A particle filtering method for improving the accuracy of the DEM simulation of the crushing capacity of a semi-autogenous mill

Abstract

This invention discloses a particle filtration method for improving the accuracy of DEM simulation evaluation of the crushing capacity of a semi-autogenous mill, belonging to the field of mineral processing technology. The method includes the following steps: S1, measuring the discrete element physical parameters, angle of repose, and static friction coefficient of the ore particles and liner material to establish a simulation model of the ore in the DEM; S2, initializing the autogenous mill equipment and related models and parameters for particle crushing and filtration in the DEM; S3, setting the maximum simulation duration and performing simulation calculations for ore particle crushing and filtration; S4, outputting the particle size change history after the simulation ends, obtaining the stable crushing particle size distribution under specific lifting stripe morphology and specified rotational speed of the semi-autogenous mill. Using the above method, this invention can dynamically identify, remove, or filter out small particles deposited at the bottom that contribute little to the effective crushing process or even have a negative impact, enabling a comprehensive and detailed evaluation of the true crushing capacity of the semi-autogenous mill liner structure.

Description

Technical Field

[0001] This invention relates to the field of mineral processing technology, and in particular to a particle filtration method for improving the accuracy of DEM simulation evaluation of crushing capacity of a semi-autogenous mill. Background Technology

[0002] Semi-autogenous mills are core grinding equipment in large mines and mineral processing plants. They utilize steel balls and the ore itself as grinding media for crushing, offering high efficiency and a simplified process. Accurately assessing the crushing capacity of the internal structure of a semi-autogenous mill is crucial for optimizing equipment design, increasing throughput, reducing energy consumption, and extending equipment life. In actual DEM simulations of semi-autogenous mills, especially when simulating ore crushing, a large number of small particles are continuously generated. Due to gravity and relatively weak collision energy, these small particles tend to deposit in the bottom region of the mill cylinder, forming one or more relatively stationary or slowly moving "cushions." This hinders particle movement and collisions, leading to a significant underestimation of the effective crushing energy calculated by the DEM simulation, severely impacting the effectiveness of the semi-autogenous mill liner structure in assessing its true crushing capacity.

[0003] Current techniques for solving fine-grained problems in DEM simulations significantly increase model complexity and computational costs, while also being cumbersome and inefficient. Summary of the Invention

[0004] The purpose of this invention is to provide a particle filtration method to improve the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill. This method can dynamically identify, remove, or filter out small particles deposited at the bottom that contribute little to the effective crushing process or even have a negative impact during the DEM crushing process simulation of a semi-autogenous mill. It can comprehensively and in detail assess the true crushing capacity of the semi-autogenous mill liner structure.

[0005] To achieve the above objectives, the present invention provides a particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill, comprising the following steps: S1. Measure the discrete element physical parameters, angle of repose, and static friction coefficient of the ore particles and lining material, and establish a simulation model of the ore in the DEM. S2. Initialize the autogenous mill equipment and related models and parameters for particle crushing and filtration in the DEM; S3. Set the maximum simulation duration and perform simulation calculations for ore particle crushing and filtration. S4. After the process ends, output the particle size change history to obtain the specific lifting strip morphology and stable crushing particle size at the specified speed of the semi-autogenous mill.

[0006] Preferably, the discrete element physical parameters in S1 include: particle density, shear modulus, and Poisson's ratio of ore particles and autogenous mill steel balls, which are measured through several repeated experiments using drainage test, inclined plate test, uniaxial compression test, particle size sieving test, and cylindrical injection test, respectively.

[0007] Preferably, the process of S2 is as follows: S21. Divide the liner material in S1 into two groups. One group is installed with the material facing forward, and the other group is installed with the material facing backward, to obtain two sets of semi-autogenous mill models. S22. Initialize the geometric model and operating parameters of the semi-autogenous mill model; S23. Initialize the parameters of the particle crushing model and the contact model; S24. Initialize the global variables for particle filtering.

[0008] Preferably, the semi-autogenous mill model is provided with a particle removal area and a particle generation area.

