Ionic Liquid Design and Process Simulation for Decarbonization of

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Ionic Liquid Design and Process Simulation for Decarbonization of Shale Gas Xinyan Liu, Ying Huang, Yongsheng Zhao, Rafiqul Gani, Xiangping Zhang, and Suo-jiang Zhang Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b00029 • Publication Date (Web): 06 May 2016 Downloaded from http://pubs.acs.org on May 7, 2016

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Industrial & Engineering Chemistry Research

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Ionic Liquid Design and Process Simulation for Decarbonization of

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Shale Gas

3

XinyanLiu,†,‡ Ying Huang,† Yongsheng Zhao, † RafiqulGani,§ XiangpingZhang,*,†

4

SuojiangZhang,†

5

†

Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase

6

Complex Systems, Key Laboratory of Green Process and Engineering, Institute of Process

7

Engineering, Chinese Academy of Sciences, Beijing 100190, China

8

‡

Sino-Danish Center for Education and Research, University of Chinese Academy of Sciences,

9 10

Beijing 100049, China §

Department of Chemical & Biochemical Engineering, Technical University of Denmark, DK

11

2800 Kgs. Lyngby, Denmark

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*Corresponding author: Xiangping Zhang [email protected]

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Abstract: Ionic liquids (ILs) have been receiving increasing attention as a potential

14

decarbonization solvent. However, the enormous number of potential ILs that can be synthesized,

15

makes it a challenging task to search for the best IL for CO2 removal from methane. In this work,

16

a method was proposed to screen suitable ILs based on the COSMO-RS (Conductor-like screening

17

model for real solvents) model, an absorption mechanism as well as experimental data. Besides

18

the Henry’s constant, the viscosity and toxicity of ILs should also need to be taken into

19

consideration for an industrial decarbonization process. Furthermore, process simulation was

20

performed to evaluate the new IL-based decarbonization technology. Considering CO2 solubility,

21

CO2/CH4 selectivity and toxicity and viscosity of ILs, [Bmim][NTf2] has been screened to be the

22

potential solvent among 90 classes of ILs. Based on reliable experimental data, rigorous

23

thermodynamic model was established. The simulation results have been found to agree well with

24

the available experimental results. Two process flow sheet options, use of two single-stage flash

25

operations or a multi-stage flash operation following the absorber have been simulated and

26

assessed. Compared with the well-known MDEA (methyldiethanolamine) process for CO2 capture,

27

the single-stage and multi-stage process alternatives would reduce the total energy consumption

28

by 42.8%, 66.04%, respectively. 1

ACS Paragon Plus Environment


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Keywords: Ionic liquids (ILs); Decarbonization; Methane; COSMO-RS; Process simulation

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1. Introduction

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Coal has become a key energy source since the industrial revolution. However, the use of

4

this non-renewable resource may lead to various kinds of environmental pollution. In an effort

5

to satisfy the rising global demand for energy and simultaneously to combat the

6

environmental impacts, such as global greenhouse gas (GHG) emissions, it is necessary to

7

search for other energy supply alternatives, such as shale gas, natural gas, and biogas1. China

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possesses a large potential source of shale gas, which is estimated to be around 31.02 trillion

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cubic meter.2 Compared to other fossil fuels, this gas mixture, primarily composed of methane,

10

some light hydrocarbons and impurities, is considered as a “clean” fuel. Before utilization,

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this clean fuel needs to be purified since the impurities. As increasing attention given to

12

climate change and CO2 emission, decarbonization has become a very important purification

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technology that requires further investigation.

14

Decarbonization technologies usually employ adsorption, low temperature distillation,

15

membrane separation or solvent-based absorption. Since the solvent-based absorption has

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many advantages, such as low cost, easy operation and mature technology, it is therefore also

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widely used in industries. The common solvent-based absorption methods include physical

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absorption, chemical absorption and physical-chemical absorption. In physical absorption, a

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suitable solvent used to absorb and desorb CO2 by changing the conditions of operation.

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Typical solvents, to name a few, are N-methyl pyrrolidone (NMP), polyethylene glycol

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dimethyl ether, methanol and sulfolane. In chemical absorption CO2 is removed by reacting

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with the solvent. Usually, the reaction is reversible, and the CO2 is absorbed under high

23

pressure and low temperature, and then regenerated by decreasing pressure and raising

24

temperature. The most common solvents for this type of absorption are aqueous amines such

25

as monoethanolamine (MEA), diethanolamine (DEA) and methyldiethanolamine (MDEA),

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which is regarded as the most effective way of separating CO2 from other gases3. However,

27

these kinds of solvents have four main disadvantages: 1) low volatility temperature; 2) large

28

solvent-loss; 3) high energy costs for solvent regeneration; 4) corrosion on the equipment 2


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because of high alkalinity.

2

With increasing calls for “green” technology, ionic liquids (ILs) have been paid much

3

attention. ILs are a class of low-temperature molten salts, which are composed of an organic

4

cation and an inorganic anion. Because of non-volatility and good stability, it has been

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considered as new potential solvent for separation. ILs for CO2 absorption has been reported

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by Blanchard4. Zhang et al.5 synthesized a novel dual amino-functionalized IL with a capacity

7

of 18.5 wt% for CO2 capture and a good thermal stability. Zhao et al.6 investigated the

8

solubility of CO2 in IL-amine hybrid solvents, which has the advantage of cost saving, high

9

capacity of CO2 and non-volatility. For shale gas decarbonization, not only solubility of CO2,

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but also selectivity of CO2 to CH4 is the key factor for IL screening. Solubility and selectivity

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of CO2 to CH4 in some ILs have been reported by Ramdin et al.7-9 and Mortazavi-Manesh et

12

al.10. These studies mainly focused on the experiment of CO2 absorption in a kind of ILs.

13

However, the enormous number of potential ILs also makes it a challenging task to search for

14

the best candidates for CO2 removal. To increase the efficiency of IL screening, prediction

15

some of thermodynamic properties of ILs becomes much crucial. The COSMO-RS model is

16

regarded as an effective method for predicting some of the thermodynamic properties of ILs.

