Monte Carlo Simulation of Supercritical Carbon Dioxide Adsorption in

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Monte Carlo simulation of supercritical carbon dioxide adsorption in carbon slit pores Shuaiwei Gu, Beibei Gao, Lin Teng, Yuxing Li, Chunyan Fan, Stefan Iglauer, Datong Zhang, and Xiao Ye Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b01344 • Publication Date (Web): 02 Aug 2017 Downloaded from http://pubs.acs.org on August 8, 2017

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0.45 0.40

Effective adsroption density

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0.35 0.30 0.25

P=2MPa P=5MPa P=8MPa P=12MPa P=16MPa P=20MPa

0.20 0.15 0.10 0.8

1.0

1.2

H/nm

ACS Paragon Plus Environment

1.4

1.6

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Monte Carlo simulation of supercritical carbon dioxide adsorption in carbon slit pores Shuaiwei Gu 1, Beibei Gao 1, Lin Teng 1, Yuxing Li 1*, Chunyan Fan 2, Stefan Iglauer 3, Datong Zhang 1, Xiao Ye 1 1. Shandong Provincial Key Laboratory of Oil & Gas Storage and Transportation Security China University of Petroleum (East China), Qingdao 266555, China 2. Department of Chemical Engineering, Curtin University of Technology, Bentley, WA 6102, Australia 3. Department of Petroleum Engineering, Curtin University of Technology, Bentley, WA 6102, Australia

Abstract CO2 adsorption on carbon is a CO2 geo-storage mechanism, and a potential technique for CO2 removal from flue gas or pressurized fuel gas streams produced from hydrocarbon reservoirs (which frequently contain CO2, sometimes at very high concentrations). However, the detailed mechanism how precisely CO2 is adsorbed on the carbon surface is only poorly understood. We thus simulated supercritical CO2 adsorption in carbon slit pores at the molecular level by Grand Canonical Monte Carlo calculations. Adsorption isotherms and microscopic structural properties were examined for different pore widths, pressures and temperatures. Our results demonstrate that the excess adsorption density isotherm of supercritical CO2 in a carbon slit pore has a maximum value, and it is not a monotonically increasing function of pressure. However, supercritical CO2 cannot be effectively adsorbed at very high temperatures (≥ 850 K) as the excess adsorption density is extremely small. Mechanistically, multi-adsorption layers were observed in large slit pores (pore width=20Å); these were defined as contact layers, inner layers and free layers, respectively. Finally, the optimum pore widths for supercritical CO2 adsorption under *Corresponding author. Tel: +86 05328399022; E-mail: [email protected] (Y. Li).


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different conditions were determined, which is vital for optimizing adsorbent and CO2 geo-storage efficiency. 1. Introduction In recent decades, burning of fossil fuels has produced large amounts of greenhouse gases, particularly carbon dioxide (CO2), which is recognized as the major contributor to global warming due to its widespread emissions mainly from industrial sources and coal-fired power plants

1,2

. Furthermore, the proportion of energy

supplied from fossil fuels still accounts for more than 80% of the total and global CO2 emissions will further accumulate in large quantities in the years to come 3. Note that if CO2 emissions are not mitigated, the CO2 concentration in the atmosphere may reach 500 ppm by 2050 and 800 ppm by 2100 4. Therefore, carbon capture, storage and utilization (CCUS) technology 5-6 has been considered as an important strategy to mitigate CO2 emissions from large industrial sources. Geological target formations for captured CO2 include saline aquifers, depleted oil or gas reservoirs, and unmineable coal seams 7. Among these different choices, unminable coal seams have attracted great attention due to its high efficiency and considerable abundance 8. Also their advantage resides in the proven capacity of retaining significant amounts of gas, and that the CO2 adsorbs on the coal surface in micropores and fractures (adsorption trapping), with the additional benefit of displacing methane originally adsorbed on the coal, which can then be produced (coal-bed methane recovery)

