Evaluation of the Gas Drainage Effect in Deep Loose Coal Seams Based on the Cloud Model

Abstract

The gas drainage effect is one of the important elements in the study of gas drainage in coal mines. It is critical to establish an effective evaluation model of the gas drainage effect for coal mines because the result of gas drainage is directly related to the safety of the coal mines. Through the research related to the safety evaluation in the existing coal mining process, we discovered that there are few studies on the evaluation of the impact of deep and soft gas drainage, and the evaluation methods are not sufficiently effective to resolve the complex problems arising in the process of gas drainage. This paper took “three soft” coal seams in the Lugou Coal Mine as the research object and constructed the evaluation index system on the basis of thoroughly analyzing the factors of coal seam drainage. We then employed a combination weighting method to attain the optimal weight by organically integrating the Analysis Hierarchical Process subjective weighting method and the Criteria Importance Through Interaction Correlation objective weighting method and utilized the cloud model to compute the numerical characteristic value of the evaluation index. In the end, this method obtained an evaluation result of the gas drainage effect evaluation. The evaluation result grade is good. Additional analysis was performed according to the evaluation factors, and corresponding improvement measures were proposed. This is of great importance in promoting safe production and improving the efficiency of gas drainage.

1. Introduction

It is gas drainage in which gas is pumped to the surface from the coal seam or goaf by using different drainage equipment. When the amount of gas emissions in a coal mine is vast, it is hard to dilute and eliminate gas by ventilation. The method of drainage can be used to eliminate gas and decrease the burden of ventilation. Gas drainage is an important measure to avoid gas disasters in coal mines. Not only that but the extracted gas can still be developed and used. Gas drainage is one of the most effective methods for gas control, and the effect of gas drainage determines the success or failure of mine gas control to a certain extent [1,2,3].

At present, domestic and foreign research on the evaluation of gas drainage effects are as follows: Ding Xiajun [4] used the method of constructing subordinating degree function to make a fuzzy comprehensive evaluation of the coal mine, and the result obtained by fuzzy comprehensive evaluation is consistent with reality. Zhang Shujin [5] quantitatively evaluated the maturity of the gas drainage method through an analytic hierarchy process and put forward the concept of gas drainage technology maturity. According to the basic principle of the analytic hierarchy process, Wu Gang [6] quantitatively studied the determination of gas pressure and established an evaluation model on this basis. Combined with field investigation and laboratory test analysis according to the industry standard, the impact of pre-drainage gas in Tunbao Coal Mine on 11,141 working face was evaluated by Shan Dakuo [7]. Field measurements and theoretical calculations were used to evaluate the effect of gas drainage in the working face to solve the problem of high gas content in the Dongfeng Coal Mine by Wang Chao [8]. Wang Leigang also applied the standard of “Interim Provisions on Reaching the Standard of Gas Extraction in Coal Mines” to evaluate gas extraction in the working face [9]. Li Shugang combined the analytic hierarchy process with fuzzy comprehensive evaluation, and a mathematical model of assessing the gas extraction outcome in the Cuijiagou coal-mining goaf was constructed to synthetically evaluate the coal-mining in Cuijiagou [10]. Shu Shihai established a model to evaluate the influencing factors of gas drainage restrictions in outburst mines. The methods AHP and entropy weight are used to determine the main and objective weight. The weighted average method was used to determine the comprehensive weight of the evaluation model [11]. Based on extension theory, an evaluation model was constructed to promote pumping by injecting water into the coal seam by Fang Xinliang [12]. Gao Kangya integrated a simple correlation function method and an analytic hierarchy process to determine the index weight, and extenics theory was introduced to establish a coal mine gas drainage system evaluation extension model [13]. The game theory-fuzzy comprehensive evaluation model was established by integrating the weighting method of game theory with the fuzzy comprehensive evaluation method by Yang Ming, and the abandoned Gushuyuan Mine was used as an example to verify the rationality of the evaluation model [14].