17

Zhang et al.11 used the COSMO-RS model to screen ILs for CO2 capture, while they mainly

18

focused on the Henry’s constant of CO2 in ILs. Lee et al.12 used COSMO-SAC model to

19

predict the activity coefficient and also developed a simple model for fugacity of 4 gas

20

calculation, but their screening process highlighted the new model neglecting some practical

21

application related properties. And Satyro et al.13 predicted the unsymmetrical activity

22

coefficient based on COSMO-RS. Fallanza et al.14 applied it to screen ILs for

23

propane/propylene separation system which is different from this work. However, those

24

screening work only considered one limited aspect of solvent property. All of them neglected

25

some other crucial properties of ILs during practical industrial. This work established a more

26

comprehensive and systematic method for screening of ILs. Not only CO2 solubility and

27

CO2/CH4 selectivity, but also some properties of viscosity and toxicity are taken into

28

consideration. Further, the energy consumption of the process was evaluated based on the 3



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screening result.

2

System simulation and assessment is indispensable for developing new processes.

3

However, since the shortage of rigorous thermodynamic data for complicated systems,

4

IL-based (IL-based) process simulation is rarely reported 15. Shiflett et al. 16 simulated a CO2

5

capture process using pure [Bmim][Ac]. Compared with the commercial MEA-based process,

6

the IL-based process showed a higher recovery of 91.3% and achieved higher CO2 purity of

7

98.7%. Moreover, the IL-based system has reduced energy consumption by 16% and the

8

assessment results showed that the investment for the new IL-based process was 11% lower

9

than the MEA-based process and also 12% lower in equipment footprint, respectively. Later

10

they also found that [Bmim][PF6] and [Emim][Tf2N], as solvents, can separate an azeotropic

11

mixture containing tetrafluoroethylene(TFE) and carbon dioxide. Based on available

12

experimental solubility data, Shiflett et al.17 simulated this process, which included a single

13

absorption column and flash tank. Besides these steady-state analyses, Valencia-Marquez et al.18

14

also simulated a CO2 capture process based on ILs, and compared it with MEA process through

15

techno-economic and dynamic analysis. The results turned out that the IL-based process featured

16

lower energy demand and the dynamic analysis showed the CO2 capture IL-plant was a highly

17

nonlinear process.

18

In this work, a more comprehensive and systematic method is proposed to screen

19

suitable ILs than previously reported works based on the COSMO-RS model, absorption

20

mechanism as well as available experimental data, and further evaluated the process. In the

21

solvent screening section, in addition to considering CO2 solubility and CO2/CH4 selectivity,

22

the toxicity and viscosity of the ILs are also taken into consideration in evaluating potential

23

solvents for an industrial decarbonization process. Based on the available experimental data,

24

parameters for the appropriate thermodynamic model are established. Finally, two process

25

cases have been investigated and compared against well-known conventional processes.

26

2. ILs Screening Method

27

This method consists of four parts: (1) screening ILs based on the COSMO-RS model,

28

then Henry’s constant of gas in ILs are calculated and the selectivities are evaluated. (2) The 4


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1

temperature-dependent properties, such as densities, viscosities, surface tensions, heat

2

capacities, and thermal conductivities, are predicted and compared with available

3

experimental data. (3) Gas-liquid equilibrium (GLE) of gas-IL system is established by using

4

NRTL model. (4) Process assessment is evaluated under almost the same gas recovery.

5

2.1. Screening of ILs

6

2.1.1. COSMO-RS Model

7

The thermodynamic models are crucial for the prediction of some basic properties of

8

solvent. In a system at equilibrium, several parameters, such as temperature (T), pressure (P),

9

liquid phase components mole fraction (xi), vapor phase components mole fraction (yi) are

10 11 12 13

14

15 16 17 18

necessary to be determined. The typical thermodynamic models for these gas-IL systems are mainly based on the cubic equation of state (EoS) for the gas phase and activity coefficient models 19. The vapor-liquid equilibrium equation based on EoS is as below. =

(1)

=

(2)

Based on activity coefficient, the vapor-liquid equilibrium equation is in Eq. (2) Where is mole fraction of component i in vapor phase; is the fugacity coefficient of component i; is the mole fraction of component i in liquid phase; is the total pressure

of system; is the fugacity of component i under standard state; is activity coefficient of component i.

19

The COSMO-RS model, developed by Klamt et al.20 in 1993, has been used to predict

20

some of thermophysical properties in various systems 21, 22 and also for qualitative analysis. It

21

is considered as an effective method for predicting some of the thermodynamic properties of

22

ILs, providing a unique and apriori computational tool for designing ILs with specific

23

properties. In the COSMO-RS model, each molecule is described by the surface charge

24

densities. Then the interaction between molecules is characterized by the surface charge

25

densities () of mutual contacted area of the two molecules, as shown as in Eq.(3).

5



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, = , =

2

+

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(3) +

#$ , = #$ , = %#$ &'( )0, + #$ ,

1 2 3 4 5 6

Where is the contacted surface area;

8 9 10

is the energy factor; and are the net

screening charge density; is the misfit energy of two molecules. #$ is the

hydrogen interaction energy. and #$ are the misfit energy and hydrogen interaction energy of unit area.

As a result, the chemical potential of each fragment 01 can be calculated as below:

as given in Eq.(5).

01 = −34 × 6( 78 1 9

7

(4)

01 − , : ; < 34

Where 1 is surface screening charge density distribution, it is also called profile;

, is the total molecular interaction energy, which is the sum of and #$ . R is gas constant; T is absolute temperature.

The activity coefficient of the solute can be described as in Eq.(6) 0 − 0 = exp 34

11 12

(5)

(6)

Where 0 is chemical potential; 0 is chemical potential under standard state

In this work, all COSMO-RS calculations were implemented with the COSMOtherm 23

13

program

14

this work.