9,10

. In order to

maximize CO2 storage capacity and to improve methane displacement efficiency, CO2 is typically injected as supercritical phase. A similar scenario has also been suggested 2


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for shale gas reservoirs 11-13. Moreover, in order to maximize storage capacity, CO2 needs to be purified from flue gas or gas produced from hydrocarbon reservoirs (which frequently contain CO2, sometimes at very high concentration

14

). Thus various CO2 separation methods are

used and new methods are developed, among these are carbon-based adsorbents 15,16. It is thus vital to fully understand the detailed adsorption mechanisms so that CO2 geo-storage capacity and containment security can be predicted with high confidence, and advanced adsorbents can be developed. In recent years, numerical simulation is becoming an effective way in CO2 sequestration modelling. Extensive studies have been put into effect to study CO2 adsorption behavior. Builes and co-workers 17 studied the capacity for CO2 adsorption of aminosilica hybrid products combining experimental-molecular simulations approach. They concluded that the high amine density hybrid products have desirable features required for CO2 adsorption capacity and efficiency. A conceptual model was presented by Gensterblum et al 18 to explain the adsorption of CH4 and CO2 on coal in the presence of water. They found that absorbed molecules are more localised when water is present in the adsorbed phase and the thermodynamic properties of adsorbed molecules are mainly caused by adsorbate–adsorbate interactions. Liu et al 8 and Lu et al

19

investigated the adsorption of CO2 and mixed gases on heterogeneous surface

models of coal systematically through the density functional theory and grand canonical Monte Carlo (GCMC). The calculations showed that the amount of absorbed gases increases gradually with the pressures up and tends to achieve the 3


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equilibrium after high pressure. Also in this context various studies were reported, where the adsorption of CO2 and CH4 on porous and non-porous carbon were investigated at atomistic level

20-22

. However, to the best of our knowledge, the

mechanism of supercritical CO2 absorbed on carbon surface has not been systematically investigated in detail so far. Also, in these Monte Carlo simulations, the CO2 molecule was simulated via the 1C-LJ model which has been proven inaccurate for CO2 as it ignores the CO2’s high quadrupole moment 23. In addition, coal is a very complex material with a highly complex and heterogeneous pore morphology

24,25

.

Thus pores of effectively all sizes are found in coal, and to gauge adsorption capacity, CO2 adsorption as a function of pore size needs to be analyzed systematically. And the optimal pore widths for CO2 adsorption have not been determined so far despite their vital importance. Consequently there is a serious lack of understanding in terms of the precise CO2 adsorption mechanism on carbon. Here we performed Grand Canonical Monte Carlo (GCMC) simulations to examine CO2 adsorption in carbon slit pores and evaluate the influence of temperature, pore width and pressure on that process. Further, the systems microstructure was characterized for different pore widths under various operating conditions. The results provided a molecular-level insight into the interactions between the effective adsorption densities and pore widths and provided useful information for CCS and carbon adsorbents as well.

4


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2. Models and methods 2.1. Interaction energies A number of potential models for CO2 have been proposed in the literature, including the HMT 26, TraPPE 27, five-charge 28 and 1C-LJ model 29. Here we used the TraPPE model with three LJ dispersive sites located on the carbon and oxygen atoms. A set of three discrete charges were located at the same position as the LJ dispersive sites. The total potential energy (ΦT) in a slit pore then consists of the fluid-fluid (Φff) and solid-fluid (Φfw) potential: Φ = Φ +Φ T ff fw

(1)

When the pore width H is constant, the total potential energy can be written as:

ΦT =Φff +Φfw ( z ) +Φfw ( H - z )

(2)

2.1.1. Multi-site potential model The potential energy of interaction was assumed to be pairwise additive. Interaction between a site “a” on a molecule “i” with a site “b” on a molecule “j” was then described by the 12-6 Lennard-Jones(LJ) equation 30: ( a ,b )

ϕi , j = 4ε

( a ,b )