The above is relevant research on the abstract effect of the evaluation of coal-mine gas. Further research on the evaluation of coal mine safety includes the fuzzy comprehensive evaluation model and index system of coordinated development of coalbed methane and coal being established, and the effect of coordinated development of coal mine was preliminarily evaluated and analyzed by Liu Jianzhong [15]. Xu Xing used the combination of an analytic hierarchy process and a fuzzy analytic hierarchy process to weight each evaluation index. This was then combined with the fuzzy comprehensive evaluation method to build a fuzzy comprehensive evaluation model based on the combination of weight and effectively carry out the potential water hazard evaluation of the coal mine safety production system [16]. Ana Claudia Nen used a pressure response system to conduct a systematic evaluation of the mining process, and the results of this pressure response system were excellent [17]. According to the theory of matter element and extension, Du Zhenyu established the extension goodness evaluation model of coal mine safety evaluation, and the evaluation results clearly reflected the safety level of the coal mine [18]. Based on the principle and method of multi-objective decision making based on set pair analysis, Li Fanxiu established the multiple contact number model of coal mine safety evaluation. The evaluation results indicate that this method is straightforward and efficient, can successfully evaluate coal mine safety, and can also rank the advantages and disadvantages of coal mine safety. The model has important practical significance in the evaluation of coal mine safety [19].

In the above studies, scholars have conducted relevant studies on gas drainage and safety evaluation in the coal mining process according to specific research priorities. There are still a few studies on the evaluation of the effect of gas drainage in goaf, and the above evaluation methods cannot solve the complex problems in the process of gas drainage in goaf. Therefore, starting from the evaluation factors, this paper completes the evaluation of gas drainage by selecting reasonable evaluation indexes and establishing a scientific and reasonable evaluation system, which is conducive to finding out the weak elements in gas drainage and improving the efficiency of gas drainage and has significant guiding significance for safe production. This paper takes the “three soft” coal seams of Lugou Coal Mine as the research object, carries out the effect evaluation of gas extraction in the soft deep coal seams, and guides the gas process according to the evaluation results so as to improve the efficiency of gas extraction.

2. Basic Information about the Research Subjects

2.1. Main Geological Formations and Coal Seam Deposits in the Mine

Lugou Coal Mine is located in Yuecun Town, 27 km southwest of Zhengzhou City, in the north of Xinmi coalfield. The Lugou coal deposit belongs to the Songji district in the North China strata area. According to the development from old to new, the strata in the mining area include Cambrian, Ordovician, Carboniferous, and Permian in Paleozoic, Triassic in Mesozoic, Neogene, and Quaternary in Cenozoic. The overall structure is a monoclinic structure with the strata dipping to the south. The dip angle of the formation is generally about 20°, with a certain fluctuation, and a broad and moderate southward inclination—Weizhai northward inclination—is formed in the southwest. There are 13 faults with a development drop greater than 20 m, which are divided into two groups: NW~SE and near EW. The structural traces are primarily stepped normal faults rising in the south and falling in the north.

The coal-bearing strata in the Lugou Wellfield are the Upper Carboniferous Taiyuan Group, the Lower Permian Shanxi Group, the Lower Shi Box Group, and the Upper Permian Upper Shi Box Group, containing nine coal-bearing sections with a total thickness of 667.19 m, 25 coal-bearing seams, a total coal seam thickness of 18.19 m and a coal-bearing factor of 2.73%. The Taiyuan Group contains nine coal seams, of which the Ⅰ1 coal seam is a partly mineable coal seam; the Shanxi Group is the major coal-bearing stratum, developing three seams, including the II0, II1, and II3 coals, with the lower II1 coal seam being the majority of the mineable coal seam and the rest of the coal seams being un-mineable. The Upper and Lower Shi Box Groups contain eight and five seams of coal, respectively, both of which are un-mineable. The total thickness of the recoverable coal seams is 7.94 m, and the coal content factor of the recoverable coal seams is 1.19%.

2.2. Mine Gas Profiles

The elevation of mine opening surge is −317 m. Measuring the specific situation of the mine obtained the highest measured stress in the vertical direction and pressure in the mine, at 15.498 Mpa and 0.35 MPa, respectively. Partial coal seam gas stress and pressure test results are shown in

Table 1. Partial coal seam gas stress and pressure.

