Application of Grey Analytic Hierarchy Process in Optimization of Mining Methods in Zhaoputezhuang Iron Mine

Zhao Chuang case of iron ore is one of the major producing mines Wuyang mining companies. The engineering geological conditions of the mine are complex, the grade of ore is low, the ore body is gently inclined and there are many ore interlayers. The stability of the surrounding rock of the ore body is poor, and the roof is more serious. At present, the mining method is carried out by the stratified filling method of the upward approach, and there are problems such as complicated mining process, large amount of mining engineering, and small production capacity, and it is urgent to optimize the mining method. Since the selection of mining options involves many different quantitative and qualitative indicators, a comprehensive and comprehensive analysis of the indicators is necessary to protect them from subjective experience [1-3].
Using gray analytic hierarchy process, the qualitative analysis and comparison of qualitative and quantitative factors are carried out, and the phase-oriented approach is applied to the stratified full tailings cementation filling method. Based on the existing mining of the mine, the structural parameters of the stope are optimized and the mining is reduced. Field support engineering quantity and mining quantity, reduce mining cost, improve mine production efficiency and recovery rate, reduce ore depletion rate [4], and achieve safe and efficient mining.
1 Gray analytic method comprehensive evaluation principle
(1) Gray correlation analysis method. The optimal index of each factor in the evaluation system is selected as the reference sequence. By comparing the comparison sequence index with the reference sequence index, the correlation coefficient of each factor in the evaluation system is obtained, and then the optimization analysis is carried out [2].
(2) Analytic hierarchy process. The complex system problem is decomposed into the interrelated levels of objectives, criteria, and programs. The hierarchical order is used to determine the relative importance order weights of each factor in each level, and the decision analysis is performed by calculating the total order [1].

(3) Gray analytic hierarchy process. On the basis of the gray correlation analysis to determine the grey correlation coefficient of each evaluation index, the analytic hierarchy process is used to rank each evaluation index hierarchically, and the weight of each evaluation index is obtained. The grey correlation coefficient and weight are comprehensively correlated, so that different Comprehensive evaluation of mining methods.
2 preferred mode of mining method
2.1 Analytic hierarchy process to determine the weight of influencing factors
2.1.1 Constructing a comparison judgment matrix
According to the 1~9 and its reciprocal scale method proposed by the AHP method, the comparison judgment matrix A is constructed by comparing the relative importance of all factors of the previous layer to all factors of the upper layer.
2.1.2 hierarchical single sort
Hierarchical single sorting is a sort of relative importance of the upper part of the evaluation evaluation index factor. Usually, the feature vector of the largest eigenvalue of the comparison judgment matrix is ​​calculated, and the feature vector is normalized, that is, the ranking weight of the factor is obtained. Commonly used methods include the square root method, the eigenvalue method, and the method. In this paper, the sum method (the arithmetic mean of the column vectors) is used. The specific calculation steps are as follows.
(1) Normalize the column vector of the comparison judgment matrix A to obtain a matrix B = [W1, W2, ..., Wn].
(2) The matrix B rows are summed to obtain a column vector W0=(W1+W2+...+Wn).
(3) Normalize the column vector W0 to obtain a feature vector W = [a1, a2, ..., an]T, and calculate AW = [b1, b2, ..., bn]T.
(4) Calculate the maximum eigenvalue of the judgment matrix, and construct a simplified calculation formula of λ according to AW=λW:

(5) Calculate the deviation consistency index of the judgment matrix CI

(6) Calculate the random consistency ratio CR of the judgment matrix

When CR<0.1, it is considered that the comparison judgment matrix has satisfactory consistency, otherwise it is adjusted to reduce the degree of inconsistency until the consistency test is passed. The RI values ​​are shown in Table 1.

