Sage Journals: Discover world-class research

Abstract

Methane adsorption isotherm experiments on semianthracite (2.00-2.33% $R_{o, \max}$ ) collected from the Xin’an coal mine, Henan Province, China, were conducted to investigate the effects of pore structure, coal quality, coal maceral, and coal rank on methane adsorption capacity with applications of univariate and multivariate analyses. Methane adsorption capacity varies significantly from 12.03 to 28.40 cm³/g. In univariate analysis, methane adsorption capacity has a strong positive correlation with pore specific surface area, weak positive correlations with pore volume and ash content, and weak negative correlations with moisture content and inertinite content. No correlation is observed between methane adsorption capacity and coal rank. In multivariate analysis, the mathematical model of methane adsorption capacity affected by the combined individual variables is established based on quantification theory I. There are similarities and differences between the two analyses. The similarities are that pore specific surface area has the greatest contribution to methane adsorption capacity, while coal rank has the least contribution. The differences are reflected in two aspects. Firstly, the other influencing factors contribute differently to methane adsorption capacity. Secondly, the positive or negative correlations of some influencing factors present the opposite. The mathematic model synthetically covers the combined effects of the influencing factors, which is more representative in evaluating methane adsorption capacity.

1. Introduction

Different from conventional gas resources, coalbed methane (CBM) belonging to unconventional gas resources is retained in coal reservoirs in three different forms, including adsorbed gas, free gas, and dissolved gas [1–5]. Among that, the adsorbed state is predominant [4–12]. Understanding of adsorption behavior is extremely important in estimating CBM resource and determining CBM productivity [9, 13]. More importantly, during underground coal mining, a deeper understanding of methane adsorption capacity is critical to prevent gas-related problems, such as explosive and outburst hazard [14, 15]. These significances make studies on methane adsorption capacity become one of the most valuable topics [16–18].

Many researches have been performed to investigate factors affecting methane adsorption capacity. In the traditional view, the influencing factors can be divided into two aspects: inherent properties of coal (e.g., coal rank, coal maceral, coal quality, and coal lithotype) and external conditions (e.g., temperature, pressure, burial history, sequence stratigraphy, water occurrence states, and water invasion) [6–8, 13, 19–36]. Additionally, other parameters of coal property, including coal deformation [29], macromolecular structure or crystallite structure characteristics of coal [37, 38], chemical structure of coal organic matters (e.g., aromatic structure, aliphatic structure, and coal surface functional group) [6, 14, 28, 39], and pore structure characteristics (e.g., pore specific surface area, pore volume, pore size distribution, and fractal characteristics) [7, 28, 30, 40–44], also play important roles in methane adsorption capacity. Coal rank is generally considered to be the dominant parameter affecting methane adsorption capacity [12, 45]. Coal samples used in previous studies, however, show wide range of variation in coal rank [25, 39, 46], which may mask the effects of other influencing factors. Therefore, for coals with similar rank, more in-depth analyses are essential to understand influencing factors on methane adsorption capacity and to establish a comprehensive mathematical model of methane adsorption capacity. This paper examines the variation of methane adsorption capacity and its influencing factors of the No. 2₁ coal in the Xin’an coal mine, Henan Province, China. The two major objectives are to (1) individually analyze the effects of influencing factors in terms of pore structure, coal quality, coal maceral, and coal rank on methane adsorption capacity and (2) comprehensively establish a mathematic model containing the influencing factors of methane adsorption capacity using multivariate statistical analysis. The research result may serve as an important geological basis for the safety production of the unmined area in the coal mine.

2. Experiments and Methods

The study area, the Xin’an coal mine, is located in northwestern Henan Province, China (Figures 1(a) and 1(b)). It spans within a homocline, with NE strike and SE trend. The No. 2₁ coal within the Lower Permian Shanxi Formation is economically minable. The coal underwent extreme tectonic deformation and is classified as tectonically deformed coal. A total of eleven coal samples were collected from six working faces (Figure 1(c)). One of the samples belongs to the working face 12201, two to the working face 13151, five to the working face 14211, one to the working face 14221, and the remaining two to the working faces 15051 and 15061, respectively. The samples were sieved directly and reduced in size to 2 mm (maximum particle size). Several subsamples of each coal sample were obtained by coning and quartering for proximate analysis, petrographic analysis, low-pressure N₂ adsorption analysis, and methane adsorption isotherm experiment.

Figure 1

(a) Location of the Henan Province. (b) Location of the Xin’an coal mine. (c) Map showing sampling points, working faces, and burial depth of the No. 2₁ coal in mining area of the Xin’an coal mine.

Proximate analysis was conducted in accordance with ASTM Standards D3173-11 [47], ASTM Standards D3175-11 [48], and ASTM Standards D3174-11 [49]. The polished samples for petrographic analysis were prepared according to the procedure described in Mardon et al. [50]. Maceral analysis (500 point counts) and mean maximum vitrinite reflectance ( $R_{o, \max}$ ) measurements were conducted on the same polished sections using a Leitz MPV-3 photometer microscope.

Low-pressure N₂ adsorption analysis was performed to obtain pore structure parameters including Brunauer-Emmett-Teller (BET) specific surface area ( $S_{BET}$ ) using BET model [51], Barrett-Joyner-Halenda (BJH) pore volume ( $V_{BJH}$ ), and pore size distribution (PSD) according to BJH model [52]. The samples between 0.18 mm and 0.25 mm were used for the experiment with a Micromeritics ASAP 2020 surface area and porosity analyzer. Prior to analysis, the samples were first degassed under vacuum at 105°C for 12 h. Both adsorption and desorption isotherms were measured at 77.35 K for the relative pressure ranging from 0.01 to 0.995. The adsorption isotherms were used to interpret $S_{BET}$ , $V_{BJH}$ , and PSDs.

