A coal gangue sorting method based on Geant4 simulation

Abstract

The present application belongs to the technical field of coal gangue separation, and particularly relates to a coal gangue separation method based on Geant4 simulation. The steps of the present application are as follows: S1. performing element analysis on the selected coal gangue in a mining area; S2. establishing the physical process of X-ray penetrating the coal gangue in Geant4; S3. calculating the photon collision probability per unit relative volume by using Geant4; S4. calibrating the optical path of the coal gangue in actual X-ray imaging; S5. obtaining the actual gray value of the coal gangue; S6. calculating the actual photon collision probability; and S7. realizing the discrimination of coal and gangue. The present application simulates the physical process of X-ray penetrating the coal gangue by using Geant4, and calculates the photon collision probability per unit relative volume, which can effectively simplify the separation process, has the advantages of simple process and high recognition accuracy, and can ensure the rapid recognition requirement of coal and gangue.

Description

Technical Field

[0001] This invention belongs to the field of coal gangue sorting technology, specifically relating to a coal gangue sorting method based on Geant4 simulation. Background Technology

[0002] With the increasing coal mining volume year by year, high-quality coal resources have been almost exhausted, and coal resources are now trending towards being "poor, fine, and complex." To improve coal quality, coal preparation plants are now implementing full-scale coal washing. Currently, the mainstream coal washing methods are divided into wet and dry methods. For wet coal washing, heavy media separation has become the dominant process, offering high precision, but its disadvantages are also particularly obvious: the dewatering and demediuming processes are complex and require large amounts of water, necessitating subsequent coal slurry water treatment. Dry coal washing mainly includes air-heavy media fluidized bed dry coal washing, air separation, combined dry separation, and X-ray separation. Because it requires no water and the process is simple, it has become a research hotspot. However, dry coal washing has lower separation precision and causes severe air pollution, thus its application is limited, mainly confined to water-scarce areas in Northwest China. Later, gamma-ray and X-ray coal preparation technologies began to come into the view of coal preparation workers, and some X-ray coal preparation equipment has begun to be used in the field, as described in Chinese patent publications "CN110000109A" and "CN115532649A". However, although this type of X-ray coal preparation equipment has certain effects, its drawbacks are also quite obvious: First, most of the equipment uses gamma rays as the radiation source. Although the penetration power is strong, source management is a problem, and residual gamma rays can cause radiation damage to maintenance workers. Second, this type of X-ray coal preparation method is generally only suitable for separating coal and gangue with a particle size of 5cm to 20cm. When the particle size exceeds 20cm, the rays need to penetrate thicker materials, resulting in a weaker energy spectrum received by the X-ray detection device. The energy spectrum of coal and gangue is not significantly different, and the separation accuracy cannot be guaranteed. In particular, some equipment currently uses pseudo-dual-energy X-rays for the sake of source safety and cost savings, which further reduces the accuracy of the equipment in identifying coal and gangue. As a result, the ratio of gangue mixed with clean coal and the ratio of clean coal mixed with gangue both fail to meet the accuracy requirements of coal preparation plants for the main beneficiation equipment. Therefore, this issue urgently needs to be addressed. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a coal and gangue sorting method based on Geant4 simulation. By using Geant4 to simulate the physical process of X-rays passing through coal and gangue, the photon collision probability per unit relative volume is calculated, which can effectively simplify the sorting process. It has the advantages of simple process and high recognition accuracy, and can ensure the need for rapid identification of coal and gangue.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A coal and gangue separation method based on Geant4 simulation, characterized by the following steps:

[0006] S1. Perform elemental analysis on the coal gangue from the selected mining area;

[0007] S2. Establish the physical process of X-ray penetration of coal gangue in Geant4;

[0008] S3. Use Geant4 to calculate the photon collision probability per unit relative volume;

[0009] S4. Optical path calibration of coal gangue in actual X-ray imaging;

[0010] S5. Obtain the actual ash value of the coal gangue;

[0011] S6. Calculate the actual photon collision probability;

[0012] S7. Compare the photon collision probability per unit relative volume calculated by Geant4 in step S3 with the actual photon collision probability in step S6 to confirm the density level of coal gangue in the actual X-ray imaging and to distinguish between coal and gangue.

[0013] Preferably, step S1 includes the following sub-steps:

[0014] Using kg / L as the density unit, coal samples with a density <1.2 and those falling within the density ranges [1.2, 1.3), [1.3~1.4), [1.4~1.5), [1.5~1.6), [1.6~1.7), and [1.7~1.8], and gangue samples with a density >1.8 were taken from the selected mining area. Elemental analysis was performed on each sample, and the elements in each sample were sorted from high to low content. The top n elements were selected as the reference elements for that type of sample, where n>1 and is an integer.

