Molecular simulation study on the effect of coal rank and moisture on

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Molecular simulation study on the effect of coal rank and moisture on CO2/CH4 competitive adsorption yang li, Zhaozhong Yang, and Xiao-Gang Li Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.9b01805 • Publication Date (Web): 26 Aug 2019 Downloaded from pubs.acs.org on August 30, 2019

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Molecular simulation study on the effect of coal rank and moisture on CO2/CH4 competitive adsorption Yang Li a, Zhaozhong Yang a, *, Xiaogang Li a a

State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest

Petroleum University, Xindu Avenue 8, Chengdu 610500, PR China Keywords: CO2/CH4 competitive adsorption; coal rank; moisture; adsorption selectivity; CO2ECBM.

Abstract: The CO2-enhanced coalbed methane recovery (CO2-ECBM) technique is based on competitive adsorption. In this study, three models of different coal ranks were established using the molecular dynamics (MD) method. A combination of MD and grand canonical Monte Carlo (GCMC) simulations was used to investigate the competitive adsorption of CO2/CH4 on dry and moist coal. The effects of coal rank and moisture content on pore structure, chemical structure, mixed gas adsorption capacity, and adsorption selectivity are discussed in detail. Simulation results show that from low- to high-rank coals, the total pore volume, porosity, and proportion of effective pores increase, which leads to an increase in their adsorption capacity. Besides, the oxygencontaining functional groups on the pore surface of coal enhance the displacement effect of CO2 on CH4 and with an increase in coal rank the adsorption selectivity of CO2/CH4 decreases. Moreover, the adsorption capacity of CO2 and CH4 will decrease owing to moisture. Water

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molecules will preferentially occupy the high-energy adsorption sites on the pore surface of coal, and then hydrogen bonding and capillary condensation will form water clusters. Therefore, in the case of moist coal, the adsorption selectivity of CO2 to CH4 fluctuates and shows different patterns of variation according to the different effective pore volumes of different coal ranks. From the perspective of CO2-ECBM, achieving a certain moisture content can have a beneficial effect, and the optimal moisture content of medium- and high- rank coal should be higher than that of lowrank coal. Under low-pressure conditions, the adsorption selectivity of CO2/CH4 of dry and moist coal is larger than that of high-pressure conditions. In our work, we advance the understanding of the microscopic mechanism of competitive adsorption of CO2/CH4, which provides a theoretical basis for improving CO2-ECBM technology.

1. Introduction Coalbed methane (CBM) constitutes a significant portion of the world’s natural gas reserves. However, geological conditions and complex influencing factors have led to a low recovery of CBM, which limits its commercial development [1][2][3]. CO2-enhanced CBM recovery has been considered as a promising method to address the problem of energy supply, reduce coal mining accidents, and diminish greenhouse gas emissions [4][5][6][7][8]. The CO2-ECBM technique is based on competitive adsorption [9][10][11]. Better understanding of the behavior of CO2/CH4 competitive adsorption is critical for CBM recovery. Coal rank is a quantity used to describe the degree of coalification, which is affected by the physical and chemical properties of coal. It is the most important factor affecting the production of methane in coal seams [12]. Coal macromolecule structure represent the organic composition of coal and account for the level of coalification [13]; they are characterized by high heterogeneity and complexity and contain a large amount of aliphatic side chains and functional groups around


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the aromatics [14][15]. Thus, the investigation of coal rank by means of coal macromolecular model is effective. Mathews and Chaffee [13] provided a dedicated review of molecular representations of coal with ranks ranging from lignite/brown coals to anthracite coals, and these models

have

greatly

facilitated

the

study

of

CH4

adsorption

simulations

[9][10][15][16][17][18][19][21]. Mosher et al. [20] adopted a simplified graphite-based molecular model to predict the adsorption isotherms for slit pores of a given width, spanning both the microand mesoporous regimes. Zhang et al. [10] studied the competitive adsorption behavior of CO2/CH4 in the micro-pores of an intermediate-ranked bituminous coal using Monte Carlo (MC) simulations. Dang et al. [9] selected brown coal as a model to explore gas adsorption mechanisms and investigated the influence of various functional groups on the CO2 adsorption capacity. Song et al. [21] investigated the promotion effects of CO2 adsorption on the desorption of CH4 onto the low-rank coal vitrinite by density functional theory. The coal rank is closely related to compositional parameters and to a molecular structure containing functional groups and microporosity, which greatly affect the gas holding capacity of coal. Further understanding of CO2/CH4 competitive adsorption behaviors in different coal ranks can raise the prospect of designing a CO2ECBM technique. In addition to coal rank, the moisture contained in most CBM reservoirs can significantly influence the gas sorption and diffusion in the coal seam [22][23]. The coal seam contains a large amount of coexistence of inherent water and CBM, due to the heterogeneity of the chemical structure of the coal surface, the interaction between water and coal is very complicated [24][25]. Moreover, gas recovery from coal beds requires stimulation techniques such as hydro-fracking, which increases the reservoir moisture contents by injecting water-based fluids. Various studies have been performed in the laboratory or with simulations on water sorption on coal or gas sorption


