A method for optimizing blasting size distribution based on grey correlation theory and trend analysis

Abstract

The present application relates to the field of blasting, in particular to a method for optimizing blasting fragmentation based on grey correlation theory and trend analysis method, comprising the following steps: determining evaluation indexes and related influencing factors of blasting fragmentation to be analyzed according to the site conditions of the mine; designing and carrying out field blasting test aiming at the related influencing factors to form a related influencing factor matrix; taking pictures of blasting fragmentation and analyzing by using image processing software to obtain blasting fragmentation data and form an evaluation index matrix; calculating the mean matrix and the zero matrix of the two matrices; calculating the grey absolute correlation matrix, determining the primary and secondary order of the influencing factors of each evaluation index through weight; determining the main influencing factors of each evaluation index according to the weight of the grey absolute correlation matrix. The present application solves the problem of difficult determination of the influence degree of the influencing factors of blasting fragmentation, has the advantages of high efficiency and pertinence in determining blasting parameters, and improves the effect of blasting fragmentation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of blasting, in particular to a method for optimizing blasting size based on grey correlation theory and trend analysis method. BACKGROUND

[0002] In the mining blasting operation, due to the complex rock mechanics conditions of the ore body and the unreasonable blasting operation parameters, etc., it is easy to cause the problems of poor blasting size effect such as more powder ore, small average size, high powder ore rate and high large block rate after blasting, resulting in low production efficiency and further causing the economic benefit of the enterprise to be damaged.

[0003] Blasting size is an important basis for optimizing blasting parameters in mines, and reasonable blasting parameters are beneficial to improve the size effect. However, blasting is complex and comprehensive, and is the joint action of factors such as hole spacing, row spacing, hole depth, plug length, row delay time and hole delay time. Most of the traditional blasting parameter optimization adopts the experience method to determine, which has strong subjectivity and is difficult to determine the optimal blasting parameters to improve the blasting size effect with high efficiency and pertinence. Therefore, it is of great significance to efficiently and quantitatively determine the main influencing factors of blasting size, and to design blasting parameters to optimize the blasting size effect.

[0004] Therefore, we propose a method for optimizing blasting size based on grey correlation theory and trend analysis method to solve the above problems. SUMMARY

[0005] The present application provides a method for optimizing blasting size based on grey correlation theory and trend analysis method to solve the technical problems of efficiently determining the main influencing factors of blasting size evaluation index for different mines and different blasting schemes, analyzing the influence trend of the main influencing factors on the evaluation index, and optimizing the blasting parameters to improve the blasting size effect.

[0006] In order to achieve the above purpose, the present application adopts the following technical scheme: a method for optimizing blasting size based on grey correlation theory and trend analysis method, comprising the following steps:

[0007] (1) According to the blasting size problem and blasting design scheme of the mine site, the evaluation index and related influencing factors of the blasting size to be analyzed are determined;

[0008] (2) For the related influencing factors of blasting size, design a blasting test scheme and carry out a field blasting test, collect the blasting test parameter data, and form a related influencing factor matrix;

[0009] (3) Take pictures of the blasting test pile size, use image processing software to analyze the size pictures, get the blasting test size distribution data, and form the evaluation index matrix of the blasting size;

[0010] (4) calculating the mean image matrix of the relevant influence factor matrix and the evaluation index matrix;

[0011] (5) calculating the initial point zero image matrix of the relevant influence factor matrix and the evaluation index matrix;

[0012] (6) calculating the grey absolute correlation matrix of the evaluation index of the blasting fragmentation and the relevant influence factor according to the grey absolute correlation degree calculation formula, and obtaining the weight of the relevant influence factor on each evaluation index;

[0013] (7) determining the main influence factor of each evaluation index according to the weight of the grey absolute correlation matrix;

[0014] (8) determining the influence trend of each main influence factor on each evaluation index by using the trend analysis method, and comprehensively considering the blasting test effect to determine the blasting parameters for optimizing the blasting fragmentation.