, and the latest parameterization method BP_TZVP_C30_1401

23

was adapted in

15

In the COSMO-RS model, the IL molecules are treated as equimolar mixtures of cation

16

and anion, and the ions are usually treated separately as electro-neutral. Then the COSMO-RS

17

model system can be considered as ternary system (cation + anion + gas). However the value

18

of Henry’s constant should be calculated under binary system (IL + gas) due to the integrity of

19

solvent. In the infinitely dilute solution, for a 1:1 IL, the activity coefficient of the gas

20

molecule should be converted by a scaling factor of 0.5 from the ternary system to the binary

21

system24. The Henry’s law constant @AB is calculated by Eq.(7).23 ACS Paragon Plus Environment

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1

C @AB = AB DAB

(7)

C Where AB and DAB are the activity coefficient at infinite dilution and the saturated vapor

2

pressure of gas, respectively. The calculated Henry’s law constants of gas in ILs were all

3

under the temperature of 303K.

4

2.1.2. Comprehensive screening

5

In this work, to separate CO2 from CH4, not only CO2 solubility and CO2/CH4

6

selectivity, but also the viscosity and toxicity of solvent are important factor to consider when

7

judging a solvent’s potential for industrial applications25. The viscosity of the solvent affects

8

much on the CO2 absorption capacity and the toxicity of solvent is the key factor for

9

environmental sustainability which needs serious attention when screening a potential CO2

10

removal solvent. A comprehensive database on toxicity of ILs is used in this work26. The

11

structure has an intimate relationship with its toxicity. Quantitative Structure-Activity

12

relationships (QSAR) model is employed to predict the toxicities (EC50 values) of various ILs

13

toward the Leukemia rat cell line IPC-8126. The term half maximal effective concentration

14

(EC50) refers to the concentration of a drug, antibody or toxicant which induces a response

15

halfway between the baseline and maximum after a specified exposure time. It is commonly

16

used as a measure of safety. Thus the “green” degree of ILs increases with EC50 value. In this

17

work, a ranking system was developed for screening different ILs for CO2 removal with the

18

consideration of various solvent properties including solubility of CO2, selectivity of

19

CO2/CH4, viscosity and toxicity.

20

2.2. Prediction of Thermodynamic Properties

21

Thermodynamic properties, phase equilibrium, and the chemical reaction of the

22

MDEA-H2O-CO2 system are modeled on the basis of the electrolyte nonrandom two-liquid

23

model (E-NRTL). This property method has been widely applied for simulating the amine

24

solutions scrubbing process.3,

25

modeling are made after regressing the Non-Random two liquids (NRTL) model parameters

26

with the available experimental data.

27

2.2.1. Scalar Properties

15

For ILs, the property prediction and phase equilibrium

7



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1

The scalar properties include the critical and volumetric properties of gas and ILs, the

2

normal boiling point and acentric factor, which can be used in many corresponding state

3

correlations for thermodynamic properties, binary parameters. The detailed critical

4

information is obtained from literature.27, 28

5

2.2.2. Temperature-Dependent Basic Properties

6

In the simulation, the temperature-dependent properties are correlated by empirical

7

equations29-33, where the coefficients correlated based on the experimental data and the range

8

of temperature for those equations are listed in Table 1. These properties are essential for

9

simulation-based assessment, which is important in analyzing energy demand of the whole

10

process.

11

2.3. Vapor-liquid Phase Equilibria Modeling

12

In this work, solubility of the light hydrocarbons and N2 in ILs is very low.

10, 37

These

13

components are non-soluble in ILs. As a result, the phase equilibria system contains

14

MDEA-H2O-CO2 system, IL-CO2 system and IL-CH4 system. The physical properties and

15

binary parameters between MDEA and its electrolytic ions were added into the simulation

16

model via a special insert data package of MDEA, called KEMDEA, which is available in

17

Aspen Plus. This method has been widely applied in CO2 capture assessment.38, 39 However,

18

since ILs are a kind of new components in the modeling system, the binary parameters of

19

gas-liquid phase are very necessary for the establishment of phase equilibria model in Aspen,

20

which are determined as described in the following.

21 22

Usually, the solubility of the gas in a solvent are represented by Henry’s constant. The relationship of Henry’s constant and other parameters is shown as follows:40

D = lim IJ → IJ →

@E = lim

23 24 25 26 27

(8)

Where p, yi and xi are total pressure, mole fraction of component i in vapor phase and liquid

phase, respectively. is the fugacity coefficient in the vapor phase. HiA is the Henry’s constant in bar of component i in solvent A.

The temperature dependence of the Henry’s constants are correlated by the following equation:40 8


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1

ln@ = LM +

L+ + LN 6(4 + LO 4 4

(9)

Where Ci is the equation parameters, H is the Henry’s constant in bar.

2

According to the Redlich-Kwong (R-K) equation of state 41, the fugacity coefficient in

3

vapor phase is calculated. Based on the experimental data solubility, the fugacity coefficients

4 5

are obtained by the Redlich-Kwong (R-K) equation of state. Activity coefficient is calculated by NRTL model, as shown in Eq.(14) 19. 6( =

∑1QTM Q RQ SQ ∑1UTM U SU

1

+ VW

AJ] Â]]

XRQ −

(10)

components; x is the mole fraction; R is the gas constant; T is the absolute temperature; and

Q

=

YZ

7

_`

= Q + aQ /4,

∑1UTM U SUQ

where SQ = exp[−

9

=

∑1UTM U SUQ

∑1TM RQ SQ

6

8

Q RQ \ , RQ

QTM

Q SQ

Q

, c is the number of

SQ is a dimensionless interaction parameter; dQ is energy interaction parameter;

Q

is the

nonrandomness factor which can be fixed at 0.3 used in most polar systems.

10

2.4. Process Assessment

11

2.4.1. Gas recovery

12

The process is aimed at CO2 removal from shale gas which mainly contains CH4 and

13

light hydrocarbons. The feed-stream components for the CO2 removal processes simulation

14

are taken from a typical component of the shale gas

15

Table 2. As the shale gas is derived from underground at relatively high pressure (20~100 bar),

16

the inlet gas pressure can assumed at 60 bar.