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on moist coal [23][25][26][27][28][29][30][31][32]. It is generally accepted that moisture has a negative impact on the gas adsorption capacity of coal. In contrast, some scholars observed that the moisture in a shale reservoir might have a positive effect. Middleton et al. [33] showed the advantage of moisture, which can not only carry proppants, but also maintain the concentration of brine to avoid the precipitation of minerals that may block small throats. Huang et al. [34] observed that the displacement efficiency of CH4 by CO2 may increase under a certain amount of moisture and thus enhance gas recovery. Although the interaction of moisture and coal has been intensively investigated, there is still a need for a deep understanding of this subject because coal has unevenly distributed chemical composition and physical structure, and its complex CO2/CH4 competitive adsorption behavior takes place around the interfaces of the coal matrix. In this study, the MD method was employed to construct three models of different coal ranks representing of brown coal, bituminous coal, and anthracite coal. The porosity, density, and CH4 excess adsorption isotherms obtained from experimental data and simulated results were compared and the validity of the models was assessed. On the basis of a previous study [19], moist coal models with four moisture contents (0.6, 1.2, 1.8, and 3.0 wt%) were used to in the simulation. Then, the competitive adsorption of CO2/CH4 on dry and moist coal was investigated by MD and GCMC simulations. The impacts of moisture content and coal rank on pore structure, chemical structure, mixed gas adsorption capacity, and adsorption selectivity are discussed in detail. In the case of dry coals, the radial distribution function (RDF) between the surface functional groups and gas molecules was analyzed. For moist coals, we explain the mechanism of the effect of water molecules on the gas adsorption capacity. Lastly, we summarize the effects of coal rank and moisture content in terms of their implications for the CO2-ECBM technique.


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Considerable research efforts have been devoted to gas sorption in coal, but many specific details still remain unexplained because of geological conditions, heterogeneous structure, and complex influencing factors. In our work, an effort was made to investigate CO2/CH4 competitive adsorption behaviors for different coal ranks and moisture contents in the hope of further understanding the microscopic mechanism of CO2/CH4 competitive adsorption and provide a theoretical basis for the improvement of CO2-ECBM technology.

2. Model and computational methodology 2.1 Molecular model for different coal ranks Coal is a complex, heterogeneous material exhibiting an amorphous molecular structure [35][36]. The construction of realistic coal molecular models is essential for solving practical problems [14], and the study of individual coal macerals is helpful for counteracting the influence of coal type change [37]. In this work, we focused on three models of different coal ranks representing brown coal (C39H37NO10S) [9], bituminous coal (C100H82N2O5S2) [38], and anthracite coal (C199H146N2O9) [39] (Figure 1). The brown coal and bituminous coal models were reasonable molecular models applied to investigate the gas adsorption properties of the corresponding coal ranks [9][10]. The anthracite coal model was constructed based on the results of proximate and ultimate analysis, 13C-NMR spectra, and XPS spectra, from the Chengzhuang coal mine [39] and was for the first time applied to investigate gas adsorption properties.


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Figure 1. Three optimized models of different coal ranks. (a) Brown coal with a chemical formula of C39H37NO10S; (b) bituminous coal with a chemical formula of C100H82N2O5S2; (c) anthracite coal with a chemical formula of C199H146N2O9. Color atom correspondence: gray for C, white for H, red for O, yellow for S, and blue for N. 2.2 Simulation details In this work, an all-atom representation and the condensed-phase optimized molecular potentials for atomistic simulation studies force field [40] were used. The force field was confirmed to reproduce the physical properties of coal structures [41] and to compute a density that was extremely close to its true value [32][42]. Simulations in this work were performed with Accelrys Materials Studio (MS) software [43]. The electrostatic interaction was calculated via the Ewald summation method. The Lennard-Jones 9−6 potential was used to describe the van der Waals (vdW) force and the potential has the functional form:   ij E = ij  2    rij 

9

   ij   3    rij

  

6

   