Serial Number	Sampling Location	Elevation of Measuring Point (m)	Burial Depth (m)	Stress in the Vertical Direction (MPa)	Stress in the Horizontal Direction (MPa)	Gas Pressure (MPa)
1	32141 Water exploration lane pumping No.6 measuring point 16 m west	−328	573	15.471	12.8925	0.22
2	32141 The water exploration lane was pumped 9 m before 13 o’clock	−329	574	15.498	12.915	0.34
3	32141 4 m drill field before 15 o’clock of water exploration lane pumping	−327	572	15.444	12.87	0.26
4	32141 The 2# hole of the bottom pumping lane is 25 m away from the stop-mining line	−285	513	13.851	11.5425	0.24
5	32141 lower pay lane 75 m to the working face, from 32 transport down 545 m	−329	574	15.498	12.915	0.34
6	32141 united lane	−290	517	13.959	11.6325	0.18
7	32141 water lane	−315	545	14.715	12.2625	0.22
8	32141 exploration lane	−330	575	15.525	12.9375	0.22
9–10	32 Transport uphill 350 m (hole No. 1)	−104	349	9.423	7.8525	0.18
	32 Transport uphill 350 m (hole No. 2)	−104	349	9.423	7.8525	0.19

.

• Calculated gas content during wellfield exploration

During the geological team’s exploration of Mi County in 1957–1958, the geological team calculated the gas content of the borehole coal seam. According to the results obtained, the gas content of the No. 2 coal seam is 0.0632~3.862 m³/t, which belongs to the low gas content.

Supplemental exploration was conducted from 2007 to 2013. According to the test results of borehole core coal samples during supplementary exploration, methane content in II1 coal seam ranges from 0.0 to 4.37 m³/t, with an average methane content of 1.21 m³/t and a methane composition of 0.0 to 68.61%, with an average methane composition of 17.16. The gas content of the II1 coal seam is 1.93~5.54 m³/t, and the average gas content of each layer is 3.76 m³/t.

According to the test results of borehole core coal samples during exploration, the methane content of II1 coal seam ranges from 0.0 to 4.37 m³/t, with an average of 1.21 m³/t, and the methane composition ranges from 0.0 to 68.61%, with an average of 17.16, as shown in

Table 2. Table of gas composition and content analysis results of borehole coal core.

Coal Seam	Project	Gas Composition (%)			Content of Gas (m3/t)
		CO2	CH4	N2	CO2	CH4	N2
II1	Minimum	0.266	0.00	31.053	0.100	0.00	0.452
	Maximum	4.925	68.607	99.398	0.676	4.366	6.026
	Average	2.058	17.16	80.446	0.253	1.213	2.913
	Count	15	15	15	15	15	15

.

2.

Calculated gas content during production

The 21 mining area is the output mining area, which is at the end of mining and is currently a mining pillar. Due to the influence of mining, the initial gas content in this area cannot be confirmed. Before mining, the rock roadway (or drilling field) should be used to predict the regional outburst risk in this area, and the gas content in this area should be measured. The maximum measured gas content is 5.79 m³/t.

The 32 mining areas are the output mining areas. At present, there is only one normal working face left in the mining area: the 32,141 working face. The initial gas content of the working face was measured by the floor rock roadway through layer drilling in the mining area, and the maximum measured gas content of this mining area was 7.33 m³/t.

Area 31 is a pioneering mining area. When testing the original gas content along the return wind downhill, transport downhill, and track downhill, as well as the rocky road at the bottom, the maximum gas content measured was 10.49 m³/t during the pioneering of this mining area.

3. System of Evaluation Indicators

3.1. Preliminary Analysis of the Indicator System

The deep coal seam is characterized by soft coal quality, low permeability, strong gas content, and complicated geological structure, so the gas drainage effect is not perfect, and the influencing factors of the deep coal seam are too complex. Combined with the gas drainage case of the II1 coal seam in Lugou Coal Mine and a large number of soft coal seam extraction cases, the relevant factors of deep soft coal seam extraction are investigated by the method of investigation and the safety system engineering method. Through the analysis of relevant factors by the method of investigation and safety system engineering method, it is found that the indicators of gas drainage impact can be evaluated from three aspects: the environmental conditions of geology, gas drainage equipment and gas drainage technology, and personnel operation and management.