[0009] Preferably, the global variables in S24 include: Flag variables for performing traversal and removal operations Save the total mass of removed particles Monitoring the total mass of ore particles in a semi-autogenous grinding mill Determine whether new ore particles need to be added. ; The geometric parameters of the particle removal region include the sector inner diameter. sector outer diameter Sector starting angle and sector termination angle ; The geometric parameters for generating the particle region include the width of the generated region. ,high ,thickness and coordinates in the Cartesian coordinate system .

[0010] Preferably, the process of S3 is as follows: S31. Set the maximum simulation time and use two sets of semi-autogenous mill models to perform simulations. The simulation steps for the two sets of semi-autogenous mill models are the same. S32. Determine whether the simulation time has reached an integer multiple of the step size. If it is an integer multiple, proceed to S33; otherwise, proceed to S36. S33, Perform the small particle removal process; S34. After the small particle removal process is complete, use the Particle Factory module to add new particles to the model; S35. After the new particles are added, the abnormal particles are processed, and then proceed to S36. S36. Perform discrete element calculation; S37. After the calculation is completed, determine whether the maximum simulation time has been reached. If the maximum simulation time has been reached, proceed to S4; otherwise, return to S32 and loop.

[0011] Preferably, the process of S33 is as follows: S331. Traverse all particles and obtain the total number, type, mass, position, and size information of the particles; S332, Set the particle counter and total mass of particles Set to 0; S333: Select a particle and determine its type. If it is ore, proceed to S334; otherwise, let... Add current particle weight update Then proceed with S337; S334. Determine if the particle's position falls within the removal area. If it does, proceed to S335; otherwise, let... Add current particle weight update Then proceed with S337; S335. Determine if the particle mass is less than the critical mass. If it is less than the critical mass, proceed to S336; otherwise, let... Add current particle weight update Then proceed with S337; S336, Remove index as The particles make Add current particle weight update ; S337, Order Return to S333 to select the next particle again, until all particles have been selected.

[0012] Preferably, the process of S34 is as follows: S341. After completing S33, initialize the random number seed using computer time information; S342. Determine the total mass of particles. With the threshold for adding new ore particles ,like If so, proceed to S343; otherwise, exit the Particle Factory module. S343. Use a random number seed to add new particles at random positions in the particle generation area via the particle factory module; S344, Order Weight update with added new particles Then return to S342 to re-determine the relationship between the total particle mass and the threshold.

[0013] Preferably, the process of S35 is as follows: S351. After completing S34, traverse all collisions and obtain the number, position and overlap of particles at this time. S352, Collision Counter The value is reset to 0; S353. Select a particle and determine whether it is ore. If it is ore, proceed to S354; otherwise, proceed to S356. S354. Determine whether the collision and overlap of ore particles are abnormal. If there is no abnormality, proceed to S355; otherwise, proceed to S356. S355. Determine whether the ore particles fall within the particle generation area. If they fall within the generation area, remove the pair of contacting particles that collided, and then proceed to S356. S356, Order Return to S353 to select the next particle again, until all particles have been selected.

[0014] Preferably, the process of S4 is as follows: S41. Output the particle size change history of the two sets of simulation outputs respectively; S42. By analyzing and comparing the two sets of change processes, the specific lifting strip morphology and stable crushing particle size at the specified speed of the semi-autogenous mill are obtained.

[0015] Therefore, the particle filtration method of the present invention, which uses the above-described structure to improve the accuracy of DEM simulation evaluation of crushing capacity of a semi-autogenous mill, has the following advantages: (1) Significantly reduce the total number of particles involved in the calculation, especially those stationary and low-speed small particles that have a large impact on the calculation but contribute little to the crushing process, thus shortening the simulation time.

[0016] (2) It eliminates the interference of non-physical sedimentation layers on particle flow, energy distribution and impact crushing events, making key results such as steel ball trajectory, impact energy distribution, power consumption, liner stress and product particle size prediction closer to physical reality, and greatly improving the reliability of the evaluation of the actual crushing capacity and working performance of the mill.

[0017] (3) Implemented via API, the method is highly customizable. Users can flexibly adjust the filtration criteria (size threshold, speed threshold, residence time, etc.) and filtration area according to the specific mill model, ore characteristics, and operating conditions.