(1)

where  ij is potential well depth, Kcal/mol;  ij is interatomic cut-off distance, Å; rij is the distance between the atoms i and j, Å. The Lorentz−Berthelot combining rules was used to calculate the parameters between different species:

 ij =  ii   jj  2

 ij =  ii   jj

(2)

To construct an appropriate structure configuration for the bulk coal models, 35, 15, and eight optimized coal molecules were first built into an amorphous cell for brown coal, bituminous coal, and anthracite coal, respectively. Then, geometry optimization was performed on these cell units using the smart minimization algorithm to achieve the initial constructed cells. The initial structures were equilibrated and relaxed through a series of MD simulations. We adopted 15.5 Å


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as the cutoff distance and vdW interactions were determined by the atom-based method. Annealing dynamics were performed to search for the global minimum-energy configuration under the canonical ensemble (NVT). The initial temperature of ten simulated annealing cycles was set to 300 K; then, the temperature was increased to 600 K and a 1000 ps of MD simulation was performed to make the configuration stable with the lowest energy. Then, we performed successive MD simulations with the isothermal isobaric ensemble (NPT), a fixed atom number N, a pressure of P = 10MPa, and a temperature of T = 298 K. To ensure that the density and energy fluctuated around some values, a sufficiently long simulation time of 2000 ps with a time step 1.0 fs was employed for the NPT run [32]. Lastly, a 1 ns simulation was performed on the final structure configurations to obtain constant energy structures for the analysis and calculation. The Forcite module of MS was used to optimize the adsorbate molecules and the fixed loading task of the Sorption module was used to construct models with different moisture contents [34][44]. The number of water molecules in the different coal rank unit cells was calculated [19][23], and different coal rank models with absolute moisture contents of 0.6%, 1.2%, 1.8%, and 3.0% were generated. The GCMC method was used to investigate the competitive adsorption behaviors of CO2/CH4 on dry and moist coal. Depending on the Metropolis algorithm [45], decide whether to accept each test or return the old configuration based on the specified probability of each test move. The GCMC simulation method examines the properties of real molecules, so the pressure is expressed in terms of fugacity. The fugacity represents the effective pressure of the actual gas in chemical thermodynamics, which is equal to the pressure of an ideal gas having the same chemical potential under the same conditions. The Peng-Robinson equation of state was chosen to obtain the fugacity of each component from the pressure [46][47]. The total number of MC steps in the grand


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canonical simulations was about 3×107; the first 2×107 steps were used for equilibration and the remaining 1×107 steps were performed to calculate the amount of adsorption. The coal structures were held fixed during the GCMC simulation process, we only consider the fluid-solid interactions so that can save enormous computational time. The Nosé-Hoover thermostat method is adopted to maintain the system temperature and Andersen barostat method was utilized to control the pressures. The amount of adsorption calculated in the molecular simulation is the absolute adsorption, while the adsorption amount obtained in the experiment is the excess adsorption amount. Therefore, to compare the calculated results with the experimental results, the absolute adsorption amount and the excess adsorption amount are converted according to formula (3) using the free pore volume and the gas density at a certain temperature and pressure

N e  N a  Vp 

(3)

where N e is the excess adsorption capacity, g; N a is the absolute adsorption capacity, g; Vp is the free pore volume of adsorbent, cm3; and  is the density of adsorbate at one temperature and pressure, g/cm3. The free pore volume Vp can be obtained from the Atom Volumes & Surface tool in MS [48]. The different interaction intensities of CH4 and CO2 are the main reason for the competitive adsorption of CO2 over CH4 under different coal ranks and moisture contents. The selectivity coefficient (S) is usually employed to characterize the adsorption intensity of binary mixtures and is defined as [49] SCO2 /CH 4 

xCO2 / xCH 4

(4)

yCO2 / yCH 4


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where xCH4 and xCO2 represent the mole fractions of CH4 and CO2 in the adsorption phase, respectively, whereas yCH4 and yCO2 represent the corresponding mole fractions in the bulk phase. If the selectivity coefficient SCO2 /CH4 > 1, the selective adsorption capacity of CH4 is inferior to that of CO2, showing that CO2 is more easily adsorbed.