[0015] Preferably, n=5.

[0016] Preferably, step S2 includes the following sub-steps:

[0017] In the Geant4 development package environment, a tube voltage of the same intensity as the actual radiation source is determined to form a simulated radiation source; target material is set, and the physical entity of the sample containing the reference element is established using the content of each reference element obtained in step S1; finally, an X-ray detector is established to detect the value of the X-ray intensity emitted by the simulated radiation source after passing through the physical entity.

[0018] Preferably, the physical entity is a square with a side length of 10cm.

[0019] Preferably, step S3 includes the following sub-steps:

[0020] Calculate the photon collision probability R per unit relative volume for each density of physical entities in step S2 using the following formula:

[0021]

[0022] in:

[0023] i represents the number of detection units in the X-ray detector built in Geant4;

[0024] E i The intensity of the X-rays detected by the i-th X-ray detector after passing through the physical entity;

[0025] E represents the intensity of X-rays emitted by the simulated source;

[0026] d i Let be the horizontal distance between the i-th X-ray detector and the light source;

[0027] a is the vertical distance between the X-ray detector and the simulated radiation source;

[0028] s represents the area of ​​the detection unit.

[0029] Preferably, step S4 includes the following sub-steps:

[0030] With the actual radiation source turned on and the belt unloaded, record the average gray value G1 of the current image; with the actual radiation source turned off, record the average gray value G2 of the current image.

[0031] Preferably, step S5 includes the following sub-steps:

[0032] Convert the pixel grayscale values ​​of the image in step S4 into a grayscale matrix A:

[0033]

[0034] in:

[0035] g1 is the grayscale value of the first pixel, g2 is the grayscale value of the second pixel, and so on, g... 100 This is the grayscale value of the 100th pixel.

[0036] Preferably, step S6 includes the following sub-steps:

[0037] The grayscale matrix A obtained in step S5 is calculated as follows:

[0038]

[0039] in:

[0040] r is the actual photon collision probability;

[0041] J is the J-th pixel in the grayscale matrix A;

[0042] g J Let J be the grayscale value of the J-th pixel.

[0043] Preferably, step S7 includes the following sub-steps:

[0044] The photon collision probability R per unit relative volume of each physical entity at each density, calculated using Geant4 simulation, is stored in a PC to form judgment data. Among them, the judgment data corresponding to physical entities with a density < 1.2 is R1, the judgment data corresponding to physical entities with a density within the density level [1.2, 1.3) is R2, the judgment data corresponding to physical entities with a density within the density level [1.3~1.4) is R3, and so on, until the judgment data corresponding to physical entities with a density within the density level [1.7~1.8] is R7, and the judgment data corresponding to physical entities with a density > 1.8 is R8.

[0045] If the actual photon collision probability r < R8, then the mineral is coal, and the density level of the mineral is obtained based on the specific landing point of r; if r ≥ R8, then the mineral is gangue.

[0046] The beneficial effects of this invention are as follows:

[0047] This invention utilizes Geant4 to simulate the physical process of X-rays passing through coal gangue and calculates the photon collision probability per unit relative volume. Its main advantages are as follows:

[0048] 1) This invention utilizes Geant4 simulation technology and relies on elemental detection to establish a high-precision coal and gangue model, and finally calculates the photon collision probability of coal and gangue; this method utilizes the differences between coal and gangue at the atomic level to achieve ultra-high precision separation.

[0049] 2) This invention relies on high-precision test data and advanced simulation technology, enabling it to adapt to complex underground environments. While separating coal and gangue, this invention can also determine the density range of the coal and obtain its grade, effectively simplifying subsequent separation processes.

[0050] 3) The identification method of the present invention is simple and has high identification accuracy. It does not require complex algorithms in the implementation process and can be implemented by ordinary industrial computers, thereby increasing the code running speed and improving the sorting efficiency.

[0051] 4) The principle of this invention is simple, does not require complex auxiliary equipment, and can effectively reduce equipment and maintenance costs. Attached Figure Description

[0052] Figure 1 This is a block diagram illustrating the working principle of the present invention;

[0053] Figure 2 This is a graph showing the X-ray intensity detected by the X-ray detectors of each detection unit.

