Prediction of top-coal caving and drawing characteristics by the analytic hierarchy process-fuzzy discrimination method in extra-thick coal seams

Abstract

The cavability assessment of thick coal seams, which contain over half of the world coal reserves, is quite topical. However, assessment of top-coal caving and drawing characteristics (CDC) in extra-thick (over 20 m) coal seams are addressed in scarce publications, due to the problem intricacy. In this study, the analytic hierarchy process (AHP)-fuzzy discrimination method is proposed for the top-coal CDC analysis as applied to extra-thick coal seam of the Laosangou mine field in China. For the AHP model elaboration, six factors were selected as secondary indicators, namely: coal burial depth, coal seam strength, caving ratio, fractures, roof conditions, gangues; whereas 13 more factors, including mechanical mining height, water absorption rate, porosity, etc., were used as tertiary indicators. For verification, this method was applied to the case studies of Tashan and Tongxin mines in China, for which the top-coal CDC calculations were made using the available experimental data under 36 particular conditions. The latter involved various combinations of caving ratios, gangue positions, and thickness values. The calculated results strongly indicate that the mechanical mining height and gangue position are the influencing factors controlling the top-coal CDC, as well as revealed certain particular patterns of their effect. Moreover, partitioning of the first mining area was performed based on the mechanical mining height criterion. This article successfully combines the fuzzy theory with mining engineering, which possesses high application prospects and academic merits.

Keywords

Fuzzy evaluation influencing factors caving and drawing characteristic membership function caving ratio gangue position gangue thickness

1 Introduction

Thick coal seams in China occupy an important status in coal production since they account for over 40% of the total coal reserve and about 45% of the coal yield. Top-coal caving is mainly used for the mining of thick coal seams [1 –3], due to the advantages of high per unit area yield, low energy consumption, and simple roadway arrangement [4]. The top-coal caving method has been advanced in Europe, implemented in China in the late 1980s, and nowadays gained a wide popularity worldwide [5]. The research efforts related to top-coal caving include the theoretical analysis [5, 6], physical simulation [7], numerical simulation [8], and field measurements [9], which cover a wide range of influencing factors [10]. The latter range includes mining depth, coal seam thickness/strength, gangue properties, roof conditions, and many other factors. Different top-coal caving techniques induce diverse sub-level caving patterns under varying geological conditions, which are investigated in numerous studies aimed at the top-coal caving ratio improvement that, in turn, are controlled by the particular CDC [11, 12]. Therefore, a reasonable assessment of the top-coal CDC is an integral component of the top-coal caving process.

However, the influencing factors of the top-coal CDC may be coupled, complex, and have intrinsic features of fuzziness, complexity, and uncertainty, which makes elaboration and application of any reliable and universal discrimination methods to these factors quite problematic, moreover that their impact degrees introduce another type of uncertainty. To resolve the problem related to fuzzy and uncertain issues [11, 13], it is expedient to develop a reliable assessment method based on fuzzy mathematics [14], given the top-coal CDC influencing factors being interrelated and coupled [15 –18]. Thus, the fuzzy discrimination method was applied by several researchers to the analysis of top-coal CDC in coal seams of the thickness not exceeding 12 m, with the assessment indicator system being constructed using the fuzzy mathematics principles [19, 20]. In another study, the top-coal CDC impacting factors were identified, and the membership function was derived based on large arrays of measured data, with a further quantitative prediction of the CDC [21]. Construction of the assessment indicator system based on the measured data turned out to be quite efficient and reliable for the CDC prediction, which strongly suggests the fuzzy discrimination method applicability to this research issue and its promising perspectives in the engineering practice.

Therefore, a scientific substantiation of TCC should be based on a comprehensive analysis of the key factors controlling it, i.e., top-coal caving and drawing characteristics (CDC), including coal burial depth, coal seam thickness, gangue properties, and roof conditions. However, the available studies on top-coal CDC of extra-thick coal seams are mainly based on the field experience and are too scarce to form a scientifically substantiated CDC evaluation system [1 , 22–24]. When conditions change, the renewed argument and analysis are required. To tackle this problem, a universal evaluation method is proposed in this article, with a practical implementation of the results on top-coal caving for extra-thick coal seams of the Laosangou coal mine. To ensure the reliability of the research results, the Tashan and Tongxin coal mines were also taken as examples for the method verification.

The authors adopted a fuzzy analytic hierarchy process to investigate the top-coal CDC of extra-thick coal seams, which provides a theoretical foundation for the exploitation of extra-thick coal seams. The research results effectively combine the fuzzy theory with the practical application of mining engineering, which facilitates the development of an interdisciplinary approach to this problem.