The geological environment of the gas-bearing coal seam has a considerable impact on the effectiveness of gas drainage. The geological environment conditions of gas drainage principally include eight factors, including geological structure, gas content, gas pressure, initial gas emission velocity, deep ground stress, gas flow attenuation coefficient, the coal seam width, and the gas permeability of coal seam [20].

In addition to geological and environmental conditions, gas drainage equipment and gas drainage technology also have a critical impact on the effect of gas drainage. Among them, gas drainage contains two main factors: extraction pumps and extraction pipelines. The gas drainage technology primarily involves negative pressure extraction, drilling diameter, drilling spacing, the hole sealing method, hole sealing length, and the extraction method: a total of six factors.

Personnel operations and management also have a certain impact on extraction, with a total of five factors involving extraction time, monitoring and surveillance, extraction organization and systems, extraction safety management, and personnel quality and safety education.

3.2. Indicator Screening

According to preliminary analysis of the index system, a total of 21 factors can influence the deep soft coal seam. The hard-to-prevent has certain correlations with the evaluation index. According to the preliminary analysis of the index system, consider also may decrease and disperse the main evaluation index’s importance and may also create unnecessary and extravagant increases in workload in the calculation. Therefore, the index system for preliminary analysis should be based on a reasonable method to screen the required evaluation indicators and select the appropriate and main evaluation indicators for analysis.

The SMART principle is a proposed method for identifying performance indicators. The SMART principle is essentially composed of five principles: the principle of scientificity, the principle of operability, the principle of practicability, the principle of applicability, and the principle of corporeality. According to the SMART principles, we can make a reasonable choice of the index system obtained from preliminary analysis and then carry out the corresponding analysis according to the index system, which can not only guarantee the accuracy of the evaluation results but also be more effective.

Consequently, according to the index system gained from the preliminary analysis and following the SMART principles, the extraction factors of the deep soft coal seam are divided into 10 factors comprising geological structure, gas content, gas pressure, extraction pipeline, drilling diameter, sealing method, coal permeability, extraction time, monitoring and investigating and personnel quality.

4. Determination of Evaluation Indicator Weights

Weight is the quantity that reflects the influence degree of each evaluation index on gas drainage, which can be judged and computed by splitting multiple level indexes. In order to make weight acquisition more scientific and accurate, first, the Analytic Hierarchy Process (AHP) and the CRITIC method are used, respectively, to obtain the subjective weight and objective weight of indicators. Second, the combined weight method is used to organically combine the Analytic Hierarchy Process (AHP) and CRITIC method to obtain the final weight of indicators.

4.1. Subjective Empowerment Approach

The Analytic Hierarchy Process (AHP) is a subjective weighting method, which takes a complex multi-objective decision-making problem as a system, decomposes the objective into multiple objectives or criteria, and then decomposes several levels of multiple indicators. The hierarchical single ranking and the overall ranking are computed by the qualitative index fuzzy quantification method. The complex problem is reduced to a single-layer problem by the systematic approach of a multi-scheme optimization decision. The principal process of the Analytic Hierarchy Process (AHP) is as follows: construct a judgment matrix according to the scale method by pairwise comparison between evaluation factors: the weight vector, eigenvalue, and eigenvector of the judgment matrix are successively calculated [21].

Compared with the commonly used scales, the 0.1 to 0.9 scale is straightforward and more suitable [22,23], so the 0.1 to 0.9 scale is used for comparison in this paper. We invited 10 experts to give an evaluation table of the importance of the 10 evaluation indicators to help consider the high empirical requirements for weight allocation (see Table 1). The indicator factors are the structure of geological (C1), gas content (C2), the pressure of the gas (C3), extraction pipeline (C4), borehole diameter (C5), sealing method (C6), coal seam permeability (C7), time of extraction(C8), monitoring and surveillance (C9) and personnel quality (C10) in a total of 10 factors.

Table 3. Expert scoring table for extraction factors in deep loose coal seams.