3. Results and discussion 3.1 Coal model validation The simulated density of the three coal models, the elemental analysis results and the atomic ratios (H/C, O/C) are as shown in Table 1. Ordinarily, as the degree of coalification increases, the structure of coal becomes more compact, resulting in a continuous increase in coal density. Nevertheless, vitrinite is the representative microlithologic component of the coal body, and the chemical structure of coal is essentially the structure of vitrinite [50][51]. Our simulated models of different coal ranks were constructed using the coal macromolecular, which mainly represents the organic macerals [15][52], i.e., the structure of vitrinite. As pointed out in some studies [53][54][55][56], the density of vitrinite is negative correlated to the degree of coalification, when the degree of coalification is low, i.e., the mass fraction of carbon is w(C) < 89%. From the elemental analysis results and atomic ratios shown in Table 1, when w(C) < 89%, the reduction in O/C is greater than that of H/C as the coal rank increases. Because oxygen decreases rapidly and the molar mass of the oxygen atom is larger than that of the carbon atom, the relative growth rate of the carbon atom mass is lower than that of the oxygen atom, which makes the density of coal decrease relatively. When w(C) = 89% the minimum density is reached [56]. As noted above, the trend of our simulated density is consistent with previous research results. Table 1


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Density and elemental analysis of different coal ranks Elemental analysis % Coal rank

Chemical formula

Atomic ratio

Density g/cm3 C

H

O

N

S

H/C

O/C

Brown coal

C39H37NO10S

1.273

65.8

5.20

22.5

1.97

4.50

0.949

0.256

Bituminous coal

C100H82N2O5S2

1.207

82.5

5.64

6.19

1.93

4.40

0.820

0.050

Anthracite coal

C199H146N2O9

1.168

88.2

5.40

5.32

1.03

0.00

0.734

0.045

In this work, probes were adopted to calculate the free pore volume of different coal rank models based on vdW forces [57]. The pore size distributions are different for different diameters of probe molecules. Thus, we first utilized a helium probe (interval = 0.015) to compute the porosities of the coal models [58]. The micro-porosities of brown coal and bituminous coal are 9.38% and 17.63%, respectively. The anthracite coal micro-porosity is 21.29%. The micro-pores in the three-dimensional (3D) macromolecular structure are shown in Figure 2. The simulated results were consistent with the research reported by Liu et al. [59], and the coal micro-porosity and its evolution were primarily determined from the coal molecular structure. With increasing coal rank, the micro-pore structure was mainly controlled by the aromatic parts and the coal crystallite structure, which both resulted in the increase in the micro-pore volume [59]. From Figure 2, with increasing coal rank, more of these micro-pores exist in the anthracite coal model and they have a better inter-connectivity compared to the micro-pores in the brown coal model, which are fewer and isolated from other pores. As above, the pore structure results further confirm that our coal models are reliable.


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Figure 2. The micro-pores (helium probe) in the 3D macromolecular structures of different coal ranks. (a) Brown coal; the model length is 31.9 Å; (b) Bituminous coal; the model length is 31.6 Å; (c) Anthracite coal; the model length is 31.9 Å. To further prove the validity of our coal models, we computed the CH4 excess adsorption isotherms at 298 K using equation (3) and compared them with results measured in the laboratory (Figure 3). Both the experiments and simulation were performed under the same isothermal conditions and a different pressure range. The experimental results for the CH4 adsorption amount of different coal ranks, low to high, were carried out on samples from Ili Basin (No. YL-2, vitrinite reflectance Ro = 0.65%) [60], Monsacro Basin (No. ES02-MVB, Ro = 1.24%) [61], and Yunnan Basin (No. 2, Ro = 3.42%) [62]. From the shown comparison in Figure 3, the simulated excess adsorption isotherms of CH4 in our coal models are qualitatively consistent with the experimental data. Moreover, our simulation results of the excess adsorption capacity of CH4 are in the order: brown coal < bituminous coal < anthracite coal, which is consistent with the reported experimental conclusion of previous studies [63][64], when the Ro < 4.0% methane adsorption capacity increases with increasing maturity of coal samples. On the basis of these results we conclude that our coal models are rational and can be utilized to study the competitive adsorption behaviors of CH4 and CO2.