Methane adsorption isotherm experiments on coal samples were conducted on an Isotherm Adsorption/Desorption System ISO-200, following Chinese National Standard GB/T 19560-2008 [53]. The coal samples on the air dry basis were sieved into a particle size fraction of 0.18–0.25 mm and moisture equilibrated under the controlled relative humidity (RH) condition using saturated salt solutions of K₂SO₄ (97% RH) for at least four days. The pretreated moisture-equilibrated samples were put into the sample cell of the ISO-200 for the adsorption isotherm experiment. The experimental temperature and equilibrium pressure were 30°C and up to 8 MPa, respectively. The measured adsorption data were fitted by the Langmuir model [54] to determine the Langmuir constants, i.e., Langmuir volume ( $V_{L}$ ) and Langmuir pressure ( $P_{L}$ ), and the analytical results were reported on the air dry basis.

3. Results and Discussion

3.1. Methane Adsorption Capacity

Coal rank of the samples varies in a relatively small range with $R_{o, \max}$ of 2.00-2.33% (Table 1). However, methane adsorption capacity (as indicated by $V_{L}$ ) varies widely from 12.03 cm³/g to 28.40 cm³/g (averaging at 19.66 cm³/g) (Table 1), which suggests a remarkable difference for the coal samples with similar rank. The observation is consistent with Zou et al. [55], where they demonstrated a significant change in methane adsorption capacity of the isorank coals. Methane adsorption capacity greater than 25 cm³/g is located in the working face 13151. Methane adsorption capacity of 20-25 cm³/g is concentrated in the working faces 14221, 15051, and 15061. Methane adsorption capacity less than 20 cm³/g is distributed in the remaining two working faces 12201 and 14211.

Table 1

Summarized results of petrographic analysis, proximate analysis, pore structure parameters, and Langmuir constants of the No. 2₁ coal in the Xin’an coal mine.

Coal sample	$R_{o, \max}$	$I_{mmf}$	$V_{mmf}$	$M_{ad}$	$A_{d}$	$V_{daf}$	$S_{BET}$	$V_{BJH}$	$V_{L}$	$P_{L}$
Coal sample	%	%	%	%	%	%	m²/g	cm³/g	cm³/g	MPa
12201-1	2.04	13.35	86.65	0.66	8.16	11.74	0.53	0.004193	15.11	0.92
13151-1	2.17	6.33	93.67	0.56	22.54	14.31	1.64	0.006137	27.69	1.81
13151-2	2.15	7.07	92.93	0.58	26.26	14.79	1.54	0.003232	28.40	2.01
14211-1	2.03	7.55	92.45	0.81	15.53	13.40	0.47	0.005334	17.91	1.23
14211-2	2.00	6.54	93.46	0.83	22.01	14.04	0.71	0.004805	17.38	1.05
14211-3	2.24	9.83	90.17	0.98	19.69	14.05	0.36	0.003372	15.68	1.01
14211-4	2.18	7.33	92.67	0.69	21.92	13.71	0.60	0.005001	16.65	1.08
14211-5	2.30	7.13	92.87	0.65	22.76	14.41	0.46	0.003980	12.03	0.89
14221-1	2.17	6.16	93.84	0.95	11.60	12.23	0.71	0.005120	23.69	1.17
15051-1	2.24	6.26	93.74	0.57	8.59	13.53	0.69	0.012206	21.65	0.94
15061-1	2.33	4.90	95.10	0.68	10.87	16.54	0.63	0.004905	20.07	1.42

Notes: $R_{o, \max}$ : mean maximum vitrinite reflectance; $I_{mmf}$ : inertinite content; $V_{mmf}$ : vitrinite content; $M_{ad}$ : moisture content; $A_{d}$ : ash content; $V_{daf}$ : volatile matter yield; $S_{BET}$ : BET specific surface area; $V_{BJH}$ : BJH pore volume; $V_{L}$ : Langmuir volume (ad); $P_{L}$ : Langmuir pressure (ad); ad: air dry basis; $d$ : dry basis; daf: dry and ash-free basis; mmf: mineral matter-free basis.

Methane adsorption capacity of different rank coals has been researched in previous publications. For low-rank coal, methane adsorption capacity of coals with 0.34-0.69% $R_{o, \max}$ varies from 3.33 cm³/g to 17.22 cm³/g in Moore et al. [56], and that of coals with 0.46-0.73% $R_{o, \max}$ is between 5.06 cm³/g and 13.37 cm³/g in Xu et al. [57]. For medium-high-rank coal, methane adsorption capacity is from 9.59 cm³/g to 21.38 cm³/g of coals with 0.68-1.51% $R_{o, \max}$ in Li et al. [58], from 18.28 cm³/g to 23.20 cm³/g of coals with 1.23-1.90% $R_{o, \max}$ in Meng et al. [59], from 12.01 cm³/g to 25.36 cm³/g of coals with 1.60-2.50% $R_{o, \max}$ in Yao et al. [34], and from 14.16 cm³/g to 43.36 cm³/g of coals with 0.96-2.93% $R_{o, \max}$ in Liu et al. [37]. Compared with the data in the literature mentioned above, the experimental results in our study suggested that the No. 2₁ coal has medium-high methane adsorption capacity.

3.2. Influencing Factors of Methane Adsorption Capacity

3.2.1. Pore Structure

The low-pressure N₂ adsorption and desorption isotherms of the coal samples are presented in Figures 2(a)–2(c). With the similar trend, these isotherms exhibit the feature of physisorption isotherm type IV with hysteresis loop H3 [60, 61]. The occurrence of hysteresis loop at the relative pressure from 0.45 to 0.995 is considered to be associated with capillary condensation in mesopores [61, 62]. The shape of hysteresis loop is identified with the particular pore structure [63]. The adsorbed nitrogen quantity at the maximum relative pressure varies among coal samples from different working faces, with the maximum value of 7.91 cm³/g at the sample 15051-1 and the minimum value of 2.18 cm³/g at the sample 14211-3. This substantial difference translates into the evident variations in $S_{BET}$ , $V_{BJH}$ , and PSD. As shown in Table 1, the $S_{BET}$ and $V_{BJH}$ of the samples are in the range of 0.36-1.64 m²/g (0.76 m²/g, on average) and 0.0032-0.0122 cm³/g (0.0053 cm³/g, on average), respectively. This is mainly attributed to the heterogeneous pore structure of the coal samples in different locations due to the intense tectonic deformation. The largest $S_{BET}$ and $V_{BJH}$ are present in the sample 13151-1 and sample 15051-1, respectively, while the smallest values are in the sample 14211-3 and sample 13151-2, respectively. The PSDs interpreted from the adsorption isotherms are illustrated in Figure 2(d) and can be classified as three types (I, II, and III). Type I (represented by the samples 13151-1 and 13151-2) presents a predominant peak at 2.00-2.60 nm. Type II (represented by the samples 14211-2, 14211-3, 14211-4, 15051-1, and 15061-1) shows the minor peak(s) at the beginning (approximately 2 nm) and/or the end (approximately 200-240 nm) of PSD. Type III (represented by the samples 12201-1, 14211-1, 14211-5, and 14221-1) exhibits no peak with respect to PSD.

