DEM simulation-based semi-autogenous mill crushing capacity evaluation method

Abstract

The invention discloses a semi-autogenous mill crushing capacity evaluation method based on DEM simulation, and relates to the technical field of mineral processing, and the method comprises the following steps: measuring discrete element calculation required parameters of ores, steel balls and lining plate materials, and establishing a semi-autogenous mill geometric model; importing a geometric model of the semi-autogenous mill, and initializing discrete element calculation required parameters and particle processing parameters; a DEM simulation experiment is carried out, when simulation time reaches integral multiples of the step length, all particle information in a simulation system is traversed, particles meeting the judgment condition are removed, the mass of removed ore particles is obtained, new particles meeting the set condition are added, abnormal particles are processed, and the ore particles are obtained. The residual mass of the ore particles with the particle size set by the system within the simulation time and the mass of the removed ore particles at each moment are obtained, the removal rate of the ore particles at each moment in the simulation process is calculated, the crushing capacity evaluation result of the semi-autogenous mill is obtained, the evaluation calculation time is short, the accuracy is high, and the problem of small particle deposition is effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mineral processing, and particularly relates to a semi-autogenous mill crushing capacity evaluation method based on DEM simulation. BACKGROUND

[0002] The semi-autogenous mill is a core grinding equipment of large mines and mineral processing plants, which uses steel balls as grinding medium to perform ore crushing work, and has high efficiency and simplified process. Accurate evaluation of the crushing capacity of the internal structure of the semi-autogenous mill is crucial for optimizing equipment design, improving grinding medium processing capacity, reducing energy consumption and prolonging equipment life.

[0003] In the past, the discrete element (DEM) simulation was mainly used to evaluate the crushing capacity of the semi-autogenous mill by using the key fracture number based on the Bonding model and the particle crushing ratio based on the Trvares crushing model and the particle size distribution comparison method, but they all have certain defects: when the Bonding model is used for simulation, the calculation efficiency is low and the particle size distribution is difficult to simulate; when the particle crushing ratio based on the Trvares crushing model is used for evaluation, a large number of small-size particles are continuously generated, and these small particles are easy to deposit at the bottom of the cylinder to form a "cushion layer", which hinders the movement and collision, resulting in that the effective crushing energy calculated by DEM is seriously underestimated, and the authenticity of the evaluation of the liner structure of the semi-autogenous mill is affected. SUMMARY

[0004] In order to overcome the defects of low calculation efficiency and inaccurate evaluation results in the prior art when simulating the semi-autogenous mill, the present application provides a semi-autogenous mill crushing capacity evaluation method based on DEM simulation.

[0005] To achieve the above purpose, the present application adopts the following technical scheme, a semi-autogenous mill crushing capacity evaluation method based on DEM simulation, characterized in that it comprises:

[0006] S1: measuring the discrete element calculation required parameters of the ore, steel ball and liner material, and establishing a semi-autogenous mill geometric model; the semi-autogenous mill geometric model comprises a semi-autogenous mill liner back-to-back installation geometric model or a semi-autogenous mill liner sequential installation geometric model; the steel ball is a grinding medium;

[0007] S2: importing the semi-autogenous mill geometric model, initializing the discrete element calculation required parameters and particle processing parameters; the particle processing parameters comprise particle filtering characteristic parameters, particle removal and generation space region parameters and global variable parameters;

[0008] S3: performing DEM simulation experiment, when the simulation time reaches an integer multiple of the step size, traversing all the particle information in the simulation system, removing the particles meeting the judgment conditions, obtaining the removed ore particle mass, adding new particles meeting the set conditions, and processing abnormal particles;

[0009] S4: Obtain the remaining mass of uncrushed ore particles in the system during the simulation time and the mass of ore particles removed at each time point. Calculate the ore particle removal rate at each time point during the simulation to obtain the evaluation results of the semi-autogenous mill's crushing capacity.