2 Factors for evaluating top-coal CDC and corresponding membership functions

2.1 Geology of the Laosangou coal mine

The Laosangou coal mine is located in the northern section of central Jungar Coalfield, about 15 kilometers northwest of the town of Xuejiawan in Jungar Banner, Erdos City, Inner Mongolia Autonomous Region. The #6 coal seam, the main seam in the mine’s first mining area, has an average coal burial depth of 573.4 m and roughly ranges between 17.5 m and 30 m thick (averaging 20 m). The parameters of the coal are as follows: uniaxial compressive strength: 1.28 to 1.64 MPa; porosity: 19.36%; pore water content: 7.92%; and softening coefficient: 0.48. This coal seam exhibits fractures, primarily tensile ones. Ranging between 1μm and 10μm in width, they are easily detected by scanning electron microscopy at 150× magnification. The direct roof overlying the seam is a layer of mudstone, which has an average thickness of 6.9 m and a compressive strength of 3.98 MPa. The main roof is a layer of fine-grained sandstone, whose average thickness is 10.8 m and compressive strength is 30 MPa. Gangues within this coal seam are primarily composed of mudstone; their average compressive strength is 14.7 MPa. The gangues are classified into three categories: upper, middle, and bottom gangues, depending on their vertical locations in the seam. Upper gangues are located within the upper one-third of the top coal, middle gangues lie within the middle one-third of the top coal, and bottom gangues are contained in the bottom one-third of the top coal above the roof supports. Based on thickness, these gangues are subdivided into three groups: thin gangues (<0.4 m), medium thick gangues (0.4 to 1.0 m) and thick gangues (>1.0 m). A survey of the site shows that thin, medium thick, and thick gangues account for 2.3%, 83.7%, and 14% of the total number of gangue layers throughout the coal seam, respectively.

2.2 Influencing factors and membership functions

The key to evaluating top-coal CDC is to identify influencing factors of CDC. A membership function that quantifies CDC can directly reflect the extent to which a factor affects CDC. Therefore, reasonable membership functions are the basis for evaluation of CDC. In the light of the results of previous studies, the coal burial depth, coal seam strength, seam thickness, and three additional parameters were selected as the influencing factors, and corresponding membership functions were constructed.

Coal burial depth (H)

The coal burial depth mainly affects the magnitude of in-situ stress in top coal and has a direct effect on the support pressure. During top-coal caving mining, the top coal tends to experience deformation, fracture, loosening, and drawing. The extent, to which the coal is fractured during the fracturing stage, is the key factor that affects CDC; it is directly correlated with the support pressure. Using the modified Griffith theory, one can derive the critical depth required for top-coal caving mining via the following equation [25]: $H = \frac{R_{c}}{υ [K - λ \cdot \frac{\sqrt{1 + {tg}^{2}} + tg φ}{\sqrt{1 + {tg}^{2}} + tg φ}]}$ (1)

where λ is lateral pressure coefficient; υ is Poisson’s ratio; K is stress concentration factor of working surface; R_c is the uniaxial compressive strength of coal; φ is internal friction angle of coal.

Normally, top-coal caving is not applied to coal seams buried at depths shallower than 100 m, while coal seams deeper than 500 m are suitable for top-coal caving [1, 5]. In this study, the ratio of actual coal burial depth (H) to the critical one, at which the rock pressure is just enough to break coal (H_min), denoted as NH, was used to determine the membership function for the coal burial depth [26]:

$\begin{matrix} u_{1} (N_{H}) \\ = {\begin{matrix} 1, N_{H} > 1 \\ 0.8 + \frac{4}{3} (N_{H} - 0.85), 0.85 < N_{H} \leq 1 \\ 0.4 + 1.6 (N_{H} - 0.6), 0.6 < N_{H} \leq 0.85 \\ 0.2, N_{N} \leq 0.6 \end{matrix} \end{matrix}$ (2) where H is the actual coal burial depth, and the value of H_min is derived via Equation (1)

Coal seam strength (Rc)

Pressure from the overlying strata is the primary force that causes top coal to break, followed by the support pressure. The uniaxial compressive strength of a coal seam represents its ability to respond to stress and is a key indicator of how difficult it is to break up this seam. It affects the process and the degree of top coal fragmentation under the support pressure. The higher a seam’s R_c, the greater the stress required to break it up and the poorer its CDC. The membership function for R_c is as follows [6]: $u_{2} (R_{c}) = {\begin{matrix} 1, R_{c} < 10 \\ 0.8 + 0.04 (15 - R_{c}), 10 \leq R_{c} < 15 \\ 0.4 + \frac{2}{15} (30 - R_{c}), 15 \leq R_{c} < 30 \\ 0.2, R_{c} \geq 30 \end{matrix}$ (3)

Seam thickness (M)/caving ratio (C)