Figure 2

(a–c) Low-pressure N₂ adsorption and desorption isotherms of the No. 2₁ coal samples from different working faces in the Xin’an coal mine. (d) Pore size distributions obtained from adsorption branches of low-pressure N₂ adsorption isotherms.

(b)

(c)

(d)

Pore structure parameters including pore specific surface area and pore volume have significant implications on methane adsorption capacity [7, 12, 20, 34, 44, 55, 64], with distinctly different mechanisms (surface adsorption versus pore volume filling) [65]. Moore [12] and Zhou et al. [44] suggested that pore specific surface area is the most important parameter in determining methane adsorption capacity, whereas Clarkson and Bustin [7] concluded that pore volume is more important. It is of great importance to have a better understanding of their roles in methane adsorption capacity in our study. The scatter plot (Figure 3(a)) displays that there is a significant positive correlation between methane adsorption capacity and $S_{BET}$ with the correlation coefficient of $r = 0.87$ . This is because gas is predominantly adsorbed on the surface of coal matrix [4, 6, 12, 43]. The larger surface area supplies substantial adsorption sites [24, 66] and stronger adsorbate-adsorbent interaction energy to methane [65] because of the adsorption force on the surface area [5]. This results in the strong positive correlation between methane adsorption capacity and $S_{BET}$ . Compared with $S_{BET}$ , the weak positive correlation between methane adsorption capacity and $V_{BJH}$ ( $r = 0.21$ ) (Figure 3(b)) suggests that the effect of $V_{BJH}$ on methane adsorption capacity may be covered by that of $S_{BET}$ .

Figure 3

Scatter plots of (a) BET specific surface area, (b) BJH pore volume, (c) moisture content, (d) ash content, (e) inertinite content, and (f) coal rank versus methane adsorption capacity of the No. 2₁ coal in the Xin’an coal mine.

(b)

(c)

(d)

(e)

(f)

3.2.2. Coal Quality

Coal quality correlates significantly with methane adsorption capacity [39, 40]. The results of proximate analysis of the samples are listed in Table 1. The No. 2₁ coal displays low moisture content (0.56-0.98%, 0.72% on average). The scatter plot (Figure 3(c)) shows that methane adsorption capacity has an insignificant negative correlation with moisture content with the $r = - 0.32$ . Generally, methane adsorption capacity decreases with the increasing moisture content until a certain critical moisture content (equilibrium moisture) is reached, after which no influence on methane adsorption capacity is observed [8, 23]. The presence of moisture weakens the interaction between coal and methane [17, 67] and competes with methane for adsorption sites [13, 25, 68]. Because of the polarity of water molecule, moisture is preferentially adsorbed [69, 70], which leads to less space available for methane [17]. Once all possible adsorption sites for moisture are occupied, methane adsorption capacity will not decrease any further [23, 70]. Meanwhile, moisture may block the accesses of gas to micropores [13, 71]. As a result, methane adsorption capacity decreases with an increase in moisture content [8, 19, 25, 31, 68, 69].

Inorganic matter (as indicated by ash content) plays a critical role in methane adsorption capacity [8, 10, 19, 25, 44, 68, 72–74]. Ash content of studied samples ranges from 8.16% to 26.26% (Table 1), which is classified as special low ash to medium ash coal according to Chinese National Standard GB/T 15224.1-2010 (Ash yield <10% for special low ash coal, 10.01-20% for low ash coal, and 20.01-30% for medium ash coal). There is a positive association between ash content and methane adsorption capacity but the relationship lacks statistical significance ( $r = 0.14$ ) (Figure 3(d)). Methane is mainly adsorbed on organic matter rather than mineral matter [31, 56, 75]. Acting as the simple diluent, mineral matter reduces the affinity of methane to the surface of coal matrix [39] and is not likely contribute to methane adsorption sites [13, 21, 25]. The increase in ash content results in the decrease in methane adsorption capacity [7, 19, 35, 56, 69]. However, methane adsorption capacity presents an increasing trend with an increase in ash content in our study. This anomaly suggests that the effect of ash content on methane adsorption capacity may be masked by other influencing factors.

3.2.3. Coal Maceral

As indicated in Table 1, coal maceral is dominated by vitrinite, followed by inertinite, with no occurrence of liptinite. Vitrinite and inertinite range from 86.65% to 95.10% and from 4.90% to 13.35%, averaging at 92.50% and 7.50%, respectively. Submaceral of vitrinite is mainly telinite and telocollinite, while that of inertinite contains mainly fusinite and some sclerotinite.

The influence of coal maceral on methane adsorption capacity is rank dependent, but with controversies. Laxminarayana and Crosdale [13] indicated that methane adsorption capacity increases with the increasing vitrinite content at high-volatile bituminous coal and decreases at semianthracite and anthracite, whereas methane adsorption capacity is not associated with coal maceral at low-medium-volatile bituminous coal. Liu et al. [26] suggested that methane adsorption capacity of vitrinite is weaker than that of inertinite for low-rank coal, and the opposite is true for high-rank coal. Flores [73] concluded that vitrinite-rich coal is characterized by greater methane adsorption capacity than inertinite-rich coal up to low-volatile bituminous coal. In contrast, Ettinger et al. [76] supported the opposite. But they have the consensus that methane adsorption capacity of coal maceral is similar at higher-rank coal (Ettinger et al. [76]; [73]). Chalmers and Bustin [45] suggested that there is no significant difference in methane adsorption capacity of coal maceral for lower-rank coal, but for higher-rank coal, vitrinite-rich coal has higher methane adsorption capacity. For the high-rank coal in our study, the negative correlation ( $r = - 0.44$ ) occurs in inertinite content and methane adsorption capacity (Figure 3(e)), which is in support of the findings of Chalmers and Bustin [45] and Liu et al. [26]. Vitrinite-rich coal is characterized by much more mesopores and micropores than inertinite-rich coal [7, 19, 45, 64] and can provide more adsorption sites for methane [70], which results in the decreasing methane adsorption capacity with an increase of inertinite content.