 σ ( a ,b ) 12  σ ( a ,b ) 6   a ,b  -  a ,b    ri (, j )   ri (, j )    

(3)

Where r(ai,,j,b) is the inter-site distance between two atoms. σ(a,b) is the cross collision diameter and ε(a,b) is the cross well depth, which can be determined by using the Lorentz-Berthelot(LB) mixing rule:  σ ( a,a ) + σ (b,b )  σ ( a,b ) =   2  

5


(4)

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(

a,b a,a b,b ε( ) = ε( ) ε( )

)

0.5

(5)

Where σ(a,a) ,σ(b,b) and ε(a,a) , ε(b,b) are collision diameters and well depths of carbon and oxygen atom respectively. All L-J parameters are available from literature

29, 31

and are listed in Table 1. By knowing the site-site interaction, the interaction between two molecules can then be calculated from: M

φi, j =

M

∑ ∑ φ(

a,b )

(6)

i, j

a=1 b=1

Where M is the number of sites on the molecule, here M is 3 for CO2.

Table 1. Potential Parameters of Carbon Dioxide and Slit Carbon graphite Wall CO2

Carbon wall -1

σff/nm σ(c,c)=0.28 σ(o,o)=0.305

εff*kB /K ε(c,c)=27 ε(o,o)=79

σww/nm 0.34

εww*kB-1/K 28

To improve the efficiency of calculation, the continuous potential energy was usually modeled by the cut and shifted LJ potential

Φ ff =

{

ΦLJ ( r ) - Φ LJ ( rc ) 0

( rrc )

(7)

Where ΦLJ is the LJ potential, r is the inter-site distance, rc is the cutoff radius (here rc=5σff) and then

Φ LJ ( rc )

is -2.12×10-25 J.

These calculations above predict the interaction caused by dispersive forces. However, the CO2 quadrupole moment due to the set of discrete charges on each molecule was reasonably high, which could not be ignored during simulation. Thus the interaction between a charge on one molecule and a charge on another molecule was carried out in the same way as with the dispersive interaction sites above. 6


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Specifically, it was calculated by Coulomb’s law: ( α, β )

ψ q,i, j

α β 1 qi q j = 4πε0 ri,( jα, β )

(8)

Where ε0 is permittivity of free space (ε0 = 8.8543×10-12 C2J-1m-1). r(αi,j ,β) is the distance between two charges α and β on molecules i and j, respectively. qαi is value of the charge α on molecule i and qβj is value of the charge β on molecule j. Electrostatic interaction between two molecules is then given by Mq

Mq

∑ ∑ ψ(

ψ q,i, j =

α, β )

q,i, j

(9)

α=1 β =1

Where Mq is the number of charges on one CO2 molecule, here it is chosen to be 3 for CO2.

2.1.2 Solid-fluid potential The carbon slit pores were assumed to be homogenous and were composed of two parallel graphite walls (cp. Figure 1). The interaction between a CO2 molecule and a solid wall can be calculated by the 10-4-3 Steele potential 32 (a )

Φ i,s

 1  σ ( a,s ) 10 1  σ ( a,s )  4  = 4πρ w ε  σ  ∆   a  -  a   5  z i  2  z i  4    σ ( a,s )     -  6∆ ( 0.61∆ + z a )3   i    ( a,s )

( a,s )  2

( 10 )

Where ∆ is the spacing between two adjacent graphite layers (0.3354 nm) and ρw is the carbon atom density (114 nm-3). The molecular parameters, σ(a,s) and ε(a,s), can be obtained via the Lorentz-Berthelot mixing rules as given in Eq. (4) and (5). Solid-fluid energy is usually adjusted with the binary interaction parameter (ksf), such 7


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that the experimental Henry constant can be reproduced by the GCMC simulations, that is

(

a,s ε ( ) = 1 - ksf

) ( ε(

a,a )

s,s ε( )

)

( 11 )

In this work, we assumed that the solid-fluid binary interaction parameter was the same for all interaction sites of a CO2 molecule (of the order of 0.05).