Seam thickness affects the caving ratio. For a certain seam thickness, a smaller caving ratio indicates a smaller top coal thickness. Like a false roof, thin top coal tends to fall together with the underlying coal, making it difficult to ensure that the top coal will collapse behind the tail of the supports. As a result, the direct roof is usually broken in advance and then drawn together with the caving top coal. This can affect the coal quality and recovery rate. Conversely, a high caving ratio indicates a thick layer of top coal. If the top coal is too thick, it is difficult to achieve the sufficient loosening of top coal in the roof control area and ensure that cave-ins of top coal occur within the caving zone. Therefore, a reasonable caving ratio is of vital importance to top-coal caving. In this study, four caving ratios, including 1/3, 1/3.4, 1/4, and 1/4.7, were determined according to the seam thickness and existing coal burial depths. The corresponding membership functions are as follows [21]: $u_{3} (C) = {\begin{matrix} 0.2, \frac{1}{5} \leq C \leq \frac{1}{4.5} \\ 0.4, \frac{1}{4.5} \leq C \leq \frac{1}{4} \\ 0.6, \frac{1}{4} \leq M \leq \frac{1}{3.5} \\ 0.8, \frac{1}{3} \leq C \leq \frac{1}{3} \\ 1, C \leq \frac{1}{3} \end{matrix}$ (4)

Fractures (j)

Fractures affect top-coal CDC in three ways. Firstly, the occurrence of fractures can make a coal seam more prone to deformation by reducing its strength and integrity. A coal seam with a higher fracture density is less integrated and more easily broken by the pressure from both overlying strata and roof supports, and thus has better CDC. Secondly, the coal breaking process is, in effect, a process of fracture formation and propagation. Therefore, the distribution of fractures influences the sizes of coal blocks. Thirdly, the porosity and pore water content of a coal seam determine the level of difficulty in top-coal caving. The evaluation of the influence of fractures included the following four aspects [26]:

Fractal index of fracture $μ_{4_{a}} (ND) = {\begin{matrix} 0.2, ND \geq 7 \\ 0.6, 7 < ND \leq 15 \\ 0.8, 15 < ND \leq 20 \\ 1, ND > 20 \end{matrix}$ (5)

Effect of water absorption $μ_{4_{b}} (w) = {\begin{matrix} 0.2, w \leq 1 \\ 0.75, 1 < w \leq 3 \\ 0.85, 3 < w \leq 10 \\ 1.0, w \geq 10 \end{matrix}$ (6)

Effect of porosity $μ_{4_{c}} (A) = {\begin{matrix} 0, A < 4 % \\ 0.4, 4 % \leq A < 6 % \\ 0.6, 6 % \leq A < 8 % \\ 0.8, 8 % \leq A < 10 % \\ 1, A \geq 10 % \end{matrix}$ (7)

Softening coefficient $u_{4_{d}} (Z) = {\begin{matrix} 0.2, Z < 0.6 \\ 0.6, 0.6 \leq Z < 0.7 \\ 0.8, 0.7 \leq Z < 0.8 \\ 1, Z \geq 0.8 \end{matrix}$ (8)

Roof conditions (K)

The coal mine roof consists of two major parts: the direct roof and the main one. If the direct roof caves readily or the caving roof blocks are too large, the top coal cannot be drawn smoothly. This will cause a loss of coal. An ideal direct roof should be: sufficiently thick, able to cave together with the top coal, occupy nearly the entire drawing space, and exhibit a distinct coal-rock interface. The movement of the main roof is a critical factor in breaking top coal. A horizontally bedded and vertically jointed fractured main roof forms the so-called Voussoir beam, which can transfer mechanical loads/forces to the underlying coal seam. Movement and destabilization of the main roof affect the level of abutment pressure in front of the coal face, which plays a decisive role in breaking the top coal. Therefore, an increase in the hardness of the main roof will increase the roof weighting step and peak abutment pressure, thereby facilitating break-up of the top coal [1 , 24–26].

Direct top thickness $u_{5_{a}} (k_{z}) = {\begin{matrix} 1, k_{z} \geq 1 \\ 0.8 + 4 (k_{z} - 0.95), 0.95 \leq k_{z} < 1 \\ 0.6 + \frac{4}{3} (k_{2} - 0.8), 0.8 \leq k_{z} < 0.95 \\ 0.2, k_{z} < 0.8 \end{matrix}$ (9)

Relative strength of direct roof and coal seam $u_{5_{b}} (k_{d}) = {\begin{matrix} 1, k_{d} \geq 1 \\ 0.6 + 2 (k_{d} - 0.8), 0.8 \leq k_{d} < 1 \\ 0.2, k_{d} < 0.8 \end{matrix}$ (10)

Ratio of basic roof thickness to the coal seam thickness $u_{5_{c}} (N) = {\begin{matrix} 0.2, N < 1 \\ 0.6, 1 < N \leq 2 \\ 0.8, 2 < N \leq 4 \\ 1.0, N > 4 \end{matrix}$ (11)

Influence of gangues (F)