2.2 Gray correlation analysis
2.2.1 Determining the reference sequence and the comparison sequence
The reference sequence is the optimal value of each factor in the evaluation system, which is recorded as: X0=(X0(1), X0(2),...,X0(n)); the comparison sequence is the index factor value of each analysis scheme in the evaluation system. , denoted as X1, X2, ..., Xn, where X1 = (X1 (1), X1 (2), ..., X1 (n)), ..., Xk = (Xk (1), Xk (2), ..., Xk(n)).
2.2.2 Correlation coefficient and relevance
The correlation coefficient can be expressed by the difference between the reference sequence and the comparison sequence curve, but the number of correlation coefficients is large, which is not convenient for visually determining the correlation size. Correlation degree is to treat the correlation coefficient of each index factor into one value, that is, to average (5). In this paper, the correlation coefficient is processed by the weight of the index factor, and the comprehensive correlation degree between the comparison sequence and the reference sequence is obtained. For the gray correlation analysis of the reference sequence X0 and the comparison sequence X1, X2, ... Xn, the gray correlation coefficient can be expressed as

In the formula, ξ is the resolution coefficient, which is generally 0.5. The gray correlation coefficient vi(k) is obtained by comparing the different mining method index sequences with the reference sequence, and the gray correlation coefficient matrix C is formed, which is the evaluation value of the mining method evaluation index.
2.3 Comprehensive correlation calculation
Combining the analytical results of the AHP and the gray correlation analysis method, according to the weight set W and the gray correlation coefficient matrix C judged by the mining method, the comprehensive correlation coefficient V is obtained:

3 engineering examples

According to the ore body characteristics of the Zhaozhuang Iron Mine, four mining methods were selected in the primary selection stage: 1 upward horizontal stratified tailings cement filling filling method (I); 2 upper approaching tailings cement filling filling Mining method (II); 3 up-to-horizontal stratified panel tailings junction filling mining method (III); 4 phase approaching to stratified full tailings cementing filling mining method (IV). Mining method evaluation indicators have both quantitative and qualitative indicators. For the quantification of qualitative indicators in the comprehensive optimization process of mining methods, it is determined according to the 1~9 scale method, in which the simpler the mining process, the higher the importance; the higher the safety, the higher the importance. The main technical indicators of mining methods are shown in Table 2.

According to the analytic hierarchy process, the factors of the mining method evaluation index are analyzed, and the hierarchical order is obtained. The weight set of the mining method is W=(0.27, 0.09, 0.27, 0.14, 0.14, 0.09). After calculating λ=6.0142, the consistency test result is RI=1.26, CI=0.0028, CR=0.0022<0.1, so the judgment matrix satisfies the consistency requirement.
Using the gray correlation analysis method, the sequence of each factor value of the four mining method evaluation indexes in Table 2 is compared with the reference value sequence, and the gray correlation coefficient of each factor is obtained to form the evaluation value of the mining method. The mining method evaluation index values ​​are shown in Table 3.

Substituting the weight set W and the gray correlation coefficient matrix C of the mining method into the formula (5), the comprehensive correlation coefficient V=(0.7649, 0.7639, 0.8310, 0.8615) can be obtained, that is, the gray level is applied. The mining method of the analytical method is preferred. Taking into account the qualitative and quantitative indicators, the comprehensive correlation coefficient of the four mining methods of Zhaozhuang Iron is obtained. Therefore, it can be judged that the optimal mining method among the four mining methods in the primary selection stage is the method of filling the mining method to the stratified full tailings cementation.
4 Conclusion
(1) Optimization of mining methods involves the decision-making of numerous indicators for numerous programs. Based on the theory of grey correlation degree and the theory of analytic hierarchy process, six qualitative and quantitative indicators of four mining methods were comprehensively analyzed, and the comprehensive correlation coefficient of four mining methods in the primary selection stage of the mine was obtained.
(2) Calculating and comparing the maximum eigenvalue and eigenvector of the judgment matrix, taking into account the complexity and unnecessaryness of the exact calculation, using the sum method to calculate, simplifying the calculation process, avoiding complex matrix operations, and simplifying the accuracy of the calculation results. Analysis requirements.

(3) Through comparative analysis, the directional layered full tailings cementing filling method is used for mining, and the structural parameters of the stope are optimized on the basis of the existing upper stratified filling method mining of the mine, with stope support The advantages of small amount of engineering and mining engineering, large production capacity of mining blocks and high production efficiency of mines indicate that the gray level theory has high reliability and practicability in the optimization of mining methods.
references
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