Mechanized mining technical process (MMTP) related to the control method of the shearer is a vital process in thin coal seam mining operations. An appropriate MMTP is closely related to safety, productivity, labour intensity, and efficiency. Hence, the evaluation of alternative MMTP is an important part of the mining design. Several parameters should be considered in MMTP evaluation, so the evaluation is complex and must be compliant with a set of criteria. In this paper, two multiple criteria decision-making (MCDM) methods, Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE), were adopted for this evaluation. Then, the most appropriate MMTP for a thin coal seam working face was selected in China.
The reserves of thin coal seam (less than 1.3 m in thickness) are enormous in China. Among 95 national key coal enterprises, a total of more than 750 thin coal seams exist in 445 coal mines. The recoverable reserves of thin seam are about 6.5 billion tons, accounting for 19% of the total recoverable coal reserves [
Currently, the intensity of excavating thin coal seams in the above minefields is increasing year by year. However, mechanized mining of thin coal seams develops slowly due to the special mining conditions. Limited by the detrimental factors, such as high labor intensity, low degree of mechanization, low safety level, and low economic efficiency, the production of thin coal seams takes merely 10.4% of the total coal production nationwide [
Currently, mechanized mining of thin seams is mainly focused on the horizontal and slightly inclined coal seams. Relatively developed fully mechanized mining techniques include longwall mining involving coal shearer, or coal plough, auger mining, and room and pillar mining by continuous miners. The latter two techniques have been rarely used due to their low recovery rates in China [
With the development of mechanizing equipment for thin coal seam, the corresponding mechanized excavating technical modes have been improved. According to the controlling method of the shearer, mechanized mining technical process (MMTP) of thin coal seam working face in China can be categorized into conventional MMTP, end-controlled MMTP [
Selecting the most appropriate MMTP is a multicriterion and multiobjective decision bound by a set of constraints. In the literature, there are many applications of decision-making techniques. One possible solution considered that the complexities encountered in this decision could be accomplished by the Analytic Hierarchy Process (AHP) and fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) method [
With respect to coal mining, Liu et al. improved the grey cluster method by assigning weights for criteria. By the cluster analysis, the technical and economic effects of three alternative mining methods including fully mechanized mining, conventional mechanized mining, and blast mining were conducted [
The main objective of this study is to select the most appropriate MMTP for thin coal seam combining AHP and PROMETHEE method. Decision-making criteria of the study that can be effective for the selection process were defined from the literature and field observation. This paper is divided into six sections. Firstly, there is an introduction of the studied problem and a literature research. Secondly, MCDM model proposed is briefly described. Then, a group of single weights was obtained from AHP. The next section presents AHP and PROMETHEE approach for MMTP evaluation. The section before the last involves an application of the proposed approach used for a real world example. The last section of this paper concludes the study with the discussion of the results.
Taking multiple factors into consideration, the MCDM system for MMTP selection of thin coal seam working face was established. These factors were obtained from index data of the links in a typical fully mechanized mining process. The system was divided into four layers: the Goal (
MCDM model for MMTP selection.
In the conventional MMTP, the operators of the shearer are involved in the simultaneous controlling shearer to complete the coal cutting operation. In the end-controlled MMTP, the operators are involved in operating the shearer at both ends of the working face to complete the process of coal cutting. Manual subdivision controlled MMTP is to divide the working face reasonably according to the distance of remote control of the shearer. The shearer is operated remotely by operators in designated subdivisions in turns to complete the coal cutting process. For automatic subdivision controlled MMTP, the subdivisional location of the shearer is measured by the remote control center according to the positioning devices. According to the stored subdivisional information, remote instructions are sent out to complete the coal cutting process. During automatic subdivision control, the full length of the working face is first subdivided and the subdivisional information is stored on the control center of the shearer. The shearer only receives the remote instructions sent by the radio transmitter in that subdivision. Memory assisted cutting is adopted in this MMTP. However, the location and state parameters of the shearer should be timely corrected by the operator according to the feedback from video surveillance system at the working face when the thickness of the thin seam varied or the geologic exploration is unidentified. As the latest one, MMTP with presetting trajectory cutting is operated with the operating parameters of shearer preset. Before presetting the parameters, 3D geological model consisting of thickness of seam, gas, structure, and so forth should be established. As a result, shearer can be operated in the parameters preset with unmanned intervention on site. The technical comparison among alternative MMTPs is presented in Table
Technical comparison of MMTPs.
