Method for predicting particle size change in ore grinding process through multi-scale energy and mass coupling

Abstract

The invention relates to a method for predicting particle size change in an ore grinding process through multi-scale energy and mass coupling, which comprises the following steps: respectively establishing multi-scale models such as a single particle model, a group particle model and an equipment level particle size distribution model, carrying out analogue simulation, and then coupling the models, and simulating and predicting the particle size change of ore particles in the ore grinding process. According to the method provided by the invention, multi-scale simulation is carried out on the particle size change of single particles, group particles and equipment levels, the change of the particle size of the particles along with parameters in the whole-flow processing process is explored, and calculation is carried out by applying a near-field dynamics method, a discrete particle method and a group balance method; the particle size change process of particles in the ore grinding equipment can be simulated and predicted in a multi-level and multi-scale mode, and the obtained prediction result has high consistency with actual processing data in the production process.

Description

Technical Field

[0001] The invention relates to the technical field of mineral processing, and in particular to a method for predicting particle size changes in a grinding process through multi-scale energy-mass coupling. Background Art

[0002] Grinding is an important part of mineral processing. It is a process with high energy consumption, high complexity and strong hysteresis, involving the interaction between particles, energy transfer between particles and equipment, and coupling of multiple physical fields. The particle size distribution of grinding products is of great significance to subsequent selection indicators, and predicting the particle size distribution of products is also an important research direction at present.

[0003] CN117299335A discloses a ball mill discharge particle size control method and system based on digital twin technology, including: S1, collecting industrial field data; S2, establishing a mechanism model of discharge particle size; S3, establishing a state and output deviation compensation model based on historical data and the mechanism model, and the data-driven deviation compensation model and the mechanism model together constitute a digital twin model of the grinding system; S4, using the digital twin model of the grinding system to train reinforcement learning controller parameters; S5, transmitting the trained controller parameters to the field controller, and the field controller gives control instructions according to the predicted particle size distribution; S6, when the control system performance deteriorates or the process indicators change, the controller parameters are retrained and the data-driven model is updated online.

[0004] CN117521365A discloses a method for predicting the particle size of a grinding product based on the principle of grinding dynamics, comprising: (1) conducting grinding tests on fine-grained materials at different times, and performing particle size analysis on the grinding products; (2) by comparing the yields of materials of different particle sizes before and after grinding, the lnln(Q0 / Q) values ​​at different grinding times can be obtained respectively, and the grinding dynamics equations of different particle sizes can be obtained by linear fitting using the least squares algorithm, and then the slope m and intercept lnk of the equation can be obtained; (3) different particle sizes and the corresponding m and k values ​​are respectively brought into a linear coordinate system; then the best fitting function is obtained by nonlinear curve fitting, and the fitting function is substituted into the n-th order grinding dynamics equation to obtain the optimized grinding dynamics equation; (4) the grinding time t value is substituted to obtain the particle size distribution of the grinding products at different grinding times, and the calculated values ​​at different grinding times are compared with the actual values ​​of the grinding test.

[0005] However, the above method lacks multi-scale considerations, fails to combine the crushing behavior of microscopic particles with the operation of macroscopic equipment, ignores the energy-mass coupling between particles and equipment, and cannot accurately reflect the complex grinding process. Therefore, providing a method that can predict the particle size change of the grinding process by multi-scale energy-mass coupling is a technical problem that needs to be solved in the current field. Summary of the invention

[0006] In view of the above problems, the purpose of the present invention is to provide a method for predicting particle size changes in the grinding process by multi-scale energy-mass coupling. Compared with the prior art, the present invention simulates models of single particles, groups of particles and equipment levels, combines the crushing behavior of microscopic particles with the operation of macroscopic equipment, and can effectively analyze the energy conversion between particles and equipment, thereby achieving accurate prediction of the particle size of grinding particles.

