A parameter calibration method for discrete element crushing model of ore particles in semi-autogenous mill

Abstract

The present invention discloses a method for calibrating parameters of discrete element crushing model of ore particles in a semi-autogenous mill, comprising the following steps: determining discrete element physical parameters and static friction coefficient of ore particles through experiments; establishing an angle of repose regression model, and obtaining other contact parameters of ore particles through comparison of angle of repose simulation and angle of repose test; calibrating Tavares crushing model parameters by single particle impact and compression test based on a catapult-type falling ball impact device; establishing a discrete element particle model by 3D scanning the three-dimensional morphology of the ore; establishing a discrete element crushing model of ore particles according to the above process to perform single particle impact simulation, and taking the cumulative mass distribution of particle crushing as an evaluation index to determine the discrete element crushing model parameters of ore particles. The present invention adopts the above method for calibrating parameters of discrete element crushing model of ore particles in a semi-autogenous mill, which can improve the accuracy of ore particle crushing simulation in a semi-autogenous mill system, so as to better visualize the crushing process of ore particles.

Description

Technical Field

[0001] The invention relates to the technical field of mineral processing, in particular to a parameter calibration method for a discrete element crushing model of ore particles in a semi-autogenous mill. Background Art

[0002] Semi-autogenous grinding technology is an indispensable part of ore processing. It mainly relies on point contact and strong impact force of steel ball media. Through impact and grinding, the target mineral is dissociated as much as possible. It has unique advantages in the field of ore crushing. However, in the semi-autogenous grinding machine, the grinding process is like a "black box", and it is difficult to deeply study the crushing process of the ore inside it. With the development of computer technology, the discrete element method (DEM) has gradually become an effective method to solve engineering problems of granular and discontinuous materials. In DEM simulation, it is very important to set the simulation parameters correctly. If the parameters are not selected accurately, it may lead to deviations in the simulation results, thereby affecting the prediction and analysis of the actual crushing process.

[0003] In the prior art, most researchers have not calibrated or have incompletely calibrated the discrete element crushing model parameters of ore particles, which will significantly affect the accuracy of the simulation results. Therefore, it is necessary to provide a method for calibrating the discrete element crushing model parameters of ore particles in a semi-autogenous mill. Before using DEM to simulate the crushing process of ore particles in a semi-autogenous mill system, the discrete element crushing model parameters of ore particles are comprehensively and detailed calibrated to improve the accuracy of the simulation results. The simulation results can be used to guide the design and optimization of the semi-autogenous mill. Summary of the invention

[0004] The purpose of the present invention is to provide a method for calibrating discrete element crushing model parameters of semi-autogenous mill ore particles. Before DEM simulation of the crushing process of ore particles in a semi-autogenous mill system, the discrete element crushing model parameters of ore particles are comprehensively and detailedly calibrated to improve the accuracy of simulation results.

[0005] To achieve the above object, the present invention provides a method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill, comprising the following steps:

[0006] S1. Determine the discrete element physical parameters, angle of repose and static friction coefficient of ore particles;

[0007] S2. A quadratic polynomial regression model of the repose angle is established by using the response surface method. The restitution coefficient, dynamic friction coefficient and rolling friction resistance of the ore particles are obtained by comparing the repose angle simulation with the repose angle test; wherein the discrete element contact parameters include the static friction coefficient, dynamic friction coefficient, restitution coefficient and rolling friction resistance;

[0008] S3, using the discrete element physical parameters and contact parameters obtained in S1 and S2 to simulate the angle of repose, and comparing the angle of repose and the stacking shape through the angle of repose test to verify the accuracy of the basic parameters of the discrete element of the ore particles;

[0009] S4. Perform a single particle impact test based on a catapult-type falling ball impact device, and obtain the crushing model parameters of the ore particles by fitting the logarithmic normal distribution function curve of the crushing probability and the impact specific energy of the ore particles by changing the impact specific energy;

[0010] S5. Determine the crushing index t by measuring the particle size distribution of the crushed products under different impact specific energies through a single particle impact test. 10 ;

[0011] S6. By performing uniaxial compression tests on single particles, the changes in axial stress under different displacements are recorded, and the cumulative damage constant of the ore particles is fitted;

[0012] S7, scanning and simplifying the three-dimensional morphology of the ore particles by a 3D scanner to establish a particle model of the ore;

[0013] S8. According to S1-S7, a discrete element crushing model of ore particles is established, and a single-particle impact simulation is performed by simulating the catapult-type falling ball impact equipment. By comparing the cumulative mass distribution curves of the simulation and the test, the accuracy of the established discrete element crushing model is verified, and the discrete element crushing model parameters of the ore particles are determined.