[0015] Preferably, in step (1), the evaluation index of the blasting fragmentation can be divided into main optimization indexes and secondary control indexes, including but not limited to the average fragmentation, the qualified rate, the fine ore rate and the large block rate; the relevant influence factors of the blasting fragmentation include but are not limited to the bench height, the overburden, the hole spacing, the row spacing, the plugging length, the inter-hole delay time and the inter-row delay time.

[0016] Preferably, in step (2), the blasting test scheme is a series of different test schemes designed according to the relevant influence factors, and the same test scheme is repeated for multiple times to form the relevant influence factor matrix.

[0017] Preferably, in step (3), the evaluation index matrix selects the average value of the evaluation index of the blasting fragmentation in multiple repeated tests of the same test scheme as the calculation data.

[0018] Preferably, in steps (4) and (5), the mean image matrix is determined by using the mean operator for non-dimensional processing, and the zero image matrix is determined by using the zero operator for the mean image matrix.

[0019] Preferably, in steps (6) and (7), the weight value is proportional to the influence of the influence factor on the evaluation index, and the greater the weight value, the greater the influence of the influence factor on the evaluation index; for the main optimization indexes in the evaluation index of the blasting fragmentation, two influence factors with the largest weight are selected as the main influence factors, and for the secondary control indexes in the evaluation index of the blasting fragmentation, one influence factor with the largest weight is selected as the main influence factor.

[0020] Preferably, in step (8), the trend analysis method is to use 6 groups of test schemes, draw the trend graph of each main influencing factor on its evaluation index and analyze its change trend, and finally determine the blasting parameters to optimize the blasting fragmentation according to the site test fragmentation and the analysis results.

[0021] The beneficial effects of the present application are:

[0022] The present application uses the grey correlation theory to analyze the related influencing factors of each blasting fragmentation evaluation index, solves the problem of determining the degree of influence of the influencing factors on the fragmentation effect, and can efficiently and quantitatively determine the primary and secondary relationships of the influencing factors, thereby providing support for blasting parameter optimization.

[0023] Further, the trend analysis method is used to analyze the main influencing factors, the influence trend of the main influencing factors on the evaluation index can be easily determined, and the optimal blasting parameters can be determined by combining the blasting test, thereby providing a basis for blasting fragmentation optimization, and the blasting fragmentation optimization is targeted, thereby breaking through the subjectivity of the traditional blasting fragmentation optimization relying on experience. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 is a method flowchart of the blasting fragmentation optimization based on the grey correlation theory and the trend analysis method of the present application;

[0025] Figure 2 is a trend graph of the main influencing factors of the trend analysis method of the present application on each evaluation index. DETAILED DESCRIPTION

[0026] In order to further explain the technical scheme of the present application, the following specific embodiments are described in detail.

[0027] As shown in Figure 1 , the present application provides a method for optimizing blasting fragmentation based on the grey correlation theory and the trend analysis method, comprising the following steps:

[0028] (1) determining the evaluation index and related influencing factors of the blasting fragmentation to be analyzed, which can be determined according to the mine site blasting fragmentation problem and the blasting design scheme, wherein the evaluation index of the blasting fragmentation can be divided into main optimization index and secondary control index, including but not limited to average fragmentation, qualified rate, fine ore rate and large block rate; the related influencing factors of the blasting fragmentation include but are not limited to bench height, overburden, hole spacing, row spacing, plug length, inter-hole delay time and inter-row delay time:

[0029] According to the problem of large amount of fine ore and small average size of blasting block in the mine site, while ensuring that the qualified rate and large block rate are within a reasonable range, the evaluation indexes of the blasting block size effect to be analyzed are determined as the fine ore rate, average size, large block rate and qualified rate; according to the blasting design scheme and the actual situation on site, the related influencing factors of the blasting block size effect are determined as the bench height, overburden, hole spacing, row spacing, plugging length, inter-hole delay time and inter-row delay time.