42

after desulphurization, as given in

17

This feed gas is used for both the IL-based plant and the MDEA-based plant simulation

18

and analysis. As a way to measure the amount of CO2 captured by these processes, the CO2

19

mole fraction in the purified gas and CO2 and CH4 recovery indicators are used. According to

20

the specification of the commercial natural gas, the CO2 should be less than 3% in mole

21

fraction43. The gas recovery is calculated as follows: efgh ijklim =

L@O ijklim =

22

nfgh oph qrstuv nfgh wttx

nfyz {|sJwJtx }uq qrstuv nfyz wttx

(11) (12)

Where nfgh u~Jx }uq is the flow rate of CO2 in the CO2 stream and nfgh wttx is the flow rate 9



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1 2 3 4

Page 10 of 41

of CO2 in the feed gas (gasin) stream; nfyz }uq|r qrstuv is the flow rate of CH4 in purified gas (gasout) stream and nfyz wttx is the flow rate of CH4 in the feed gas (gasin) stream (see notation in Figure 1and 2). 2.4.2. Energy Consumption Estimation

5

Energy consumption of a whole process is an important indicator 4of process

6

evaluation44. The total energy consumption includes the thermal energy and the electricity

7

consumption. The total energy consumption (TEC) is calculated as shown in Eq.(13).

8

Additionally, various kinds of raw gas and different technologies make it hard to evaluate the

9

process. Thus, specific energy consumption (SEC) is proposed as an indicator for comparison

10

of different process. It can be calculated as highlighted in Eq.(14). TEC = + # × e SEC =

11 12 13

4L &k

(13) (14)

Where is the electricity consumption in compressor 1 and pump 1; # is the heat duty

in regenerator; e is conversion efficiency of thermal energy to electricity, which is usually around 0.3-0.445-48, so 0.384 is used in this work. The SEC and TEC are the specific energy

14

consumption (MJ/kg CH4), total energy consumption (MJ/h) respectively.

15

3. Process Description

16

In this work, the conceptual design of the individual unit operations (Figure 1 and 2)

17

used to remove CO2 from shale gas to meet the specification of purified gas was performed

18

with the support of Aspen Plus process simulations. The flowsheet of IL-based process is

19

evaluated as shown in Figure 1. The feed gas enters from bottom of absorber at the condition

20

of 293.15K and 60 bar, contacting the aqueous IL counter-currently. Rich solvent flows into

21

the flash1 (15 bar) to recycle the light hydrocarbons. The CO2 is released in flash2 through

22

slightly increasing of the temperature to 300.15K and reducing the pressure to 1 bar. The lean

23

solvent is cooled to 298.15K and pumped to 60 bar again for reusing. The flow sheet in Aspen

24

Plus is shown in Figure S2 of Supporting Information.

25

Besides this two single-stage flash process, another decarburization process49 has been 10


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1

paid much attention for its lower heat consumption, as shown in Figure 2. The regeneration of

2

physical solvents can be achieved by reducing the pressure of rich solvent stream in a series

3

of multi-stage flash vessels. The flow sheet in Aspen Plus is shown in Figure S3 of

4

Supporting Information.

5

The aim of the steady-state design of CO2 removal IL-based process is to evaluate the

6

energy consumption. A typical MDEA decarbonization process with the same feed gas

7

condition is also simulated for making a comparison with IL-based process under the same

8

CO2 recovery and CH4 recovery. This process mainly includes an absorber and a regenerator.

9

Feed gas contacts the solvent in a packed counter-current absorber under 60 bar. The lean

10

solvent can be regenerated by a reboiler and has a heat exchange with the rich solvent to save

11

much energy. The flow sheet in Aspen Plus is shown in Figure S1 of Supporting Information.

12

4. Results and Discussion

13

4.1. Selection of ILs

14

Based on some previous studies7, 10, 50 about solubility of CO2 and CH4, 90 kinds of

15

mostly common ILs have been analyzed to select a set of IL-solvents with convenient

16

properties as CO2 absorbents for CO2and CH4 gas mixture. The cations include [HOemim],

17

[Emim], [Bmim], [Hmim], [Omim], [MeButPyrr], [N4111], [N-bupy], [Hmpy]; anions

18

include [DEP], [C2SO4], [CH3SO4], [NO3], [OTF], [FEP], [BF4], [Tf2N], [DCA], [TCA]. The

19

solubility is evaluated by the Henry’s constant, which is calculated directly by the activity

20

coefficients and the vapor pressures of the compounds in COSMO software. Combined with

21

the toxicity and viscosity, a prospective IL is chosen as a potential solvent to absorb CO2 from

22

the shale gas.

23

4.1.1. Henry’s constant of CO2 and CH4 in ILs

24

The Henry’s law constants of CO2 in these 90 ILs are predicted by the COSMO-RS

25

method at 303.15K, as shown in Figure 3. Detailed data is given in Table S1 of Supporting

26

Information.

27

Based on the color variance, it confirms that the solubility of CO2 increases with the

28

length of alkyl chain in the cation of same kind of ILs. This is due to the fact that the longer 11



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Page 12 of 41

1

alkyl chain has bigger volume of anion, which leads to strong interactions among the related

2

molecules and large absorption capacity. And the cations have less effect on the solubility

3

than the anions. These characteristics are consistent with the conclusions in the literature 24, 37,

4

51, 52

5 6

. Similarly, the predicted Henry’s constants of CH4 in ILs are shown in Figure 4 and data

is listed in Table S2 of Supporting Information.

7

The predicted data demonstrates that the Henry’s constant of CH4 in ILs is much higher

8

than that of CO2. As Henry’s constant is inversely proportional to solubility, the solubility of

9

CO2 is higher than the solubility of CH4. This can be explained by the highest molecular

10

quadrupole moments of CO2 and the highest interaction energies between IL and CO2 53. Then

11

it is possible to absorb CO2 from the mixed gases containing CH4 using the selected IL. The

12

general tendency for solubility of CH4 in ILs is similar to that of CO2, which the solubility of

13

CH4 increases with the alkyl chain in the cation of same kind of ILs and the anion affects

14

mostly.

15

4.1.2. Selectivity of CO2 to CH4

16 17

A higher selectivity value commonly results in an easy separation process for the gas mixture. The CO2/CH4 selectivity is calculated by the Eq. (15).8 fgh /fyz =

18 19 20

@fyz @fgh

(15)

Where fgh /fyz is the selectivity of the absorbing component CO2 over CH4; @fyz and

@fgh are the Henry’s constant of component CO2 and CH4 in ILs, respectively.

