Evaluation of mining capacity of mines using the combination weighting approach: A case study in Shenmu Mining Area in Shaanxi Province,China

Abstract

Aiming at the low mining rate in mines, Xingelao, Dabianyao, and Dongliang Coal Mines in Shenmu Mining Area, Shaanxi Province, China were taken as research objects. Based on this, this study constructed an evaluation index system for the mining capacity of the mines from the perspectives of geological factors, mechanical equipment, humans, and mining design. Moreover, the factors influencing the mining capacity of the mines were evaluated using a combination weighting approach based on an improved analytic hierarchy process and an entropy weight method. A standard cloud was generated based on the mapping standards of each index and a comprehensive cloud was obtained according to comprehensive weight and a backward cloud algorithm. Finally, by combining the comprehensive cloud with local and overall scores of the mines, the mining capacities of the mines were evaluated. The research results demonstrate that the key factor restricting the mining capacity of the mines is the geological environment and five major third-grade indexes affecting mining capacity are igneous rock intrusion, collapse column, scouring zone of the ancient river bed, mechanization level and coal pillar width. In addition, the corresponding suggestions and measures were put forward according to the main factors influencing the mining rate of the mines. In accordance with the weights and scores of each index, the overall scores of the mines were calculated. Dongliang, Dabianyao, and Xingelao Coal Mines were ranked in order based on scores. The research results provide a theoretical basis for improving the mining capacity of the mines under similar geological conditions.

Keywords

Improved analytic hierarchy process entropy weight method combination weighting approach mining rate capacity evaluation

Introduction

Coal is the main fossil energy in the world. With the gradual exploitation of resources on the earth, coal resources diminish gradually.^1–3 Due to the strong dependence of each country, coal plays the dominant role in the energy structure of each country in the world.^4–7 In recent years, to pursue short-term benefits, many coal mining enterprises only mine thick coal seams rather than thin ones, which wastes lots of coal resources and results in a low mining rate on the whole.^8–11 It is an inevitable choice to properly reduce the scale of coal development and improve the mining rate under the changing trend of energy consumption and the current situations of coal resources.^12–15 Evaluation of the mining capacity of mines using the combination weighting approach can intuitively and clearly reflect the level of mining capacity of mines and improve weak links to raise the mining rate of mines.^16–18

Based on the theory of the combination weighting approach, many experts have carried out in-depth studies on factors affecting the mining rate of mines. Qiao et al.¹⁹ systematically analyzed various factors influencing the mining rate from the perspectives of hydrogeology, characteristics of coal seams, technical process, and basic work before mining and proposed corresponding measures to improve the mining rate. To solve the problems of a low mining rate and a serious waste of resources at the current stage, Wu et al.²⁰ revealed the primary causes for the low mining rate, including cutthroat competition, giving up the high mining rate, insufficient inspection and supervision, poor geographical location, and backward technical conditions. Moreover, they put forward corresponding suggestions and measures to control the low mining rate. Zhang et al.²¹ applied short-wall backfill mining with tailing paste into Zhangzhao Coal Mine (Zibo City, Shandong Province, China), which allows the mining rate of the mine to reach above 85%, thus achieving goals of releasing coal resources, controlling surface subsidence, and effectively disposing of solid wastes. Yavue et al.²² studied the optimum support design for the main haulage road in deep coal seams by using an analytic hierarchy process (AHP) method and finally selected a better supporting mode. Bogdanovic²³ and Ataei et al.²⁴ analyzed geological, geotechnical, safety, and economic factors by utilizing the AHP method and finally selected a method most suitable for mining the Jajarm Mine in Iran. Charya et al.²⁵ ranked and analyzed the relative importance of artificial intelligence factors in the manufacturing industry based on the AHP method and improved relatively important factors to increase efficiency in industrial automation. Sun et al.²⁶ evaluated agricultural water management in irrigation districts of north China through an improved AHP and classified the evaluation system into five second-grade and 14 third-grade indexes. The evaluation results show that the engineering index and management index are two important factors affecting agricultural water use in irrigation districts.

In conclusion, the main factors affecting the mining capacity of mines include mine geology, hydrogeological characteristics, technical process, management, and mechanical equipment. These studies provide ideas, methods, and theoretical bases for this research. On this basis, this study improved the selection of scales for the traditional AHP method^27–31 and showed that the improved scales could more accurately express scores. By using a combination weighting approach based on the improved AHP and entropy weight method (EWM), this research analyzed weights of factors influencing the mining rate of Xingelao, Dabianyao, and Dongliang Coal Mines in Shenmu Mining Area, Yulin City, Shaanxi Province, China, and evaluated mining capacity.