[0010] Preferably, in step S1, the parameters required for discrete element calculation include density, shear modulus, Poisson's ratio, coefficient of restitution, static friction coefficient, rolling friction coefficient, and rotational speed; the measurement methods include drainage test, inclined plate test, uniaxial compression test, particle size sieving test, and cylindrical injection test.

[0011] Preferably, in step S2, the particle filtration characteristic parameters include the step size T for performing the particle filtration operation. step The expected total mass M of ore within the system target Type labeling of ore particles P type The mass threshold M of each ore particle to be removed remove And the mass M of newly generated ore particles new The parameters for particle removal and generation include the maximum angle A of the particle removal spatial region in the cylindrical coordinate system established by the rotation center of the semi-autogenous mill. ngleMax and minimum angle A ngleMin The maximum radius R of the particle removal space region in the cylindrical coordinate system established by the rotation center of the semi-autogenous mill. Max and minimum radius R Min The vertex coordinates (x, y) of the particle generation region in the global Cartesian coordinate system GenerateMin ,y GenerateMin ,z GenerateMin ) and the length, width, and height (w) of the particle generation region in the global Cartesian coordinate system eightGenerate ,h eightGenerate ,l engthGenerate The global variable parameter includes the sum of the masses of the removed ore particles, M. moved And the total mass M of ore particles in the system at the current time current .

[0012] Preferably, in step S3, the process of removing particles that meet the determination criteria includes:

[0013] SA1: Traverse the particle information at the current time step, obtain the total number of particles, and obtain the type, mass and position of each particle;

[0014] SA2: Sequentially determine whether each particle is an ore, whether its mass is within the expected ore particle mass threshold, and whether its location is within the particle removal area; if yes, remove the current ore particle and record the mass of the removed ore particle; if no, record the total mass of particles in the current system.

[0015] Preferably, in step S3, the process of adding new particles that meet the set conditions includes:

[0016] SB1: Initialize the random number seed; the random number seed is used to set the addition position in the particle generation area;

[0017] SB2: Determine if the total ore mass in the system is less than the expected total ore mass; if yes, add ore particles one by one at the set addition position and update the total ore mass in the system; if no, stop adding ore particles and exit the ore particle addition program.

[0018] Preferably, in step S3, the process of processing abnormal particles includes:

[0019] SC1: Traverse the particle collision information at the current time step to obtain the total number of collisions, collision locations, particle types involved in the collisions, and overlap information;

[0020] SC2: Sequentially determine whether the type of each collision is ore particle, whether there is overlap, and whether the collision location is within the particle generation area; if yes, remove the contacting particles of the current collision; if no, the abnormal particle handling procedure ends.

[0021] Preferably, in step S4, the removal rate of ore particles at time k is... The calculation method is as follows:

[0022]

[0023] Where k is the system simulation time; The difference time interval; This represents the sum of the masses of ore removed during system simulation time k.

[0024] Preferably, in step S4, the evaluation result of the semi-autogenous mill's crushing capacity is obtained based on the remaining mass of the uncrushed ore particles in the system and the removal rate of the ore particles. When the change in the remaining mass of the uncrushed ore particles in the system tends to be stable, it indicates that the system's crushing process tends to be stable. When the system's crushing process tends to be stable, the greater the removal rate of the ore particles, the stronger the crushing capacity of the semi-autogenous mill.

[0025] A semi-autogenous mill crushing capacity evaluation system based on DEM simulation includes a memory and a processor. The memory stores a computer program, and the processor is connected to the memory. The processor executes the computer program to implement a semi-autogenous mill crushing capacity evaluation method based on DEM simulation.

[0026] A computer program product comprising a computer program / instructions that, when executed by a processor, implement a method for evaluating the crushing capacity of a semi-autogenous mill based on DEM simulation.