3.2.4. Coal Rank

As the most important indicator of metamorphism, $R_{o, \max}$ varies from 2.00% to 2.33% with the average value of 2.17% (Table 1). The coal is classified as semianthracite, according to the ASTM classification [77]. Coal rank is generally considered to be the dominant parameter affecting methane adsorption capacity [8, 12, 13, 25, 31, 34, 38, 45]. A correlation of second-order polynomial trend (“U” shaped) exists between coal rank and methane adsorption capacity with the minimum value occurring at high- or medium-volatile bituminous coal [10, 13, 19, 39, 46, 71]. Coal rank in our study exceeds medium-volatile bituminous coal, and methane adsorption capacity is supposed to increase with the increasing coal rank. Nevertheless, there is no correlation between coal rank and methane adsorption capacity ( $r = - 0.005$ , Figure 3(f)). The extremely weak negative correlation coefficient suggests that coal rank is not the main factor affecting methane adsorption capacity because of its relatively small variation range.

3.3. Mathematic Model of Methane Adsorption Capacity

Methane adsorption capacity is affected by these various factors, and the contributions of the individual variables to methane adsorption capacity may interact and be likely to be incorrect in univariate analysis. It is necessary to develop a computational scheme of methane adsorption capacity affected by the combined individual variables. Quantification theory I belongs to multivariate statistical analysis method, which can associate quantitative and qualitative variables simultaneously. The CBM geological mathematical model software was developed on the basis of quantification theory I and used to relate methane adsorption capacity to the combined effects of the independent variables in our study. The quantitative variables including $S_{BET}$ , $V_{BJH}$ , moisture content, ash content, inertinite content, and $R_{o, \max}$ were selected as the independent variables, while methane adsorption capacity as the dependent variable. There are no qualitative variables in the independent variables. The mathematical model containing all analytical factors for methane adsorption capacity established by the software is $\begin{matrix} (1) & y = 14.3877 d 1 + 211.9483 d 2 + 12.4757 d 3 - 0.2695 d 4 - 0.3185 d 5 + 2.6019 d 6, \end{matrix}$ where $y$ is methane adsorption capacity, cm³/g; d1 is $S_{BET}$ , m²/g; d2 is $V_{BJH}$ , cm³/g; d3 is moisture content, %; d4 is ash content, %; d5 is inertinite content, %; d6 is $R_{o, \max}$ , %.

The model was checked using ANOVA (i.e., analysis of variance, $F$ -test) method [78]. The $F$ -distribution for $F$ -test of the mathematical model is 12.98, which is larger than the $F_{0.1} = 4.01$ (the value of the $F$ -distribution at the 10% significance level). The $F$ -test suggests that the mathematical model is proved to be significant. Consequently, the mathematical model can be used to evaluate methane adsorption capacity of the No.2₁ coal in our study area.

The measured values from the experiment and the predicted values from the mathematical model of methane adsorption capacity are listed in Table 2. The deviation of the measured values to the predicted values varies from -1.75 cm³/g to 2.06 cm³/g. The maximum and minimum deviations are belonging to the working face 13151, which is mainly attributed to the relatively high methane adsorption capacity. The relative error varies from 0.08% to 9.31% with the average value of 4.59%. The correlation coefficient between the measured values and the predicted values of methane adsorption capacity is 0.98, as illustrated in Figure 4, indicating there is a good consistency between the mathematical model prediction and the experimental measurement.

Table 2

Comparison of measured values and calculated values of methane adsorption capacity of the No. 2₁ coal in the Xin’an coal mine.

Coal sample	Measured value, $V_{M}$	Predicted value, $V_{P}$	$Deviation V_{M} - V_{P}$	$(∣ V_{M} - V_{P} ∣) / V_{M}$
Coal sample	cm³/g	cm³/g	cm³/g	%
12201-1	15.11	15.60	-0.49	3.24
13151-1	27.69	29.44	-1.75	6.32
13151-2	28.40	26.34	2.06	7.25
14211-1	17.91	16.69	1.22	6.81
14211-2	17.38	18.78	-1.40	8.06
14211-3	15.68	15.51	0.17	1.08
14211-4	16.65	15.73	0.92	5.53
14211-5	12.03	13.15	-1.12	9.31
14221-1	23.69	23.71	-0.02	0.08
15051-1	21.65	21.15	0.50	2.31
15061-1	20.07	20.16	-0.09	0.45

Figure 4

Scatter plot of measured versus predicted methane adsorption capacity of the No. 2₁ coal in the Xin’an coal mine.

3.4. Comparison of Univariate Analysis and Multivariate Analysis

For the univariate analysis expressed by the scatter plots, the correlation coefficients suggest that the contributions of the analytical factors to methane adsorption capacity in a descending order are as follows: $S_{BET}$ , inertinite content, moisture content, $V_{BJH}$ , ash content, and $R_{o, \max}$ . Methane adsorption capacity is positively correlated with $S_{BET}$ , $V_{BJH}$ , and ash content, is negatively correlated with inertinite content and moisture content, and has no correlation with $R_{o, \max}$ . For the multivariate analysis expressed by the mathematical model, the partial correlation coefficients of independent variables are, in same order as the mathematical model, 0.96, 0.28, 0.74, 0.73, 0.43, and 0.18, respectively. This implies that the contributions of independent variables to methane adsorption capacity from large to small are $S_{BET}$ , moisture content, ash content, inertinite content, $V_{BJH}$ , and $R_{o, \max}$ . Methane adsorption capacity has positive relationships with $S_{BET}$ , $V_{BJH}$ , moisture content, and $R_{o, \max}$ and negative relationships with ash content and inertinite content according to the mathematical model.