Evaluation index system for mining capacity of the mines

The selection of evaluation indexes for factors affecting the mining capacity of the mines should follow five principles: scientificity, comprehensiveness, operability, timeliness, and standardability.^32,33 Based on the above five principles, by referring to existing studies,^34,35 it can be concluded that geological environment, mechanical equipment, humans, and mining design are factors influencing the mining rate of the mines. Therefore, a target layer, a rule layer, and an index layer were comprehensively and reasonably constructed, in which the evaluation index system including four second-grade indexes and 16 third-grade indexes is shown in Figure 1.

Figure 1.

Evaluation index system for the mining capacity of the mines.

Evaluation of the mining capacity of mines

The basic idea for evaluating the mining capacity of the mines using the combination weighting approach based on the improved AHP and EWM is shown as follows: Firstly, the mapping standard and the score matrix are determined. Secondly, the weight of each index is determined by combining it with subjective and objective methods. Finally, the local and overall scores are calculated, and the evaluation grades are determined according to scores. The specific steps are displayed in Figure 2. Note that the cloud model is based on probability theory and fuzzy set theory, and forms a model of conversion between qualitative concept and quantitative representation through specific algorithms. The cloud model can take into account the randomness and fuzziness of the real world, and organically combine the two. After the construction of the mapping standard, to convert the actual conditions of the mine into corresponding scores and then calculate, the target layer and the criterion layer of the mine are evaluated later. Mine total score and local score is a form of expression, which can quantitatively analyze the situation of the mine target layer and criterion layer.

Figure 2.

Steps for evaluating the mining capacity of the mines.

Establishment of cloud model

Based on probability theory and fuzzy set theory, a bidirectional cognitive model, namely a cloud model to solve the problem of uncertainty was proposed, realizing interconversion between qualitative concepts and quantitative data. C is a qualitative concept in the universe X. The distribution of mapping from the universe X to the interval [0, 1] in the universe space is called a membership cloud (a cloud for short). Each x is known as a cloud droplet.³⁶ As shown in Figure 3, the abscissa represents the scope of qualitative concepts, and the ordinate denotes the degree of membership. The digital features of Ex, En, and He reflect the overall characteristics of the cloud model, which are expressed as expectation, entropy, and superentropy, respectively.^37–39

Figure 3.

Cloud model.

Weights of each index are determined by using the combination weighting approach combining the improved AHP with EWM, which can not only learn theories and experience from senior experts but also reduce subjective arbitrariness of weighting so that evaluation results are more authentic and reliable. Note that senior experts come from coal mines or government departments. We sent evaluation forms to these experts and obtained evaluation results through an anonymous questionnaire.

Selection of scales for improving the traditional AHP

The scales of 1–9 of the traditional AHP are not in harmony with the habit of language judgment. Moreover, the scores of grades of the importance of factors generally do not reach 7 or 9, and there are often scores at grades of 2, 4, and 6. This may lead to some errors in evaluation conclusions and consistency tests.^39,40 The improved AHP method plays a role to reasonably improve the scales of evaluation indexes.^41,42

The specific method for establishing scales is shown as follows: if the relative importance of a for b as well as of b for c are known, then the relative importance of a for c is transitive. It is supposed that the scores of equal importance, slight importance, obvious importance, strong importance, and extreme importance are 1, a², a³, a⁴ and a⁵, and a:c = (a:b)/ (b:c). The limit of the judgment of Saaty in a numerical value is 9 and a⁵ = 9, that is, a = 1.5518 (values above a⁵ are all recorded as 9). Therefore, the scales of indexes can be presented and the indexes can be scored by taking the scale of exponent indexes and their reciprocal as scales, as displayed in Table 1.

Table 1.

Scale definition.

Grade of importance	Score b^ij	Description
Factor i is extremely important compared with factor j	9	Maximum difference in judgment between the two indexes in the allowable range.
Factor i is strongly important compared with factor j	5.799	Strong difference in judgment between the two indexes.
Factor i is obviously important compared with factor j	3.737	Obvious difference in judgment between the two indexes.
Factor i is slightly important compared with factor j	2.408	Slight difference in judgment between the two indexes.
Factor i is equally important compared with factor j	1	No difference in judgment between the two indexes.