[0007] In order to achieve the purpose of the invention, the present invention adopts the following technical solutions:

[0008] The present invention provides a method for predicting particle size change in a grinding process by multi-scale energy-mass coupling, the method comprising the following steps:

[0009] S1, establish the initialization model of single particle, group particle and device level;

[0010] S2, based on the single particle initialization model, the single particle model is constructed by using the peridynamic method and simulation is performed to determine the particle breakage threshold and breakage distribution function;

[0011] S3, based on the group particle initialization model, the discrete particle method is used to construct the group particle model and perform simulation to analyze the crushing behavior of the group particles;

[0012] S4, based on the equipment-level initialization model, the group balance method is used to construct the equipment-level particle size distribution model to obtain the change characteristics of particle size in the grinding equipment over time;

[0013] S5, the single particle model, the group particle model and the equipment-level particle size distribution model are coupled to simulate the particle size change of ore particles during the grinding process.

[0014] In the method for predicting particle size changes during grinding by multi-scale energy-mass coupling provided by the present invention, the grinding equipment is simplified, the particle physical properties and motion boundary conditions are set, a multi-scale simulation model of single particles, group particles and equipment levels is established, and the near-field dynamics method and discrete particle method are used to analyze and predict the particle crushing behavior, ultimately achieving good prediction accuracy.

[0015] Preferably, the establishment of the initialization model of the single particle and the group of particles in step S1 includes determining the physical property parameters of the ore particles and establishing the initial state of the particles.

[0016] Preferably, the physical property parameters of the ore particles include any one of particle size, density, Young's modulus, elastic modulus or Poisson's ratio, or a combination of at least two thereof.

[0017] In the present invention, it is also necessary to determine the physical properties of the grinding medium, which may be, for example, grinding balls. The physical properties include any one or a combination of at least two of particle size, density, Young's modulus, elastic modulus or Poisson's ratio, which are mainly used for modeling of group particles.

[0018] Preferably, the establishment of the initialization model of the equipment level in step S1 includes: simplifying and modeling the geometric structure of the grinding equipment, extracting characteristic data of the equipment, and determining the motion boundary conditions and equipment operation parameters of the grinding equipment.

[0019] In the present invention, the geometric structure of the grinding equipment is simplified and modeled, for example, including the simplification and modeling of key structures such as the mill cylinder, liner, and lifting bar.

[0020] Preferably, the characteristic data includes any one of the diameter of the cylinder, the length of the cylinder, the upper base of the lifting bar, the lower base of the lifting bar, the height of the lifting bar or the number of the lifting bars, or a combination of at least two of them.

[0021] In the present invention, the lifting bar is in the shape of a trapezoid, including an upper base, a lower base and a height. Lifting bars of other shapes in the art may also be selected as required.

[0022] Preferably, the motion boundary condition includes any one of the rotation axis direction, rotation direction or rotation speed of the grinding equipment, or a combination of at least two of them.

[0023] Preferably, the equipment operating parameters include any one of the ratio of balls to materials, mill speed, operating time, gravitational acceleration or time step, or a combination of at least two of them.

[0024] Preferably, the construction of the single particle model in step S2 includes: regarding the single particle as a number of discrete material points, each material point is connected by a bond, within the limited area of ​​the bond action, represented by the radius δ, for each material point x, following the peri-field dynamics model, importing the peri-field dynamics model into the single particle initialization model to obtain the single particle model.

[0025] Preferably, the peridynamic model is:

[0026]

[0027] Where ρ represents the mass density of the ore particles; u represents the material point displacement; represents the material point acceleration; T [x,t] <x ′ -x> represents the force vector state; b(x,t) represents the external load body force density; H x =H(x,δ)=(|x ′ -x|≤δ|x ′∈Ω), represents the near field of the material point x in the spatial domain Ω, and δ represents the neighborhood radius.

[0028] Preferably, the relationship between acceleration and stress-strain when a single particle is subjected to force is analyzed according to the single particle model, the stress field and strain field inside the particle are solved, and the particle breakage threshold is determined.

[0029] In the present invention, step S2 regards a single particle as a number of discrete material points according to a peridynamic method, each material point is connected by a bond, thereby establishing a micromechanical structure of a single particle, and adopts a peridynamic method to analyze the external force of the single particle, calculate the interaction force between the interior and the boundary of the single particle, thereby determining its crushing threshold. A peridynamic model is adopted and simulated by a simulation tool, and according to Newton's second law, the acceleration and stress-strain relationship when the single particle is stressed are analyzed, and the stress field and strain field inside the particle are solved. When the crushing threshold is reached, the starting position of the single particle crushing and the extension path of the crushing crack are determined.