The position, thickness, and strength of the gangues exert influence on the top-coal CDC. Weak and thin gangues form weak planes, which are favorable for top-coal caving. In contrast, strong and thick gangues can impede the breakage and caving of top coal. The gangues within reach of the shearer will be directly cut by the shearer and, thus, have no influence on the CDC. The influence of a middle gangue on the CDC depends on its distance from the top of the support beneath it. If this distance is small, the middle gangue can easily break and cave under the action of the support, and thus has a relatively small influence on the CDC; otherwise, it will strongly affect the CDC. The influence of an upper gangue depends on its thickness: a thin upper gangue can cave together with top coal, while a thick upper gangue does not break easily and, thus, has a stronger effect on CDC [26].

the thickness of gangues $u_{6_{a}} (h) = {\begin{matrix} 0.8, h \leq 0.4 \\ 0.6, 0.4 < h \leq 1 \\ 0.2, h > 1.0 \end{matrix}$ (12)

the position of gangues $u_{6_{b}} (l_{c}) = {\begin{matrix} 0.5 - 0.4 (6 - l), 0 < l < 6 \\ 0.5 + 0.4 (l - 6), 6 \leq l < 12 \\ 1, l > 12 \end{matrix}$ (13)

Where l is the distance between the roof boundary and coal seam gangue midline

strength of gangues $u_{6_{c}} (N_{c}) = {\begin{matrix} 0.2, N_{c} \geq 1 \\ 0.6 - 2 (N_{c} - 0.8), 0.6 \leq N_{c} < 1 \\ 1.0, N_{c} > 0.6 \end{matrix}$ (14)

Where N_c is the gangue-to-coal seam strength ratio (N_c = R_g/R_c).

3 Mathematical modeling and weight calculation

3.1 Mathematical modeling

Since the top-coal CDC values are affected by multiple fuzzy factors, it is feasible to systematically analyze the latter by fuzzy evaluation [7 , 14]. In this study, the analytic hierarchy process (AHP) and fuzzy evaluation were used to evaluate the CDC of the extra-thick coal seams. Six factors and 13 sub-factors were identified through an analysis of influencing factors of CDC. They were then combined to elaborate a structured mathematical model (Fig. 1).

Fig.1

AHP hierarchy for evaluating the top-coal CDC.

In this AHP hierarchy, level A denotes the overall goal, level B is the level of evaluation criteria, and level C comprises the sub-criteria. Judgment matrices were constructed by this hierarchy, to achieve the numerical representation and quantification of top-coal CDC. Each factor involved in the evaluation was organized into a domain of discourse U [26]:

$\begin{matrix} U & = & {u_{1}, u_{2}, u_{3}, u_{4}, u_{5}, u_{6}} \\ = & {H, R_{c}, M, j, k, F} \end{matrix}$ (15)

Let the fuzzy subset V denote the domain of discourse of CDC. The B value, which indicates the level of CDC, is the degree of membership of U in the fuzzy subset V. The fuzzy set V is defined by Equation (16) [26]: $V = {I, II, III, IV, V}$ (16)

The B value can be derived via the following equation [26]: $B = \sum_{i = 1}^{6} w_{i} u_{i} (u_{i})$ (17) where u_i (u_i) is the membership degree of the i-th factor in V and w_i is the weight of the i-th factor.

The CDC of top coal (V) can be classified as very good, good, medium, poor, and very poor. Then, the numerical representation and classification of top-coal CDC can be achieved by calculating the B values and comparing the results with the respective criterion.

3.2 Determination of weights of the factors and sub-factors

Weights given to the factors and sub-factors evaluated structurally quantify the evaluation system and represent the relative importance of the influencing factors in the AHP hierarchy. They are essential to unifying the structure and functions of the evaluation system. The weights of the factors were determined through the following six steps: (1) build an AHP model; (2) construct judgment matrices; (3) rank the relative importance of the sub-factors under each factor and check the consistency of the judgments; (4) rank the relative importance of the factors; (5) check the consistency of the judgments about the relative importance of the factors; (6) calculate the weights of the factors and sub-factors. Comparison matrices are reciprocal matrices, expressed as $A = [\begin{matrix} 1 & a_{12} & \dots & a_{n} \\ \frac{1}{a_{12}} & 1 & \dots & a_{2 n} \\ \dots & \dots & \dots & \dots \\ \frac{1}{a_{n}} & \frac{1}{a_{2 n}} & \dots & 1 \end{matrix}]$ [25].