After establishing an AHP model for the consistency test, experts in the field are asked to make a pairwise comparison and score the importance of each factor. The importance of m factors to a certain factor A in the upper layer is compared. Two factors B_i and B_j are set for comparison and the comparison results are b_ij. Based on the scoring results, the comparison value b_ij and the judgment matrix A are separately obtained.

The largest characteristic root and weight of AHP

The m root of square k_i of the product of elements in each row of the judgment matrix A is calculated.

k_{i} = \sqrt[m]{\prod_{j = 1}^{m} b_{i j}} (i = 1, 2, \dots m; j = 1, 2, \dots, m)

(1)

Q_i after normalization of K_i is calculated.

Q_{i} = K_{i} / \prod_{i = 1}^{m} K_{i}

(2)

where,

Q_{i} = [Q_{1}, Q_{2}, \dots, Q_{m}]^{T}

. The normalized components indicate the relative importance of the elements, that is, weights.

The largest characteristic root λ_max is obtained.

λ_{m ax} = \sum_{i = 1}^{m} (B_{i} / m Q_{i}) (i = 1, 2, \dots m; j = 1, 2, \dots, m)

(3)

where B_i represents the ith element in the hierarchical single ranking (namely a superiority and inferiority ranking B) of the largest characteristic root λ_max calculated by vectors and

B = A Q

. The relatively rigorous ranking method is the largest characteristic root method, which can obtain the largest characteristic root and its eigenvector.

Finally, the consistency test is carried out. RI is the average random consistency index of the judgment matrix. When CR < 0.1, it meets the consistency requirements.

To reduce the impact of subjective factors, questionnaires were used in this study. To be specific, a total of 20 questionnaires were issued to mine professionals and teaching and research staff and the weight of each index in each questionnaire was calculated by using Yaahp software. Yaahp is a comprehensive evaluation auxiliary software based on the AHP and fuzzy comprehensive evaluation method, which provides help in model construction, calculation and analysis for the decision-making process using the AHP and fuzzy comprehensive evaluation method. According to weight vectors, clustering analysis was performed by utilizing SPSS software to cluster 20 questionnaires into several categories. The weight coefficient of each category is calculated based on formula (4) and finally, the weight of each index is determined by formula (5).²⁸

λ_{i} = ϕ_{i}^{2} / \sum_{i = 1}^{n} ϕ_{i}^{2}

(4)

α = \sum_{i = 1}^{n} λ_{i} ϖ

(5)

where, $λ_{i}$ , $φ_{i}$ and $ϖ$ separately indicate the weight coefficient, bulk weight and average weight, of each category.

Concept of EWM

EWM is used to determine the objective weight according to the variability of indexes. The smaller the entropy of an index, the more information the index has, the greater the role it plays in the evaluation, and the greater the weight it has.²⁹ The steps for determining weights using the EWM are shown as follows:

Constructing the evaluation index matrix

Assuming that there are m objects and n indexes for evaluation, the evaluation index matrix is constructed.

Data processing

Very large and very small data are obtained, and the data are normalized.

l_{i j} = x_{i j}^{,} / \sum_{i = 1}^{m} x_{i j}^{,} (j = 1, 2, \dots, n)

(6)

Calculating the entropy of the nth index

e_{j} = - κ \sum_{j = 1}^{m} l_{i j} \ln l_{i j} (j = 1, 2, \dots, n)

where,

κ = 1 / \ln m

e_{j} = - \sum_{j = 1}^{m} l_{i j} \ln l_{i j} / \ln m (j = 1, 2, \dots, n)

(7)

Determining weights of indexes

The coefficient of variability $g_{j}$ of the nth index is $g_{j} = 1 - e_{j}$ and the weight of the index is $β_{j} = g_{j} / \sum_{j = 1}^{n} g_{j}$ .

Determining weights of indexes using the combination weighting approach

The combination weighting approach is to use the geometric average method to combine subjective weight vector $α = (α_{1}, α_{2}, \dots, α_{j})$ with objective weight vector $β = (β_{1}, β_{2}, \dots, β_{j})$ determined by the EWM, which is calculated as follows:

w_{j} = \sqrt{α_{j} β_{j}} / \sum_{j = 1}^{n} \sqrt{α_{j} β_{j}}

(8)

Determination of mapping standard and standard cloud

In the evaluation index system for the mining capacity of mines, the third-grade indexes include three qualitative indexes and 13 quantitative indexes. During the evaluation, the qualitative indexes are quantified by field professionals with specific scores following the actual situations. In the meanwhile, the quantitative indexes are also mapped as specific scores according to measured values. Mapping standards are displayed in Table 2. According to the score matrix R_ij of each index, the local score is calculated by combining with weights of each index determined by the combination weighting approach, as shown below:

c_{i} = \sum_{j = 1}^{n} w_{ij} R_{ij}

(9)

Table 2.