The predicted selectivity of CO2/CH4 is shown in Figure 5. It highlights that the

21

selectivity of CO2/CH4 decreases with the length of alkyl side chain in the cation of same kind

22

of the ILs. For example, the rank of selectivity of CO2/CH4 in imidazolium ILs is

23

[Emim][NTf2] > [Bmim][NTf2] > [Hmim][NTf2] > [Omim][NTf2]. Therefore, the trend of

24

selectivity of CO2/CH4 is inverse to the trend of solubility for the same kind of ILs. The same

25

behavior is observed to the property of permeability and separation factor of CO2/CH4 in

26

polymer membrane 54. As a result, these are trade-off parameters as the selectivity of CO2/CH4

27

decreases with increasing solubility of CO2 in same kind of ILs. It is very difficult to select a 12


Page 13 of 41

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1

kind of IL with both high solubility and high selectivity. Imidazolium-based ILs can be the

2

potential ILs for two main reasons: 1) relatively high solubility of CO2 due to the

3

hydrogen-bond interaction between CO2 and the C2 carbon on imidazole ring52,

4

available for sufficient experimental data of some basic properties for process simulation. As

5

a result, imidazolium-based ILs was chosen for further research. Moreover, a longer cation

6

side chain is commonly accompanied by high viscosity56, 57. Hence, it is better to choose ILs

7

containing [Emim]+ or [Bmim]+. However, [Emim]+ may crystallize easily under low

8

temperature58 and has relatively lower solubility59. As a result, [Bmim]+ is chosen as a

9

potential cation. The comparison of solubility of CO2 and selectivity of CO2/CH4 in various

10 11

55

; 2)

ILs with [Bmim]+ is shown in Table 3 According to Table 3, [Bmim][TCA], [Bmim][NTf2] and [Bmim][DCA] can be applied

12

as the potential decarbonization solvent due to their relatively high solubility and selectivity.

13

4.1.3. Toxicity and viscosity of ILs

14

Table 4 gives the EC50 value

26, 60

and viscosity

29, 30, 34, 35, 61, 62

of ILs with [Bmim] as

15

cation, higher EC50 indicates lower toxicity. IL with [DEP], [C2SO4], [FEP] has the lower

16

selectivity. The raw material of [TCA] synthesis contains cyanogen chloride

17

kind of colorless and highly toxic inorganic compound. Hence, they were not chosen as the

18

optimal IL for decarbonization process in industrial application. What’s more, [Bmim][NTf2]

19

shows the highest EC50 indicates and relative lower viscosity among the studied ILs. Hence,

20

[Bmim][NTf2] is selected as the optimal solvent.

21

4.2. Validation of Thermodynamic Property Models

63

, which is a

22

Scalar properties collected from the literature are listed in Table S3 of Supporting

23

Information. As shown in Figure 6, the predicted value for physical properties in Table 1

24

displays high accordance with the experimental data with the AARD less than 8%.

25

4.3. Phase Equilibrium Calculation Results

26 27 28

The parameters of Henry’s constant in binary system are listed in Table S4 of Supporting Information. And the NRTL parameters are given in Table 5. Thus the phase equilibrium data for this system is established. Figure 7 shows the 13



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Page 14 of 41

1

comparison of experimental results with calculated results. It shows that the calculated values

2

are in good agreement with the experimental data64, 65 with an AARD of 8.54%, 4.36%,

3

respectively. The detailed value is shown in Supporting Information of Table S5 and S6 This

4

good accordance indicates the reliability of the thermodynamic model in the availability of

5

process simulation.

6

4.4. Sensitivity analysis

7

4.4.1. Stages number of absorber and consumption of ILs

8

Figure 8 shows the effect of the ILs demand and the stage number. It is known that the

9

separating efficiency in the absorber increases with the stage number. Based on Figure 8, 10

10

trays for the absorber are enough to meet the separation specification. Adding more trays have

11

little effect on the mole fraction of CO2 in purified gas. Ultimately, the stage number of

12

absorber was determined at 10.

13

After fixing stage number at 10, the relationship between flowrate of ILs and CO2

14

concentration of the purified gas is shown in Figure 9. It is obvious that the optimal

15

circulating solvent is determined to 600000 kg/h.

16

4.4.2. Pressure of Flash tank

17

As in Figure 1, the regeneration system includes two flash columns, the first one is for

18

methane recovery, and the second one is for solvent and CO2 regeneration. The pressure of the

19

flash-1 determines the quantity of methane recycle, which simultaneously affects the acid gas

20

purity in flash 2.

21

As shown the blue line in Figure 10, the recovery of CH4 linearly decreased with the

22

increase of pressure of flash-1. From this aspect, it seems that the pressure should be as low as

23

possible. However, based on the red line in Figure 10, under low pressure, the recovery of

24

CO2 is below 60%. And as the pressure increasing, the recovery of CO2 increases sharply at

25

first. When the pressure reaches to 15 bar, the CO2 recovery increases slightly. What’s more,

26

the low pressure may also lead to high energy consumption for CH4 recycle. Therefore, to

27

make sure a relatively high CO2 recovery rate, the flash 1 pressure is decided at 15 bar.

28

The flash 2 pressure is studied to achieve the minimal electricity-specific consumption, 14


Page 15 of 41

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1

which means the electricity needed for recycling 1kg CO2, as shown in Figure 11.

2

As shown in Figure 11, the electricity-specific consumption decreases at the start and

3

when the flash pressure exceeds 1 bar, it starts to increase a little. Then the optimal value of

4

flash2 pressure is about 1 bar. The main reasons for such a trend are as follows. At low flash

5

pressure, with the flash pressure increasing, it needs less energy for compressing CO2 to

6

recycle. While the pressure increases to 1 bar, the CO2 regeneration rate starts to decrease,

7

which contributes to the increase of energy consumption for per kilogram of CO2.

8

4.4.3. Comparison of different components of raw gas

9

The energy consumption also has a close relationship with the initial CO2 concentration

10

of raw gas. In this section, the influence of initial concentration of CO2 on electricity, heat

11

duty and solvent consumption are investigated under the same solvent lean load for IL-based

12

initial decarbonization process, as shown in Figure 12. Detailed information is shown in Table

13

S7 of Supporting Information.