Elements in this matrix were determined through a series of pairwise comparisons. When element A is used as the criterion, the scale a_ij (positive integers from 1 through 9 and their reciprocals) denotes the relative importance or preference (reciprocal) of the i-th element with respect to the j-th element in the lower level. The value of a_ij increases with the relative importance of the i-th element with respect to the j-th one. The following judgment matrices were obtained based on the structured mathematical model and the importance of the influencing factors: $W_{A \sim B} = [\begin{matrix} 1 & 3 & 4 & 7 & 1 / 2 & 5 \\ 1 / 3 & 1 & 3 & 5 & 1 / 4 & 3 \\ 1 / 4 & 1 / 3 & 1 & 3 & 1 / 7 & 1 / 2 \\ 1 / 7 & 1 / 5 & 1 / 3 & 1 & 1 / 6 & 1 / 5 \\ 2 & 4 & 7 & 6 & 1 & 3 \\ 1 / 5 & 1 / 3 & 2 & 5 & 1 / 3 & 1 \end{matrix}]$ (18) $W_{B 3 \sim C} = [\begin{matrix} 1 & 7 & 3 & 5 \\ 1 / 7 & 1 & 1 / 5 & 1 / 3 \\ 1 / 3 & 5 & 1 & 3 \\ 1 / 5 & 3 & 1 / 3 & 1 \end{matrix}]$ (19) $W_{B 4 \sim C} = [\begin{matrix} 1 & 1 / 2 & 2 \\ 2 & 1 & 3 \\ 1 / 2 & 1 / 3 & 1 \end{matrix}]$ (20) $W_{B 6 \sim C} = [\begin{matrix} 1 & 7 & 5 \\ 1 / 7 & 1 & 1 / 3 \\ 1 / 5 & 3 & 1 \end{matrix}]$ (21)

The largest eigenvalues of the judgment matrices were calculated to check their consistency. Firstly, the column vectors of the matrices were normalized, yielding ${\tilde{A}}_{ij} = (\frac{a_{ij}}{\sum_{i = 1}^{n} a_{ij}})$ and ${\tilde{A}}_{ij}$ was then column-normalized, resulting in ${\tilde{W}}_{ij} = (\sum_{j = 1}^{n} \frac{a_{1 j}}{\sum_{i = 1}^{n} a_{ij}}, \sum_{j = 1}^{n} \frac{a_{2 j}}{\sum_{i = 1}^{n} a_{ij}}, \dots, \sum_{j = 1}^{n} \frac{a_{nj}}{\sum_{i = 1}^{n} a_{ij}},)^{T}$ . Secondly, normalization of of $\tilde{W}$ yields the priority vector W = (w₁, w₂, …, w_n) ^T; the largest eigenvalues being obtained for $λ_{max} = \frac{1}{n} \sum_{i = 1}^{n} \frac{(AW)_{i}}{w_{i}}$ . The consistency of each matrix was then tested by the dependence $CR = \frac{CI}{RI}$ , where CR is the consistency ratio [27], CI is the consistency index, which is defined as $CI = \frac{λ_{max} - n}{n - 1}$ ; and RI is the random consistency index, whose values are by Saaty’s mean random consistency index (Table 2). If CR <0.1, the judgment matrix is considered to pass the consistency test; otherwise, the matrix needs to be adjusted. Table 3 lists the results of the consistency test on the judgment matrices. It shows that all four matrices satisfy the consistency requirements. Finally, the membership degrees of the factors and sub-factors were calculated with the Matlab software. The results are presented in Table 4.

Table 1

Criteria for evaluating the top-coal CDC

The classification of caving property	I	II	III	IV	V
Classification	very good	good	medium	poor	very poor
Comprehensive evaluation value	>0.9	0.8∼0.89	0.7∼0.79	0.6∼0.69	<0.6

Table 2

Saaty’s mean random consistency index RI [27]

n	1	2	3	4	5	6	7	8	9
RI	0	0	0.58	0.94	1.12	1.24	1.32	1.41	1.45

Table 3

Results of the consistency test on the judgment matrices for the Laosangou coal mine

Matrix	Sort vector	λ _max	CI	RI	CR
A∼B	[0.28,0.15,0.06,0.03,0.38,0.10]^T	6.43	0.086	1.24	0.069
B₃∼C	[0.76,0.06,0.35,0.14]^T	4.14	0.048	0.94	0.051
B₄∼C	[0.31,0.49,0.16]^T	3.02	0.011	0.58	0.019
B₆∼C	[0.85,0.07,0.20]^T	3.09	0.048	0.58	0.083

Table 4

Weights of factors and sub-factors involved in the evaluation

Layer criterion weight		Layer sub-criterion weight
Coal Seam Thickness (B1)	0.2881	Coal Seam Thickness (C1)	0.2881
Coal burial depth (B2)	0.1388	Mining height (C2)	0.1388
Fractures of Coal Seam (B3)	0.0586	Fractal Index (C3)	0.0331
		Water Absorption (C4)	0.0032
		Porosity of Coal Seam (C5)	0.0154
		Softening Coefficient (C6)	0.0069
Roof Conditions (B4)	0.0318	Direct Roof Thickness (C7)	0.0094
		Direct Roof Strength (C8)	0.0172
		Thickness Ratio of Direct Roof to Coal Seam (C9)	0.0052
Coal Seam Strength (B5)	0.3787	Coal Seam Strength (C10)	0.3787
Gangue Location (B6)	0.1040	Gangue Layer Position (C11)	0.0760
		Gangue Strength (C12)	0.0084
		Gangue Thickness (C13)	0.0196
Total	1.0000	Total	1.0000

After the weights of all factors and sub-factors had been derived, the influencing factors and corresponding membership functions were applied to the Tashan and Tongxin coal mines to examine their validity.