Mapping standards of each index.

Evaluation grade	[0, 40) weaker capacity	[40, 55) weak capacity	[55, 70) medium capacity	[70, 85) strong capacity	[85, 100] stronger capacity
Thickness of coal seam (m)	≤0.8	[0.8, 1.5)	[1.5, 2)	[2, 4)	[4, 6)
Number of fault fracture zones	≥16	[12, 16)	[8, 12)	[4, 8)	[0, 4)
Area of igneous rock intrusion (m²)	≥5000	[2500, 5000)	[1000, 2500)	[500, 1000)	[0, 500)
Area affected by collapse column (m²)	≥4000	[2000, 4000)	[800, 2000)	[300, 800)	[0, 300)
Area affected by scouring zone of ancient river bed (m²)	≥3000	[1500, 3000)	[800, 1500)	[400, 800)	[0, 400)
Qualified rate of maintenance (%)	[50, 60)	[60, 70)	[70, 80)	[80, 90)	[90, 100]
Serviceability rate of equipment (%)	[50, 60)	[60, 70)	[70, 80)	[80, 90)	[90, 100]
Mechanization level	[0, 40)	[40, 55)	[55, 70)	[70, 85)	[85, 100]
Equipment failure rate (%)	[60, 100)	[45, 60)	[30, 45)	[15, 30)	[0, 15]
Matching degree of height of a coal mining machine with coal thickness (%)	[50, 60)	[60, 70)	[70, 80)	[80, 90)	[90, 100]
Cultural competence of staffs (average education years per person)	[0, 6)	[6, 12)	[12, 16)	[16, 19)	[19, 23]
Technical quality of staffs (working years per person)	[0, 3)	[3, 6)	[6, 9)	[9, 12)	[12, 30]
Staff training rate (%)	[50, 60)	[60, 70)	[70, 80)	[80, 90)	[90, 100]
Layout of working face	[0, 40)	[40, 55)	[55, 70)	[70, 85)	[85, 100]
Coal pillar width (m)	[16, 20)	[12, 16)	[8, 12)	[4, 8)	[0, 4)
Coal mining method	[0, 40)	[40, 55)	[55, 70)	[70, 85)	[85, 100]

Based on the local score, the calculation formula for the overall evaluation score of the mining capacity of the mines is

c = \sum_{i = 1}^{4} w_{i} c_{i}

(10)

According to the above cloud model theory and basic mapping standards, the corresponding cloud model of evaluation grade in the model for the mining capacity of the mines is generated by using Matlab software, as shown in Figure 4. In the figure, the ordinate represents the degree of membership of cloud droplets to different evaluation grades, while the abscissa indicates the quantitative value of the index at different grades. Different colors are used to distinguish the degree of membership of the same variable at different grades. From left to right, blue, red, yellow, purple and green represent grade I (weaker capacity), grade II (weak capacity), grade III (medium capacity), grade IV (strong capacity) and grade V (stronger capacity), respectively.

Figure 4.

Standard cloud model of evaluation grades.

Case study and analysis

Determination of measured value and mapping score of each mine

Against the engineering backgrounds of Xingelao, Dabianyao, and Dongliang Coal Mines in Shenmu City of Shaanxi Province, the mining capacities of the three mines were evaluated. The three mines all had shallow-buried nearly horizontal coal seams with a mining depth of about 110 m. The three mines were mined with the longwall retreat full-seam mining along the strike and belonged to mines with low gas content, in which coal dust was explosive. The coal seams under mining were combustion spontaneous ones in class I and Xingelao Coal Mine was adjacent to Dabianyao Coal Mine. Protective coal pillars with a width of 30 m were reserved on both sides and there were no facilitates, such as buildings, rivers, and roads on the surface. The measured values and mapping scores of each index of the three mines are demonstrated in Table 3.

Table 3.

Measured values and mapping scores of each index.