[0054] Figure 3 A graph showing the horizontal distance between the X-ray detector and the light source for each detection unit;

[0055] Figure 4 This is a drop pattern after testing 1000 mineral samples in Example 1. Detailed Implementation

[0056] For ease of understanding, this section combines... Figure 1-4 The actual workflow of the present invention is further described below:

[0057] Example 1:

[0058] The operation procedure of Example 1 is as follows: Figure 1 As shown, the specific steps include:

[0059] 1. Taking a certain mining area as an example, using kg / L as the unit of density, coal samples with a density <1.2 and those falling within the density ranges [1.2, 1.3), [1.3~1.4), [1.4~1.5), [1.5~1.6), [1.6~1.7), and [1.7~1.8], and gangue samples with a density >1.8 were collected. This density classification is a conventional classification method, and other sorting modes are also used, so they will not be described in detail.

[0060] Elemental analysis was performed on the above samples, and the results are shown in Table 1 below:

[0061] Table 1

[0062] -1.2 1.2-1.3 1.3-1.4 1.4-1.5 1.5-1.6 1.6-1.7 1.7-1.8 gan Name Atomic% C 81.12 76.17 63.42 48.82 50.46 49.5 40.65 24.72 O 12.63 15 23.89 34.82 34.09 33.84 41.95 48.34 Si 1.96 2.91 5.65 6.56 6.33 4.78 5.14 13.39 Al 1.33 2.42 4.48 5.43 5.74 4.23 6.31 10.58 N 2.34 2.51 1.56 2.58 1.99 3.24 0.94 0.37 Ti 0.06 0.33 0.05 0.13 0.09 0 0.13 0.05 Fe 0.08 0.23 0.14 0.16 0.1 1.07 0.62 0.13 Na 0.1 0.15 0.18 0.38 0.29 0.51 0.59 0.63 P 0.2 0.1 0.04 0 0 0 0.01 0.02 Ca 0 0.08 0.17 0.19 0.1 0.89 0.97 0.06 Mg 0.05 0.07 0.19 0.49 0.14 1.15 0.56 0.19 Mn 0 0.03 0.16 0.31 0.16 0.17 0.2 0.23 K 0 0 0 0.12 0 0.17 0 1.17 S 0.12 0 0.06 0 0.52 0.44 0 0.1

[0063] It can be seen that in coal samples with a density <1.2Kg / L, the five elements with higher content are C, O, Si, Al and N; the same applies to other samples.

[0064] 2. In the Geant4 development environment, determine the tube voltage with the same intensity as the actual radiation source, set the target material, and reconstruct the physical entity of each density of coal using the content of the five elements mentioned above. This physical entity is a square with sides of 10cm. Finally, build the X-ray detector and obtain the X-ray intensity map detected by the X-ray detector of each detection unit, such as... Figure 2 As shown.

[0065] 3. Calculate the photon collision probability R per unit relative volume using the following formula:

[0066]

[0067] Where i = 100; s = 0.1cm 2 E = 160kV; E i Values ​​such as Figure 2 shown; a=1m; d i Values ​​such as Figure 3 As shown.

[0068] Substituting the above data into the formula, the photon collision probability per unit relative volume of a coal sample with a density <1.2 kg / L is calculated to be R1 = 31.54%. Similarly, R2 = 37.31%, R3 = 44.69%, R4 = 52.94%, R5 = 63.42%, R6 = 69.78%, R7 = 77.13%, and R8 = 83.21%.

[0069] 4. Turn on the actual source and obtain G1 = 216.3, G2 = 11.18.

[0070] 5. After imaging the coal gangue, import the image into a PC and convert the pixel grayscale values ​​in the image into the following grayscale matrix A:

[0071]

[0072] 6. Perform the following calculations on the grayscale matrix A:

[0073]

[0074] The gray value of the first pixel in grayscale matrix A is 143.6. The photon collision probability r1 corresponding to this gray value can be calculated as follows:

[0075]

[0076] Similarly, the photon collision probability corresponding to all gray values ​​in the gray matrix A can be calculated, and then the average value can be taken to finally obtain the actual photon collision probability r = 56.42%.

[0077] 7. Compare the photon collision probabilities R per unit relative volume calculated by Geant simulations, i.e., R1 = 31.54%, R2 = 37.31%, R3 = 44.69%, R4 = 52.94%, R5 = 63.42%, R6 = 69.78%, R7 = 77.13%, and R8 = 83.21%.

[0078] It can be seen that if the calculated r is between R4 and R5, then the mineral is coal, and it is coal with a density of [1.4 to 1.5].

[0079] 8. Following the above steps, an experiment was conducted using 1000 pieces of coal and gangue from the mining area. The results are as follows: Figure 4 As shown.

[0080] Of course, those skilled in the art will recognize that the present invention is not limited to the details of the exemplary embodiments described above, but also includes the same or similar structures that can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention.