3.3 Parameter validity test

The membership functions and weights given to the factors were applied to the Tashan and Tongxin coal mines in Datong to test their validity. Being located within the Datong coalfield, the two coal mines are geographically close to the Laosangou coal mine (about 140 km), and the conditions of their coal seams are similar to those observed in the Laosangou coal mine (Table 5). Moreover, the top-coal caving has been successfully carried out and yielded good results at the 8105 and 8101 coal faces of the Tashan and Tongxin coal mines, respectively. Therefore, through a comparison with the actual observations from the two coal mines, the test can effectively verify whether the influencing factors and membership functions are valid and robust, i.e., provide reliable results when applied to mega coal seams.

Table 5
General conditions of the Tashan and Tongxin coal mines

Index Indicator Description

Ta Shan 8105 Tong Xin 8101

Coal burial depth 347–447 m Average 447.5 m

Coal Seam Thickness The average thickness is 16.8 m, the maximal one exceeding 20 m The average thickness is 17.1 m, the maximal one exceeding 23.64 m

Coal Seam Strength Compressive strength: 9.49–12.3 MPa Average compressive strength is 10.2 MPa

Gangue Location Coal seam contains 4–14 gangue layers, uneven distribution, carbonaceous mudstone is dominant compressive strength 27.51–36.48 MPa, occasional kaolinite, and mudstone, compressive strength 26.7 MPa Coal seam contains 5–10 gangue layers, the lithology is dominated by carbonaceous mudstone, compressive strength 27.51 MPa, occasional kaolinite, sandy mudstone, siltstone or fine sandstone, the total thickness of gangue is 2.75 m

Roof Conditions The direct top layer is limestone, 2.4–6.3 m thick, compressive strength is 112.76 MPa. The basic top is the sandstone, 11.31–27.36 m thick, compressive strength 79 MPa The direct top layer is carbonaceous mudstone with the thickness of 4.14 m and compressive strength of 26.4 MPa. The basic top layer is coarse sandstone with compressive strength of 43.87 MPa

Fractures SEM observation of layer surface with magnification up to x100 revealed numerous clastic structures, while at x1000, obvious joints and fissure patterns were observed Fractures development, brittle fracture

Coal Caving B = 0.87, II grade B = 0.81, II grade

Index	Indicator Description
Coal burial depth	347–447 m	Average 447.5 m
Coal Seam Thickness	The average thickness is 16.8 m, the maximal one exceeding 20 m	The average thickness is 17.1 m, the maximal one exceeding 23.64 m
Coal Seam Strength	Compressive strength: 9.49–12.3 MPa	Average compressive strength is 10.2 MPa
Gangue Location	Coal seam contains 4–14 gangue layers, uneven distribution, carbonaceous mudstone is dominant compressive strength 27.51–36.48 MPa, occasional kaolinite, and mudstone, compressive strength 26.7 MPa	Coal seam contains 5–10 gangue layers, the lithology is dominated by carbonaceous mudstone, compressive strength 27.51 MPa, occasional kaolinite, sandy mudstone, siltstone or fine sandstone, the total thickness of gangue is 2.75 m
Roof Conditions	The direct top layer is limestone, 2.4–6.3 m thick, compressive strength is 112.76 MPa. The basic top is the sandstone, 11.31–27.36 m thick, compressive strength 79 MPa	The direct top layer is carbonaceous mudstone with the thickness of 4.14 m and compressive strength of 26.4 MPa. The basic top layer is coarse sandstone with compressive strength of 43.87 MPa
Fractures	SEM observation of layer surface with magnification up to x100 revealed numerous clastic structures, while at x1000, obvious joints and fissure patterns were observed	Fractures development, brittle fracture
Coal Caving	B = 0.87, II grade	B = 0.81, II grade

At the 8105 coal face in the Tashan coal mine, the actual recovery rate reaches 88.9%, and the maximum mining height is 5 m. This face was estimated to have produced 10.849 million tons of raw coal in 2011. High production and high efficiency have been achieved at the 8101 coal face in the Tongxin coal mine, where the mining height was 4 m, while the mine wall and roof were so stable that no significant roof or rib falls have occurred. The results of the evaluation are consistent with the actual conditions of the two mines, demonstrating that the influencing factors and membership functions are suitable for evaluating the top-coal CDC in the Laosangou coal mine.