MMTP | Advantages | Disadvantages |
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Conventional | Lower equipment investment; simple technology; high maturity | High labour intensity; poor man-machine environment |
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End-controlled | Liberating operators of shearer | Poor adaptability |
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Manual subdivision controlled | Lower equipment investment; reducing labour intensity | Adding several operators in site; lower safety degree |
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Automatic subdivision controlled | Liberating operators of shearer | Adding one operator in site; higher automated equipment investment; very poor adaptability |
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Presetting trajectory cutting | Liberating operators of shearer; high adaptability | High exploration input before mining; lower maturity |
The AHP, proposed by an operational research scientist named Saaty in 1980, is a decision-making technique with qualitative and quantitative analysis. It is well adapted to complex decision situations with complicated structure of hierarchy and relative lack of necessary data. Based on the decision criteria system for MMTP selection above, the AHP was used to assign weights for each criterion. The process normally includes three steps: constructing the hierarchy judgment matrices, calculating the hierarchy relative weights, and checking the consistency of the judgments [
In this study, supposing a set of alternatives
Scale for pairwise comparisons.
Relative intensity | Definition | Explanation |
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1 | Of equal value | Two requirements are of equal value |
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3 | Slightly higher value | Experience slightly favors one requirement over another |
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5 | Essential or strong value | Experience strongly favors one requirement over another |
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7 | Very strong value | A requirement is strongly favored and its dominance is demonstrated in practice |
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9 | Extreme value | The evidence favoring one over another is of the highest possible order of affirmation |
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2, 4, 6, and 8 | Intermediate values between two adjacent judgments | When compromise is needed |
AHP judgement matrix.
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1 | 1/2 | 1 |
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1 | 1 |
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1 | 3 | 2 |
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1 | 1/4 | 1/3 |
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2 | 1 | 1 |
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1 | 1 |
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1/3 | 1 | 1/3 |
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4 | 1 | 2 |
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1 | 1 | 1 |
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1/2 | 3 | 1 |
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3 | 1/2 | 1 |
In general, to meet the requirements of the precision in calculation of the relative weight by using the AHP, it is enough to conduct an approximate calculation using the square root method as follows.
Calculating the geometric means of the elements on each row of the judgment matrices,
Thus, adaptability and labor intensity are the most important criteria for MMTP selection based on the opinion survey.
The consistency of the judgment matrices must be checked to measure its credibility. The consistency criterion used is
Consistency indices of randomly generated reciprocal matrices.
Order of matrix | 1 | 2 | 3 | 4 | 5 | 6 | 7 |
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0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 |
By the formula above, the consistency checking results of the judgment matrices were obtained, as shown in Table
Consistency test results of AHP judgment matrix.
Judgment matrix |
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3 | 3.0536 | 0.0462 |
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2 | 2 | 0 |
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3 | 3.0536 | 0.0462 |
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3 | 3.0183 | 0.0158 |
Claimed by Belgian Professor Brans in 1984, PROMETHEE is a method used in MCDM problems to rank the alternatives, which takes advantage of preference function, criterion value, and criterion weight given by decision-makers to determine the scheme with optimal order [
Suppose scheme
In PROMETHEE I, the positive direction and the negative direction of preference priority rating of
Brans and other professors have offered six kinds of preference functions [
In this study, supposing a set of alternatives
Obviously, some evaluation indicators could not be quantified accurately in this paper, such as the adaptability (
Triangular fuzzy numbers.
In this paper, the decision-makers had 6 choices [
In the evaluation, equipment investment (
Fuzzy expressions of evaluation index.
Number | Attribute value | Flexibility | Maturity | Automation | Management difficulty | Labour intensity | Yager index |
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VB |
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Very inflexible | Very immature | Very low | Very hard | Very high | 0.933 |
B |
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Inflexible | Immature | Low | Hard | High | 0.800 |
W |
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General | General | General | General | General | 0.600 |
M |
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Medium | Medium | Medium | Medium | Medium | 0.400 |
G |
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Flexible | Mature | High | Easy | Low | 0.200 |
VG |
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Very flexible | Very mature | Very high | Very easy | Very low | 0.067 |
Eventually, in the initial judgment matrix, we could foresee from the introduced methodology that the results obtained will be fuzzy numbers, and, according to a conclusion in our selection problem, these results of fuzzy numbers have to be ranked with respect to the principles of PROMETHEE method, and this means that fuzzy numbers have to be compared. In order to compare the fuzzy numbers, Goumas and Lygerou (2000) proposed to use Yager index [
In the MMTP evaluation process, the evaluation indicators can be quantified through field research, consultation with experts, and fuzzy evaluation (Table
Values of indicators.
Indicators | Alternatives | ||||
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0 | 0 | 0 | 1.5 | 2.0 |
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10000 | 10000 | 5000 |
5000 | 5000 |
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VG/0.067 | VB/0.933 | G/0.2 | B/0.8 | G/0.2 |
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VG/0.067 | W/0.6 | G/0.2 | W/0.6 | B/0.8 |
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VB/0.933 | VB/0.933 | VB/0.933 | G/0.2 | VG/0.067 |
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W/0.6 | M/0.4 | B/0.8 | M/0.4 | M/0.4 |
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VB/0.933 | G/0.2 | M/0.4 | G/0.2 | VG/0.067 |
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0 |
Notes: all indicators are cost indexes.