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Figure 3. Excess adsorption isotherms of CH4 determined by molecular simulations and experiments in different coal ranks at 298 K. Bro, Bitu, Anth represent Brown coal, Bituminous coal, Anthracite coal; simu and exp represent simulation result and experiment result. 3.2 Competitive adsorption on dry coal models 3.2.1 Effect of pore structure characteristics The pore structure, the contained functional groups, molecular structure and compositional parameters are the decisive factors for the coal rank, which significantly influence the gas-holding capacity of the coal. To investigate the effect of the pore structure on the competitive adsorption of CO2/CH4, the GCMC simulation method was applied to the adsorption behaviors of binary gases with equimolar on different coal rank models (Figure 4). It is clear that the adsorption capacity of CH4 from the binary CH4/CO2 mixture is much lower than that from the singlecomponent gas (Figure 3 and Figure 4a). The CO2 adsorption capacity is significantly larger than that of CH4; such behavior could be attributed to the CO2 being preferentially adsorbed in the coal (Figure 4). The simulated adsorption capacity of both CH4 and CO2 on different coal models is in the order: brown coal < bituminous coal < anthracite coal, which is consistent with the microporosity computed with the helium probe (9.38, 17.63, and 21.29%), and the adsorption capacity of brown coal is much lower than that of the other two models. For deeper insight into the effect of the pore structure characteristics on the competitive adsorption capacity of the gas, the pore size distribution (PSD) (Figure 5) and the ratio of the effective to the total pore volume (Figure 6) were computed. The PSD was obtained by the method proposed by Gelb and Gubbins [65]; from Figure 5, the pore sizes showed a mono-peak type, and the peak site was at ∼1.7 Å. In general, pores in coal reservoirs are classified into three types by the International Union of Pure and Applied Chemistry: micro-pores, meso-pores or transitional pores, and macro-pores


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[66][67]. It has been found that micro-pores (< 2 nm in diameter) in coal play a vital role in CH4 storage [20][68][69][70]. In this study, we defined pores that are not accessible to gas molecules as ineffective pores, which have diameters smaller than 3.8 Å, and effective pores as those with diameters bigger than 3.8 Å. The probe size is based on the kinetic diameter of CH4 (3.758 Å) and CO2 (3.3 Å) [71]. In order to improve the simulation certainty, the simulation of different configurations of three coal rank models were performed three times and the average value was taken as the final outcomes. As shown in Figure 5, the effective pores of brown coal detected by the probe are fewer than those in the other two models. Accordingly, the adsorption capacity of brown coal is generally lower than that of bituminous coal and anthracite coal, which is composed of different pore structures. As shown in Figure 6, the fraction of effective pores increases with the coal rank and the ineffective pores occupy most of the total volumes. Liu et al. [59] reported that when the Ro varies from 1.4% to 4.0%, the micro-pore volume increases with an increase in aromatic carbon content. The aromatic carbon ratio (fa) of the anthracite and bituminous coal model is 0.87 and 0.78, respectively [38][39]. Consequently, the adsorption capacity of anthracite coal is higher than that of bituminous coal. The amount of effective pores as a percentage of total pore volume is 12.07, 33.18, and 39.24% for brown, bituminous, and anthracite coal models, respectively, which coincides with the gas adsorption amount (Figure 4).


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Figure 4. The adsorption amount of CH4 (a) and CO2 (b) in an equimolar bi-component on dry coal models of different rank at 298 K.

Figure 5. Pore size distribution of different coal rank models.

Figure 6. The ratio of different types of pores to the total pore volume. 3.2.2 Effect of chemical structure characteristics Coal structures have diverse chemical compositions, and the variations of coal chemical structures have a significant impact on gas adsorption [50][72]. The CO2/CH4 adsorption selectivities of the different models were calculated (Figure 7) using the simulation result of the


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absolute adsorption amount (Figure 4). In the low-pressure range, the selectivity increases as the pressure rises, and then after reaching a certain peak begin to fall; this trend is in good agreement with the results reported by previous research [9][10][36]. The selectivity values (4.1 - 8.7) are always larger than one, which demonstrates that CO2 is preferentially adsorbed on coal and has a larger selective adsorption capacity compared to that of CH4. When the pressure is low, the selectivity is higher, showing that CO2 adsorption is more advantageous in low-pressure range than in high pressure, which is due to the interaction energy between the gas molecules and the pore wall. The interaction between CO2 and pore wall is stronger than that of CH4, and CO2 is preferentially adsorbed. With an increase in pressure, the amount of CO2 adsorption increases, but when the pressure reaches a certain stage, the CO2 adsorption in the pore will reach the limit. Then, to occupy the lower energy adsorption sites on the pore wall, CO2 and CH4 begin to compete for adsorption [34]. As shown in Figure 7, the sequence of selectivity for different coal models is: brown coal > bituminous coal > anthracite coal, which is in good agreement with the O/C ratio in Table 1. The oxygen-containing functional groups can strengthen the adsorption of CO2 with respect to CH4 [9][72]. From Figure 1, we can see that the functional groups of the different coal molecular models and the heterogeneity diminish with increasing coalification [50]. That is why the selectivity of brown coal is higher than that of the other two coal models.