B	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10
C1	0.5	0.6	0.5	0.4	0.3	0.3	0.4	0.3	0.1	0.2
C2	0.4	0.5	0.4	0.3	0.2	0.2	0.3	0.2	0.1	0.1
C3	0.5	0.6	0.5	0.4	0.3	0.3	0.4	0.3	0.1	0.2
C4	0.6	0.7	0.6	0.5	0.4	0.4	0.5	0.4	0.2	0.3
C5	0.7	0.8	0.7	0.6	0.5	0.5	0.6	0.5	0.3	0.4
C6	0.7	0.8	0.7	0.6	0.5	0.5	0.6	0.5	0.3	0.4
C7	0.6	0.7	0.6	0.5	0.4	0.4	0.5	0.4	0.2	0.3
C8	0.7	0.8	0.7	0.6	0.5	0.5	0.6	0.5	0.3	0.4
C9	0.9	0.9	0.9	0.8	0.7	0.7	0.8	0.7	0.5	0.4
C10	0.8	0.9	0.8	0.7	0.6	0.6	0.7	0.6	0.6	0.5

presents the evaluation judgment matrix resulting from the synthesis of the experts’ opinions, as follows:

$$B=[\begin{array}{cccccccccc}0.5& 0.6& 0.5& 0.4& 0.3& 0.3& 0.4& 0.3& 0.1& 0.2\\ 0.4& 0.5& 0.4& 0.3& 0.2& 0.2& 0.3& 0.2& 0.1& 0.1\\ 0.5& 0.6& 0.5& 0.4& 0.3& 0.3& 0.4& 0.3& 0.1& 0.2\\ 0.6& 0.7& 0.6& 0.5& 0.4& 0.4& 0.5& 0.4& 0.2& 0.3\\ 0.7& 0.8& 0.7& 0.6& 0.5& 0.5& 0.6& 0.5& 0.3& 0.4\\ 0.7& 0.8& 0.7& 0.6& 0.5& 0.5& 0.6& 0.5& 0.3& 0.4\\ 0.6& 0.7& 0.6& 0.5& 0.4& 0.4& 0.5& 0.4& 0.2& 0.3\\ 0.7& 0.8& 0.7& 0.6& 0.5& 0.5& 0.6& 0.5& 0.3& 0.4\\ 0.9& 0.9& 0.9& 0.8& 0.7& 0.7& 0.8& 0.7& 0.5& 0.4\\ 0.8& 0.9& 0.8& 0.7& 0.6& 0.6& 0.7& 0.6& 0.6& 0.5\end{array}]$$

The subjective weight vector is expressed as Equation (1).

$${\dot{\omega }}_i^{}=\frac{\overline{M_i}}{{\displaystyle \sum _{i=1}^n\overline{M_i}}}$$

(1)

In the above equation, $\overline{M_i}$ is the transformation of matrix B into elements in a fuzzy consistent matrix.

According to Equation (1), the weights are then normalized:

$$\dot{\omega }=(0.1148,0.1229,0.1148,0.1047,0.0946,0.0946,0.1047,0.0946,0.0748,0.0793)$$

4.2. Objective Empowerment Approach

The CRITIC weighting method is an objective weighting method that uses a comparison of strength and conflict between evaluation indicators to measure the weight of indicators [24]. Comparison intensity refers to the value gap between various evaluation schemes of the same index, which is expressed in the form of a standard deviation. The deeper the standard deviation is, the greater the fluctuation. That is, the larger the value gap between schemes, the higher the weight will be. The conflict between indicators is represented by the correlation coefficient. If there is a strong positive correlation between the two indicators, it means that the less conflict there is, the lower the weight will be.

The CRITIC weight method first needs to construct the initial judgment matrix m × n. In the matrix m × n, element m represents m experts, and element n represents the risk assessment value of n indicators given by experts. If the risk evaluation value provided is an interval value, the average value is taken as the risk assessment value.

The standard deviation of each evaluation indicator is then computed, followed by the quantified value of the conflict and the information content of the indicator, resulting in an objective weighting value.

$${\sigma }_i=\sqrt{\frac{1}{m}{\displaystyle \sum }_{k=1}^m{\left(x_i^{\left(k\right)}-\overline{x_i}\right)}^2}$$

(2)

$$y_i={\displaystyle \sum _{q=1}^n\left(1-\frac{Cov(i,q)}{{\sigma }_i{\sigma }_q}\right)}$$

(3)

$${\varphi }_i={\sigma }_i{y}_i$$

(4)

$${\ddot{\omega }}_i^{}=\frac{{\varphi }_i}{{\displaystyle \sum _{i=1}^n{\varphi }_i}}$$

(5)

According to the purpose of the evaluation, the results of all the evaluations made on the subject were assessed, and the following set of comments was calculated according to the degree of influence of each factor to be evaluated on gas drainage from deep soft coal seams.

$$V=\{V1,V2,V3,V4\}$$

In the set of comments, V₁ shows enormous influence, V₂ shows great influence, V₃ shows universal influence, V₄ shows minor influence.