[0030] In the present invention, the continuous and discontinuous displacement change processes are unified in the mathematical framework in the near-field dynamics model of step S2. During the simulation, the generation and diffusion of particle cracks are both natural, and only the breaking of bonds between material points needs to be focused on. When the elongation is greater than a certain critical value, the breaking of the bonds cannot be restored and cracks begin to occur.

[0031] Preferably, the construction of the group particle model in step S3 includes: simulating the particle crushing process by replacing large particles with small particle clusters, determining whether particles collide, calculating the collision energy and determining whether crushing occurs, thereby obtaining the crushing behavior of the group particles, and further obtaining the position, velocity and particle size information of the particles.

[0032] Preferably, the calculation formula of the collision energy is:

[0033]

[0034] In the formula, represents the tangential contact force; F s represents the normal contact force; ξ n represents the tangential overlap; ξ s represents the normal overlap; E represents the collision energy.

[0035] In the present invention, step S3 adopts a discrete particle method to simulate the movement and interaction of group particles, adopts a spring-damping simulation to calculate the crushing process of particles in the grinding equipment, and adopts a method of replacing large particles with small particle clusters to complete the simulation of the large particle crushing process. Ore particles change from large particles to small particles and then to powder during the grinding process. Whether crushing occurs during the process is determined based on the calculation of the collision energy, thereby analyzing the crushing behavior of the group particles and describing the collision, friction and crushing process between the group particles.

[0036] Preferably, the construction of the equipment-level particle size distribution model in step S4 includes: simulating the macroscopic movement of ore particles in the grinding equipment, judging whether the particles are broken according to the collision energy and minimum crushing energy received by the particles, describing it using the crushing rate function and crushing distribution function in the group balance equation, and solving the group balance equation to obtain the change characteristics of the particle size in the grinding equipment over time.

[0037] Preferably, the macroscopic motion includes motion between particles, motion between particles and device walls, and rotation of the device.

[0038] Preferably, the movement of the ore particles in the grinding equipment includes any one of throwing, leaking or grinding, or a combination of at least two of them.

[0039] Preferably, the minimum crushing energy is obtained by simulating single particle crushing using a laboratory falling ball experiment.

[0040] In the present invention, the fragmentation rate function in the group balance equation in step S4 is expressed as:

[0041]

[0042] The fragmentation distribution function is expressed as:

[0043]

[0044] In the formula, λ k is the number of collisions per second when the energy is k; m j,k is the mass of the particle broken into the jth particle size when the energy is k; m i,k is the mass of the particle with the i-th particle size broken when the energy is k; b ij,k is the crushing function when the energy is k; H is the total mass of the material; the data obtained from the experiment is substituted into the equation, and the best fitting value is obtained by the least squares method to obtain the parameter α.

[0045] In the present invention, the collision energy received by the particles is compared with the minimum breakage energy. When the collision energy between the particles exceeds the minimum breakage energy, the particles break. The greater the collision energy and the collision frequency, the greater the particle breakage rate. By solving the group equilibrium equation, the change of particle size distribution over time is obtained.

[0046] Preferably, the coupling method in step S5 includes:

[0047] S501, using a peridynamic method in a single particle model to determine the particle breakage threshold and the breakage distribution function; while using a discrete particle method in a group particle model to calculate the collision energy, count the effective collisions that exceed the breakage threshold and calculate the energy input of each particle size;

[0048] S502, obtaining a selection function according to the energy input and crushing distribution function of each particle size;

[0049] S503, substituting the selection function and the crushing distribution function into the particle size distribution model of the equipment level, and predicting the change of the particle size of the ore particles over time during the grinding process.

[0050] Preferably, the particle size distribution model of the equipment level is expressed as:

[0051]

[0052] In the formula, m i represents the mass fraction of the i-th particle size, S i is the selection function, which indicates the probability of particle breaking at the i-th particle size, b ij is the breakup distribution function, which indicates the mass fraction of particles with a particle size of j that are broken into particles with a particle size of i, and t indicates time.