2.2 Monte Carlo simulations We performed GCMC simulation to obtain adsorption isotherms and structure properties. A schematic diagram of the pore model is shown in Fig.1. The Heff is the width that is accessible to pore width. The dimensions of the simulation box in the xand y- directions (which were treated as infinite in the steele potential model) were set to be 20 times the collision diameter of CO2, while z-direction was dependent on the pore width. Periodic boundary conditions were imposed both in the x- and ydirections, and the cutoff radius was 5 times the collision diameter of CO2. For each given pressure, we generated 2×108 configurations with 100000 cycles run for both the equilibrium and sampling stages. Each cycle consisted of 1000 displacement moves and exchanges which included insertion and deletion with equal probability. In the equilibration stage, the maximum displacement length was initially set as half of the largest dimension of the box and was adjusted at the end of each cycle to give an acceptance ratio for displacement of 20%.

8


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Fig.1. Schematic of the carbon slit pore.

2.3

Mesoscopic analysis The adsorption isotherm is generally represented by the average density (ρ) of

the fluid in slit pore. Its dimensionless quantity ρ* can be given by ρ* =

1 H*

∫

H*

0

ρ * ( z* ) dz*

( 12 )

Where ρ*(z*) is the local density distribution function, shown in Eq. 14, The excess adsorption density (Γ) can be calculated from Γ = ρ - ρb

( 13 )

Where ρb is the bulk phase density, which we can obtain from a Johnson equation of state 33. Two particle density distributions were collected here: the local density distribution and the radius density distribution. The local density of adsorbed fluid in the slit pores can then be defined as:

( )

( ) / (A

ρ* z* = N z*

*

• ∆z*

)

( 14 )

Where N (z*) is the number of particles in the bin bounded by [z, z+∆z] and A* • ∆z*is the volume of the bin, < > represents the ensemble average. The radial density 9


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distribution function is a measure of the probability of finding a particle at a distance r away from a given reference particle, defined as g (r) =

ρ (r) ρ

( 15 )

Where ρ(r) is the local time-averaged density at a distance r from a given particle and ρ is the average number density of particles.

3. Results and Discussion 3.1 Excess Adsorption Isotherms In order to verify the accuracy of the simulation method, predicted excess adsorption isotherms of supercritical CO2 are compared to experimental data on an activated carbon sample. Two red lines showing the experimental data measured by Bai

34

are given in Fig.2 as a comparison. Predictions of the excess adsorption

isotherms have the same trend as the experimental data at 323 K and 360 K that it is not a monotonically increasing function of adsorption pressure. Instead, it firstly reaches a maximum and then decreases. To explain this, the bulk phase density of CO2 versus pressure for different temperatures is plotted in Fig.3. At relatively low pressure, the CO2 bulk phase density is smaller than that of the absorbed phase. When the slope of the adsorption isotherm gets equal to the slope of bulk phase density, then the maximum is observed. Beyond this pressure point, the excess adsorption density decreases. However, the GCMC simulation results under-predict the excess adsorption density at higher pressures. Two possible reasons can account for these deviations. One is due to the different pore width assumed in the simulation (here the 10


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pore width is 15 Å while in a real porous system there exists a linear combination of various pore widths). Furthermore, real pore structures of activated carbon are much more complex than the ideal slit pore model used in this study.

25

3

Excess Density [kmol/m ]

30

20

15 experimental data at 323K prediction at 323K experimental data at 360K prediction at 360K

10

5 0

2

4

6

8

10

12

14

P/MPa Fig.2. Comparison of the predictions and the experimental data

20 T=315K T=323K T=348K T=360K T=400K T=500K T=600K T=850K

3

Bulk Phase Density [kmol/m ]