[0081] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0082] The technologies, shapes, and structures not described in detail in this invention are all known technologies.

Claims

1. A coal and gangue separation method based on Geant4 simulation, characterized in that... Includes the following steps: S1. Perform elemental analysis on the coal gangue from the selected mining area; S2. Establish the physical process of X-ray penetration of coal gangue in Geant4; S3. Use Geant4 to calculate the photon collision probability per unit relative volume; S4. Optical path calibration of coal gangue in actual X-ray imaging; S5. Obtain the actual ash value of the coal gangue; S6. Calculate the actual photon collision probability; S7. Compare the photon collision probability per unit relative volume calculated by Geant4 in step S3 with the actual photon collision probability in step S6 to confirm the density level of coal gangue in the actual X-ray imaging and to distinguish between coal and gangue.

2. The coal and gangue separation method based on Geant4 simulation according to claim 1, characterized in that: Step S1 includes the following sub-steps: Using kg / L as the density unit, coal samples with a density <1.2 and those falling within the density ranges [1.2, 1.3), [1.3~1.4), [1.4~1.5), [1.5~1.6), [1.6~1.7), and [1.7~1.8], and gangue samples with a density >1.8 were taken from the selected mining area. Elemental analysis was performed on each sample, and the elements in each sample were sorted from high to low content. The top n elements were selected as the reference elements for that type of sample, where n>1 and is an integer.

3. The coal and gangue separation method based on Geant4 simulation according to claim 2, characterized in that: n＝5。 4. The coal and gangue separation method based on Geant4 simulation according to claim 2, characterized in that: Step S2 includes the following sub-steps: In the Geant4 development package environment, a tube voltage of the same intensity as the actual radiation source is determined to form a simulated radiation source; target material is set, and the physical entity of the sample containing the reference element is established using the content of each reference element obtained in step S1; finally, an X-ray detector is established to detect the value of the X-ray intensity emitted by the simulated radiation source after passing through the physical entity.

5. A coal gangue separation method based on Geant4 simulation according to claim 4, characterized in that: The physical entity is a square with a side length of 10cm.

6. The coal and gangue separation method based on Geant4 simulation according to claim 4, characterized in that: Step S3 includes the following sub-steps: Calculate the photon collision probability R per unit relative volume for each density of physical entities in step S2 using the following formula: in: i represents the number of detection units in the X-ray detector built in Geant4; E i The intensity of the X-rays detected by the i-th X-ray detector after passing through the physical entity; E represents the intensity of X-rays emitted by the simulated source; d i Let be the horizontal distance between the i-th X-ray detector and the light source; a is the vertical distance between the X-ray detector and the simulated radiation source; s represents the area of ​​the detection unit.

7. A coal and gangue separation method based on Geant4 simulation according to claim 6, characterized in that: Step S4 includes the following sub-steps: With the actual radiation source turned on and the belt unloaded, record the average gray value G1 of the current image; with the actual radiation source turned off, record the average gray value G2 of the current image.

8. A coal gangue separation method based on Geant4 simulation according to claim 7, characterized in that: Step S5 includes the following sub-steps: Convert the pixel grayscale values ​​of the image in step S4 into a grayscale matrix A: in: g1 is the grayscale value of the first pixel, g2 is the grayscale value of the second pixel, and so on, g... 100 This is the grayscale value of the 100th pixel.

9. A coal and gangue separation method based on Geant4 simulation according to claim 8, characterized in that: Step S6 includes the following sub-steps: The grayscale matrix A obtained in step S5 is calculated as follows: in: r is the actual photon collision probability; J is the J-th pixel in the grayscale matrix A; g J Let J be the grayscale value of the J-th pixel.

10. A coal and gangue separation method based on Geant4 simulation according to claim 9, characterized in that: Step S7 includes the following sub-steps: The photon collision probability R per unit relative volume of each physical entity at each density, calculated using Geant4 simulation, is stored in a PC to form judgment data. Among them, the judgment data corresponding to physical entities with a density < 1.2 is R1, the judgment data corresponding to physical entities with a density within the density level [1.2, 1.3) is R2, the judgment data corresponding to physical entities with a density within the density level [1.3~1.4) is R3, and so on, until the judgment data corresponding to physical entities with a density within the density level [1.7~1.8] is R7, and the judgment data corresponding to physical entities with a density > 1.8 is R8. If the actual photon collision probability r < R8, then the mineral is coal, and the density level of the mineral is obtained based on the specific landing point of r; if r ≥ R8, then the mineral is gangue.

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