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Figure 7. CO2/CH4 adsorption selectivity of dry coal models versus pressure. The RDF is an efficient tool for analyzing the intimacy of the CO2 and CH4 molecules to the adsorption sites of the coal surfaces. The RDF can reflect the ordering characteristics of particle aggregation and can be interpreted as the ratio of the local density to the average bulk density of the system, which can provide the affinity between the gas and the adsorption sites on the coal [10]. The expression for RDF is as follows [52]:

gij (r ) 

dN 4 jr 2 dr

(5)

where dN is the number of j particles at a distance r to r  dr from i particles and  j is the density of j particles. To obtain further chemical information on the adsorbed CH4 and CO2 in different coal rank models, the RDFs between CH4/CO2 and atoms (C, H, O, N, S) in different coal models were computed in order to reveal the possible high-energy adsorption sites (Figure 8). By comparing the main peak-to-peak values of the RDF between CO2 and the coal surface polar functional groups, it is easily to see that the interaction between CO2 and oxygen-containing functional groups in coal is much stronger than that of other polar groups (Figure 8b, d, f). In


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contrast, the RDFs between CH4 and various polar functional groups in the coal model indicate that the interaction strength between CH4 and oxygen-containing functional groups is weaker than that of other polar groups (Figure 8a, c, e). The RDF of both CH4 and CO2 in different models show that the CO2 molecule forms a main peak at ~0.29 nm and CH4 forms a main peak at ~0.41 nm. The peak disparity indicates that the CO2 in the adsorption layer is much more compact than the CH4. This observation suggests that CO2 is strongly adsorbed on the sites of oxygen-containing functional groups in coal of different coal ranks. On the other hand, CH4 is more likely to adsorb on the sites of sulfur-containing functional groups in the bituminous coal model, whereas the CH4 in the brown and anthracite coal models is more inclined to adsorb on the high-energy sites of nitrogen-containing functional groups. For both the brown and bituminous coal models, CH4 show a weaker affinity to the sites of oxygen-containing functional groups, which is supported by previous studies [9][10].


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Figure 8. RDFs of between CH4/CO2 and atoms in coal (C, O, H, N, S) for equimolar adsorption. (a) Brown coal model-CH4; (b) brown coal model-CO2; (c) bituminous coal model-CH4; (d) bituminous coal model-CO2; (e) anthracite coal model-CH4; (f) anthracite coal model-CO2.


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3.3 Competitive adsorption on moist coal models 3.3.1 Effect of moisture content The influence of the moisture content on the CO2/CH4 adsorption capacity is discussed in this section. As can be seen from Figure 9, the absolute adsorption amount of CO2 and CH4 shows a negative correlation to the moisture content. In terms of adsorption capacity, that of in moist coal significantly decreases compared to that of dry coal. Then, we derive the change in pore structure by evaluating the change in the effective pore volumes of the coal models under different moisture contents. As shown in Figure 10, the trend is consistent with the absolute adsorption amount of CO2 and CH4 at different moisture contents. That can be explained by water molecules covering most of the enterable pores, which are then divided into many ineffective pores after the water molecules form a cluster. As shown in Figure 9, when the moisture content is greater than 1.8 wt%, the absolute adsorption amount drops to a quite low level; this can be explained by the occupation of enterable pores by water molecule clusters. A possible explanation is that water molecules preferentially occupy the main adsorption sites on the coal surface and, because of the hydrogen bonding between water molecules and the strong vdW potential of water molecules and microporous walls, water molecules will adsorb the agglomeration of the water clusters [73] and block the diffusion channel of gases, which will hinder the adsorption of CO2 and CH4. To gain more insight into the impact of water on CO2 and CH4 adsorption, Figure 11 presents the formation of water clusters in different coal models at 298 K. As the moisture content (i.e., the number of water molecules) increases, most of the water clusters gradually grow until they are integrated. Previous studies [74][75][76] have suggested that multi-molecular layer adsorption and capillary condensation of water


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molecules in the coal pore structure diminish the effective pores and reduce the gas adsorption amount.

Figure 9. The absolute adsorption capacities of CO2 and CH4 in equimolar mixtures at different moisture contents, 298 K, and 10 MPa.

Figure 10. Proportion of effective pore volumes at different moisture contents for different coal models.