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15

10

5

0 0

5

10

15

P/MPa

Fig.3. Bulk phase density of CO2 11


20

16

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Excess adsorption isotherms for the 15 Å pore width in the 315-850 K temperature range are displayed in Fig.4. Isotherms of temperatures slightly above the critical temperature decline rapidly after reaching the maximum, and the closer the temperature is to the critical CO2 temperature, the more significant is the decline. Consequently, the curve pattern changes at high pressures. In addition, due to the heat release during the adsorption process, the maximum decreases and shifts to higher pressures with increasing temperature. As a result, there is no maximum observed at pressures less than 20 MPa when temperatures are much higher than the critical value. It is also important to note that excess density is extremely small at 850 K (less than 3 Kmol/m3), which indicates that supercritical CO2 cannot be effectively adsorbed at much high temperatures in carbon slit pores. 30 T=315K T=323K T=348K T=360K

25

T=400K T=500K T=600K T=850K

3

Ecxess density [Kmol/m ]

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20

15

10

5

0 0

5

10

15

20

P/MPa

Fig.4. Excess adsorption density isotherms in the pore with a 15 Å width

The excess adsorption isotherms in the carbon slit pores are presented in Fig.5 12


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where the excess adsorption density is plotted as a function of adsorption pressure and pore width. As the pore width increases, the maximum in the isotherm decreases gradually and occurs at higher pressure. This is caused by the decreasing adsorption potential exerted by solid walls on CO2 molecules. Thus, higher pressures are required to build-up the condensed phase inside the pore when compared to smaller pores. 40 H=0.8nm H=1.0nm H=1.2nm H=1.5nm H=2.0nm H=2.2nm

35 3

Excess Density [kmol/m ]

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30 25 20 15 10 5 0 0

5

10

15

20

P/MPa Fig.5. Excess adsorption isotherms at T=323 K as a function of adsorption pressure and pore width

3.2 Microstructure of CO2 in carbon slit pores The typical one-dimensional fluid density profiles along z-direction in the carbon slit pores at T=323 K are plotted in Fig.6. For the slit pore of H=20 Å, five adsorption layers are observed inside the pore (shown in Fig.7); we define these as contact layer, inner layer and free layer, respectively. It is interesting to note that the two contact layers (pore density = 86.8436 Kmol/m3) are more distinct than the two inner layers (pore density = 30.5939 Kmol/m3) and the free layer (pore density = 13


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24.6502 Kmol/m3), indicating that the adsorption capacity of the contact layer is larger than that of the inner layer while the free layer has the lowest adsorption capacity. This is because the interaction exerted by pore walls on the CO2 decreases as the distance from the carbon wall increases. As the pore width decreases to 15 Å and 12 Å, the free layer disappears and only three layers are observed inside the pore, once again, the two contact layers close to the pore walls are denser than the inner layer. In the case of the 8 Å pores, due to the overlap of the strong solid-solid and CO2-solid interactions, the condensed phases are concentrated in the middle of the pores, with a higher density.

100

Pore density [Kmol/m3]


120 100 80 60 40 20 0

80 60 40 20 0

0

2

4

6

8

0

2

4

6

8

10

12

Distance in z

Distance in z

Pore width=8 Å

Pore width =12 Å

100 Pore density [Kmol/m3]

100


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80 60 40 20

80 60 40 20 0

0 0

2

4

6

8

10

12

0

14

5

Distance in z

10

15

Distance in z

Pore width =15 Å

Pore width =20 Å

14


20

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Fig.6. Local density profiles of supercritical CO2 in carbon slit pores at T=323 K and P=10 MPa

Fig.7. Adsorption structure of supercritical CO2 in slit pore of H=20 Å

To further describe the microstructure of CO2 confined in the slit pore, the radial density distribution function is calculated. For illustration, some results for the pore width of H=20 Å at 273, 323and 400 K are given in Fig.8. The maximum value of the radial distribution function occurs at 3.70 Å, 3.85Å, 4.10 Å, respectively. As the temperature increases, the position of the peak shifts to the right and the value decreases gradually, implying weaker interactions between CO2 molecules at higher temperatures. In addition, due to the significant thermal motion of CO2 molecules at higher temperatures and the large distance between carbon walls, the position of the radial density valley shifts to the right and valley width increases.