There are similarities and differences between the univariate analysis and the multivariate analysis. The similarities are that $S_{BET}$ has the greatest contribution to methane adsorption capacity, while $R_{o, \max}$ has the least contribution. The differences are reflected in two aspects. Firstly, the contributions of the other influencing factors ( $S_{BET}$ and $R_{o, \max}$ not included) are in different orders. Taking inertinite content as an example, it ranks second in the univariate analysis but fourth in the multivariate analysis. Secondly, the positive or negative relationships of some influencing factors such as ash content present the opposite in the two analyses. Because the mathematic model synthetically covers the combined effects of the influencing factors, it is more representative in evaluating methane adsorption capacity.

4. Conclusions

Methane adsorption isotherm experiments on semianthracite collected from the Xin’an coal mine, Henan Province, China, were conducted to investigate the effects of pore structure, coal quality, coal maceral, and coal rank on methane adsorption capacity using univariate analysis expressed by the scatter plots and multivariate analysis expressed by the mathematical model. The key conclusions are summarized as follows: (1)

Methane adsorption capacity varies widely from 12.03 cm³/g to 28.40 cm³/g, suggesting a remarkable difference for the coal with similar rank (2.00-2.33% $R_{o, \max}$ )

(2)

In univariate analysis, methane adsorption capacity has a strong positive correlation with $S_{BET}$ ( $r = 0.87$ ), weak positive correlations with $V_{BJH}$ ( $r = 0.21$ ) and ash content ( $r = 0.14$ ), weak negative correlations with moisture content ( $r = - 0.32$ ) and inertinite content ( $r = - 0.44$ ), and no correlation with $R_{o, \max}$ ( $r = - 0.005$ )

(3)

In multivariate analysis, the mathematical model of methane adsorption capacity is established: $\begin{matrix} (2) & y = 14.3877 d 1 + 211.9483 d 2 + 12.4757 d 3 - 0.2695 d 4 - 0.3185 d 5 + 2.6019 d 6, \end{matrix}$ where $y$ is methane adsorption capacity; d1 to d6 represent $S_{BET}$ , $V_{BJH}$ , moisture content, ash content, inertinite content, and $R_{o, \max}$ , respectively

(4)

S_BET has the greatest contribution to methane adsorption capacity, while $R_{o, \max}$ has the least contribution in both univariate analysis and multivariate analysis. The differences in the two analyses are that the other influencing factors contribute differently to methane adsorption capacity, and the positive or negative correlations of some influencing factors present the opposite

(5)

The mathematic model is more representative in evaluating methane adsorption capacity because it covers the combined effects of the influencing factors

Footnotes

Data Availability

All data generated or analyzed during this study are included in this article.

Conflicts of Interest

The authors declare that there is no conflict of interest.

Acknowledgments

We gratefully acknowledge the financial support from the National Natural Science Foundation of China (grant numbers 41972184 and 41902177) and the research foundation of Jiangxi Provincial Department of Education of China (grant number GJJ190570). We sincerely appreciate the anonymous reviewers and the associate editor for their constructive comments to substantially improve the manuscript.

References

Alexeev

A. D.

Ulyanova

E. V.

Starikov

G. P.

Kovriga

N. N.

Latent methane in fossil coals

Fuel 2004 83

6654140

10 1407 1411

10.1016/j.fuel.2003.07.001

2-s2.0-1842685722

Gunter

W. D.

Gentzis

Rottenfusser

B. A.

Richardson

R. J. H.

Deep coalbed methane in Alberta, Canada: a fuel resource with the potential of zero greenhouse gas emissions

Energy Conversion and Management 1997 38

6654140

S217 S222

10.1016/S0196-8904(96)00272-5

Noak

Control of gas emissions in underground coal mines

International Journal of Coal Geology 1998 35

6654140

1-4 57 82

10.1016/S0166-5162(97)00008-6

2-s2.0-0032004042

Rice

D. D.

Law

B. E.

Rice

D. D.

Composition and origins of coalbed gas

Hydrocarbons from Coal 1993

AAPG Studies in Geology

159 184

Rightmire

C. T.

Rightmire

C. T.

Eddy

G. E.

Kirr

J. N.

Coalbed methane resource

Coalbed Methane Resources of the United States 1984

AAPG Studies in Geology

1 13

Busch

Gensterblum

CBM and CO₂-ECBM related sorption processes in coal: a review

International Journal of Coal Geology 2011 87

6654140

2 49 71

10.1016/j.coal.2011.04.011

2-s2.0-79960371036

Clarkson

C. R.

Bustin

R. M.

Variation in micropore capacity and size distribution with composition in bituminous coal of the Western Canadian Sedimentary Basin: implications for coalbed methane potential

Fuel 1996 75

6654140

13 1483 1498

10.1016/0016-2361(96)00142-1

2-s2.0-0001680664

Hildenbrand

Krooss

B. M.

Busch

Gaschnitz

Evolution of methane sorption capacity of coal seams as a function of burial history—a case study from the Campine Basin, NE Belgium

International Journal of Coal Geology 2006 66

6654140

3 179 203

10.1016/j.coal.2005.07.006

2-s2.0-32644445572

Karacan

C. Ö.

Okandan

Assessment of energetic heterogeneity of coals for gas adsorption and its effect on mixture predictions for coalbed methane studies

Fuel 2000 79

6654140

15 1963 1974

10.1016/S0016-2361(00)00055-7

2-s2.0-0343777288

10.

Levine

J. R.

Law

B. E.

Rice

D. D.

Coalification: the evolution of coal as source rock and reservoir

Hydrocarbons from Coal 1993

AAPG Studies in Geology

39 77

11.

Milewska-Duda

Duda

Nodzeñski

Lakatos

Absorption and adsorption of methane and carbon dioxide in hard coal and active carbon

Langmuir 2000 16

6654140

12 5458 5466

10.1021/la991515a

2-s2.0-0033707709

12.

Moore

T. A.

Coalbed methane: a review

International Journal of Coal Geology 2012 101

6654140

36 81

10.1016/j.coal.2012.05.011

2-s2.0-84865049192

13.