[0053] In the present invention, in step S5, the models of single particles, group particles and equipment levels are coupled to ensure the consistency of energy-mass transmission between different scales, and the overall crushing behavior of group particles is predicted by the crushing process of single particles. The particle size change of the product over time is then predicted by the group balance method, and finally the particle size change in the simulation grinding process is realized.

[0054] Preferably, the method further includes step S6, comparing the particle size change of the ore particles during the grinding process simulated in step S5 with the actual production data, calculating the relative deviation, and verifying the reliability of the multi-scale energy-mass coupling method for predicting the particle size change during the grinding process.

[0055] In the present invention, step S6 simulates the particle size change of the particles during the grinding process by using a computer to compare the simulation result data with the actual production data, and can also use visualization software to virtually display the equipment structure and the predicted simulation results. If the simulation result is significantly different from the actual production data, the reason for the difference is analyzed to check whether the model assumptions and parameter settings are reasonable.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The method for predicting particle size changes in the grinding process by multi-scale energy-mass coupling provided by the present invention combines single particles, group particles and equipment levels, can combine the crushing behavior of microscopic particles with the operation of macroscopic equipment, analyze the energy conversion between particles and equipment, and realize the prediction function of the particle size distribution of the grinding product. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A flow chart of a method for predicting particle size changes during grinding by multi-scale energy-mass coupling provided in Example 1 of the present invention;

[0059] Figure 2 A virtual demonstration image of the device-level particle size distribution model provided in step S4 in embodiment 1 of the present invention;

[0060] Figure 3 This is a comparison chart between the data obtained by simulation in step S5 in Example 1 of the present invention and the actual production data. DETAILED DESCRIPTION

[0061] The technical solution of the present invention is further described below by specific implementation methods. It should be understood by those skilled in the art that the embodiments are only to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0062] Example 1

[0063] This embodiment provides a method for predicting particle size changes during grinding by multi-scale energy-mass coupling, and its flow chart is as follows: Figure 1 As shown, the method comprises the following steps:

[0064] S1, establish the initialization model of single particle, group particle and device level.

[0065] In step S1, the establishment of the initialization model of single particles and group particles includes determining the physical parameters of the ore particles and establishing the initial state of the particles, wherein the physical parameters of the ore particles include any one of particle size, density, Young's modulus, elastic coefficient or Poisson's ratio or a combination of at least two thereof, and the initial state of the particles includes the initial distribution of the particles and the generation range of the particles; the establishment of the initialization model of the equipment level includes: simplifying and modeling the geometric structure of the grinding equipment, extracting the characteristic data of the equipment, and determining the motion boundary conditions and equipment operation parameters of the grinding equipment, wherein the characteristic data includes any one of the diameter of the cylinder, the length of the cylinder, the upper bottom of the lifting bar, the lower bottom of the lifting bar, the height of the lifting bar or the number of the lifting bars or a combination of at least two thereof, the motion boundary conditions include any one of the rotation axis direction, the rotation direction or the rotation speed of the grinding equipment or a combination of at least two thereof, and the equipment operation parameters include any one of the ball-to-material ratio, the mill speed, the operation time, the acceleration of gravity or the time step or a combination of at least two thereof;

[0066] Specifically, the simplified grinding equipment in this embodiment includes a cylinder and a lifting bar, wherein the diameter of the cylinder is D ball =0.3m, the length of the cylinder L ball =0.3m, the lifting bar is a trapezoid, the upper base of the lifting bar a = 0.01m, the lower base of the lifting bar b = 0.01m, the height of the lifting bar h = 0.01m, and the number of lifting bars n = 4;