[0018] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0019] Figure 1This is an overall flowchart of a particle filtration method for improving the accuracy of DEM simulation evaluation of crushing capacity of a semi-autogenous mill, according to the present invention. Figure 2 This is a simulation process diagram of S3 in a particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill according to the present invention. Figure 3 This is a flowchart of particle removal in S33 of a particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill, according to the present invention. Figure 4 This is a flowchart of step S34, adding particles, in a particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill, according to the present invention. Figure 5 This is a flowchart of the S35 abnormal particle processing method in a particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill, according to the present invention. Figure 6 This is a model diagram of a semi-autogenous grinding mill model assembled using liner material, used in a particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous grinding mill according to the present invention; wherein... Figure 6 (a) is a diagram of the formal model; Figure 6 (b) is a diagram of the reverse-mounted model; Figure 7 This is a schematic diagram of the particle removal area in an embodiment of a particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill according to the present invention. Figure 8 This is a schematic diagram of the particle generation area in an embodiment of a particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill according to the present invention. Figure 9 This is a schematic diagram illustrating the change of large particle mass over time in an embodiment of a particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill according to the present invention. Figure 10 This is a partial structural diagram of a semi-autogenous grinding mill model, illustrating a particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous grinding mill according to the present invention; wherein... Figure 10 (a) is a structural diagram of the main body section; Figure 10 (b) is a structural diagram of the reverse assembly section. Detailed Implementation

[0020] Example To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0023] like Figures 1-10 As shown, this invention discloses a particle filtering method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill. When simulating the crushing process of a semi-autogenous mill using DEM, small particles are dynamically removed via an application programming interface (API). Based on a set removal threshold and region, small particles are dynamically removed from the computational domain. Simultaneously, to maintain material balance, after removing a certain amount of small particles, coarse particles of corresponding mass are dynamically generated proportionally in the blank areas of the mill slab, thereby maintaining the relative stability of the material load within the mill. Finally, the actual crushing capacity of the semi-autogenous mill liner structure is evaluated by statistically analyzing the total mass and removal rate of the small particles removed. The method includes the following steps: S1. Measure the discrete element physical parameters, angle of repose, and static friction coefficient of the ore particles and lining material, and establish a simulation model of the ore in the DEM. The particle density, shear modulus, and Poisson's ratio of the ore particles and the autogenous mill steel balls were measured through several repeated experiments using drainage tests, inclined plate tests, uniaxial compression tests, particle size sieving tests, and cylindrical injection tests.

[0024] Five repeatable experiments were conducted in this embodiment, and the parameters obtained are as follows: the Poisson's ratio of the ore particles is approximately 0.245, the density is approximately 3620 kg / m3, the elastic modulus is approximately 31.8 GPa, and the calculated shear modulus G is approximately 1.3 × 10¹⁰ Pa; the Poisson's ratio of the steel is taken as 0.3, the density as 7800 kg / m3, and the shear modulus as 8 × 10¹⁰ Pa; the coefficient of restitution (steel-steel): 0.7, the static friction coefficient (steel-steel): 0.2, and the rolling friction coefficient (steel-steel): 0.01, which are expressed in Table 1.

[0025] Table 1 Mechanical and contact parameters of ore, steel balls, and liner plates

[0026] S2. Initialize the autogenous mill equipment and related models and parameters for particle crushing and filtration in the DEM; Any semi-autogenous mill model, operating parameters, contact model parameters, and particle crushing model can be arbitrarily set to determine the particle removal area location, stable filling rate, and particle removal threshold in the particle filtration method.

[0027] S21. Divide the liner material in S1 into two groups. One group is installed with the material facing forward, and the other group is installed in reverse to obtain two semi-autogenous mill models. Set up two sets of calculation examples for each model, one using particle filtration technology and the other not using particle filtration technology. The semi-autogenous mill model includes a particle removal area and a particle generation area.

[0028] S22. Initialize the geometric model and operating parameters of the semi-autogenous mill model; S23. Initialize the parameters of the particle crushing model and the contact model; S24. Initialize the global variables for particle filtration. The initial values ​​of the global variables are shown in Table 2. The global variables represent regions A and B as follows: Figure 7 and Figure 8 As shown.