15


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Fig.8. Radial density distribution function of CO2 at 10 MPa for various temperatures

3.3 Effects of temperature Density distributions for 20 Å pores at a pressure of 10 MPa and a series of temperatures are shown in Fig.9. The density of the contact layer and the inner layer decreases as temperature increases. At 298 K, below the critical CO2 temperature, the density distributions peak in the regions close to the solid surface and in the center of the pore, which are far from uniform. In the case of temperatures above the critical value, the influence of the solid-solid is weaker, and CO2 are more uniformly distributed. The CO2 density is high near the pore walls, and is close to the bulk density in the middle of the pores.

16


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120

100 Pore density [Kmol/m3]

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

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298K 323K 348K

80

60

40

20

0

0

5

10

15

20

Distance in z

Fig.9. Density distribution of CO2 in the 20 Å carbon slit pore for different temperatures at 10 MPa

3.4 Effects of pressure In reality, the local CO2 density distribution is difficult to measure, but is important for an accurate prediction of the CO2 storage capacity. To investigate the effect of pressure on the CO2 density, the local density distributions for the 20 Å pore at 323 K and various pressures are calculated, as shown in Fig.10; and Table 2 tabulates the corresponding CO2 adsorption densities. Clearly the density of the adsorbed layers increases as the pressure increases, and following observations can be made: 1.

Both contact layer and free layer densities increase with increasing

pressure, while the excess adsorption density decreases. 2.

The peak of the contact layer density shifts closer to the pore wall as

pressure increases. 17


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3.

When pressure is very high, the excess CO2 adsorption density in the

carbon slit pores is rather small. Therefore, the excess adsorption density can be simply estimated as that of the bulk phase CO2, for extremely high pressures (pressures>20 MPa). Table 2. CO2 adsorption density of the 20 Å carbon slit pore at 323 K for different pressures Pressures（MPa）

5

10

20

Contact layer (Kmol/m3)

22.8602

25.9627

28.1042

Free layer (Kmol/m3)

2.4370

8.5207

17.3133

Excess adsorption (Kmol/m3)

20.4232

17.4420

10.7909

Fig.10. Density distribution of CO2 in the 20 Å carbon slit pore at 323 K for different pressures

3.5 Optimum pore width and optimum pressure Both CO2 capture and storage processes require a thorough understanding of CO2 adsorption properties in micro porous carbon-based materials, even though carbon capture and storage are distinctively separated processes. Improving CO2 18


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adsorption efficiency and predicting CO2 adsorption capacities on carbon still remains a challenge 35,36. Hence, the development of rational strategies to enhance CO2 capture, separation and storage is not only necessary, but also practical and urgent. Before the supercritical CO2 is injected, it has to be captured by micro carbon-based materials as much as possible. Moreover, also in order to maximize storage capacity, CO2 needs to be purified from flue gas or gas produced from hydrocarbon reservoirs. And in gas separation applications, it is normally required to release the absorbed gas from high pressure to ambient pressure 35. Here the release pressure is chosen to be 0.122 MPa, and the effective storage capacity is defined as ρs ：

ρs = ρ − ρe

( 16 )

Where ρe is the residual adsorption density of CO2 absorbed at 0.122 MPa. For small pore widths, the CO2 absolute adsorption density increases rapidly at pressures less than 0.2 MPa while it is almost linear for large pores (as shown in Fig 11). Therefore, the effective storage density ρs could be rather small when CO2 is released at low pressure. However in larger pores, the CO2 emission is also limited, because of the small excess adsorption density in the pore (shown in Fig.5). As a consequence, there must be an optimum width at which the CO2 emissions will reach the maximum. Fig.12 shows the effective storage density as a function of pore width at 323 K, which is higher than the critical temperature (304 K). Effective storage density is relatively small for a pore width of H=7-8 Å, and as the pore width increases, the effective storage density increases gradually and reaches a maximum at H=11-12 Å. After then, it decreases smoothly with the pore size. Apart from the effect 19


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3

absolute adsorption density [Kmol/m ]

of pore width, CO2 has a larger effective storage capacity at higher pressures.