[0027] The present application has the advantages of:

[0028] (1) The present application measures the required parameters of the ore, steel ball and lining plate material through discrete element calculation, establishes a semi-autogenous mill geometric model, imports the semi-autogenous mill geometric model, initializes the required parameters and particle processing parameters of the discrete element calculation, performs DEM simulation experiment, when the simulation time reaches an integer multiple of the step size, traverses all the particle information in the simulation system, removes the particles meeting the determination condition, obtains the removed ore particle mass, adds new particles meeting the set condition, processes abnormal particles, obtains the remaining mass of the ore particles of the system set particle size and the mass of the removed ore particles at each time during the simulation time, calculates the removal rate of the ore particles at each time during the simulation process, and obtains the semi-autogenous mill crushing capacity evaluation result. The evaluation calculation time is short, the accuracy is high, the problem of small particle deposition is effectively solved, and more reliable simulation basis is provided for accurately evaluating the mill crushing capacity and optimizing the mill design and process parameters.

[0029] (2) The present application significantly reduces the total number of particles participating in the calculation by removing small ore particles, especially those small particles that are stationary and low-speed and have little contribution to the crushing process, thereby shortening the simulation time.

[0030] (3) The present application eliminates the interference of the non-physical deposition layer on the particle flow, energy distribution and impact crushing event by removing the smaller ore particles after crushing, so that the steel ball motion trajectory, impact energy distribution, power consumption, lining plate stress and product particle size prediction and other key results are closer to the physical reality, and the reliability of the evaluation of the actual crushing capacity and working performance of the mill is greatly improved.

[0031] (4) The present application is implemented through API, and users can flexibly adjust the filtering criteria (size threshold, speed threshold, residence time, etc.) and filtering area according to the specific mill model, ore characteristics and running conditions, and the method has high customizability. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The present application is a method flow chart;

[0033] Figure 2 The present application is a DEM simulation flow chart;

[0034] Figure 3 The present application is a particle removal flow chart;

[0035] Figure 4 The present application is a particle addition flow chart;

[0036] Figure 5 The present application is an abnormal particle processing flow chart;

[0037] Figure 6Figure for back-to-back installation geometry model of liner plate;

[0038] Figure 7 Figure for sequential installation geometry model of liner plate;

[0039] Figure 8 Figure for particle removal area model;

[0040] Figure 9 Figure for particle generation area model;

[0041] Figure 10 Figure for residual mass curve of 150mm particle in system;

[0042] Figure 11 Figure for particle removal rate change curve of back-to-back installation of liner plate;

[0043] Figure 12 Figure for particle removal rate change curve of sequential installation of liner plate;

[0044] Figure 13 Figure for comparison of semi-autogenous mill liner plate installation modes. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0046] As shown in Figures 1-13 , the present application proposes a semi-autogenous mill crushing capacity evaluation method based on DEM simulation, which comprises:

[0047] S1: Measure the parameters required for discrete element calculation of ores, steel balls and liner plate materials, and establish a semi-autogenous mill geometry model to lay the foundation for subsequent DEM simulation. The steel ball is a crushing medium. The parameters required for discrete element calculation include density, shear modulus, Poisson's ratio, recovery coefficient, static friction coefficient, rolling friction coefficient and rotational speed. The measurement methods include drainage test, inclined plate test, uniaxial compression test, particle size screening experiment and cylinder injection test.

[0048] When carrying out field mapping and geometric modeling on the semi-autogenous mill, the discrete element calculation parameter values are shown in Table 1 below. Since the discrete element calculation parameters of the liner plate material are similar to those of the steel ball, they are not written in the table. The semi-autogenous mill geometry model includes a back-to-back installation geometry model of semi-autogenous mill liner plate or a sequential installation geometry model of semi-autogenous mill liner plate as shown in Figure 6 and Figure 7 .

[0049] Table 1 Discrete element calculation parameter values

[0050]

[0051] S2: Import the semi-autogenous mill geometric model, initialize the parameters required for discrete element calculation, particle processing parameters for DEM simulation. Particle processing parameters include particle filtering characteristic parameters, particle removal and generation spatial region parameters, and global variable parameters.