Evaluation index	Xingelao Coal Mine	Dabianyao Coal Mine	Dongliang Coal Mine
Thickness of coal seam (m)	1.6 (58)	1.7 (61)	2.7 (75)
Number of fault fracture zones	8 (70)	6 (78)	3 (90)
Area of igneous rock intrusion (m²)	0 (100)	0 (100)	0 (100)
Area affected by collapse column (m²)	0 (100)	0 (100)	0 (100)
Area affected by scouring zone of ancient river bed (m²)	0 (100)	0 (100)	0 (100)
Qualified rate of maintenance (%)	85 (78)	80 (70)	85 (78)
Serviceability rate of equipment (%)	86 (79)	83 (75)	90 (85)
Mechanization level	80	85	85
Equipment failure rate (%)	18 (82)	16 (84)	12 (88)
Matching degree of the height of a coal mining machine with coal thickness (%)	93 (90)	96 (94)	92 (88)
Cultural competence of staffs (average education years per person)	13 (58)	12 (55)	13 (58)
Technical quality of staffs (working years per person)	6 (55)	7 (60)	6 (55)
Staff training rate (%)	92 (88)	94 (91)	95 (93)
Layout of working face	80	85	85
Coal pillar width (m)	12 (55)	10 (63)	10 (63)
Coal mining method	75	80	80

Calculation of combined weights

The 20 questionnaires were numbered as 1–20, and then the weights of each index in each questionnaire were calculated by Yaahp software. Finally, clustering analysis on weight vectors was performed by using SPSS software. The 20 questionnaires were clustered into four categories. Based on formula (6), the weight coefficients of the rule layer in the index system were calculated as 0.4679, 0.2157, 0.1007, and 0.2157 and then weights of each index were calculated through formula (7). Furthermore, according to the measured values of each index in the field, the weights were calculated using the EWM. Finally, by virtue of formula (8), the weights determined by AHP were combined with those determined by the EWM to calculate the final weight. The calculation results of weights are shown in Table 4.

Table 4.

Weights of evaluation indexes.

Index	Weight	Index	Weight $α_{j}$ determined by the AHP	Weight $β_{j}$ determined by the EWM	Combined weight $w_{j}$	Total ranking weight
A	0.4679	A1	0.0666	0.0086	0.0248	0.0116
		A2	0.1234	0.026	0.0587	0.0275
		A3	0.2699	0.3217	0.3055	0.1429
		A4	0.2699	0.3217	0.3055	0.1429
		A5	0.2699	0.3217	0.3055	0.1429
B	0.2157	B1	0.0789	0.0241	0.0522	0.0112
		B2	0.1341	0.0340	0.0808	0.0174
		B3	0.1695	0.0241	0.0764	0.0164
		B4	0.4117	0.9063	0.7306	0.1576
		B5	0.2064	0.0122	0.0600	0.0129
C	0.1007	C1	0.1743	0.1656	0.1700	0.0176
		C2	0.6513	0.6460	0.6489	0.0653
		C3	0.1743	0.1882	0.1812	0.0182
D	0.2157	D1	0.5693	0.0916	0.3208	0.0691
		D2	0.1136	0.8045	0.4246	0.0916
		D3	0.3169	0.1037	0.2545	0.0549

By finding the index of maximum weight subordinating to the rule layer, a strategic quadrilateral with A3, B4, C2, and D2 for mining capacity of the mines was constructed based on four-quadrant analysis, as displayed in Figure 5.

Figure 5.

Strategic quadrilateral of the mining capacity of the mines.

The mining rate of the mines was mainly evaluated from the geological environment, mechanical equipment, humans, and mining design. Although the geological environment exerts great influences on the mining rate of the mines, it is formed under natural conditions and is an inherent influence factor. The mechanical equipment and mining design are assigned with the same weight, but the weight on the human factor is small. By analyzing the overall ranking of weights, it is obtained that the area of igneous rock intrusion, area affected by collapse column, area affected by scouring zone of the ancient river bed and mechanization level occupy larger proportions in the total weight, which have large effects on mining capacity of the mines.

According to Figure 5, the area of triangles in each quadrant of the strategic quadrilateral can be calculated. Since the area is relatively small, the area of each triangle is multiplied by 100 for comparison. Therefore, the areas of triangles A₃OD₂, A₃OB₄, C₂OB₄ and, C₂OD₂ are 1.3090, 2.2564, 1.0291, and 0.5981, respectively. Through analysis, the factors mainly affecting the mining capacity of the mines are AB, CB, DA, and CD, ranking in descending order. The triangle A₃OB₄ has the largest area; so, AB should be improved to raise the mining rate of the mines.