[0014] Preferably, in S1, the discrete element physical parameters include particle density, shear modulus, Poisson's ratio, and the measurement methods include drainage test, inclined plate test, uniaxial compression test, particle size screening test and cylinder injection test.

[0015] Preferably, in S2, the quadratic polynomial regression model of the angle of repose is established by using a significance analysis test, a steepest climbing test and a response surface test design, and the quadratic polynomial regression model of the angle of repose is:

[0016] Y=31.30+1.88*A+1.46*B+0.96*C+0.60*AB+0.12*BC+0.41*A 2 +0.087*B 2 -0.26*C 2 (1)

[0017] Among them, A is the dynamic friction coefficient between ore and steel, B is the dynamic friction coefficient between ore and ore, and C is the rolling friction resistance of ore particles.

[0018] Preferably, in step S4, during the single particle impact test, an electromagnet is used to grab a steel ball and control the steel ball to fall. The impact energy E of the ore particle is calculated as follows:

[0019]

[0020] The impact energy per unit mass of particles, that is, the impact specific energy W m , kin for:

[0021]

[0022] Among them, m g is the mass of the steel ball, m i is the mass of ore particles, h g is the height of the photoelectric gate, H is the height of the falling ball, v g It is the speed of the steel ball measured by the photoelectric gate;

[0023] By changing the impact specific energy, the log-normal distribution function fitting curve of the crushing probability of ore particles and the impact specific energy is studied. According to Formula 4 and Formula 5, the median mass specific fracture energy e is obtained. 50 Then, the crushing model parameters of ore particles are fitted according to Formula 6;

[0024]

[0025] Among them, P0(e) is the probability of breakage, e * is the relative fracture energy, e max is the impact energy of all particles breaking in a single impact, e is the impact energy of particles in a single impact, and e 50 is the median particle specific fracture energy, σ 2 is the variance of the log-normal distribution of fracture energy, d p is the particle size, e ∞ 、d0、 are the parameters of the fragmentation model fitting the experimental data.

[0026] Preferably, in S5, during the single particle impact test, the particle size distribution of the crushed product under different impact specific energies is measured by a catapult-type falling ball impact device to determine the crushing index t 10 ;

[0027] t 10 =A[1-exp(-b×E cs )] (7)

[0028] Among them, E cs is the impact specific energy, that is, the impact energy and the mass of the ore sample m i A is the maximum damage in a single impact event, that is, the percentage of passing, and b is the standard JK slope parameter.

[0029] Preferably, in step S6, when calculating the cumulative damage constant γ of the ore particles, the damage variable is calculated after calculating the theoretical value of the axial stress using the Hertzian theory, as shown in Formula 8 to Formula 11;

[0030]

[0031] Among them, e n is the specific fracture energy of the particle without fracture after n loading cycles; D n * is the damage fraction of the particle in the nth loading cycle; γ is the damage accumulation coefficient; e imp,n is the instantaneous impact specific energy in the particle at the end of the nth loading cycle; F expt is the test force, F Hertzian is the elastic force obtained from the curve of Hertzian elastic contact theory; Δp is the particle displacement, and Δp* is the deformation of the particle at fracture.

[0032] Preferably, in S7, the three-dimensional morphology of the ore particles is simplified by a mesh optimization method in a 3D scanner.

[0033] Therefore, the present invention adopts the above-mentioned discrete element crushing model parameter calibration method for semi-autogenous mill ore particles, and the beneficial effects are as follows:

[0034] (1) The present invention can comprehensively and detailedly calibrate the parameters of the discrete element crushing model of ore particles, improve the accuracy of ore particle crushing simulation, and visualize the ore particle crushing process.