[0030] (2) For the related influencing factors of the blasting block size, a blasting test scheme is designed and a field blasting test is carried out, the blasting test parameter data is collected, and a related influencing factor matrix is formed, the blasting test scheme is designed according to a series of different test schemes of the related influencing factors, according to the actual situation, the same test scheme is repeatedly tested for multiple times, and the related influencing factor matrix is formed:

[0031] The blasting test scheme is designed, and the related influencing factor matrix is formed. According to the adjustable situation on site, the bench height is set as 12.5, 14.5, 15.4, 16.1 and 17 m, the overburden is set as 1.5, 1.6 and 2 m, the hole spacing is set as 6.0, 6.5, 7.0, 7.5 and 8.0 m, the row spacing is set as 4.8 and 5.0 m, the plugging length is set as 4.0, 4.5, 5.0 and 6.0 m, the inter-hole delay time is set as 9, 17 and 25 ms, and the inter-row delay is set as 42, 65 and 80 ms. A total of 9 test schemes are designed, as shown in Table 1, and 3 blasting tests are carried out for each test scheme.

[0032] Table 1. Related influencing factor data of medium-length hole bench blasting block size in a certain open-pit mine in Heilongjiang

[0033]

[0034] (3) The blasting test heap block size pictures are taken, the image processing software is used to analyze the block size pictures, the blasting test block size distribution data is obtained, the evaluation index matrix of the blasting block size is formed, and the average value of the evaluation indexes of the blasting block size of multiple repeated tests of the same test scheme is selected as the calculation data;

[0035] The blasting heap block size of each test scheme is comprehensively taken, and the pictures are taken when the blasting heap is not out of the mine and when the mine is out. 15 pictures are taken for each test, and a total of 45 pictures are taken for each test scheme. The software is used to process and analyze the blasting heap block size pictures of each test, and the block size distribution data curve is obtained. The evaluation indexes of the blasting block size effect of each test scheme are selected as the average value of the 3 test data, as shown in Table 2.

[0036] Table 2. Evaluation index data of the blasting block size in a certain open-pit mine in Heilongjiang

[0037]

[0038] (4) Calculate the mean image matrix of the relevant influence factor matrix and the evaluation index matrix. Since the data units are not unified, the data values need to be dimensionless. The mean operator is used The mean image of each column of the matrix is calculated as follows:

[0039]

[0040]

[0041] The specific steps are as follows:

[0042] a. Calculate the average value of each column of the relevant influence factor matrix and the evaluation index matrix. The calculation formula is:

[0043]

[0044]

[0045] b. Use the average value to perform dimensionless processing on each column of the relevant influence factor matrix and the evaluation index matrix. The calculation formula is:

[0046]

[0047]

[0048] c. Form the mean image matrix of the relevant influence factor matrix and the evaluation index matrix:

[0049]

[0050]

[0051] (5) Calculate the zero image matrix of the relevant influence factor matrix and the evaluation index matrix. Through the mean image matrix, the zero image operator is used The zero image matrix can be calculated as follows:

[0052]

[0053]

[0054] a. Use the first row of each column of the mean image matrix And Subtract the value of each row of each column corresponding to it, which is calculated as follows:

[0055]

[0056]

[0057] b. Forming the starting point zeroization matrix of the correlation influence factor matrix and the evaluation index matrix:

[0058]

[0059]

[0060] (6) According to the formula of grey absolute correlation degree, the grey absolute correlation matrix of the evaluation index of blasting fragmentation and the correlation influence factor is calculated, and the weight of the correlation influence factor on each evaluation index is obtained. The calculation of the grey absolute correlation degree is as follows:

[0061]

[0062] Among them: , , .