[0052] Particle filtering characteristic parameters include T step , M target , P type , M remove , and M new ; particle removal and generation spatial region parameters include A ngleMin , A ngleMax , R Min , R Max , (x GenerateMin , y GenerateMin , z GenerateMin ), and (w eightGenerate , h eightGenerate , l engthGenerate ); global variable parameters include M moved and M current .

[0053] The meanings of each parameter are as follows:

[0054] Tstep, double type, is the step length for performing particle filtering operations, and functions to actively execute the particle filtering process when the simulation time is an integer multiple of Tstep, to avoid excessive memory traversal leading to high computational cost.

[0055] Mtarget, double type, is the total mass of ore desired in the system, and functions to continue adding new ore when the mass of ore in the system is found to be lower than Mtarget when the ore addition process is running, and to stop when the mass of ore is greater than or equal to Mtarget.

[0056] Ptype, int type, is the type label of the ore particles, and functions to analyze and judge only ore particles of type Ptype when the particle filtering process is executed; for steel ball particles that are not of type Ptype, the subsequent judgment process is skipped, saving computational resources.

[0057] Mremove, double type, is the mass threshold for each ore particle to be removed, i.e., the maximum removal mass, and functions to determine whether an ore particle needs to be removed based on its position when the mass of the ore particle is less than Mremove; when the mass of the ore particle is greater than Mremove, the subsequent judgment process is skipped, saving computational resources.

[0058] Mnew, double, is the mass of the newly generated ore particle, and is used to determine the size of the newly generated ore particle.

[0059] RMin, RMax, AngleMin and AngleMax, all double, are parameters defining the position and size of the particle removal space region, and a cylindrical coordinate system is established with the center of the rotating SAG mill as the origin. RMin and RMax are the maximum and minimum radii of the particle removal space region, respectively; and AngleMin and AngleMax are the maximum and minimum angles of the particle removal space region, respectively, as shown in FIG. 2. Figure 8

[0060] xGenerateMin, weightGenerate, yGenerateMin, heightGenerate, zGenerateMin and lengthGenerate, all double, are parameters defining the position and size of the particle generation space region. (xGenerateMin, yGenerateMin, weightGenerate) are the coordinates of the top vertex of the particle generation region in the global Cartesian coordinate system, which is a cuboid region, as shown in FIG. 3. Figure 9 Figure 9

[0061] Mmoved, double, is the sum of the masses of the removed ore particles during the execution of the particle filtering method, and is a global variable of the system. The variable is initialized to 0 at the beginning of the simulation, and Mmoved and the simulation time will be recorded after each execution of the particle filtering process.

[0062] Mcurrent, double, is the total mass of the ore particles in the system at the current time during the execution of the particle filtering method, and is a global variable of the system. The variable is used to determine whether new ore particles need to be added to the system. The variable is initialized to 0 at the beginning of the simulation, and is reset to 0 after each execution of the particle filtering process.

[0063] The initial values of the characteristic parameters of the particle filtering and the space region parameters of the particle removal and generation after initialization are shown in Table 2.

[0064] Table 2 Initial values of the characteristic parameters of the particle filtering and the space region parameters of the particle removal and generation

[0065] ​​​

[0066] S3: When the simulation time reaches an integer multiple of the step size, iterate through all particle information in the simulation system, remove particles that meet the determination condition, obtain the mass of the removed ore particles, add new particles that meet the set condition, and process abnormal particles until the simulation system tends to be stable.

[0067] The process of removing particles that meet the determination condition is shown in Figure 3 , and includes:

[0068] SA1: Iterate through the particle information of the current time step, obtain the total number of particles Nump, the type Typei, mass mi, and position pi of each particle. At the same time, initialize the particle number counter i = 0 and the total mass of the particles Mcurrent = 0.