14

The total amount of raw gas is 2000 kmol/h. The ratios of volume for CO2/CH4 are set

15

to 1/4, 2/3, 3/2, 4/1, respectively. As shown in Figure 12 (a) and (c), with the increasing of

16

CO2 concentration, the consumption of electricity, heat duty and circulating solvent all

17

increase gradually. This is ascribed to the fact that more CO2 capture needs more energy to

18

regenerate and recycle. However, looking into the specific energy consumption (SEC) in (b),

19

it is interesting to find that the SEC of CO2 decreases with the increase of CO2 concentration.

20

While for CH4, it displays a reverse tendency. This is due to higher CO2 concentration has

21

higher CO2 partial pressure which is captured easily and mostly by the solvent under relative

22

low pressure and same solvent lean load.

23

4.5. Comparison of the MDEA and IL-Based Processes

24

Following processes are simulated: (1) 50wt% conventional MDEA process is simulated

25

as reference case. (2) two single-stage IL-based process is established. (3) multi-stage flash

26

IL-based process is simulated as the optimal process.

27

These processes are aimed to purify the shale gas, as a way to measure the process, gas

28

recovery is an important parameter. And the comparison of these processes should be made 15



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1

under relatively the same gas recovery (see the given values in Table 6).

2

4.5.1. Solvent Demand

Page 16 of 41

3

As shown in figure 13, the solvent demand in IL-based process is larger than that of the

4

MDEA process. This is because the IL-based process is physical absorption while MDEA is

5

chemical absorption, which leads to a larger solubility of acid gas in MDEA than that of in

6

ILs. Although the IL-based decarbonization process needs large amount of solvent, it

7

possesses several advantages such as little loss rate, thermostability, non-corrosion and

8

non-volatility, which gives long-term benefits. Under almost the same gas recovery, the

9

multi-stage flash method process needs 33.3% less solvent than that in two single-stage

10

IL-based process.

11

4.5.2. Energy Consumption

12 13

The energy consumption in this process includes two parts: electricity and thermal energy. Figure 14 shows the comparison result of three processes.

14

As shown in Figure 14 (a), the IL-based process consumes more electricity than MDEA

15

process. This main difference lies in the recovery of light hydrocarbon pressurized again into

16

the absorber. Nevertheless, as for the thermal energy consumption, it shows that the IL-based

17

process requires less regeneration thermal energy than the MDEA process, which is attributed

18

to the difference of regeneration mechanism. In IL-based process, the CO2 removal is a

19

physical absorption and the regenerator is flash column, where CO2 can be regenerated by

20

adding a little heat and reducing the pressure. In the MDEA process, the regenerator is a

21

stripper containing a reboiler at the bottom, where large thermal duty is essentially a sum of

22

the energy utilized for three main purposes: raising the temperature of CO2-loaded solution to

23

the boiling point, breaking the chemical bonds between CO2 and MDEA, and stripping CO2

24

and water from the rich solvent. Figure 14 (a) shows the total energy consumption (TEC) in

25

MDEA is two times larger than IL-based process. The same behavior is also observed for

26

SEC, as shown in Figure 14 (b). Comparing with the two IL-based processes, the multi-stage

27

flash process can save 39.4% energy consumption. It is better to choose the multi-stage flash

28

IL-based decarburization process. 16


Page 17 of 41

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1

5. Conclusion

2

This work has evaluated the energy consumption of IL-based decarbonization process

3

through rigorous thermodynamic property modeling and process simulation as well as a

4

comparison with the traditional MDEA decarbonization process.

5

The COSMO-RS model has been used in this work for choosing the effective solvent for

6

shale gas decarbonization. 9 kinds of cations and 10 kinds of anions have been chosen for the

7

screening of the IL candidates. Based on COSMO-RS, the Henry’s constant of CO2 and CH4

8

in these ILs have been calculated. Under additional consideration of toxicity and viscosity,

9

[bmim][NTf2] has been chosen for further assessment. Thermodynamic property models

10

related to this IL-based decarbonization process, including the physicochemical properties

11

and phase equilibrium, have been developed. The calculated results show good agreement

12

with the experimental data, which indicates the models and parameters are reliable for use in

13

process simulation.

14

Two processing options have been considered for the IL-based decarburization process.

15

Reliable and validated thermodynamic property models have been used in the simulations. In

16

two single-stage process, an absorber with stage number of 10 and two flashes at different

17

pressure (15 bar, 1 bar,) are set up through sensitivity analysis. In multi-stage flash process,

18

the IL-rich solvent is regenerated using the pressure-swing option with four adiabatic flash

19

vessels arranged in series at pressures 55, 40, 30 and 0.4 bar, respectively. These flash drums

20

lead to a lower energy consumption (739.18 kWe) comparing with the initial process (1245.72

21

kWe).

22

Compared with the MDEA process, the two single-stage and multi-stage flash processes

23

show 42.8%, 66.04% reduction in total energy consumption, respectively. Detailed

24

calculation of energy distribution depicted that the electricity consumption of IL-based

25

process is higher than that in MDEA process, while the thermal energy consumption is less a

26

lot. A detailed cost analysis will be performed in our future work.

27 28

In summary, the IL-based process could realize energy-saving and environmental friendly carbon capture, which provides a perspective capture technology for the future. 17



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1

Page 18 of 41

Supporting Information

2

Following tables and discussion: Henry’s constant of CO2 and CH4 in 90 kinds of ILs,

3

basic parameters of correlations for physicochemical properties prediction, comparison of

4

predicted value and experimental value for CO2 and CH4 solubility in ILs, key operation

5

parameters of key equipment in MDEA and IL-based decarbonization processes, column

6

profiles of the absorber and regenerator of those processes, and mass and energy balance

7

information on the process streams. This information is available free of charge via the

8

Internet at http://pubs.acs.org/.

9 10

AUTHOR INFORMATION

11

Corresponding Authors

12

*Tel./fax:+86-010-82544875. Email:[email protected]

13

*E-mail:[email protected]

14

Notes

15

The authors declare no competing financial interest.