[0063] The specific steps are as follows:

[0064] a. According to the formula, the grey absolute correlation matrix of the correlation influence factor and the evaluation index can be calculated, as shown in Table 3:

[0065] Table 3. Grey absolute correlation matrix of deep hole blasting fragmentation in a lead-zinc mine

[0066]

[0067] Among them, the greater the value of the grey absolute correlation degree, the greater the influence of the influence factor on the evaluation index;

[0068] b. According to Table 3, the weight order of the correlation influence factor of the blasting fragmentation effect evaluation index is obtained:

[0069] The weight order of the influence factor of the fine ore rate is: delay time between rows > delay time between holes > step height > hole spacing > blockage length > row spacing > overdeep.

[0070] The weight order of the influence factor of the qualified rate is: row spacing > overdeep > blockage length > hole spacing > step height > delay time between rows > delay time between holes.

[0071] The weight order of the influence factor of the large block rate is: step height > delay time between rows > hole spacing > delay time between holes > blockage length > row spacing > overdeep.

[0072] The weight order of the influence factor of the average block size is: overdeep > row spacing > blockage length > hole spacing > step height > delay time between rows > delay time between holes.

[0073] (7) According to the weight of the grey absolute correlation matrix, the main influence factors of each evaluation index are determined;

[0074] According to the importance of evaluation indexes, the number of main influencing factors is determined, 2 influencing factors are selected according to the weight order of main evaluation indexes, and 1 influencing factor is selected according to the weight order of secondary evaluation indexes. The selection of main influencing factors also needs to be determined in combination with the actual situation of the mine. The main influencing factors of the fine ore rate are the delay time between rows and the delay time between holes; the main influencing factors of the average lump size are the super depth and the row distance; the main influencing factors of the large lump rate are the bench height; and the main influencing factors of the qualified rate are the row distance.

[0075] (8) The influence trend of each main influencing factor on each evaluation index is determined by using the trend analysis method, the blasting parameters are determined by comprehensively considering the site blasting test effect, and the blasting lump size is optimized, the trend analysis method is to use 6 test schemes, draw the trend chart of each main influencing factor on its evaluation index and analyze the change trend, and finally determine the blasting parameters to optimize the blasting lump size according to the site test lump size and the analysis result;

[0076] The specific steps are as follows:

[0077] a. According to the 6 group scheme test blasting parameters and results in table 1 and table 2, the influence trend chart of main influencing factors on evaluation indexes is drawn, as shown in Figure 2 .

[0078] b. Analyze each trend chart to determine the influence trend of influencing factors on evaluation indexes. According to (a) in Figure 2 , it can be seen that the fine ore rate increases with the increase of the delay time between rows, the fine ore rate and the delay time between rows show obvious positive correlation, and smaller delay time between rows is beneficial to reduce the fine ore rate; combined with the blasting effect of test scheme 1, it is more appropriate to use the delay time between rows of 42 ms; according to (b) in Figure 2 , it can be seen that the fine ore rate shows an upward trend with the increase of the delay time between holes, and smaller delay time between holes is more beneficial to reduce the fine ore rate, combined with the blasting effect of test scheme 1, it is more appropriate to use the delay time between rows of 9 ms; according to (c) in Figure 2 , it can be seen that the large lump rate shows a trend of first increasing and then decreasing with the delay time between rows, and the large lump rate is the largest when the delay time between rows is 65 ms, therefore, the delay time between rows less than 65 ms and greater than 65 ms can be selected, and combined with the requirement of fine ore rate, the delay time between rows of 42 ms is more appropriate; according to (d) in Figure 2 , it can be seen that the large lump rate shows a trend of first decreasing and then increasing with the bench height, and the large lump rate is the smallest when the bench height is 14 m and 14.5 m; according to (e) in Figure 2It can be seen from (e) in the table that the average lump size increases first and then tends to be stable with the increase of overburden, and the average lump size tends to be stable at 16 cm after the overburden is 1.6 m, and the average lump size can be improved by selecting overburden above 1.6 m; according to Figure 2 It can be seen from (f) in the table that the qualified rate increases with the increase of row spacing, but the growth rate is small, and the qualified rate of 5 m row spacing fluctuates greatly, and it is considered that the qualified rate is slightly positively correlated with the row spacing but not obvious, therefore, the row spacing can be 5 m. Other blasting parameters are determined on the basis of the determination of the main influencing factor parameters, combined with the blasting test scheme and the actual situation of the mine.