As there is no exact parameter index to judge the degree of influence, the expert survey evaluation combined with the fuzzy statistics method was used to assess the influence degree of each factor by 10 relevant experts in combination with the actual situation of the extracted coal seam. The evaluation results are shown in

Table 4. Statistical table of expert evaluations.

Evaluation Factors	Order of Evaluation
	Enormous Implications	Great Influence	General Influence	Less Influence
Geological formations	6	4	0	0
Gas content	1	3	4	2
Gas pressure	2	5	3	1
Extraction pipes	0	2	7	1
Drilling diameter	2	6	2	0
Sealing method	0	4	5	1
Coal seam permeability	6	4	0	0
Extraction time	2	5	3	0
Monitoring and surveillance	3	6	1	0
Quality of staff	0	3	5	2

. In order to further quantify the evaluation results, the evaluation results of the specialists were assigned according to the uniform method, and the evaluation results are illustrated in Table 3.

The expert evaluation values from

Table 5. Table of expert evaluation level scores.

Order of Evaluation	Enormous Implications	Great Influence	General Influence	Less Influence
Scores	1	0.8	0.6	0.4

were brought into the expert evaluation statistics table to obtain the initial judgment matrix T.

$$T=[\begin{array}{llllllllll}1& 1& 1& 1& 1& 1& 0.8& 0.8& 0.8& 0.8\\ 1& 0.8& 0.8& 0.8& 0.6& 0.6& 0.6& 0.6& 0.4& 0.4\\ 1& 1& 0.8& 0.8& 0.8& 0.8& 0.8& 0.6& 0.6& 0.4\\ 0.8& 0.8& 0.6& 0.6& 0.6& 0.6& 0.6& 0.6& 0.6& 0.4\\ 1& 1& 0.8& 0.8& 0.8& 0.8& 0.8& 0.8& 0.6& 0.6\\ 0.8& 0.8& 0.8& 0.8& 0.6& 0.6& 0.6& 0.6& 0.6& 0.4\\ 1& 1& 1& 1& 1& 1& 0.8& 0.8& 0.8& 0.8\\ 1& 1& 0.8& 0.8& 0.8& 0.8& 0.8& 0.6& 0.6& 0.6\\ 1& 1& 1& 0.8& 0.8& 0.8& 0.8& 0.8& 0.8& 0.6\\ 0.8& 0.8& 0.8& 0.6& 0.6& 0.6& 0.6& 0.6& 0.4& 0.4\end{array}]$$

Based on the scoring values provided by the experts, the standard deviation ${\sigma }_i$ of each evaluation indicator according to Equations (2)–(5), and then calculating the quantified value of conflict $y_i$, and the amount of information about the indicator ${\varphi }_i$, the final objective weight value ${\ddot{\omega }}_i^{}$ is as shown in

Table 6. CRITIC empowerment method for calculating process values.

	Standard Deviations ${\mathit{\sigma }}_{\mathit{i}}$	Quantified Value of Conflict ${\mathit{y}}_{\mathit{i}}$	Indicator Information Content m ${\mathit{\varphi }}_{\mathit{i}}$	Objective Weighting Values m ${\ddot{\mathit{\omega }}}_{\mathit{i}}^{}$
T1	0.1838	3.1982	0.3303	0.1060
T2	0.1033	2.0963	0.3977	0.1277
T3	0.1897	2.7050	0.4971	0.1596
T4	0.1838	2.4051	0.2730	0.0876
T5	0.1135	1.4626	0.1950	0.0626
T6	0.1333	1.4626	0.1974	0.0634
T7	0.1350	2.0963	0.2165	0.0694
T8	0.1033	2.6162	0.3861	0.1239
T9	0.1476	2.7025	0.3418	0.1097
T10	0.1265	1.9010	0.2805	0.0900

.