4 Evaluation of Top-Coal CDC

4.1 Results and discussion

The CDC of top coal in the Laosangou coal mine were evaluated using Equation (17). The proposed membership functions for the influencing factors, in combination with the relevant parameters of the seam, such as coal burial depth, seam thickness, and coal properties, and classified according to Table 1. The evaluation was performed for 36 cases with different combinations of caving ratios, gangue thickness, and gangue locations. These cases were subdivided into three groups. The cases in the first group were designed to reveal the CDC evolution with caving ratio and gangue thickness for the given gangue locations. Those in the second group were designed to examine how CDC varied with gangue location and gangue thickness for certain caving ratios. The evaluation in the third group was intended to reveal the patterns of variation in CDC with the gangue location and caving ratio for certain gangue thicknesses.

(1) Fig. 2 illustrates the top-coal CDC variation with the caving ratio and gangue thickness for certain gangue locations.

As shown in Fig. 2, the top coal exhibited a decrease in CDC with caving ratio, when the gangue location and thickness remained unchanged. In the cases with middle or upper gangues, the CDC declined from Grade I to Grade IV as the caving ratio increased from 1/3 to 1/4.7, as compared to the decline from Class I to Class III observed in the cases with bottom gangues.

Fig.2

Top-coal CDC for certain gangue locations.

When the gangue location and caving ratio did not change, an increase in the gangue thickness caused a small decrease in CDC; the B value dropped by 0.1 as the thickness increased from “thin” to “medium thick” or from “medium thick” to “thick” values.

(2) Fig. 3 depicts the top-coal CDC variation with the gangue location and gangue thickness for certain caving ratios.

Fig.3

Top-coal CDC for certain caving ratios.

It demonstrates that, for a particular gangue thickness, the CDC dropped with the rise of gangue layer position, and the rate of this drop slowed down with caving ratio. An increase in the caving ratio also resulted in a drop of CDC. For a caving ratio of 1/3, the top coal exhibited CDC of Grade I in the most cases, while it showed Grade II CDC only in the cases with an upper gangue. When the caving ratio was 1/3.4, the top coal exhibited good CDC in the cases with bottom or middle gangues and had a medium CDC in the cases with an upper gangue. For a caving ratio of 1/4, the top-coal CDC varied slightly and belonged to the medium class in the most cases. When the caving ratio was 1/4.7, the top coal exhibited poor CDC, except medium CDC in the cases with a bottom gangue. These findings suggest that the caving ratio has a relatively strong effect on top-coal CDC and that sublevel caving should be adopted when the caving ratio exceeds 1/4.

(3) Fig. 4 reveals the patterns of variation in CDC with the gangue location and caving ratio for the given gangue thicknesses.

Fig.4

Top coal CDC for certain gangue thicknesses.

For certain gangue thickness values, the top-coal CDC is affected by the caving ratio and gangue location. When the gangue thickness and caving ratio remained unchanged, the top coal experienced a decrease in CDC as the gangue location rose. In the cases with certain gangue thickness values, the CDC decreased with the caving ratio at roughly the same rate. For certain caving ratios, the CDC variations with gangue thickness and location were slight.

The above analysis implies that the caving ratio, gangue location, and gangue thickness influence the top-coal CDC. When the caving ratio is 1/3.4 or lower, and the gangue layer is thinner than 0.4 m and located in the bottom, the top coal shows CDC of Grade I or Grade II, which makes it suitable for top-coal caving. As the caving ratio increases to 1/4 or higher, the top-coal CDC tends to decrease to Class III. When the gangue is thicker than 1 m and lies in the upper one-third of the top coal, the minimum B value occurs at 0.65; the top coal demonstrates relatively poor CDC and, thus, is more suitable for sublevel caving.

4.2 CDC of the top coal in the first mining area of the Laosangou coal mine

The main seam in the Laosangou coal mine was divided into several regions based on relevant parameters (e.g. gangue thickness, gangue location and seam thickness) and the data from 73 boreholes through this seam. The CDC of top coal was evaluated separately for each region; the results are illustrated in Fig. 5. Four mining heights were used in the evaluation: 3.5 m, 4.0 m, 4.5 m and 5.0 m. The total area of top coal of each CDC class was calculated for each mining depth. The results are plotted in Fig. 6 for a visual representation of variations in CDC of the top coal.

Fig.5

CDC of top coal in the first mining area for different mining heights.

Fig.6

Statistics on top coal areas across CDC classes for different mining depths.

Figure 6 shows that the top-coal area with medium or better CDC increased with the mining height (or with the caving ratio drop). When the mining height was 3.5 m, the top coal with medium and better CDC accounted for merely 35.55% of the total size of the first mining area, while the share of top coal with poor and very poor CDC was 64.45%. In this case, the caving ratio calculated for the local average seam thickness (20 m) reached 1/4.7. However, the seam thickness values in some boreholes were nearly 30 m. This means that the caving ratios in these locations exceeded 1/5 and, thus, the top coal there had even poorer CDC. When the mining height was 4 m, the top coal with medium and better CDC made up 53.59% of the first mining area, a significant increase from that for the mining depth of 3.5 m. For the mining height values of 4.5 m and 5 m, the shares of top coal with medium or better CDC were 86.78% and 99.7%, respectively, and the area of top coal with very poor CDC was zero, suggesting a great improvement of the top-coal CDC.