[0069] SA2: Determine whether the type of each particle is ore (Typei == Ptype?), whether the mass is within the threshold of the expected removed ore particle mass (Mi <= Mremove), and whether the position pi is within the particle removal area. Yes, remove the current ore particle and record the removed ore particle mass (Mmoved = Mmoved + mi). No, record the total mass of the particles in the current system (Mcurrent = Mcurrent + mi).

[0070] The process of adding new particles that meet the set condition is shown in Figure 4 , and includes:

[0071] SB1: Initialize a random number seed. The random number seed is used to set the addition position in the particle generation area.

[0072] SB2: Determine whether the total mass of ore in the system Mcurrent is less than the expected total mass of ore Mtarget. Yes, add ore particles one by one at the set position and update the total mass of ore in the system (Mcurrent += the mass of the currently added particle). No, stop adding ore particles and exit the ore particle addition program.

[0073] The process of processing abnormal particles is shown in Figure 5 , and includes:

[0074] SC1: Iterate through the particle collision information of the current time step, obtain the total number of collisions NumC, the collision position Pj, the type of the particles involved in the collision Typej, and the overlap information Olapj. At the same time, initialize the collision number counter j = 0.

[0075] SC2: Determine whether the type Typej of each collision is an ore particle, whether there is an overlap, and whether the collision position Pj is within the particle generation area. Yes, remove the contact particles of the current collision. No, the abnormal particle processing program ends.

[0076] S4: Obtain the remaining mass of the ore particles that are not broken in the system within the simulation time and the mass of the ore particles removed at each time, calculate the removal rate of the ore particles at each time under the two installation modes of the semi-autogenous mill liner, and obtain the evaluation result of the breaking capacity of the semi-autogenous mill.

[0077] The removal rate of the ore particles at each time The calculation method is as follows:

[0078]

[0079] Wherein, k is the simulation time of the system; is the differential time interval, which is 2s; is the sum of the removed ore mass when the simulation time of the system is k.

[0080] In this embodiment, the ore particles with the set particle size are ore particles with a particle size of 150mm, and the system simulation is completed in about half a day. The change graph of the remaining mass of the ore particles with the set particle size is shown in Figure 10 It can be seen from Figure 10 that for the simulation processes of the semi-autogenous mill systems with the two different installation modes of the liner, when the time reaches 70s, the remaining mass of the ore particles with the set particle size of the system is basically stable at about 4500kg, which proves that the breaking work of the semi-autogenous mill basically tends to be stable.

[0081] Figure 11 and Figure 12 are the curves of the removal rate of the ore particles with the two different installation modes of the semi-autogenous mill changing with the simulation time, since the removed small ore particles meet the particle filtering standard and are considered to meet the discharge conditions of the semi-autogenous mill and are discharged from the semi-autogenous mill with the water flow, the removal rate of the ore particles can be considered as the breaking rate of the ore particles in the semi-autogenous mill. It can be seen from Figure 11 and Figure 12 that after the particle breaking in the simulation system reaches stability, the average removal rate of the broken small ore particles in the semi-autogenous mill liner back-to-back installation example is 52.77kg / s; the average removal rate of the broken small ore particles in the semi-autogenous mill liner sequential installation example is 73.94kg / s, and the removal rate of the particles in the semi-autogenous mill liner sequential installation is about 140% of that in the semi-autogenous mill liner back-to-back installation.

[0082] Therefore, when the change trend of the residual mass of the ore particles which are not broken by the system tends to be stable, it is indicated that the system breaking process tends to be stable, and the greater the removal rate of the ore particles is, the stronger the breaking capacity of the semi-autogenous mill is, so the ore breaking capacity when the semi-autogenous mill liner is installed in sequence is superior to the ore breaking capacity when the semi-autogenous mill liner is installed back to back; because the ore breaking capacity when the semi-autogenous mill liner is installed in sequence is about 140% of the ore breaking capacity when the semi-autogenous mill liner is installed back to back.