[0067] Specifically, the physical property parameters of the ore particles include the particle size of the feed particles. In this embodiment, the mass proportion of the -3.35+2.80mm particle size is 15.14%, the mass proportion of the -2.80+2.36mm particle size is 5.53%, the mass proportion of the -2.36+2.00mm particle size is 15.06%, the mass proportion of the -2.00+1.70mm particle size is 9.37%, the mass proportion of the -1.70+1.18mm particle size is 12.03%, the mass proportion of the -1.18+0.85mm particle size is 6.03%, and the mass proportion of the -0.85+0.60mm particle size is 6. 92%, -0.60+0.425mm particle size accounts for 5.49%, -0.425+0.300mm particle size accounts for 4.12%, -0.300+0.212mm particle size accounts for 4.06%, -0.212+0.150mm particle size accounts for 3.78%, -0.150+0.106mm particle size accounts for 3.08%, -0.106+0.075mm particle size accounts for 2.96%, -0.075mm particle size accounts for 6.42%, and the total particle mass is 1395.6g; density ρ p =2900kg / m 3 , Young's modulus E = 1 × 107 Pa, elastic modulus k = 1000 N·m, Poisson's ratio ν = 0.33;

[0068] Specifically, the motion boundary conditions include the rotation axis direction of the grinding equipment, the rotation direction and the mill speed. The mill speed ω ball =54rpm.

[0069] S2, based on the single particle initialization model, the peri-field dynamics method is used to construct a single particle model and perform simulation to determine the particle breakage threshold and breakage distribution function.

[0070] In step S2, the construction of the single particle model includes: regarding the single particle as a number of discrete material points, each material point is connected by a bond, and within the limited area where the bond acts, it is represented by a radius δ. For each material point x, following the peridynamic model, the peridynamic model is imported into the single particle initialization model to obtain the single particle model; according to the single particle model, the relationship between acceleration and stress-strain when the single particle is subjected to force is analyzed, the stress field and strain field inside the particle are solved, and the particle breakage threshold is determined;

[0071] The peridynamic model is:

[0072]

[0073] Where ρ represents the mass density of the ore particles; u represents the material point displacement; represents the material point acceleration; T [x,t] <x ′ -x> represents the force vector state; b(x,t) represents the external load body force density; H x =H(x,δ)=(|x ′ -x|≤δ|x ′ ∈Ω), represents the near field of the material point x in the spatial domain Ω, and δ represents the neighborhood radius.

[0074] S3, based on the group particle initialization model, the discrete particle method is used to construct the group particle model and perform simulation to analyze the crushing behavior of the group particles.

[0075] In step S3, the construction of the group particle model includes: simulating the particle breakage process by replacing large particles with small particle clusters, determining whether particles collide, calculating the collision energy and determining whether breakage occurs, thereby obtaining the breakage behavior of the group particles, and then obtaining the position, velocity and particle size information of the particles;

[0076] The calculation formula of the collision energy is:

[0077]

[0078] In the formula, represents the tangential contact force; F s represents the normal contact force; ξ n represents the tangential overlap; ξ s represents the normal overlap; E represents the collision energy;

[0079] In this embodiment, the discrete particle method is used to simulate the movement and interaction of a group of particles, specifically including: during the grinding process, the ore is broken from large particles into small particles and then into powder particles, simulation technology is used to check whether collisions occur between particles during the grinding process, and whether particles are broken is determined by calculating the collision energy, and the simulation technology is combined to achieve real-time updating of the position, speed and particle size information of the particles during the grinding process.

[0080] S4, based on the equipment-level initialization model, the group balance method is used to construct the equipment-level particle size distribution model to obtain the time-varying characteristics of particle size in the grinding equipment.

[0081] In step S4, the construction of the equipment-level particle size distribution model includes: simulating the macroscopic movement of ore particles in the grinding equipment, judging whether the particles are broken according to the collision energy and minimum crushing energy of the particles, describing it using the crushing rate function and crushing distribution function in the group balance equation, and solving the group balance equation to obtain the change characteristics of the particle size in the grinding equipment over time; wherein the macroscopic movement includes the movement between particles, the movement between particles and the wall of the equipment, and the rotation of the equipment, and the movement form of the ore particles in the grinding equipment includes any one of throwing, leaking or grinding, or a combination of at least two of them;

[0082] In this embodiment, the group balance equation includes a crushing rate function and a crushing distribution function, and introduces a minimum crushing energy. The collision energy received by the particles is compared with the minimum crushing energy. When the collision energy between the particles exceeds the minimum crushing energy, the particles are crushed, and the greater the collision energy and the collision frequency, the greater the crushing rate of the particles. By solving the group balance equation, the change of the particle size distribution over time is obtained. The group balance method is used to construct a particle size distribution model at the equipment level for simulation. The impact energy generated by the interaction between the particles, steel balls and the wall during the grinding process causes the particles to be crushed. With the help of post-processing visualization software, the equipment structure and the model calculation results are virtually demonstrated on the interface, such as Figure 2 shown.