Laxminarayana

Crosdale

P. J.

Role of coal type and rank on methane sorption characteristics of Bowen Basin, Australia coals

International Journal of Coal Geology 1999 40

6654140

4 309 325

10.1016/s0166-5162(99)00005-1

2-s2.0-0033167313

14.

Hao

Wen

Chu

Effect of the surface oxygen groups on methane adsorption on coals

Applied Surface Science 2013 264

6654140

433 442

10.1016/j.apsusc.2012.10.040

2-s2.0-84870575456

15.

Zhang

Development and current situation of study on theory of methane adsorption on coal

Procedia Earth and Planetary Science 2011 3

6654140

318 324

10.1016/j.proeps.2011.09.100

16.

Heuchel

Davies

G. M.

Buss

Seaton

N. A.

Adsorption of carbon dioxide and methane and their mixtures on an activated carbon: simulation and experiment

Langmuir 1999 15

6654140

25 8695 8705

10.1021/la9904298

17.

Zhang

Clennell

M. B.

Dewhurst

D. N.

Liu

Combined Monte Carlo and molecular dynamics simulation of methane adsorption on dry and moist coal

Fuel 2014 122

6654140

186 197

10.1016/j.fuel.2014.01.006

2-s2.0-84893220740

18.

Zhang

Sang

Physical chemistry mechanism of influence of liquid water on coalbed methane adsorption

Procedia Earth and Planetary Science 2009 1

6654140

1 263 268

10.1016/j.proeps.2009.09.042

2-s2.0-71749086326

19.

Bustin

R. M.

Clarkson

C. R.

Geological controls on coalbed methane reservoir capacity and gas content

International Journal of Coal Geology 1998 38

6654140

1-2 3 26

10.1016/s0166-5162(98)00030-5

2-s2.0-0032467642

20.

Clarkson

C. R.

Bustin

R. M.

The effect of pore structure and gas pressure upon the transport properties of coal: a laboratory and modeling study. 1. Isotherms and pore volume distributions

Fuel 1999 78

6654140

11 1333 1344

10.1016/s0016-2361(99)00055-1

2-s2.0-0033190484

21.

Crosdale

P. J.

Beamish

B. B.

Valix

Coalbed methane sorption related to coal composition

International Journal of Coal Geology 1998 35

6654140

1-4 147 158

10.1016/s0166-5162(97)00015-3

2-s2.0-0032001643

22.

Guan

Liu

Wang

Zhao

The temperature effect on the methane and CO₂ adsorption capacities of Illinois coal

Fuel 2018 211

6654140

241 250

10.1016/j.fuel.2017.09.046

2-s2.0-85029684805

23.

Krooss

B. M.

van Bergen

Gensterblum

Siemons

Pagnier

H. J. M.

David

High-pressure methane and carbon dioxide adsorption on dry and moisture-equilibrated Pennsylvanian coals

International Journal of Coal Geology 2002 51

6654140

2 69 92

10.1016/S0166-5162(02)00078-2

2-s2.0-0036640880

24.

Lamberson

M. N.

Bustin

R. M.

Coalbed methane characteristics of Gates Formation coals, northeastern British Columbia: effect of maceral composition

AAPG Bulletin 1993 77

6654140

2062 2076

10.1306/bdff8fd8-1718-11d7-8645000102c1865d

25.

Laxminarayana

Crosdale

P. J.

Controls on methane sorption capacity of Indian coals

AAPG Bulletin 2002 86

6654140

201 212

10.1306/61eeda8a-173e-11d7-8645000102c1865d

26.

Liu

Jin

Sun

Meng

Coalbed methane adsorption capacity related to maceral compositions

Energy Exploration & Exploitation 2020 38

6654140

1 79 91

10.1177/0144598719870325

27.

Liu

Zhu

Liu

Tang

Temperature effect on gas adsorption capacity in different sized pores of coal: experiment and numerical modeling

Journal of Petroleum Science and Engineering 2018 165

6654140

821 830

10.1016/j.petrol.2018.03.021

2-s2.0-85043368579

28.

Mosher

Liu

Rupp

Wilcox

Molecular simulation of methane adsorption in micro- and mesoporous carbons with applications to coal and gas shale systems

International Journal of Coal Geology 2013 109-110

6654140

36 44

10.1016/j.coal.2013.01.001

2-s2.0-84874720743

29.

Pan

Hou

Bai

Zhao

Coalbed methane sorption related to coal deformation structures at different temperatures and pressures

Fuel 2012 102

6654140

760 765

10.1016/j.fuel.2012.07.023

2-s2.0-84866629051

30.

Saghafi

Pinetown

K. L.

Grobler

P. G.

van Heerden

J. H. P.

CO₂ storage potential of South African coals and gas entrapment enhancement due to igneous intrusions

International Journal of Coal Geology 2008 73

6654140

74 87

10.1016/j.coal.2007.05.003

2-s2.0-36248962149

31.

Scott

A. R.

Hydrogeologic factors affecting gas content distribution in coal beds

International Journal of Coal Geology 2002 50

6654140

1-4 363 387

10.1016/S0166-5162(02)00135-0

2-s2.0-0036587609

32.

Wang

Yao

Wen

Sun

Yuan

Effect of water occurrences on methane adsorption capacity of coal: a comparison between bituminous coal and anthracite coal

Fuel 2020 266

6654140

117102

10.1016/j.fuel.2020.117102

33.

Wang

Tang

Influence of water invasion on methane adsorption behavior in coal

International Journal of Coal Geology 2018 197

6654140

74 83

10.1016/j.coal.2018.08.004

2-s2.0-85052471892

34.

Yao

Liu

Tang

Che

Huang

Preliminary evaluation of the coalbed methane production potential and its geological controls in the Weibei Coalfield, Southeastern Ordos Basin, China

International Journal of Coal Geology 2009 78

6654140

1 1 15

10.1016/j.coal.2008.09.011

2-s2.0-59649104950

35.

Zhang

Yan

Yang

Mangi

H. N.

Wang

Yang

Zhang

Liang

She

Effects of sequence stratigraphy on coal characteristics and CH₄ adsorption capacity of the low-rank coal in Santanghu Basin, China

Journal of Natural Gas Science and Engineering 2020 81

6654140

103467

10.1016/j.jngse.2020.103467

36.