[0083] However, when the breaking capacity of the semi-autogenous mill is evaluated by using the particle breakage ratio, the particle size distribution of the ore particles in the system is generally used to evaluate the breaking efficiency of the system. In the two groups of corresponding examples, the simulation time needs to reach about one week before the simulation work is completed, and the particle size of the ore particles in each stage of the semi-autogenous mill equipment is shown in Tables 3 and 4. It can be seen from the data in Tables 3 and 4 that, under the same simulation time, the particle size distribution of the ore particles in the system is very similar under different installation modes of the semi-autogenous mill liner.

[0084] It can be seen from the data in Tables 3 and 4 that, when the particle breakage ratio is used for evaluation, the conclusion is that there is no obvious difference between the breaking capacity of the semi-autogenous mill liner installed in sequence and the breaking capacity of the semi-autogenous mill liner installed back to back.

[0085] Table 3 Particle size distribution of the ore particles in the example of the liner installed back to back

[0086]

[0087] Table 4 Particle size distribution of the ore particles in the example of the liner installed in sequence

[0088]

[0089]

[0090] After the back-to-back installed liner of the semi-autogenous mill in a certain mine is adjusted to be installed in sequence, the on-site production data is collected, and it is found that the ore breaking capacity is increased from 517 t / h to 653 t / h. Obviously, according to the results of the industrial experiment, the ore breaking capacity when the semi-autogenous mill liner is installed in sequence is about 126% times of the ore breaking capacity when the semi-autogenous mill liner is installed back to back.

[0091] It can be seen from the comparison between the evaluation results of the present application and the evaluation method using the particle breakage ratio and the actual production breaking data of the semi-autogenous mill that the evaluation results obtained by the present application are more in line with the actual breaking capacity of the semi-autogenous mill, and the simulation time of the present application is much better than the evaluation method using the particle breakage ratio.

[0092] In summary, in the simulation of the crushing process of the semi-autogenous mill by using the DEM, the small particles are dynamically removed by the application program interface (API), the small particles are dynamically removed in the calculation domain according to the set characteristic parameters of the particle filtering and the spatial region of the particle removal and generation; in order to maintain the material balance, after a certain amount of small particles are removed, the corresponding mass of coarse particles is dynamically generated in proportion in the spatial region without affecting the simulation calculation of the semi-autogenous mill, so that the relative stability of the material load in the mill is maintained; finally, the real crushing capacity of the semi-autogenous mill liner structure is evaluated by counting the total mass and removal rate of the removed small particles, the accuracy of the evaluation of the semi-autogenous mill is improved, the simulation time is shortened, and the evaluation efficiency is improved.

[0093] Of course, the present application is not limited to the details of the above-described exemplary embodiments, but also includes the same or similar structures that can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be considered as limiting the claims involved.

[0094] Furthermore, it should be understood that, although the present specification is described in terms of embodiments, not every embodiment contains only one independent technical solution, and the description of the specification is only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that those skilled in the art can understand.

[0095] The technologies, shapes, and structural parts not described in detail in the present application are well-known technologies.

Claims

1. A method for evaluating the crushing capacity of a semi-autogenous mill based on DEM simulation, characterized in that, The method comprises the following steps: S1: measuring parameters required for discrete element calculation of ores, steel balls and lining plate materials, and establishing a semi-autogenous mill geometric model; the semi-autogenous mill geometric model comprises a semi-autogenous mill lining plate back-to-back installation geometric model or a semi-autogenous mill lining plate sequential installation geometric model; the steel balls are grinding media; S2: importing the semi-autogenous mill geometric model, initializing parameters required for discrete element calculation and particle processing parameters; the particle processing parameters comprise particle filtering characteristic parameters, spatial region parameters for particle removal and generation, and global variable parameters; S3: performing a DEM simulation experiment, when a simulation time reaches an integer multiple of a step size, traversing all particle information in a simulation system, removing particles meeting a judgment condition, obtaining a removed ore particle mass, adding new particles meeting a set condition, and processing abnormal particles; S4: obtaining a remaining mass of unbroken ore particles in the system and a mass of removed ore particles at each time in a simulation time, calculating a removal rate of the ore particles at each time in the simulation process, and obtaining a semi-autogenous mill breaking capacity evaluation result.