[0083] S5, the single particle model, the group particle model and the equipment-level particle size distribution model are coupled to simulate the particle size change of ore particles during the grinding process.

[0084] In step S5, the coupling method includes:

[0085] S501, using a peridynamic method in a single particle model to determine the particle breakage threshold and the breakage distribution function; while using a discrete particle method in a group particle model to calculate the collision energy, count the effective collisions that exceed the breakage threshold and calculate the energy input of each particle size;

[0086] S502, obtaining a selection function according to the energy input and crushing distribution function of each particle size;

[0087] S503, substituting the selection function and the crushing distribution function into the particle size distribution model at the equipment level, and predicting the change of the particle size of the ore particles over time during the grinding process, wherein the particle size distribution model at the equipment level is expressed as:

[0088]

[0089] In the formula, m i represents the mass fraction of the i-th particle size, S i is the selection function, which indicates the probability of particle breaking at the i-th particle size, b ij is the breakup distribution function, which indicates the mass fraction of particles with a particle size of j that are broken into particles with a particle size of i, and t indicates time;

[0090] In this embodiment, step S5 couples the models of single particles, group particles and equipment levels to ensure the consistency of energy-mass transmission between different scales, predicts the overall crushing behavior of the group particles by the crushing process of a single particle, and then predicts the particle size change of the product over time by the group balance method, ultimately achieving the particle size change in the simulated grinding process.

[0091] S6, comparing the particle size change of the ore particles in the grinding process obtained by the simulation in step S5 with the actual production data, and calculating the relative deviation. The results show that the error is less than 5%, and the comparison between the predicted value obtained by the simulation and the actual value obtained by the actual production is Figure 3 As shown, the reliability of the multi-scale energy-mass coupling method for predicting particle size changes during grinding provided by the present invention is verified.

[0092] In step S6, the particle size change of the particles during the grinding process is simulated by using a computer, and the simulation result data is compared with the actual production data. The equipment structure and the predicted simulation results can also be virtually displayed with the help of visualization software. If the simulation results are significantly different from the actual production data, the reasons for the difference are analyzed to verify whether the model assumptions and parameter settings are reasonable.

[0093] In summary, the method for predicting particle size changes in the grinding process by multi-scale energy-mass coupling provided by the present invention combines single particles, group particles and equipment levels, can combine the crushing behavior of microscopic particles with the operation of macroscopic equipment, analyze the energy conversion between particles and equipment, and realize the prediction function of the particle size distribution of grinding products.

[0094] The applicant declares that the above is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention shall fall within the protection scope and disclosure scope of the present invention.

Claims

1. A method for predicting particle size changes during grinding by multi-scale energy-mass coupling, characterized in that: The method comprises the following steps: S1, establish initialization models of single particles, group particles and device levels; S2, based on the single particle initialization model, the single particle model is constructed by using the peridynamic method and simulation is performed to determine the particle breakage threshold and breakage distribution function; S3, based on the group particle initialization model, the discrete particle method is used to construct the group particle model and perform simulation to analyze the crushing behavior of the group particles; S4, based on the equipment-level initialization model, the group balance method is used to construct the equipment-level particle size distribution model to obtain the change characteristics of particle size in the grinding equipment over time; S5, the single particle model, the group particle model and the equipment-level particle size distribution model are coupled to simulate the particle size change of ore particles during the grinding process.

2. The method according to claim 1, characterized in that: The establishment of the initialization model of single particles and group particles in step S1 includes determining the physical property parameters of the ore particles and establishing the initial state of the particles; Preferably, the physical property parameters of the ore particles include any one of particle size, density, Young's modulus, elastic modulus or Poisson's ratio, or a combination of at least two thereof.