[0035] (2) The simulation results of the present invention can be used to guide the design and optimization of a semi-autogenous mill. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a flow chart of an embodiment of a method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill according to the present invention;

[0037] Figure 2 Schematic diagram of a method for determining basic discrete element parameters of ore particles in an embodiment of a method for calibrating discrete element crushing model parameters of a semi-autogenous mill ore particle of the present invention, wherein (a) is a schematic diagram of a density test, (b) is a schematic diagram of an inclined plate test, (c) is a schematic diagram of a mechanical property test, and (d) is a schematic diagram of an angle of repose test;

[0038] Figure 3 It is a schematic diagram of repose angle simulation and repose angle test verification of an embodiment of a method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill of the present invention, wherein (a) is a repose angle simulation effect diagram, and (b) is a repose angle test schematic diagram;

[0039] Figure 4It is a schematic diagram of a catapult-type falling ball impact device according to an embodiment of a method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill of the present invention;

[0040] Figure 5 It is a schematic diagram of the three-dimensional morphology and network optimization of ore particles in an embodiment of a discrete element crushing model parameter calibration method for ore particles in a semi-autogenous mill of the present invention;

[0041] Figure 6 It is a schematic diagram of cumulative mass distribution curves of simulation and experiment of an embodiment of a discrete element crushing model parameter calibration method for semi-autogenous mill ore particles of the present invention.

[0042] Reference numerals

[0043] 1. Ejection device; 2. Lifting device; 3. Impact base. DETAILED DESCRIPTION

[0044] The technical solution of the present invention is further described below through the accompanying drawings and embodiments.

[0045] Unless otherwise defined, technical or scientific terms used in the present invention shall have the common meanings understood by one having ordinary skills in the field to which the present invention belongs.

[0046] Embodiment 1

[0047] like Figure 1 As shown, a method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill comprises the following steps:

[0048] S1. Basic discrete element parameters of ore particles, including discrete element physical parameters and contact parameters of ore particles, as shown in Table 1. Figure 2 As shown, a series of test methods are used to determine the discrete element physical parameters, repose angle and contact parameters of ore particles:

[0049] Test the density of ore particles using the water displacement method;

[0050] The static friction coefficient of the ore particles was tested by the inclined plate method, and the ore particles were tested by sliding tests on the steel plate and steel surface with the particles attached;

[0051] The uniaxial compression test was carried out using an electro-hydraulic servo universal testing machine to obtain mechanical properties parameters such as shear modulus and Poisson's ratio of the ore particles;

[0052] The ore particles were sieved through a standard gravel sieve, and the repose angle of the ore particles was tested by the cylinder injection method.

[0053] Table 1 Basic parameters of discrete element of ore particles

[0054] Ore physical parameters Ore contact parameters <![CDATA[Density (ore, kg / m 3 )]]> Recovery coefficient (ore-ore) Shear modulus (ore, Pa) Coefficient of recovery (ore-steel) Poisson's ratio (ore) Static friction coefficient (ore-ore) Static friction coefficient (ore-steel) Dynamic friction coefficient (ore-ore) Dynamic friction coefficient (ore-steel) Rolling friction resistance of ore particles

[0055] After 5 repeated tests, the average values ​​of the parameters were obtained: the Poisson's ratio of the ore particles was about 0.245, and the density was about 3620 kg / m 3 , the elastic modulus is about 31.8 GPa, and the shear modulus G is about 1.3×10 10 Pa; the Poisson's ratio of steel is 0.3 and the density is 7850kg / m 3 , the shear modulus is 8×10 10 Pa; recovery coefficient (steel-steel): 0.7, static friction coefficient (steel-steel): 0.2, dynamic friction coefficient (steel-steel): 0.2, static friction coefficient (ore-steel) is 0.54, static friction coefficient (ore-ore) is 0.76.

[0056] The particle size distribution used in the repose angle test is shown in Table 2. The repose angle is about 32.4° and the cylinder lifting speed is 0.1 m / s.