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Figure 11. Snapshots of the formation of water clusters at 298 K in different coal models. Brown coal model at a moisture content of 0.6 wt% and 3.0 wt% and effective pore structure (a); bituminous coal model (b); anthracite coal model (c). The random coal structure is hidden and the water cluster is marked by the red circle. To achieve a better understanding of the impact of moisture, the CO2/CH4 adsorption selectivities were computed for different moisture contents, as shown in Figure 12. The adsorption selectivity of each coal model has its own curve trend. The selectivity of the bituminous coal model first declines and then rises as the moisture content increases. This trend is consistent with the results reported by Weniger et al. [77]. A decrease in the selectivity with increasing rank and pressure is also observed in Figure 12, which is consistent with the findings of previous studies [77][78][79]. The selectivity of the brown coal model decreases in the beginning, then increases


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sharply, and finally decreases as the moisture content increases. The selectivity of the anthracite coal model decreases slightly as the moisture content increases. It can be concluded that water competes with gas for sorption sites, especially high-energy sites that would be occupied by CO2 molecules in the dry state. As shown in Figure 13, at a lowmoisture state, water molecules are more inclined to adsorb on the sites compared to CO2 molecules; thus, the adsorption amount of CO2 is reduced significantly. At a high-moisture state, depending on capillary condensation, many water molecules are transported and attached to the accessible pores, which makes many high-energy sites achievable to be adsorbed by CO2, resulting in a quite slow decline in the CO2 adsorption amount and increasing selectivity. Because of the different pore size distribution of the different ranks of coal (Figure 5), the enterable pore volume of low-rank coal is small, and the enterable volume of high-rank coal is large. With the occurrence of the cluster of adsorbed water molecules [80] and the following capillary condensation, polar groups remain available for CO2 adsorption, the CO2/CH4 adsorption selectivity increases. However, the small enterable pore volume of low-rank coal is quickly filled by water clusters, which leads to the decrease in the selectivity, whereas high-rank coal can accommodate more water molecules, so the selectivity continues to decline.


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Figure 12. CO2/CH4 adsorption selectivity of moisture coal models with equimolar component at 298 K.

Figure 13. A schematic of water and gas molecular competitive adsorption on the pore wall of coal at different states. To further explain the effect of water molecules on the competitive adsorption of gas molecules at different moisture conditions, snapshots of the adsorption of H2O, CO2, and CH4 molecules in pores of different coal models are shown in Figure 14. By comparing the dry state and the high-moisture state, it can be observed that water molecules occupy most of the previous positions of CO2 and CH4 molecules. By comparing the high-moisture state of the brown coal to that of the anthracite coal model, we can find that the pores of the brown coal model are almost filled by water molecules, and the gas molecules are few. In contrast, the bituminous coal model and the anthracite coal model still coexist with gas and water molecules because of their large enterable pore volumes. From the local magnification in the structure model in Figure 14, it can be seen that compared to the case of other polar groups, the oxygen-containing functional groups


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have higher affinity to water molecules. The O/C ratio of the three coal models is brown > bituminous > anthracite coal model. Therefore, it is easier to adsorb water molecules on the pores of the brown coal model, which aggravates the reduction of gas molecule adsorption under highwater-content conditions.

Figure 14. Snapshots of water and gas molecules adsorption in pores of different coal models at the dry state and the high-moisture state. Color molecule correspondence: red for CO2, black for CH4, blue for H2O. 3.3.2 Implications for CO2-ECBM Through the study of the effect of different coal ranks and moisture contents on the competitive adsorption of CO2/CH4, we have obtained some understanding of the mechanism. By summarizing the above results, in this section we discuss how we can apply these conclusions to the field application of CO2-ECBM technology.


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By studying the effect of different coal ranks on the competitive adsorption of CO2/CH4, we determined the control mechanism of the coal rank on the adsorption capacity of coal. It is believed that the pore structure of coal is the fundamental factor controlling the adsorption capacity of different coal ranks. From low rank to high rank, the total pore volume, porosity, and proportion of effective pores increase with increasing coalification, which leads to a rapid increase in the coal adsorption capacity. Owing to the strong surface heterogeneity of coal, the role of oxygencontaining functional groups on the surface of coal enhances the effect of CO2 on CH4. The low coalification degree of low-rank coal, which has more polar functional groups and a high ratio of O/C in the molecule, results in a higher adsorption selectivity of CO2/CH4. Therefore, injecting CO2 into a low-rank coal reservoir can achieve higher CH4 replacement efficiency. If, from the perspective of sequestration, high-rank coal with more aromatic parts and a coal crystallite structure resulted in an increase in micro-pore volume, injecting CO2 into the depleted high-rank coal reservoir could store a higher amount of CO2. From the study of the effect of different moisture contents on the competitive adsorption of CO2/CH4, useful insights can be obtained for CO2-ECBM technology. Owing to the change of pore structure and surface chemical structure caused by different coalification degrees, different adsorption selectivity changes were obtained under different water contents of different coal ranks. In this study, it is concluded that low- and medium-rank coals, at a certain moisture content, can contribute to the competitive adsorption of CO2 on CH4 (Figure 12). Huang et al. [34] also pointed out that, for producing the same amount of CH4, optimal moisture conditions can reduce the amount of CO2 that needs to be pumped into the reservoir. Because of the tight character of coalbed methane, the extremely low permeability of the coalbed causes the CBM to require reservoir stimulation to improve the gas migration efficiency in the subsurface. The commonly used