Zhu

Ren

Wan

Lin

B. Q.

Yang

Methane adsorption on coals with different coal rank under elevated temperature and pressure

Fuel 2019 254

6654140

115686

10.1016/j.fuel.2019.115686

2-s2.0-85067691294

37.

Liu

Song

Nie

Wang

Sun

Coal macromolecular structural characteristic and its influence on coalbed methane adsorption

Fuel 2018 222

6654140

687 694

10.1016/j.fuel.2018.03.015

2-s2.0-85043229233

38.

Meng

Niu

Crystallite structure characteristics and its influence on methane adsorption for different rank coals

ACS Omega 2019 4

6654140

24 20762 20772

10.1021/acsomega.9b03165

31858063

39.

Lin

Wang

Zhao

Ruan

Surface properties of pulverized coal and its effects on coal mine methane adsorption behaviors under ambient conditions

Powder Technology 2015 270

6654140

278 286

10.1016/j.powtec.2014.10.020

2-s2.0-84909978983

40.

Dutta

Bhowmik

Das

Methane and carbon dioxide sorption on a set of coals from India

International Journal of Coal Geology 2011 85

6654140

3-4 289 299

10.1016/j.coal.2010.12.004

2-s2.0-79951557024

41.

Liu

Mou

Cheng

Impact of pore structure on gas adsorption and diffusion dynamics for long-flame coal

Journal of Natural Gas Science and Engineering 2015 22

6654140

203 213

10.1016/j.jngse.2014.11.030

2-s2.0-84912101099

42.

Zhao

Tang

Mathews

J. P.

Tao

Coal seam porosity and fracture heterogeneity of macrolithotypes in the Hancheng Block, eastern margin, Ordos Basin, China

International Journal of Coal Geology 2016 159

6654140

18 29

10.1016/j.coal.2016.03.019

2-s2.0-84962787226

43.

Zhao

Jiang

Chu

Methane adsorption behavior on coal having different pore structures

International Journal of Mining Science and Technology 2012 22

6654140

6 757 761

10.1016/j.ijmst.2012.11.011

2-s2.0-84873740503

44.

Zhou

Liu

Cai

Karpyn

Yao

Comparative analysis of nanopore structure and its effect on methane adsorption capacity of Southern Junggar coalfield coals by gas adsorption and FIB-SEM tomography

Microporous and Mesoporous Materials 2018 272

6654140

117 128

10.1016/j.micromeso.2018.06.027

2-s2.0-85048828120

45.

Chalmers

G. R. L.

Bustin

R. M.

On the effects of petrographic composition on coalbed methane sorption

International Journal of Coal Geology 2007 69

6654140

4 288 304

10.1016/j.coal.2006.06.002

2-s2.0-33846452492

46.

Yee

Seidle

J. P.

Hanson

W. B.

Law

B. E.

Rice

D. D.

Gas sorption on coal and measurement of gas content

Hydrocarbons from Coal 1993

AAPG Studies in Geology

203 218

47.

ASTM Standard D3173-11

Test method for moisture in the analysis sample of coal and coke 2011

West Conshohocken, PA

ASTM International

10.1520/d3173-11

48.

ASTM Standard D3175-11

Test method for volatile matter in the analysis sample of coal and coke 2011

West Conshohocken, PA

ASTM International

10.1520/d3175-11

49.

ASTM Standard D3174-11

Test method for ash in the analysis sample of coal and coke 2011

West Conshohocken, PA

ASTM International

10.1520/d3174-11

50.

Mardon

S. M.

Eble

C. F.

Hower

J. C.

Takacs

Mastalerz

Marc Bustin

Organic petrology, geochemistry, gas content and gas composition of Middle Pennsylvanian age coal beds in the Eastern Interior (Illinois) Basin: implications for CBM development and carbon sequestration

International Journal of Coal Geology 2014 127

6654140

56 74

10.1016/j.coal.2014.02.002

2-s2.0-84897077074

51.

Brunauer

Emmett

P. H.

Teller

Adsorption of gases in multimolecular layers

Journal of the American Chemical Society 1938 60

6654140

2 309 319

10.1021/ja01269a023

2-s2.0-12444272525

52.

Barrett

E. P.

Joyner

L. G.

Halenda

P. P.

The determination of pore volume and area distributions in porous substances. I. computations from nitrogen isotherms

Journal of the American Chemical Society 1951 73

6654140

1 373 380

10.1021/ja01145a126

2-s2.0-0342445773

53.

GB/T 19560–2008

Chinese National Standard

Coal, Experimental method of high-pressure adsorption isothermal to coal-capacity method 2008

National Standardization Management Committee

54.

Langmuir

The adsorption of gases on plane surfaces of glass, mica and platinum

Journal of the American Chemical Society 1918 40

6654140

9 1361 1403

10.1021/ja02242a004

2-s2.0-0007601535

55.

Zou

Lin

Liu

Zhou

Zhang

Yan

F. Z.

Variation of methane adsorption property of coal after the treatment of hydraulic slotting and methane pre-drainage: a case study

Journal of Natural Gas Science and Engineering 2014 20

6654140

396 406

10.1016/j.jngse.2014.07.024

2-s2.0-84907373531

56.

Moore

T. A.

Bowe

Nas

High heat flow effects on a coalbed methane reservoir, East Kalimantan (Borneo), Indonesia

International Journal of Coal Geology 2014 131

6654140

7 31

10.1016/j.coal.2014.05.012

2-s2.0-84902524041

57.

Tang

D. Z.

Liu

D. M.

Tang

S. H.

Yang

Chen

X. Z.

Deng

C. M.

Study on coalbed methane accumulation characteristics and favorable areas in the Binchang area, southwestern Ordos Basin, China

International Journal of Coal Geology 2012 95

6654140

1 11

10.1016/j.coal.2012.02.001

2-s2.0-84862791534

58.

Tang

Yang

The pore-fracture system properties of coalbed methane reservoirs in the Panguan Syncline, Guizhou, China

Geoscience Frontiers 2012 3

6654140

6 853 862

10.1016/j.gsf.2012.02.005

2-s2.0-84867874370

59.