H=0.7nm H=0.8nm H=0.9nm

40

H=1.3nm H=1.4nm H=1.5nm

H=1.0nm H=1.1nm H=1.2nm

30

20

10

0 0.0

0.2

0.4

0.6

0.8

P/MPa

Fig.11. Absolute adsorption density of CO2 at low pressures at 323 K

35 3

Effective adsroption density[Kmol/m ]

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30 25 20 P=2MPa P=5MPa P=8MPa P=12MPa P=16MPa P=20MPa

15 10 5 0.6

0.8

1.0

1.2

1.4

1.6

H/nm

Fig.12. Effective CO2 storage density as a function of pore width and pressure at 323 K

Based on the analysis of the effects of temperature, we have known that the adsorption density has an intimate connection with temperature. The relationships 20


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between pore width and effective storage capacity are described in Fig.13 for three different temperatures and six pressures. The following observations can be made: 1. As temperature increases, the optimum pore width becomes smaller, reaching the maximum at H=13 Å (307 K), 10 Å (400 K) and 7 Å (450 K) respectively. 2. At 450 K, the effective storage density reaches the maximum at H=7 Å and then decreases monotonically with pore width for all pressures. 3. It is interesting that a platform can be found at 307K and 400K respectively. At 307K, the effective adsorption density increases first with the pore width and undergoes a platform at 10-11 Å before getting to the maximum. The same phenomenon can also be noticed at 400K at 12-13 Å which needs to be analyzed more in the future.

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Fig.13. Effective CO2 storage density as a function of pore width, pressure and temperature

Temperature thus has a highly significant influence on ρs and ρs has different optimum pore widths at different temperatures. Furthermore, the effective adsorption density is extremely small at low pressures. Thus, CO2 should be absorbed at high pressures to achieve the large effective adsorption capacity. 22


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1. Conclusions CO2 emission from industry and power plants has become a worldwide problem with a strong link to global warming 1. This emitted CO2 can in principle be stored in unmineable coal seams and potentially organic-rich shale reservoirs 9,12. However, the CO2 has to be separated from the produced gases, which can for instance be achieved by carbon-based absorbents

35

. It is thus vital to precisely understand how CO2

adsorbs on carbon surfaces so that CO2 storage capacity and containment security can be reliably predicted and advanced adsorbents for CO2 separation can be developed. We thus examined supercritical CO2 adsorption into carbon slit pores via GCMC simulations, and from our microscopic analysis, we conclude that: 1) Excess adsorption isotherms of supercritical CO2 are not monotonically increasing functions of pressure. Instead a maximum occurs when the pressure changes and the excess adsorption density can be rather small at very high pressures. These predicted trends are in good agreement with experimental data. 2) Multi-adsorption layers form in pores with large width (contact layer, inner layer and free layer). The contact layer has the largest adsorption capacity, followed by the inner layer, while the free layer has the lowest adsorption capacity. 3) The optimum pore widths of supercritical CO2 adsorption in carbon slit pores at various temperatures are predicted. As the temperature increases, the optimum pore width decreases and when the temperature is close to the critical temperature, the optimum adsorption pore width is recommended to be 13 Å. High pressures have been confirmed as the preferable operation pressure for carbon-adsorbent based CO2 23


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separation. These results thus provide a framework with which advanced adsorbents for CO2 separation can be designed, and improved CO2 geo-storage scenarios in carbon-rich reservoirs can be developed, further enabling industrial-scale CO2 sequestration.

Acknowledge The present work is supported by National Science Foundation of China (51374231) and resources provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia.

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Monte Carlo Simulation of Supercritical Carbon Dioxide Adsorption in

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