Based on the results of this analysis, the optimal mining methods and mining heights were determined for different regions in the first mining area of the Laosangou coal mine (Fig. 7). The top coal with medium or better CDC is subdivided into four zones shown in different colors: red zone for the sublevel caving; dark green zone for the mining height from 3.5 to 4 m; light green zone for the mining depth from 4 to 4.5 m; magenta zone for the mining height from 4.5 to 5 m.

Fig.7

The mining methods for the first mining area.

At present, according to the area designated in Fig. 7, preliminary mining of the first mining area in Laosangou coal mine has been successfully carried out by adopting different mining heights for different areas. It shows that the division of the areas according to the evaluation results is scientifically justified and reasonable, which corroborates the reliability of the results obtained in this study.

5 Conclusions

Fuzzy evaluation can be utilized to predict the CDC of top coal in extra-thick seams, and the top coal caving process in extra-thick coal seam seams is different from traditional top coal caving (<12 m). In this study, we applied AHP-fuzzy discrimination method to analyse the top-coal CDC in extra-thick coal seams. AHP-fuzzy discrimination method is a scientific and effective method for evaluating the CDC in extra-thick seams, therefore the research results provide scientific basis for evaluation of CDC in extra-thick coal seam. The research conclusions are as follows:

The influencing factors of the CDC in extra-thick coal seam were comprehensively analyzed in the paper. An AHP-fuzzy discrimination model was constructed based on the analysis on the issue of weights and subordinate function of the influencing factors. Qualitative analysis and quantitative analysis were combined to overcome the limitation of relying on field experience, which made the evaluation results more reasonable and reliable.

The evaluation model composed of 6 second-level indices and 13 third-level ones was elaborated and successfully applied to the particular cases of the Tashan and Tongxin coal mines. The results show that this method can be utilized to accurately evaluate the CDC of top coal in extra-thick coal seams. The top-coal CDC was analyzed using the established assessment indicator system for 36 combinations of mining heights and coal seam thicknesses, as well as situations of gangue. Based on the findings, higher mechanical mining heights as well as lower gangue positions and thickness values are beneficial for CDC, on the contrary, the top-coal CDC will be worse.

According to the calculation results of top coal caving under 36 different conditions in combination with the classification results of 73 boreholes in the first mining area of Laosangou coal mine, the first mining area was conventionally subdivided into four sections with a mechanical mining height of 3.4–4 m, 4–4.5 m, 4.5–5 m, and 4.5–5 m, respectively. The research results provide a theoretical basis for the mining method of the first mining area in Laosangou. Moreover, the results of this study have a good reference meaning for the problems with multiple influencing factors.

Footnotes

Acknowledgments

The research is financially supported by the National Basic Research Program of China (No. 2015CB251600), the Jiangsu basic research program (Natural Science Foundation) (BK20150051), National Natural Science Foundation (51264035, 51474206, 51504240 and 51404254). We wish to thank the Laosangou Coal Mine for supporting the important work.

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Index	Indicator Description
	Ta Shan 8105	Tong Xin 8101
Coal burial depth	347–447 m	Average 447.5 m
Coal Seam Thickness	The average thickness is 16.8 m, the maximal one exceeding 20 m	The average thickness is 17.1 m, the maximal one exceeding 23.64 m
Coal Seam Strength	Compressive strength: 9.49–12.3 MPa	Average compressive strength is 10.2 MPa
Gangue Location	Coal seam contains 4–14 gangue layers, uneven distribution, carbonaceous mudstone is dominant compressive strength 27.51–36.48 MPa, occasional kaolinite, and mudstone, compressive strength 26.7 MPa	Coal seam contains 5–10 gangue layers, the lithology is dominated by carbonaceous mudstone, compressive strength 27.51 MPa, occasional kaolinite, sandy mudstone, siltstone or fine sandstone, the total thickness of gangue is 2.75 m
Roof Conditions	The direct top layer is limestone, 2.4–6.3 m thick, compressive strength is 112.76 MPa. The basic top is the sandstone, 11.31–27.36 m thick, compressive strength 79 MPa	The direct top layer is carbonaceous mudstone with the thickness of 4.14 m and compressive strength of 26.4 MPa. The basic top layer is coarse sandstone with compressive strength of 43.87 MPa
Fractures	SEM observation of layer surface with magnification up to x100 revealed numerous clastic structures, while at x1000, obvious joints and fissure patterns were observed	Fractures development, brittle fracture
Coal Caving	B = 0.87, II grade	B = 0.81, II grade