4.3. Portfolio Empowerment Approach

The combined weighting method organically combines the Analytic Hierarchy Process (AHP) and CRITIC weighting methods. The combined weighting method integrates subjective weight and objective weight so that the achieved weight results are more scientific and sensible. In order to make the calculation of comprehensive weight more balanced, the principle of minimum discriminant information is used to calculate the comprehensive weight [25,26,27,28]. The optimization model M1 was constructed. After solving, the comprehensive weight ${\omega }_i$ can be obtained.

$$\begin{array}{l}M1:\{\begin{array}{c}\min J(\omega )={\displaystyle \sum _{i=1}^n\left({\omega }_i\ln \frac{{\omega }_i}{{\omega }_{{}_i}^1}+{\omega }_j\ln \frac{{\omega }_i}{{\omega }_{{}_i}^2}\right)}\\ s.t.{\displaystyle \sum _{j=1}^n{\omega }_i=1,{\omega }_i\ge 0}\end{array}\\ \end{array}$$

(6)

$${\omega }_i=\frac{\sqrt{{\omega }_i^1\times {\omega }_i^2}}{{\displaystyle \sum _{i=1}^n\sqrt{{\omega }_i^1\times {\omega }_i^2}}}$$

(7)

Formula (7) is used to calculate the combined weight value:

$$\dot{\omega }=(0.1114,0.1266,0.1367,0.0968,0.0777,0.0782,0.0862,0.1094,0.0915,0.0854)$$

5. Effect Evaluation Based on Cloud Model

5.1. Cloud Model

The cloud model is a qualitative and quantitative transformation model based on probability theory and fuzzy mathematics. It can represent the process from qualitative concept to quantitative representation, and the process from quantitative representation to qualitative concept. The cloud model commonly represents information through expected value Ex, entropy En and super entropy He. The expected value Ex represents the expectation of the distribution of cloud droplets in the domain space, which is the most representative point of the qualitative concept and the most typical sample of quantification of this concept. Entropy En represents the measurable degree of determinacy of the qualitative concept. The broader entropy is, the more macroscopic the concept is generally. It is also a measure of the uncertainty of the qualitative concept, which is determined by the randomness and fuzziness of the concept. The super entropy He represents the uncertainty measure of entropy, which is determined by the randomness and fuzziness of entropy. It reflects the cohesion of each value under this language level [29,30].

Without the influence of specific factors, the vast majority of stochastic phenomena approximately comply with a normal distribution [31,32]. Therefore, the ordinary cloud distribution model was used for this analysis. The details are as follows:

$$\{\begin{array}{l}{E}_x=\frac{{\displaystyle \sum _{i=1}^n}{T}_i}{n}\\ E_n=-{\displaystyle \sum _{i=1}^n}P\left(T_i\right){\log }_{10}^{P\left(T_i\right)}\\ He=\eta \end{array}$$

(8)

In the end, the cloud numerical characteristics of the indicators are weighted with the weights of the indicators to obtain the final assessment of cloud numerical characteristics (see Equation (9)).

$$\{\begin{array}{l}\mathrm{Ex}={\displaystyle \sum _{i=1}^{\mathrm{n}}E{x}_i\times {\omega }_i}\\ \mathrm{En}=\sqrt{{\displaystyle \sum _{i=1}^{\mathrm{n}}E{n}_i\times {\omega }_i}}\\ \mathrm{He}={\displaystyle \sum _{i=1}^{\mathrm{n}}H{e}_i\times {\omega }_i}\end{array}$$

(9)

5.2. Evaluation Set Determination

Referring to some related studies on the cloud model and according to the five levels of the gas drainage effect, the golden section ratio method is used to grade the comment set A = {A₁,A₂,A₃,A₄,A₅}, where A₁ is excellent grade (0.8 < A₁ ≤ 1),A₂ is a good grade (0.6 < A₂ ≤ 0.8), and A₂ is a good grade (0.6 < A₂ ≤ 0.8). A₃ is the general grade (0.4 < A₃ ≤ 0.6), A₄ is the qualified grade (0.2 < A₄ ≤ 0.4), A₅ is the unqualified grade (0 < A₅ ≤ 0.2) [33,34,35].