Based on the combined weight of each index in Table 4 and specific values mapped by measured values, local and overall scores of mining capacity of the mines are separately calculated using formulas (9) and (10). Evaluation grades are determined in accordance with scores and Table 2 and the calculation results and corresponding evaluation grades are demonstrated in Table 5. Dongliang Coal Mine, Dabianyao Coal Mine, and Xingelao Coal Mine are ranked in order according to the scores of mining capacity thereof. In the rule layer “mechanical equipment” and “mining design” evaluation grade evaluation is good, stope capacity evaluation level and the total score is relatively good, big span kiln and east beam coal mining recovery rate to meet the demand, as the “mechanical equipment” and “mining design” rating for strong and moderate, Xingelao coal mining power rating is “strong”, but there is relatively low score evaluation. These two weaknesses need to be addressed further.

Table 5.

Evaluation on mining capacity of the mines.

Coal mine	Total score	Evaluation grade	Second-grade index	Local score	Evaluation grade
Xingelao Coal Mine	84.02	Strong	Geological environment	97.19	Stronger
			Mechanical equipment	80.87	Strong
			Human	61.49	Medium
			Mining design	68.10	Medium
Dabianyao Coal Mine	86.24	Stronger	Geological environment	97.74	Stronger
			Mechanical equipment	83.21	Strong
			Human	64.77	Medium
			Mining design	74.37	Strong
Dongliang Coal Mine	87.31	Stronger	Geological environment	98.79	Stronger
			Mechanical equipment	87.01	Stronger
			Human	62.40	Medium
			Mining design	74.38	Strong

Determination of the comprehensive evaluation cloud

According to the above scores of second-grade indexes and total scores of each mine, the comprehensive cloud maps of Xingelao, Dabianyao, and Dongliang Coal Mines were separately plotted by Matlab software. Cyan represents the standard cloud. From the left to right, dark yellow, purple, brown, dark blue and red separately indicates human, mining design, mechanical equipment, geological environment, and overall evaluation score of the mines, as illustrated in Figures(6) to (8).

Figure 6.

Comprehensive cloud map of Xingelao Coal Mine.

Figure 7.

Comprehensive cloud map of Dabianyao Coal Mine.

Figure 8.

Comprehensive cloud map of Dongliang Coal Mine.

By analyzing Figures (6) to (8), it can be concluded that the score of the geological environment is high, and it is evaluated to be of stronger mining capacity. Although the geological environment is the main factor affecting the mining rate of the mines, the three mines have a good geological environment, which slightly influences the mining rate. Low local scores of human factor and mining design indicate the low quality of staff management and staff themselves, and the coal pillar width is relatively large. Therefore, it is necessary to strengthen the training of staff and effectively reduce the width of coal pillars on the premise of guaranteeing safety and rationality.

Discussion

The main factors affecting the mining capacity of the mines are the area of igneous rock intrusion, the area affected by collapse column, the area affected by scouring zone of the ancient river bed, mechanization level, and coal pillar width. The mining capacity can be improved according to these main influence factors by taking the following specific measures. In the process of mining, the principle of “exploration while mining” should be adopted as far as possible, and mining areas of igneous rock intrusion, collapse columns, and scouring zones of ancient river beds should be avoided as far as possible. The mines should be mined by mostly using fully-mechanized mining, rather than blasting mining and conventional mining. The management and training of staff should be strengthened. Coal pillars need to be narrowed within a reasonable range, to improve the mining rate and reduce the waste of resources.

Conclusions

Based on the engineering backgrounds of Xingelao, Dabianyao, and Dongliang Coal Mines in Shenmu City, Shaanxi Province, the traditional AHP method was improved and weight coefficients of each index were determined by using the combination weighting approach based on the improved AHP and EWM. This not only considers subjectivity of decision makers, but also ensures accuracy of weights, which provides a scientific decision-making model for methods improving the mining rate. Therefore, the model can be widely used in the field relevant to the mining capacity of the mines, and can better guide improvement of the mining rate of the mines.

(1) The evaluation index system and standard cloud model for the mining capacity of the mines were constructed. Based on the principle for constructing the index system, the evaluation index system for mining capacity of the mines was built from the perspectives of geological environment, mechanical equipment, human factor, and mining design by referring to relevant studies and combining with the actual situations in the field.

