Optimization of Interval-parameter Systems, Automation and Remote Control, Vol. 65, p. 625-633, 2004.

Momeni, M. and Riahi, S., Prediction of Amines Capacity for Carbon Dioxide Absorption in Gas Sweetening Processes, Journal of Natural Gas Science and Engineering, Vol. 21, p. 442-450, 2014.

Muhammad, A. and Gadelhak, Y., Simulation based Improvement Techniques for Acid Gases Sweetening by Chemical Absorption: A Review, International Journal of Greenhouse Gas Control, Vol. 37, p.481-491, 2015.

Nădăban, S., Dzitac, S., and Dzitac, I., Fuzzy TOPSIS: A General View, Procedia Computer Science, Vol. 91, p. 823-831, 2016

Niu, M.W. and Rangaiah, G.P., Retrofitting Amine Absorption Process for Natural Gas Sweetening via Hybridization with Membrane Separation, International Journal of Greenhouse Gas Control, Vol. 29, p. 221-230, 2014.

Polasek, J.A.B., Selecting Amines for Sweetening Units, Bryan Research and Engineering, Inc. - Technical Papers, 2006.

Qiu, K., Shang, J.F., Ozturk, M., Li, T.F., Chen, S.K., Zhang, L.Y., and Gu, X.H., Studies of Methyldiethanolamine Process Simulation and Parameters Optimization for High-sulfur Gas Sweetening, Journal of Natural Gas Science and Engineering, Vol. 21, p. 379-385, 2014.

Rudnik, K. and Kacprzak, D., Fuzzy TOPSIS Method with Ordered Fuzzy Numbers for Flow Control in a Manufacturing System, Applied Soft Computing, Vol. 52, p. 1020-1041, 2017.

Sevastianov, P., Numerical Methods for Interval and Fuzzy Number Comparison based on the Probabilistic Approach and Dempster–shafer Theory, Information Sciences, Vol. 177, p.4645-4661, 2007.

Shokouhi, M., Jalili, A.H., Samani, F., and Hosseini-Jenab, M., Experimental Investigation of the Density and Viscosity of CO2-Loaded Aqueous Alkanolamine Solutions, Fluid Phase Equilibria, Vol. 404, p. 96-108, 2015.

Tavan, Y. and Gholami, H., Shahhosseini, S., Some Notes on Process Intensification of Amine-based Gas Sweetening Process for Better Temperature Distribution in Contactor to Reduce the Amount of Amine as a Result of Corrosion and Foaming, Journal of Loss Prevention in the Process Industries, Vol. 41, p. 169-177, 2016.

Vahid Abkhiz, I.H. and Mostafa Taheri, Operational Investigation of Multi Feed Amine Process Performance in Comparison with Conventional Process, International Journal of Oil, Gas and Coal Engineering, Vol. 2, No. 6, p. 78-83, 2014.

Zahid, U., Al Rowaili, F.N., Ayodeji, M.K., and Ahmed, U., Simulation and Parametric Analysis of CO2 Capture from Natural Gas Using Diglycolamine, International Journal of Greenhouse Gas Control, Vol. 57, p. 42-51, 2017.