[0057] Table 2 Particle size test results

[0058] Particle size Mass / g 31.5 390 26.5 2318.8 19 3383.2 16 884 13.2 895.8 9.5 1230.8 4.75 1188.6 2.36 267.6

[0059] S2. Design the repose angle regression model using the response surface method. In the process of regression model design, a quadratic polynomial regression model of the repose angle is established through significance analysis test, steepest climbing test and response surface test design, and the unknown discrete element contact parameters are obtained: dynamic friction coefficient (ore-ore, ore-steel), restitution coefficient (ore-ore, ore-steel) and rolling friction resistance of ore particles; establish the repose angle quadratic polynomial regression model:

[0060] Y=31.30+1.88*A+1.46*B+0.96*C+0.60*AB+0.12*BC+0.41*A 2 +0.087*B 2 -0.26*C 2 (1)

[0061] Among them, A is the dynamic friction coefficient (ore-steel), B is the dynamic friction coefficient (ore-ore), and C is the rolling friction resistance of the ore particles. In the above calibration process, since the influence on the repose angle is not significant, the recovery coefficient (ore-steel) is determined to be 0.3 and the recovery coefficient (ore-ore) is determined to be 0.5.

[0062] In addition, the repose angle obtained by the test is 32.4°, which is set as the target value. The optimization solution obtained by optimizing the repose angle regression model is not a unique solution, but several groups of solutions. A set of optimization parameters is selected, and the dynamic friction coefficient (ore-steel) is 0.50, the dynamic friction coefficient (ore-ore) is 0.70, and the rolling friction resistance of the ore particles is 0.16.

[0063] S3, such as Figure 3 As shown in Table 3, the basic parameters of the discrete element obtained in S1 and S2 are used to simulate the angle of repose. The angle of repose and the stacking shape are compared through the angle of repose test to verify the accuracy of the basic parameters of the discrete element of the ore particles. The simulation results of the angle of repose obtained by using this parameter have a high similarity with the results of the physical test of the angle of repose in both the angle of repose and the stacking shape, and a set of basic parameter combinations suitable for DEM simulation is determined.

[0064] Table 3. Calibration results of basic parameters of discrete element

[0065] Ore physical parameters Numeric Ore contact parameters Numeric <![CDATA[Density (ore, kg / m 3 )]]> 3620 Recovery coefficient (ore-ore) 0.30 Shear modulus (ore, Pa) <![CDATA[1.3×10 10 ]]> Coefficient of recovery (ore-steel) 0.50 Poisson's ratio (ore) 0.245 Static friction coefficient (ore-ore) 0.76 Static friction coefficient (ore-steel) 0.54 Dynamic friction coefficient (ore-ore) 0.70 Dynamic friction coefficient (ore-steel) 0.50 Rolling friction resistance of ore particles 0.16

[0066] S4, the crushing process of ore particles is realized based on the Tavares crushing model. The Tavares crushing model parameters of ore particles are calibrated through single particle impact test, as shown in Table 4;

[0067] Table 4 Tavares crushing model parameters

[0068]

[0069] like Figure 4 As shown in the figure, a single particle impact test is carried out based on a catapult-type falling ball impact device, which includes a swing-arm electromagnetic steel ball grabbing device, a lifting device, a catapult device, a chuck-type steel ball positioning device, a control console, and an impact base. Electromagnets are used to grab steel balls and control the falling of steel balls. The maximum falling height of the steel balls is 1.7m, and the maximum size of the grabbed steel balls is 150mm, so as to meet the impact crushing requirements of different energy levels.

[0070] In this embodiment, five particle sizes of -9.5+4.75mm, -13.2+9.5mm, -16+13.2mm, -19+16mm and -26.5+19mm are selected for single particle impact test. The calculation formula of the impact energy E of the ore particles is as follows:

[0071]

[0072] The impact energy per unit mass of particles, that is, the impact specific energy W m , kin It can be expressed as:

[0073]

[0074] Among them, m g is the mass of the steel ball, m i is the mass of ore particles, h g is the height of the photoelectric gate, H is the height of the falling ball, v g Steel ball speed measured by the photoelectric gate.

[0075] By changing the impact specific energy, the log-normal distribution function fitting curve of the ore particle breakage probability and the impact specific energy is studied, as shown in Formula 4 and Formula 5. The median mass specific fracture energy e is obtained. 50 Then, the Tavares crushing model parameters of the ore particles are fitted according to Formula 6;

[0076]

[0077] Among them, P0(e) is the probability of breakage, e * is the relative fracture energy, e max is the impact energy of all particles breaking in a single impact, e is the impact energy of particles in a single impact, and e 50 is the median particle specific fracture energy, σ 2 is the variance of the log-normal distribution of fracture energy, d p is the particle size, e ∞ 、d0、 are the parameters of the Tavares fragmentation model fitting the experimental data.