[0029] Flag variables for performing traversal and removal operations Save the total mass of removed particles Monitoring the total mass of ore particles in a semi-autogenous grinding mill Determine whether new ore particles need to be added. ; It is a double type, which stores the mass of all particles that have been removed, and is used for post-processing and data analysis of the simulation; It is a double type, which stores the mass of all particles that have been removed, and is used for post-processing and data analysis of the simulation; This is a double type, used to monitor the current ore particle quality in the system and determine whether new ore particles need to be added to the system. This is a double value representing the ore mass when the system's fill rate is stable, used to determine whether new ore particles need to be added to the system.

[0030] The geometric parameters of the particle removal region, defined in a cylindrical coordinate system based on the rotation center of the semi-autogenous mill model, include the sector inner diameter. sector outer diameter Sector starting angle and sector termination angle All geometric parameters are of type double; The geometric parameters for generating the particle region are of type double, including the width of the generated region. ,high ,thickness and coordinates in the Cartesian coordinate system In this embodiment, the generated particle region is a cuboid region, and the sign of the thickness value determines the relative direction between the cuboid's edges and vertices.

[0031] Table 2 Particle Removal API Settings

[0032] S3. Set the maximum simulation duration and perform simulation calculations for ore particle crushing and filtering. Figures 2-5 As shown; S31. Set the maximum simulation time and use two sets of semi-autogenous mill models to perform simulations. The simulation steps for the two sets of semi-autogenous mill models are the same. S32. Determine whether the simulation time has reached an integer multiple of the step size. If it is an integer multiple, proceed to S33; otherwise, proceed to S36. S33, Perform the small particle removal process; S331. Traverse all particles and obtain the total number, type, mass, position, and size information of the particles; S332, Set the particle counter and total mass of particles Set to 0; S333: Select a particle and determine its type. If it is ore, proceed to S334; otherwise, let... Add current particle weight update Then proceed with S337; S334. Determine if the particle's position falls within the removal area. If it does, proceed to S335; otherwise, let... Add current particle weight update Then proceed with S337; S335. Determine if the particle mass is less than the critical mass. If it is less than the critical mass, proceed to S336; otherwise, let... Add current particle weight update Then proceed with S337; S336, Remove index as The particles make Add current particle weight update ; S337, Order Return to S333 to select the next particle again, until all particles have been selected.

[0033] S34. After the small particle removal process is complete, use the Particle Factory module to add new particles to the model; S341. After completing S33, initialize the random number seed using computer time information; S342. Determine the total mass of particles. With the threshold for adding new ore particles ,like If so, proceed to S343; otherwise, exit the Particle Factory module. S343. Use a random number seed to add new particles at random positions in the particle generation area via the particle factory module; S344, Order Weight update with added new particles Then return to S342 to re-determine the relationship between the total particle mass and the threshold.

[0034] S35. After the new particles are added, the abnormal particles are processed, and then proceed to S36. S351. After completing S34, traverse all collisions and obtain the number, position and overlap of particles at this time. S352, Collision Counter The value is reset to 0; S353. Select a particle and determine whether it is ore. If it is ore, proceed to S354; otherwise, proceed to S356. S354. Determine whether the collision and overlap of ore particles are abnormal. If there is no abnormality, proceed to S355; otherwise, proceed to S356. S355. Determine whether the ore particles fall within the particle generation area. If they fall within the generation area, remove the pair of contacting particles that collided, and then proceed to S356. S356, Order Return to S353 to select the next particle again, until all particles have been selected.

[0035] S36. Perform discrete element calculation; S37. After the calculation is completed, determine whether the maximum simulation time has been reached. If the maximum simulation time has been reached, proceed to S4; otherwise, return to S32 and loop.

[0036] S4. After the process ends, output the particle size change history to obtain the specific lifting strip morphology and stable crushing particle size at the specified speed of the semi-autogenous mill.

[0037] By analyzing the particle size change process within the system, more accurate evaluation indicators for the crushing capacity of the semi-autogenous mill are obtained based on the stable particle size and the system stabilization time.