(2) The selection of scales in the traditional AHP method was improved so that the results calculated using the improved AHP method were more reasonable and reliable. By combining the improved AHP method with the EWM method, the weights of influence factors in the index system for mining capacity of the mines were calculated and analyzed by combining the subjective and objective aspects. By analyzing the weights, the main influence factors were determined and corresponding suggestions were put forward.

(3) Based on the evaluation model and cloud model, the mining capacities of Xingelao, Dabianyao, and Dongliang Coal Mines were evaluated. In accordance with evaluation results, the key factor restricting mining capacity was the geological environment. The mining capacities of the three mines were scored. According to the scores of mining capacity, Dongliang, Dabianyao, and Xingelao Coal Mines were ranked in order.

Footnotes

ORCID iD

Zhiheng Cheng

Author biographies

Haoyi Chen received his bachelor's degree in Mining Engineering from Hulunbeir College, Inner Mongolia, in 2019. He is currently a Master of Guizhou University of School of Mines. His research interests include coal mine safety and mine capacity evaluation.

Zhiheng Cheng received his PhD degree in Mining Engineering from China University of Mining & Technology, Beijing, in 2015. He is currently an associate professor with North China Institute of Science and Technology of School of Safety Engineering. His research interests include coal mine safety, mine capacity evaluation and methane recovery.

Dezhong Kong received his PhD degree in Mining Engineering from China University of Mining & Technology, Beijing in 2016. He is currently an associate professor with Guizhou University of School of Mines. His research interests include rock mechanics and coal mine safety.

References

Zou

Liu

Jiang

, et al. Gas flow laws in coal subjected to hydraulic slotting and a prediction model for its permeability-enhancing effect. Energy Sources Part A 2021: 1–15. DOI: 10.1080/15567036.2021.1936692

Black

. Review of coal and gas outburst in Australian underground coal mines. International Journal of Mining Science and Technology 2019; 29: 815–824.

Geng

Nie

, et al. Regression analysis of major parameters affecting the intensity of coal and gas outbursts in laboratory. Int J Min Sci Technol 2017; 27: 327–332.

Liang

Zou

, et al. Analysis of effects of CO₂ injection on coalbed permeability: implications for coal seam CO₂ sequestration. Energy Fuel 2019; 33: 6606–6615.

Zou

Liu

Zhang

, et al. Rationality evaluation of production deployment of outburst-prone coal mines: a case study of Nantong coal mine in Chongqing, China. Saf Sci 2020; 122: 104515.

Tong

Brandt

Yue

, et al. Forest management in southern China generates short term extensive carbon sequestration. Nat Commun 2020; 11: 129.

McCollum

Bauer

Calvin

, et al. Fossil resource and energy security dynamics in conventional and carbon-constrained worlds. Clim Change 2014; 123: 413–426.

Liang

Chang

, et al. Effects of cyclic saturation of supercritical CO₂ on the pore structures and mechanical properties of bituminous coal: an experimental study. J CO₂ Util 2020; 40: 101208.

Liang

Zou

, et al. Numerical analysis of permeability rebound and recovery during coalbed methane extraction: implications for CO₂ injection methods. Process Saf Environ 2021; 149: 93–104.

10.

Zou

Liu

Cheng

, et al. Effect of slot inclination angle and borehole-slot ratio on mechanical property of Pre-cracked coal: implications for ECBM recovery using hydraulic slotting. Nat Resour Res 2020; 29: 1705–1729.

11.

Jiang

Luo

. Development of a roof bolter drilling control process to reduce the generation of respirable dust Int J Coal Sci Technol 2021; 8: 199–204.

12.

Wang

Liang

, et al. Has China's Coal consumption already peaked? A demand-side analysis based on hybrid prediction models. Energy (Oxford) 2018; 162: 272–281.

13.

Busch

Gensterblum

. CBM and CO₂-ECBM related sorption processes in coal: a review. Int J Coal Geol 2011; 87: 49–71.

14.

Mavor

Vaughn

. Increasing coal absolute permeability in the San Juan Basin Fruitland formation, SPE Reserv. Eval Eng 1998; 1: 201–206.

15.

Liang

Zou

. Structures and fractal characteristics of pores in long-flame coal after cyclical supercritical CO₂ treatment. Fuel 2021; 286: 119305.

16.

Peng

, et al.

Experimental study on evolution law for particle breakage during coal and gas outburst

Int J Coal Sci Technol 2020; 7: 97–106.

17.

Liu

Lin

, et al. Mechanical criterion for coal and gas outburst: a perspective from multiphysics coupling. Int J Coal Sci Technol 2021. https://doi.org/10.1007/s40789-021-00447-z

18.