[0078] S5. Determine the crushing index t by measuring the particle size distribution of crushed products under different impact specific energies using a catapult-type falling ball impact device. 10 . Crushing index t 10 It refers to the cumulative mass percentage of crushed ore particles that pass 1 / 10 of the geometric average particle size of the original ore;

[0079] t 10 =A[1-exp(-b×E cs )] (7)

[0080] Among them, E cs is the impact specific energy (impact energy and the mass of the ore sample m i A is the maximum damage (percentage of damage) that can be achieved in a single impact event, and b is the standard JK slope parameter.

[0081] S6. Perform uniaxial compression tests on individual ore particles, record the changes in axial stress under different displacements, calculate the theoretical value of axial stress using Hertzian theory, and then calculate the damage variable using formulas (such as formulas 8-11) to fit the cumulative damage constant γ of the ore particles;

[0082]

[0083] Among them, e n is the specific fracture energy of the particle without fracture after n loading cycles. Therefore, e n-1 can be interpreted as the e calculated at the end of the previous loading cycle n Value; D n * is the damage fraction of the particle in the nth loading cycle; γ is the damage accumulation coefficient; e imp,n is the instantaneous impact specific energy in the particle at the end of the nth loading cycle; F expt is the test force, and F Hertzian is the elastic force obtained from the curve of Hertzian elastic contact theory; Δp is the displacement, and Δp* is the deformation of the particle at fracture.

[0084] The calibrated Tavares crushing model parameter values ​​are shown in Table 5;

[0085] Table 5 Calibration results of Tavares crushing model parameters

[0086]

[0087]

[0088] S7, such as Figure 5 As shown in FIG. 1 , the three-dimensional morphology of ore particles is scanned by a 3D scanner to establish a particle model. Since the ore particle morphology obtained by scanning is relatively complex, a mesh optimization method is used to simplify the ore morphology.

[0089] S8. According to the process of step S1 to step S7, a discrete element crushing model of ore particles is established, and a single particle impact simulation is performed by simulating a projectile-type falling ball impact device. The crushing effect of ore particles is tested and verified through a single particle impact test, and the cumulative mass distribution of ore particle crushing is used as an evaluation index. Figure 6 As shown in the figure, the cumulative mass distribution curves of simulation and experiment are compared to verify the accuracy of the discrete element crushing model established and determine the discrete element crushing model parameters of ore particles.

[0090] Therefore, the present invention adopts the above-mentioned discrete element crushing model parameter calibration method for semi-autogenous mill ore particles to comprehensively and detailedly provide discrete element crushing model parameters for ore particles. Based on the application of this parameter calibration method, the accuracy of ore particle crushing simulation can be improved, so as to better visualize the crushing process of ore particles.

[0091] Applying this set of discrete element crushing model parameters to the simulation of the semi-autogenous mill can study the influence of the structure and process parameters of the semi-autogenous mill on the flow of ore particles, the crushing process and the liner wear mechanism, and give a detailed description of the time, energy and specific crushing process of the ore particles from the feed end to the discharge end, which will be used to guide the design and optimization of the semi-autogenous mill.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill, characterized in that: The following steps are involved: S1. Determine the discrete element physical parameters, angle of repose and static friction coefficient of ore particles; S2. A quadratic polynomial regression model of the repose angle is established by using the response surface method. The restitution coefficient, dynamic friction coefficient and rolling friction resistance of the ore particles are obtained by comparing the repose angle simulation with the repose angle test; wherein the discrete element contact parameters include the static friction coefficient, dynamic friction coefficient, restitution coefficient and rolling friction resistance; S3, using the discrete element physical parameters and contact parameters obtained in S1 and S2 to simulate the angle of repose, and comparing the angle of repose and the stacking shape through the angle of repose test to verify the accuracy of the basic parameters of the discrete element of the ore particles; S4. Perform a single particle impact test based on a catapult-type falling ball impact device, and obtain the crushing model parameters of the ore particles by fitting the logarithmic normal distribution function curve of the crushing probability and the impact specific energy of the ore particles by changing the impact specific energy; S5. Determine the crushing index t by measuring the particle size distribution of the crushed products under different impact specific energies through a single particle impact test. 10 ; S6. By performing a uniaxial compression test on a single particle of ore, the changes in axial stress under different displacements are recorded, and the cumulative damage constant of the ore particles is fitted; S7, scanning and simplifying the three-dimensional morphology of the ore particles by a 3D scanner to establish a particle model of the ore; S8. According to S1-S7, a discrete element crushing model of ore particles is established, and a single-particle impact simulation is performed by simulating the catapult-type falling ball impact equipment. By comparing the cumulative mass distribution curves of the simulation and the test, the accuracy of the established discrete element crushing model is verified, and the discrete element crushing model parameters of the ore particles are determined.