[0038] S41. Output the particle size change history of the two sets of simulation outputs respectively; S42. By analyzing and comparing the two sets of change processes, the specific lifting strip morphology and stable crushing particle size at the specified speed of the semi-autogenous mill are obtained.

[0039] The final results are as follows: When particle filtration technology is not used, the crushing efficiency of the system is generally evaluated using the particle size distribution of the ore particles in the system. In the two sets of calculations without particle filtration technology, the particle size distribution of the ore at each stage of operation of the semi-autogenous mill is shown in Tables 3 and 4, and no distinction is shown between the two.

[0040] Table 3. Calculation example of reverse-loading liner, particle size distribution of ore.

[0041] Therefore, when using particle filtration technology, in both sets of calculations, the changes in large, unbroken particles within the system over time are as follows: Figure 9 As shown, the ore crushing rate in the system stabilizes after 60 seconds. At this time, in the case of back-to-back liner installation (reverse installation), the removal rate of the crushed small ore particles is 50.77 kg / s; in the case of sequential liner installation (normal installation), the removal rate of the crushed small ore particles is 73.94 kg / s.

[0042] Table 4. Calculation example of liner installation, particle size distribution of ore particles.

[0043] Furthermore, at one mine, adjusting the back-to-back lining to a front-mounted configuration increased the ore processing capacity from 547 t / h to 653 t / h. Clearly, particle filtration technology allows DEM (Damage Estimation Model) to more effectively predict the crushing capacity of a semi-autogenous mill.

[0044] Therefore, the particle filtration method of the present invention, which uses the above-described structure to improve the accuracy of DEM simulation evaluation of crushing capacity of a semi-autogenous mill, has the following advantages: (1) Significantly reduce the total number of particles involved in the calculation, especially those stationary and low-speed small particles that have a large impact on the calculation but contribute little to the crushing process, thus shortening the simulation time.

[0045] (2) It eliminates the interference of non-physical sedimentation layers on particle flow, energy distribution and impact crushing events, making key results such as steel ball trajectory, impact energy distribution, power consumption, liner stress and product particle size prediction closer to physical reality, and greatly improving the reliability of the evaluation of the actual crushing capacity and working performance of the mill.

[0046] (3) Implemented via API, the method is highly customizable. Users can flexibly adjust the filtration criteria (size threshold, speed threshold, residence time, etc.) and filtration area according to the specific mill model, ore characteristics, and operating conditions.

[0047] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A particle filtration method for improving the accuracy of crushing capacity assessment in DEM simulation of a semi-autogenous mill, characterized in that, Includes the following steps: S1. Measure the discrete element physical parameters, angle of repose, and static friction coefficient of the ore particles and liner material, and establish a simulation model of the ore in the DEM. S2. Initialize the autogenous mill equipment and related models and parameters for particle crushing and filtration in the DEM; S3. Set the maximum simulation duration and perform simulation calculations for ore particle crushing and filtration. S4. After the process ends, output the particle size change history to obtain the specific lifting strip morphology and stable crushing particle size at the specified speed of the semi-autogenous mill.

2. The particle filtration method for improving the accuracy of DEM simulation crushing capacity assessment of a semi-autogenous mill according to claim 1, characterized in that, The discrete element physical parameters in S1 include the particle density, shear modulus, and Poisson's ratio of the ore particles and the autogenous mill steel balls, which were measured through several repeated experiments using drainage tests, inclined plate tests, uniaxial compression tests, particle size sieving tests, and cylindrical injection tests.

3. The particle filtration method for improving the accuracy of DEM simulation crushing capacity assessment of a semi-autogenous mill according to claim 2, characterized in that, The process of S2 is as follows: S21. Divide the liner material in S1 into two groups. One group is installed with the material facing forward, and the other group is installed with the material facing backward, to obtain two sets of semi-autogenous mill models. S22. Initialize the geometric model and operating parameters of the semi-autogenous mill model; S23. Initialize the parameters of the particle crushing model and the contact model; S24. Initialize the global variables for particle filtering.

4. The particle filtration method for improving the accuracy of DEM simulation crushing capacity assessment of a semi-autogenous mill according to claim 3, characterized in that: The semi-autogenous mill model includes a particle removal area and a particle generation area.