5.3. Numerical Characteristics of Evaluation Indicators

Based on the expert evaluation values, the numerical features of the cloud model for the indicators were then generated in conjunction with the calculation of the numerical features, and the results of the calculations were listed (see

Table 7. Numerical characteristics of the expert indicator cloud model.

	T1	T2	T3	T4	T5	T6	T7	T8	T9	T10
Ex	0.92	0.66	0.76	0.62	0.8	0.66	0.92	0.78	0.84	0.62
En	0.2923	0.5558	0.5472	0.3482	0.4127	0.4097	0.2923	0.4472	0.3900	0.4472
He	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01	0.01

).

In this paper, the digital characteristics of the 10 evaluation indicators are drawn as cloud models (see Figure 1), from which it can be found that the second, fourth and tenth evaluation indicators have the lowest evaluation level of cloud, followed by the third and eighth evaluation indicators.

Finally, the cloud numerical characteristics of all indicators were weighted using Equation (9) to obtain C₁ [0.7583, 0.4334, 0.01] for the gas drainage effect assessments.

According to the range of characteristic values of the evaluation set, we can get that the comprehensive evaluation cloud is nearest to the “good” grade, so the evaluation result of the gas drainage effect in the growth period is determined to be “good” (as shown in Figure 2). At this time, because the gas drainage work has been carried out for a period of time, the gas in the coal seam is considerably affected by mining-induced fractures, and the gas emissions are greater, so the gas drainage effect at this time is excellent. According to the comprehensive evaluation cloud gained and the actual situation, the gas drainage can still be further enhanced.

6. Discussion

According to the results of 5.3, to improve the gas extraction efficiency of the Lugou Coal Mine, the three factors of gas content, extraction pipeline, and quality of staff should be improved first, and the two factors of gas pressure and extraction time should be improved second. Since gas content and gas pressure are geological environmental conditions, the mine focuses on improving extraction efficiency from the extraction pipeline, quality of staff, and extraction time. The specific recommendations are as follows:

In the actual construction process, considering the construction time, coal seam thickness changes (such as coal seam thinning), and the actual situation at the site, it is recommended that the multi-point hydraulic-punching scheme with 0.74 t/m of coal output from the hydraulic jet in the whole process (full coal thickness) in the II1 coal seam of the Lugou Coal Mine be adopted for the whole process of drilling through the seam. The average coal output is controlled at about 0.74 t/m, which can meet the pumping demand, and then the capsule bag type with two blocking and one injection and the sealing length of not less than 17 m. The whole process is under the sieve pipe, and the effective radius of extraction of 30 d is 6.5 m when the negative pressure of extraction is 25 kPa. The enhanced extraction and mining support technology suitable for the three soft coal seams of the Lugou Coal Mine is achieved with high efficiency and economy. As a result of the local gas drainage time being too short, around the borehole gas pressure changing with the extraction time has not been thoroughly rendered. We suggest that in the process of formal construction, it is relevant to increase the distance between the drilling and, at the same time, extend the Lugou Coal Mine gas drainage time. At the same time, it will strengthen staff training and improve posting capabilities for the purpose of maximizing the efficiency of safe and economical pumping.

7. Conclusions

(1)

Lugou Coal Mine is selected as a typical mining area as the research object of the gas drainage effect evaluation. The main influencing factors of gas drainage are evaluated, the index system is preliminarily analyzed, and reasonable selection is made according to the SMART principle so as to construct a comparatively accurate evaluation index system.

(2)

The combined weight assignment method, which combines subjective and objective weighting, combines the advantages of the Analysis Hierarchical Process (AHP) method and the CRITIC method to assign weights to the evaluation index system independently, optimizing the weights and decreasing the influence of subjective human factors on the evaluation.

(3)

The cloud model is used to carry out the evaluation of gas drainage. The cloud model combines the characteristics of fuzzy mathematics and probability theory, which can be well-converted between quantitative and qualitative and is suitable for the evaluation of the gas drainage effect. The evaluation result of the gas drainage effect is “excellent”, which is consistent with the actual situation of the mine, demonstrating that the combined weighting method and the cloud model have good applicability in the ground pressure evaluation model of phosphate rock, which makes it of practical significance in the ground pressure evaluation.

(4)

Based on the evaluation results, improvement measures for gas extraction were proposed.