2. The method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill according to claim 1, characterized in that: In S1, the discrete element physical parameters include particle density, shear modulus, Poisson's ratio, and the measurement methods include drainage test, inclined plate test, uniaxial compression test, particle size screening test, and cylinder injection test.

3. The method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill according to claim 1, characterized in that: In S2, the quadratic polynomial regression model of the angle of repose was established using significance analysis test, steepest climbing test and response surface test design. The quadratic polynomial regression model of the angle of repose is: Y=31.30+1.88*A+1.46*B+0.96*C+0.60*AB+0.12*BC+0.41*A 2 +0.087*B 2 -0.26*C 2 (1) Where A is the dynamic friction coefficient between ore and steel, B is the dynamic friction coefficient between ore and ore, and C is the rolling friction resistance of ore particles.

4. The method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill according to claim 1, characterized in that: In S4, an electromagnet is used to grab the steel ball and control the steel ball to fall during the single-particle impact test. The impact energy E of the ore particle is calculated as follows: The impact energy per unit mass of particles, that is, the impact specific energy W m , kin for: Among them, m g is the mass of the steel ball, m i is the mass of ore particles, h g is the height of the photoelectric gate, H is the height of the falling ball, v g It is the speed of the steel ball measured by the photoelectric gate; By changing the impact specific energy, the log-normal distribution function fitting curve of the crushing probability of ore particles and the impact specific energy is studied. According to Formula 4 and Formula 5, the median mass specific fracture energy e is obtained. 50 Then, the crushing model parameters of ore particles are fitted according to Formula 6; Among them, P0(e) is the probability of breakage, e * is the relative fracture energy, e max is the impact energy of all particles breaking in a single impact, e is the impact energy of particles in a single impact, and e 50 is the median particle specific fracture energy, σ 2 is the variance of the log-normal distribution of fracture energy, d p is the particle size, e ∞ 、d0、 are the parameters of the fragmentation model fitting the experimental data.

5. The method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill according to claim 1, characterized in that: In S5, the crushing index t is determined by measuring the particle size distribution of the crushed products under different impact specific energies using a catapult-type falling ball impact device during the single particle impact test. 10 ; t 10 =A[1-exp(-b×E cs )] (7) Among them, E cs is the impact specific energy, that is, the impact energy and the mass of the ore sample m i A is the maximum damage in a single impact event, that is, the percentage of passing, and b is the standard JK slope parameter.

6. The method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill according to claim 1, characterized in that: In S6, when calculating the cumulative damage constant γ of the ore particles, the damage variable is calculated after calculating the theoretical value of the axial stress using the Hertzian theory, as shown in Formula 8 to Formula 11; Among them, e n is the specific fracture energy of the particle without fracture after n loading cycles; D n * is the damage fraction of the particle in the nth loading cycle; γ is the damage accumulation coefficient; e imp,n is the instantaneous impact specific energy in the particle at the end of the nth loading cycle; F expt is the test force, F Hertzian is the elastic force obtained from the curve of Hertzian elastic contact theory; Δp is the particle displacement, and Δp* is the deformation of the particle at fracture.

7. The method for calibrating parameters of a discrete element crushing model of ore particles in a semi-autogenous mill according to claim 1, characterized in that: In S7, the three-dimensional morphology of the ore particles is simplified by the mesh optimization method in the 3D scanner.

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