16

ACKNOWLEGEMENTS

17

This work was supported by the National Natural Science Fund for Distinguished Young Scholars

18

(21425625), the National Natural Science Foundation of China (21506219), Beijing Natural

19

Science Foundation(2142029), and the External Cooperation Program of BIC, Chinese Academy

20

of Sciences (l22111KYS820150017)

21

18


Page 19 of 41

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1

LIST OF SYMBOLS

2

pis= vapor pressure of pure component i

3

P= pressure, Pa

4

T= temperature, K

5

xi= composition of component iin liquid

6

yi= composition of component iin vapor

7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

=vapor phase fugacity coefficient of component i =liquid phase fugacity coefficient of component i γi= activity coefficient

Emisfit=misfit energy of two molecules.

=energy factor.

=charge density, Coulomb/m2

=contacted surface area, m2 Ehb=hydrogen interaction energy, Coulomb

0 =chemical potential.

γi∞= the infinite dilution activity coefficient of component i

=density,g.cm-3

Ci= the coefficient of properties of IL equation

0= viscosity, Pa*S

=surface tension, mN/m

4i =relatice temperature of IL.

Tc=critical temperature of IL,K.

=thermal conducticity, W/mK. fi=fugacity, bar.

HiA= Henry’s law constant of component i in solvent A, Pa

e =the recovery of gas component i

&k, =the mass flow of iout of absorber, kg/h.

&, =the total mass flow of i in the system, kg/h 19



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1 2 3

Page 20 of 41

=the electricity consumption, kW. # =heat duty in regenerator, kW. Si=selectivity of CO2/CH4.

4

20


Page 21 of 41

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1

Abbreviations

COSMO-RS

Conductor-like-Screening Model for Real Solvents

GHG

Greenhouse gas

PSA

Pressure swing adsorption

TSA

Temperature swing adsorption

NMP

N-methyl pyrrolidone

ILs

Ionic liquids

EoS

Equation of state

SSW

Sticky-shield method

QSAR

Quantitative structure-activity relationship

AARD

Average absolute relative deviation

TEC

Total energy consumption

SEC

Specific energy consumption

[HOemim]

1-ethyl-3-methylimidazolium

[emim]

1-ethyl-3-methylimidazolium

[bmim]

1-butyl-3-methylimidazolium

[hmim]

1-hexyl-3-methylimidazolium

[omim]

1-octyl-3-methylimidazolium

[MeButPyrr]

1-butyl-1-methylpyrrolidinium

[N4111]

Butyltrimethylammonium

[N-bupy]

1-butylpyridinium

[hmpy]

1-hexyl-3-methylpyridinium

[TCA]

Tricyanomethanide

[DCA]

Dicyanamide

[BF4]

Tetrafluoroborate

[OTF]

Trifluoromethanesulfonate

[NO3]

Nitrate

21



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[CH3SO4]

Methylsulfate

[Tf2N]

Bis(trifluoromethylsulfonyl)-imide

[C2SO4]

Ethylfulfate

[FEP]

Tris(pentafluoroethyl)trifluorophosphate

[DEP]

Diethylphosphate

Page 22 of 41

1 2 3

Greek letters

4

ρ = density, g cm−3

5

η = viscosity, mPa s

6

σ = surface tension, mN m−1

7

λ = liquid thermal conductivity, W m−1 K−1

8

ω = acentric factor

22


Page 23 of 41

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(60). Ranke J.; Stolte S.; Stormann, R.; Arning, J.; Jastorff, B., Design of Sustainable Chemical Products-The Example of Ionic Liquids. Chem. Rev. 2007, 107, 2183-2206. (61). Fernańdez, A.; Torrecilla, J. S.; Garcıá, J. n.; Rodrı´guez, F., Thermophysical Properties of 1-Ethyl-3-methylimidazolium Ethylsulfate and 1-Butyl-3-methylimidazolium Methylsulfate Ionic Liquids. J. Chem. Eng. Data 2007, 52, 1979-1983. (62). Zhang, X.; Zhang, S.; Bao, D.; Huang, Y.; Zhang, X., Absorption Degree Analysis on Biogas Separation with Ionic Liquid Systems. Bioresource Technol. 2014, 175, 135-141. (63). Marszalek, M.; Fei, Z.; Zhu, D. R.; Scopelliti, R.; Dyson, P. J.; Zakeeruddin, S. M.; Gratzel, M., Application of ionic liquids containing tricyanomethanide [C(CN)3]or tetracyanoborate [B(CN)4]- anions in dye-sensitized solar cells. Inorg. Chem. 2011, 50, (22), 11561-11567. (64). Raeissi, S.; Peters, C. J., Carbon Dioxide Solubility in the Homologous 1-Alkyl-3-methylimidazolium Bis(trifluoromethylsulfonyl)imide Family. J. Chem. Eng. Data. 2009, 54, 382-386. (65). Raeissi, S.; Peters, C. J., High Pressure Phase Behaviour of Methane in 1-butyl-3-methylimidazolium Bis(trifluoromethylsulfonyl)imide. Fluid Phase Equilibria. 2010, 294, (1-2), 67-71.

20 21

27



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Page 28 of 41

1

Table captions:

2

Table 1 Equation of basic properties of [bmim][NTf2]

3

Table 2 Feed gas composition

4

Table 3 Comparison of different ionic liquids with [bmim] as cation on CO2 solubility and

5

selectivity of CO2/CH4

6

Table 4 Toxicity and viscosity value of IL with [Bmim] as cation

7

Table 5 NRTL parameters in binary system

8

Table 6 Comparison of recovery rate on main gas component

28


Page 29 of 41

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1

Table 1 Equation of basic properties of [bmim][NTf2]

Properties

Viscosity (Pa ∙ S)

Thermal

(mN/m) Thermal conductivity

3 4 5

temperature

Exp.

(K)

data

270~370

29

290~390

L = −58.96 + 3.454 − 0.004184 +

290~360

σ = 0.691 − 4i

30, 34,

h ^N.MNN.NO`s ^M.N`s ^.+`s

λ = −0.78 + 0.0086T − 2.69 × 10^ 4 + + 2.78 × 10^ 4 N

(W/mK)

2

of

7058.17 lnμ = −93.46 + + 11.72 × 6(4 4

(J/K/mol)

tension

of

ρ = 1.719 − 0.000946 × T

capacity

Surface

Ref.