Meng

Tang

Meng

Geological controls and coalbed methane production potential evaluation: a case study in Liulin area, eastern Ordos Basin, China

Journal of Natural Gas Science and Engineering 2014 21

6654140

95 111

10.1016/j.jngse.2014.07.034

2-s2.0-84906823882

60.

Gregg

S. J.

Sing

K. S. W.

Adsorption, Surface Area and Porosity 1982 2nd

New York

Academic Press

61.

Sing

K. S. W.

Everett

D. H.

Haul

R. A. W.

Moscou

Pierotti

R. A.

Rouquérol

Siemieniewska

Reporting physisorption data for gas/solid systems with special reference to the determination of surface area and porosity (Recommendations 1984)

Pure and Applied Chemistry 1985 57

6654140

4 603 619

10.1351/pac198557040603

2-s2.0-84939775172

62.

Rouquerol

Sing

K. S. W.

Maurin

Llewellyn

Rouquerol

Sing

K. S. W.

Llewellyn

Maurin

Introduction

Adsorption by Powders and Porous Solids: Principles, Methodology and Applications 2012 2nd

Amsterdam

Elsevier

1 24

63.

Cai

Y.-D.

Liu

D.-M.

Liu

Z.-H.

Zhou

Y.-F.

Che

Evolution of pore structure, submaceral composition and produced gases of two Chinese coals during thermal treatment

Fuel Processing Technology 2017 156

6654140

298 309

10.1016/j.fuproc.2016.09.011

2-s2.0-84994766874

64.

Mastalerz

Drobniak

Strąpoć

Solano Acosta

Rupp

Variations in pore characteristics in high volatile bituminous coals: implications for coal bed gas content

International Journal of Coal Geology 2008 76

6654140

3 205 216

10.1016/j.coal.2008.07.006

2-s2.0-53149139687

65.

Heller

Zoback

Adsorption of methane and carbon dioxide on gas shale and pure mineral samples

Journal of Unconventional Oil and Gas Resources 2014 8

6654140

14 24

10.1016/j.juogr.2014.06.001

2-s2.0-84923291927

66.

Gan

Nandi

S. P.

Walker

P. L.

Jr.

Nature of the porosity in American coals

Fuel 1972 51

6654140

4 272 277

10.1016/0016-2361(72)90003-8

2-s2.0-49649130354

67.

Kumar

Elsworth

Liu

Pone

Mathews

J. P.

Optimizing enhanced coalbed methane recovery for unhindered production and CO₂ injectivity

International Journal of Greenhouse Gas Control 2012 11

6654140

86 97

10.1016/j.ijggc.2012.07.028

2-s2.0-84865477706

68.

Crosdale

P. J.

Moore

T. A.

Mares

T. E.

Influence of moisture content and temperature on methane adsorption isotherm analysis for coals from a low-rank, biogenically-sourced gas reservoir

International Journal of Coal Geology 2008 76

6654140

1-2 166 174

10.1016/j.coal.2008.04.004

2-s2.0-51249124437

69.

Clarkson

C. R.

Bustin

R. M.

Binary gas adsorption/desorption isotherms: effect of moisture and coal composition upon carbon dioxide selectivity over methane

International Journal of Coal Geology 2000 42

6654140

4 241 271

10.1016/S0166-5162(99)00032-4

2-s2.0-0034162880

70.

Wang

G. G. X.

Zhang

Wei

Jiang

Qin

A review on transport of coal seam gas and its impact on coalbed methane recovery

Frontiers of Chemical Science and Engineering 2011 5

6654140

2 139 161

10.1007/s11705-010-0527-4

2-s2.0-80053272291

71.

Prinz

Littke

Development of the micro- and ultramicroporous structure of coals with rank as deduced from the accessibility to water

Fuel 2005 84

6654140

1645 1652

10.1016/j.fuel.2005.01.010

2-s2.0-18844439188

72.

Day

Fry

Sakurovs

Swelling of coal in carbon dioxide, methane and their mixtures

International Journal of Coal Geology 2012 93

6654140

40 48

10.1016/j.coal.2012.01.008

2-s2.0-84858156589

73.

Flores

R. M.

Coalbed methane: from hazard to resource

International Journal of Coal Geology 1998 35

6654140

1-4 3 26

10.1016/S0166-5162(97)00043-8

2-s2.0-0031999027

74.

Gensterblum

Busch

Krooss

B. M.

Molecular concept and experimental evidence of competitive adsorption of H₂O, CO₂ and CH₄ on organic material

Fuel 2014 115

6654140

581 588

10.1016/j.fuel.2013.07.014

2-s2.0-84882398623

75.

Butland

C. I.

Moore

T. A.

Secondary biogenic coal seam gas reservoirs in New Zealand: a preliminary assessment of gas contents

International Journal of Coal Geology 2008 76

6654140

1-2 151 165

10.1016/j.coal.2008.05.017

2-s2.0-51249109922

76.

Ettinger

Eremin

Zimakov

Yanovska

Natural factors influencing coal sorption properties. I. Petrography and sorption properties of coals

Fuel 1966 45

6654140

267 275

77.

ASTM Standard D388-15

Classification of coals by rank 2015

West Conshohocken, PA

ASTM International

10.1520/d0388-15

78.

Patten

M. L.

Understanding Research Methods: An Overview of the Essentials 2002 3rd

Los Angeles

Pyrczak Publishing

Univariate and Multivariate Analyses of Influencing Factors on Methane Adsorption Capacity of Semianthracite

Abstract

1. Introduction

2. Experiments and Methods

3. Results and Discussion

3.1. Methane Adsorption Capacity

3.2. Influencing Factors of Methane Adsorption Capacity

3.2.1. Pore Structure

3.2.2. Coal Quality

3.2.3. Coal Maceral

3.2.4. Coal Rank

3.3. Mathematic Model of Methane Adsorption Capacity

3.4. Comparison of Univariate Analysis and Multivariate Analysis

4. Conclusions

Footnotes

Data Availability

Conflicts of Interest

Acknowledgments

References