Thus, the judgment matrix can be expressed as follows:
At the beginning of the evaluation, all the indicators have been transformed into cost indexes. Hence, the priorities of the alternatives decrease with the values of the final evaluation obtained from AHP or PROMETHEE.
The performance graph (Figure
Priorities of the alternatives considering
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5~6 |
7~10 |
11~15 |
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Priorities |
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Notes:
Final priorities of the five MMTPs from AHP.
The overall weights of the alternative MMTPs were obtained by multiplying the priority of each main criterion by the priority of each alternative. As illustrated in Figure
The priorities of the alternatives considering all criteria were presented in Table
Priorities of the alternatives considering all criteria.
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Priorities |
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After the value of
Fuzzy PROMETHEE flows.
Flow values |
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0.592 |
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1.194 |
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The resulting ranking.
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2 ( |
3~9 ( |
10~12 ( |
13~15 ( |
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Partial ranking |
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Complete ranking |
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Notes:
PROMETHEE ranking.
Alternatives are ranked from the best to the worst one by using the net flow
As illustrated in Table
In the coal mining engineering, only one MMTP usually cannot meet the demand of safety, productivity, and efficiency. The specific MMTP should be adopted in the given mining stage. In this paper, several kinds of MMTPs applied successfully in China were evaluated by AHP and PROMETHEE, respectively. From the above ranking results, the best alternative MMTP selected by PROMETHEE is clearly consistent with the one by AHP. The most appropriate alternative MMTP for thin coal seam mining, MMTP with presetting trajectory cutting, is selected.
Before presetting trajectory cutting, with the usage of detection techniques, 3D geological model of thin coal seam working face which can meet mining engineering requirements should be established, including thickness of seam, gas, and structure. Then, the operating parameters of shearer can be preset in some degree according to the 3D geological model. During the normal mining, shearer in thin coal seam working face can be operated in the parameters preset with unmanned intervention on site. In this way, the operators of the shearer can be liberated utterly. MMTP with presetting trajectory cutting will have potentially broad application in the fields of thin coal seam mining.
In general, with the increasing of precision of geologic exploration of thin coal seam and key assisting intelligent techniques, MMTP should transit from other MMTPs towards MMTP with presetting trajectory cutting, to achieve the intelligent and unmanned excavation of thin coal seam.
Panel 43101 is a thin coal seam working face at Liangshuijing coal mine of Shaanxi Huisen Coal Industry Development Co., Ltd., in China. The panel mainly focuses on mining #4−3 coal seam. Fully mechanized overall height mining is adopted in this face. The thickness of #4−3 coal seam is from 1.05 m to 1.4 m, and the average is 1.14 m. The inclination is from 0° to 1°. According to statistics, the safety accident rate per operator of shearer (
Based on the results from AHP and PROMETHEE in Section
In the acceptance conference of the Research on the Mining Techniques and Equipment for Thin Seamsin Xi’an, the most appropriate MMTP was recognized unanimously by the participating experts. Moreover, MMTP with presetting trajectory cutting is successfully applied in the thin coal seam mining for panel 43101.
This paper has demonstrated the application of the AHP and PROMETHEE methods in evaluating MMTPs for thin coal seam mining in China. Unlike the conventional approach which is an empirical method for the selection of MMTPs, the AHP and PROMETHEE methods make it possible to evaluate the alternative MMTPs in a more scientific manner which preserves integrity and objectivity. The two methods are flexible, transparent, easy to comprehend, and easy to apply by decision-makers.
In the established decision-making model, five alternatives were evaluated with regard to three main criteria and their subcriteria. The evaluations by the two methods revealed that the most appropriate MMTP for thin coal seam mining is MMTP with presetting trajectory cutting (
MMTP is a crucial task in mining operations. It is closely related to safety, productivity, and efficiency. The rational evaluation of MMTPs for thin coal seam mining requires the consideration of numerous criteria, including economical, technical, and ergonomic factors. The problem is based on the comparisons of alternative MMTPs according to the identified criteria. Hence, decision-making methods to solve MCDM problem are considered to be used in the paper. For this purpose, AHP and PROMETHEE methods work together to solve this problem, and the most appropriate MMTP was selected.
The authors declare that there is no conflict of interests regarding the publication of this paper.
Financial support for this work was provided by the Chinese National 863 High Technology Plan (no. 2012AA062101) and a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions and the Fundamental Research Funds for the Central Universities (no. 2014XT01). The authors gratefully acknowledge the financial support from the organizations mentioned above.