Fan

Luo

, et al. Coal and gas outburst dynamic system. Int J Min Sci Technol 2017; 27: 49–55.

19.

Qiao

, et al. Influence of coal mining on regional karst groundwater system: a case study in west mountain area of Taiyuan City, northern China. Environ Earth Sci 2011; 64: 1525–1535.

20.

Xiao

Song

. Competitiveness analysis of coal industry in China: a diamond model study. Resour Policy 2017; 52: 39–53.

21.

Zhang

Zhao

, et al. The development and application of shortwall backfill mining with tailing paste: a case study in China mine. J Residuals Sci Technol 2017; 14: 37–43.

22.

Yavuz

Iphar

Once

. The optimum support design selection by using AHP method for the main haulage road in WLC Tuncbilek colliery. Tunnelling Underground Space Technol 2008; 23: 111–119.

23.

Bogdanovic

Nikolic

Ilic

. Mining method selection by integrated AHP and PROMETHEE method. Anais da Academia Brasileira de Ciências 2012; 84: 219–233.

24.

Ataei

Shahsavany

Mikaeil

. Monte Carlo analytic hierarchy process (MAHP) approach to selection of optimum mining method. Int J Min Sci Technol 2013; 23: 573–578.

25.

Acharya

Sharma

Gupta

. Analyzing the factors in industrial automation using analytic hierarchy process. Comput Electr Eng 2018; 71: 877–886.

26.

Sun

Wang

Hao

. An improved analytic hierarchy process method for the evaluation of agricultural water management in irrigation districts of north China. Agric Water Manage 2017; 179: 324–337.

27.

Wang

Zou

, et al. Influences of path control effects on characteristics of gas migration in a coal reservoir. Fuel (Guildford) 2020; 267: 117212.

28.

Saaty

T. L.

What is the analytic hierarchy process? Mathematical models for decision support. Springer: Germany, 1988, pp.109–121.

29.

Saaty

T. L

. A scaling method for priorities in hierarchical structures. J Math Psychol 1977; 15: 234–281.

30.

Dong

Saaty

T. L

. An analytic hierarchy process model of group consensus. J Syst Sci. Syst Eng 2014; 23: 363–375.

31.

Saaty

T. L

. How to make a decision: the analytic hierarchy process. Eur J Oper Res 1990; 48: 9–26.

32.

Sheshpari

. A review of underground mine backfilling methods with emphasis on cemented paste backfill. Electron J Geotech Eng 2015; 20: 5183–5208.

33.

Dong

Tong

, et al. Some developments and new insights of environmental problems and deep mining strategy for cleaner production in mines. J Clean Prod 2019; 210: 1562–1578.

34.

Feng

Zhang

, et al. Status and research progress for residual coal mining in China. J China Coal Soc 2020; 45: 151–159.

35.

Wang

Zhu

, et al. Influence of high mining velocity on periodic weighting during fully-mechanized mining in a shallow seam. Zhongguo Kuangye Daxue Xuebao(J China Univ Min Technol) 2012; 41: 349–354.

36.

Wang

Zou

. Bidirectional cognitive computing method supported by cloud technology. Cogn Syst Res 2018; 52: 615–621.

37.

Yao

Wang

Xue

, et al. Assessment of lake eutrophication using a novel multidimensional similarity cloud model. J Environ Manage 2019; 248: 109259.

38.

Zhang

, et al. Evaluation of the water yield of coal roof aquifers based on the FDAHP-entropy method: a case study in the Donghuantuo coal mine, China. Geofluids 2021. https://doi.org/10.1155/2021/5512729

39.

Liu

Yang

Yuan

, et al. A fuzzy analytic hierarchy process model to assess the risk of disaster reduction due to grouting in coal mining, Arab. J Geosci 2020; 13: 1–15.

40.

Cui

Wang

Zhao

, et al. China's regional sustainability assessment on mineral resources: results from an improved analytic hierarchy process-based normal cloud model. J Clean Prod 2019; 210: 105–120.

41.

Lin

Chen

, et al. Assessment of flash flood risk based on improved analytic hierarchy process method and integrated maximum likelihood clustering algorithm. J Hydrol 2020; 584: 124696.

42.

Wang

, et al. Evaluation of floor water inrush based on fractal theory and an improved analytic hierarchy process. Mine Water Environ 2017; 36: 87–95.