5. A particle filtration method for improving the accuracy of DEM simulation crushing capacity assessment of a semi-autogenous mill according to claim 4, characterized in that, Global variables in S24 include: Flag variables for performing traversal and removal operations Save the total mass of removed particles Monitoring the total mass of ore particles in a semi-autogenous grinding mill Determine whether new ore particles need to be added. ; The geometric parameters of the particle removal region include the sector inner diameter. sector outer diameter Sector starting angle and sector termination angle ; The geometric parameters for generating the particle region include the width of the generated region. ,high ,thickness and coordinates in the Cartesian coordinate system .

6. The particle filtration method for improving the accuracy of DEM simulation crushing capacity assessment of a semi-autogenous mill according to claim 5, characterized in that, The process of S3 is as follows: S31. Set the maximum simulation time and use two sets of semi-autogenous mill models to perform simulations. The simulation steps for the two sets of semi-autogenous mill models are the same. S32. Determine whether the simulation time has reached an integer multiple of the step size. If it is an integer multiple, proceed to S33; otherwise, proceed to S36. S33, Perform the small particle removal process; S34. After the small particle removal process is complete, use the Particle Factory module to add new particles to the model; S35. After the new particles are added, the abnormal particles are processed, and then proceed to S36. S36. Perform discrete element calculation; S37. After the calculation is completed, determine whether the maximum simulation time has been reached. If the maximum simulation time has been reached, proceed to S4; otherwise, return to S32 and loop.

7. A particle filtration method for improving the accuracy of DEM simulation crushing capacity assessment of a semi-autogenous mill according to claim 6, characterized in that, The process of S33 is as follows: S331. Traverse all particles and obtain the total number, type, mass, position, and size information of the particles; S332, Set the particle counter and total mass of particles Set to 0; S333: Select a particle and determine its type. If it is ore, proceed to S334; otherwise, let... Add current particle weight update Then proceed with S337; S334. Determine if the particle's position falls within the removal area. If it does, proceed to S335; otherwise, let... Add current particle weight update Then proceed with S337; S335. Determine if the particle mass is less than the critical mass. If it is less than the critical mass, proceed to S336; otherwise, let... Add current particle weight update Then proceed with S337; S336, Remove index as The particles make Add current particle weight update ; S337, Order Return to S333 to select the next particle again, until all particles have been selected.

8. A particle filtration method for improving the accuracy of DEM simulation crushing capacity assessment of a semi-autogenous mill according to claim 7, characterized in that, The process of S34 is as follows: S341. After completing S33, initialize the random number seed using computer time information; S342. Determine the total mass of particles. With the threshold for adding new ore particles ,like If so, proceed to S343; otherwise, exit the Particle Factory module. S343. Use a random number seed to add new particles at random positions in the particle generation area via the particle factory module; S344, Order Weight update with added new particles Then return to S342 to re-determine the relationship between the total particle mass and the threshold.

9. A particle filtration method for improving the accuracy of DEM simulation crushing capacity assessment of a semi-autogenous mill according to claim 8, characterized in that, The process of S35 is as follows: S351. After completing S34, traverse all collisions and obtain the number, position and overlap of particles at this time. S352, Collision Counter The value is reset to 0; S353. Select a particle and determine whether it is ore. If it is ore, proceed to S354; otherwise, proceed to S356. S354. Determine whether the collision and overlap of ore particles are abnormal. If there is no abnormality, proceed to S355; otherwise, proceed to S356. S355. Determine whether the ore particles fall within the particle generation area. If they fall within the generation area, remove the pair of contacting particles that collided, and then proceed to S356. S356, Order Return to S353 to select the next particle again, until all particles have been selected.

10. A particle filtration method for improving the accuracy of DEM simulation crushing capacity assessment of a semi-autogenous mill according to claim 9, characterized in that, The process of S4 is as follows: S41. Output the particle size change history of the two sets of simulation outputs respectively; S42. By analyzing and comparing the two sets of change processes, the specific lifting strip morphology and stable crushing particle size at the specified speed of the semi-autogenous mill are obtained.

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