Correlated Equation

Density (kg/m3)

Limitation

35

31

32, 34,

290~360

290~360

36

33

Notation: ρ is density in kg.m-3; μ is viscosity in Pa ∙ S; L is thermal capacity in J.K-1.mol-1; σ is the surface tension in mN/m. 4i equals to of IL; λ is thermal conductivity in W/mK.

` , `~

and Tc is critical temperature

Table 2 Feed gas composition 42

6 Component

mol%

CH4

80.11

C2H6

6.58

C3H8

1.92

C4H10

1.31

C5H12

0.3

CO2

7.04

N2

2.778

H2O

4.49E-5

Total flowrate of raw gas

2000 kmol/h 29



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1

Page 30 of 41

Table 3 Comparison of different ionic liquids with [bmim] as cation on CO2 solubility

2

and selectivity of CO2/CH4 Solubility

[FEP]>[TCA]>[NTf2]>[DCA]>[DEP]>[C2SO4]>[OTF]>[CH3SO4]>[NO3]>[BF4]

Selectivity

[TCA]>[DCA]>[BF ]>[OTF]>[NO ]>[CH SO ]>[ NTf ]>[C SO ]>[FEP]>[DEP] 4

3

3

4

2

2

4

3 4

5

Table 4 Toxicity and viscosity value of ILs with [Bmim] as cation ILs

Toxicity EC50

Viscosity (298.15K) (Pa.s)

[Bmim][FEP] [Bmim][OTF] [Bmim][DCA] [Bmim][CH3SO4] [Bmim][BF4] [Bmim][NTf2] [Bmim][NO3] [Bmim][C2SO4] [Bmim][DEP] [Bmim][TCA]

1.81 3 3.15 3.21 3.3 3.41 -

0.075 0.033 0.213 0.137 0.059 0.165 -

Notation: “-” means the value cannot be obtained from current literature.

6 7

Table 5 NRTL parameters in binary system

Parameters

CO2

CH4

a12

-38.6038

-1.829

b12

19030.71

152.792

a21

-1.39749

4.067

b21

233.99

-948.345

αij

0.3

0.3

8 9 10 11 12 13 30


Page 31 of 41

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1

Table 6 Comparison of recovery rate on main gas component

Recovery rate Components MDEA (50%)

IL (initial)

IL (multi-stage flash)

CH4

99.93%

97.32%

95.31%

CO2

81.45%

84.34%

82.64%

2

31



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Page 32 of 41

1

Figure captions:

2

Figure 1 Two single-stage flash process

3

Figure 2 Multi-stage flash IL-based process

4

Figure 3 Henry’s law constants of CO2 in 90 ILs predicted by COSMO-RS method at 303.15K

5

Figure 4 Henry’s law constants of CH4 in 90 ILs predicted by COSMO-RS method at 303.15K

6

Figure 5 Selectivity of CO2/CH4 in 90 ILs predicted by COSMO-RS method at 303.15K

7

Figure 6 Experimental and calculated densities (a), viscosities (b), heat capacities (c), surface

8

tensions (d), and thermal conductivities (e) of the ILs:dots and lines denote experimental

9

data from literature and calculated value, respectively.

10

Figure 7 Total pressures of the systems of CO2-[Bmim][NTf2] (a), CH4-[Bmim][NTf2] (b)

11

Figure 8 CO2 mole fraction out of absorber versus mass flow of ILs under different stage numbers

12

Figure 9 CO2 mole fraction out of absorber versus mass flow of ILs

13

Figure 10 Flash-1 pressure versus CO2 purity and CH4 recycle rate

14

Figure 11 Electricity consumption under different flash-2 pressure

15

Figure 12 the effect of initial concentration of CO2 on energy and solvent consumption in IL-based

16

two single-stage decarbonization process

17

Figure 13 Comparison of the quantity of solvent in different processes

18

Figure 14 Comparison of energy consumption in three processes

19

32


Page 33 of 41

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1 2

Figure 1 Two single-stage flash process

3

4 5

Figure 2 Multi-stage flash IL-based process

6 7 8 9 10

33



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1 2

Figure 3 Henry’s law constants of CO2 in 90 ILs predicted by COSMO-RS method at

3

303.15K

Page 34 of 41

4

5 6

Figure 4 Henry’s law constants of CH4 in 90 ILs predicted by COSMO-RS method at

7

303.15K

8

34


Page 35 of 41

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1 2

Figure 5 Selectivity of CO2/CH4 in 90 ILs predicted by COSMO-RS method at 303.15K

35



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Page 36 of 41

1 2

Figure 6 Experimental and calculated densities (a), viscosities (b), heat capacities (c), surface

3

tensions (d), and thermal conductivities (e) of the ILs: dots denote experimental data in

4

literature 29-36 and lines are fitted by Matlab.

36


Page 37 of 41

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1 2

Figure 7 Total pressures of the systems of CO2-[Bmim][NTf2] (a), CH4-[Bmim][NTf2] (b):

3

dots and lines denote experimental data from literature 64,65 and calculated value, respectively.

4 5

Figure 8 CO2 mole fraction in purified gas versus mass flow of ILs under different stage

6

numbers

7 37



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1

Page 38 of 41

Figure 9 CO2 mole fraction out of absorber versus mass flow of ILs under 10 trays

2

3 4

Figure 10 Flash-1 pressure versus CO2 purity and CH4 recycle rate

5

6 7

Figure 11 Electricity consumption under different flash-2 pressure

8

38


Page 39 of 41

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1 2

Figure 12 The effect of initial concentration of CO2 on energy and solvent consumption in

3

IL-based two single-stage decarbonization process

4

5 6

Figure 13 Comparison of the quantity of solvent in different processes

7 8 9 39



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1 2

Page 40 of 41

Figure 14 Comparison of energy consumption in three processes

3

40


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1 2

For Table of Contents Only

41


Ionic Liquid Design and Process Simulation for Decarbonization of

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