[0079] c, determine the blasting parameters of the blasting size optimization. According to the actual situation of the mine, the step height is determined to be 14.5 m. According to the analysis of b in (8), the blast hole row spacing is selected to be 5 m, combined with test schemes 1 and 3, the blast hole spacing is determined to be 6.5 m, and the plug length is selected to be 4.5 m. The overburden is set to be 2 m. The digital electronic detonator is used for hole-by-hole initiation, the inter-hole delay time is selected to be 9 ms, and the inter-row delay time is selected to be 42 ms.

[0080] The above only describes the preferred examples of the present application and is not used to limit the present application, although the present application is described in detail with reference to the foregoing examples, those skilled in the art can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for optimizing the size of blasting blocks based on grey relational theory and trend analysis, characterized in that: Includes the following steps: (1) Based on the problem of blasting block size at the mine site and the blasting design scheme, determine the evaluation index and related influencing factors of the blasting block size to be analyzed; (2) Design a blasting test scheme and conduct on-site blasting tests for the relevant influencing factors of blasting block size, collect blasting test parameter data, and form a matrix of relevant influencing factors; (3) Take pictures of the blasting block size of the on-site blasting test, analyze the block size pictures using image processing software, obtain the block size distribution data of the blasting test, and form an evaluation index matrix of blasting block size; (4) Calculate the mean matrix of the relevant influence factor matrix and the evaluation index matrix; (5) Calculate the initial zeroing matrix of the relevant influence factor matrix and the evaluation index matrix; (6) Calculate the evaluation index of blasting block size and the grey absolute correlation matrix of related influencing factors according to the grey absolute correlation formula, and obtain the weight of related influencing factors on each evaluation index. (7) Determine the main influencing factors of each evaluation index based on the weights of the grey absolute correlation matrix; (8) Use trend analysis to determine the influence trend of each major influencing factor on each evaluation index, and combine the results of the field blasting test to comprehensively determine the blasting parameters and optimize the blasting block size. In step (1), the evaluation index of the blasting block size can be divided into main optimization index and secondary control index, including average block size, qualified rate, fine ore rate and large block rate; the relevant influencing factors of the blasting block size include step height, over-depth, hole spacing, row spacing, blockage length, hole delay time and row delay time; In step (2), the blasting test plan is designed based on a series of different test plans according to relevant influencing factors. The same test plan is repeated multiple times to form a matrix of relevant influencing factors. In steps (6) and (7), for the main optimization indicators of the blasting block size evaluation index, two factors with the largest weight are selected as the main influencing factors, and for the secondary control indicators of the blasting block size evaluation index, one factor with the largest weight is selected as the main influencing factor.

2. The method for optimizing blasting block size based on grey relational theory and trend analysis as described in any one of claims 1, characterized in that: In step (3), the evaluation index matrix is ​​calculated using the average value of the evaluation index of the blasting block size from multiple repeated tests of the same test scheme.

3. The method for optimizing blasting block size based on grey relational theory and trend analysis according to any one of claims 2, characterized in that: In steps (4) and (5), the mean image matrix is ​​used with the mean-averaging operator. The dimensionless transformation process is performed to determine the nullification matrix, which is then determined using the nullification operator. The mean image matrix is ​​calculated and determined.

4. The method for optimizing blasting block size based on grey relational theory and trend analysis as described in any one of claims 3, characterized in that: In steps (6) and (7), the weight values ​​are proportional to the influence of the influencing factors on the evaluation index.

5. The method for optimizing blasting block size based on grey relational theory and trend analysis according to any one of claims 4, characterized in that: In step (8), the trend analysis method uses no less than 6 test schemes to draw trend charts of the evaluation indicators of each major influencing factor and analyze their changing trends. Finally, the blasting parameters are determined by combining the field test block size and analysis results to optimize the blasting block size.

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