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stimulation techniques, including hydraulic fracturing, foam fracturing, and CO2-based fracturing (containing liquid additives), will introduce extraneous water into the coal seam and, in addition to the coal seam itself also contain water. Therefore, whether it is extraneous water or inherent water, it can have a beneficial effect on CO2-ECBM if the coalbed controls a certain amount of moisture. Although water causes competition for gas to adsorb high-energy sites on the surface of coal pores, hydrogen bonding and capillary condensation both cause clusters of water molecules, which result in exposing some of the adsorption sites, thereby increasing the adsorption selectivity of CO2 to CH4. For a low-rank coal reservoir, pores are easily filled by water clusters, whereas the effective pore volume of medium- and high-rank coal seams is larger than that of low-rank coal, so the optimal moisture content should be higher than in the low-rank coal. From our study result, under low pressure condition, the adsorption selectivity of CO2/CH4 of dry and moist coal is larger (Figure 7 and 12), and lead us to conclude that the CO2-ECBM engineering is best carried out in shallow coal seam, which can obtain the optimal competitive adsorption selectivity. On the other hand, for the deep coal seam which the reservoir pressure is high, we can adopt the method of nitrogen (N2) injection. Based on Dalton's law of partial pressures, with the injection of N2, the partial pressure of other gases is reduced on some extent, and N2 can help to increase the permeability of the coal seam [81], thereby increasing the extraction of CBM and reduce the amount of CO2 injected [82]. The low-rank coal seam has a shallow depth and a high content of oxygen-containing functional groups [83], which means the low-pressure ranged environment, higher CO2/CH4 adsorption selectivity, and more favorable for the CO2ECBM technology. Relative to high-rank coal with deep coal seam and low oxygen content, lowrank coal reservoir is more suitable for CO2-ECBM projects.

4. Conclusion


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The pore structure of coal is the fundamental factor controlling the adsorption capacity of different coal rank. From low rank to high rank, the total pore volume, porosity, and proportion of effective pores increase with an increase in coalification, which leads to an increase in the coal adsorption capacity. The role of oxygen-containing functional groups on the surface of coal enhances the effect of CO2 on CH4. From low-rank coal to high-rank coal, the oxygen content is reduced and the adsorption selectivity of CO2 to CH4 is reduced. In the case of low pressure (5 – 7 MPa), coal has higher adsorption selectivity. The presence of moisture reduces the proportion of the effective pore volume and reduces the adsorption capacity of CO2 and CH4. Water molecules will preferentially occupy the high-energy adsorption sites on the pore surface of coal, reducing the amount of CO2 adsorption, but hydrogen bonding and capillary condensation will form water clusters and, to some extent, contribute CO2 adsorption. Therefore, in the case of moist coal, the adsorption selectivity of CO2 to CH4 fluctuates and, according to the different effective pore volumes of different coal ranks, different variation laws are observed. The selectivity of the brown coal model decreases in the beginning, then increases sharply, and finally decreases as the moisture content increases. The selectivity of the bituminous coal model first declines and then rises as the moisture content increases. The selectivity of the anthracite coal model decreases slightly as the moisture content increases. From the perspective of CO2-ECBM, the presence of moisture in coal is inevitable, and controlling the moisture content of coal can have a beneficial effect. Because the enterable pore volume increases with the coal rank, the optimal moisture content of medium- and high-rank coal should be higher than that of the low-rank coal. Under low-pressure conditions, the adsorption selectivity of CO2/CH4 of dry and moist coal is larger. Relative to high-rank coal with deep coal seam and low oxygen content, low-rank coal reservoir is more suitable for CO2-ECBM projects.


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Corresponding Author *Corresponding author at: State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Xindu Avenue 8, Chengdu 610500, PR China. E-mail: [email protected].

Acknowledgements This work is financially supported by the National Science and Technology Major Project of China (2016ZX05044-004-002). We thank Dr. Han Jinxuan for her advice and help on the research work.

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Molecular simulation study on the effect of coal rank and moisture on

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