3. The method according to claim 1 or 2, characterized in that: The establishment of the initialization model of the equipment level in step S1 includes: simplifying and modeling the geometric structure of the grinding equipment, extracting the characteristic data of the equipment, and determining the motion boundary conditions and equipment operation parameters of the grinding equipment; Preferably, the characteristic data includes any one of the diameter of the cylinder, the length of the cylinder, the upper base of the lifting bar, the lower base of the lifting bar, the height of the lifting bar or the number of the lifting bars, or a combination of at least two of them.

4. The method according to claim 3, characterized in that The motion boundary condition includes any one of the rotation axis direction, rotation direction or rotation speed of the grinding equipment, or a combination of at least two of them.

5. The method according to claim 3, characterized in that: The equipment operation parameters include any one of the ratio of balls to materials, mill speed, operation time, gravitational acceleration or time step, or a combination of at least two of them.

6. The method according to any one of claims 1 to 5, characterized in that: The construction of the single particle model in step S2 includes: treating the single particle as a number of discrete material points, each of which is connected by a bond, and within a limited area where the bond acts, represented by a radius δ, for each material point x, following a peridynamic model, importing the peridynamic model into a single particle initialization model to obtain a single particle model; Preferably, the peridynamic model is: Where ρ represents the mass density of the ore particles; u represents the material point displacement; represents the material point acceleration; T [x,t] <x ′ -x> represents the force vector state; b(x,t) represents the external load body force density; H x =H(x,δ)=(|x ′ -x|≤δ|x ′ ∈Ω), represents the near field of the material point x in the space domain Ω, and δ represents the neighborhood radius; Preferably, the relationship between acceleration and stress-strain when a single particle is subjected to force is analyzed according to the single particle model, the stress field and strain field inside the particle are solved, and the particle breakage threshold is determined.

7. The method according to any one of claims 1 to 6, characterized in that: The construction of the group particle model in step S3 includes: simulating the particle breakage process by replacing large particles with small particle clusters, determining whether particles collide, calculating the collision energy and determining whether breakage occurs, thereby obtaining the breakage behavior of the group particles, and then obtaining the position, velocity and particle size information of the particles; Preferably, the calculation formula of the collision energy is: In the formula, represents the tangential contact force; F s represents the normal contact force; ξ n represents the tangential overlap; ξ s represents the normal overlap; E represents the collision energy.

8. The method according to any one of claims 1 to 7, characterized in that: The construction of the equipment-level particle size distribution model in step S4 includes: simulating the macroscopic movement of ore particles in the grinding equipment, judging whether the particles are broken according to the collision energy and minimum crushing energy of the particles, describing it using the crushing rate function and crushing distribution function in the group balance equation, and solving the group balance equation to obtain the change characteristics of the particle size in the grinding equipment over time; Preferably, the macroscopic motion includes the motion between particles, the motion between particles and the wall of the device, and the rotation of the device; Preferably, the movement of the ore particles in the grinding equipment includes any one of throwing, leaking or grinding, or a combination of at least two of them; Preferably, the minimum crushing energy is obtained by simulating single particle crushing using a laboratory falling ball experiment.

9. The method according to any one of claims 1 to 8, characterized in that: The coupling method in step S5 includes: S501, using a peridynamic method in a single particle model to determine the particle breakage threshold and the breakage distribution function; while using a discrete particle method in a group particle model to calculate the collision energy, count the effective collisions that exceed the breakage threshold and calculate the energy input of each particle size; S502, obtaining a selection function according to the energy input and crushing distribution function of each particle size; S503, substituting the selection function and the crushing distribution function into the particle size distribution model at the equipment level to predict the change of the particle size of the ore particles over time during the grinding process; Preferably, the particle size distribution model of the equipment level is expressed as: In the formula, m i represents the mass fraction of the i-th particle size, S i is the selection function, which indicates the probability of particle breaking at the i-th particle size, b ij is the breakup distribution function, which indicates the mass fraction of particles with a particle size of j that are broken into particles with a particle size of i, and t indicates time.

10. The method according to any one of claims 1 to 9, characterized in that: The method also includes step S6, comparing the particle size change of the ore particles in the grinding process obtained by simulation in step S5 with the actual production data, calculating the relative deviation, and verifying the reliability of the method for predicting the particle size change in the grinding process by multi-scale energy-mass coupling.

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