2. The method for evaluating the breakage capacity of a SAG mill based on DEM simulation according to claim 1, characterized in that, In step S1, the parameters required for discrete element calculation comprise density, shear modulus, Poisson's ratio, recovery coefficient, static friction coefficient, rolling friction coefficient and rotating speed; and the measurement methods comprise a drainage test, an inclined plate test, a uniaxial compression test, a particle size screening experiment and a cylinder injection test.

3. The method for evaluating the breakage capacity of a SAG mill based on DEM simulation according to claim 1, characterized in that, In step S2, the particle filtration characteristic parameters include the step size T for performing the particle filtration operation. step The expected total mass M of ore within the system target Type labeling of ore particles P type The mass threshold M of each ore particle to be removed remove And the mass M of newly generated ore particles new The parameters for particle removal and generation include the maximum angle A of the particle removal spatial region in the cylindrical coordinate system established by the rotation center of the semi-autogenous mill. ngleMax and minimum angle A ngleMin The maximum radius R of the particle removal space region in the cylindrical coordinate system established by the rotation center of the semi-autogenous mill. Max and minimum radius R Min The vertex coordinates (x, y) of the particle generation region in the global Cartesian coordinate system GenerateMin ,y GenerateMin ,z GenerateMin ) and the length, width, and height (w) of the particle generation region in the global Cartesian coordinate system eightGenerate ,h eightGenerate ,l engthGenerate The global variable parameter includes the sum of the masses of the removed ore particles, M. moved And the total mass M of ore particles in the system at the current time current .

4. The method for evaluating the breakage capacity of a SAG mill based on DEM simulation according to claim 3, characterized in that, In step S3, the process of removing particles meeting the judgment condition comprises: SA1: traversing particle information of a current time step, obtaining a total number of particles, and obtaining a type, a mass and a position of each particle; SA2: judging whether the type of each particle is ore, whether the mass is within a threshold of an expected removed ore particle mass and whether the position is within a particle removal region; if yes, removing the current ore particle and recording the removed ore particle mass; if no, recording a total mass of particles in the system.

5. The method for evaluating the breakage capacity of a SAG mill based on DEM simulation according to claim 3, characterized in that, In step S3, the process of adding new particles meeting the set condition comprises: SB1: initializing a random number seed; the random number seed is used to set an adding position in a particle generation region; SB2: judging whether a total mass of ores in the system is less than an expected total mass of ores; if yes, adding ore particles at the set adding position one by one and updating the total mass of ores in the system; if no, stopping adding the ore particles and exiting an ore particle adding program.

6. The method for evaluating the breakage capacity of a SAG mill based on DEM simulation according to claim 3, characterized in that, In step S3, the process of processing abnormal particles comprises: SC1: traversing particle collision information of a current time step, obtaining a total number of collisions, a collision position, a type of particles participating in the collision and overlapping amount information; SC2: judging whether the type of each collision is an ore particle, whether there is overlapping amount and whether the collision position is within a particle generation region; if yes, removing the contact particles in the current collision; if no, ending an abnormal particle processing program.

7. The method for evaluating the breakage capacity of a SAG mill based on DEM simulation according to claim 1, characterized in that, In step S4, the removal rate of the ore particles at the kth time instant is calculated as follows: Wherein, k is the system simulation time;▽t is the differential time interval; is the sum of the removed ore mass at the system simulation time k.

8. The method for evaluating the breakage capacity of a SAG mill based on DEM simulation according to claim 1, characterized in that, In step S4, the evaluation result of the semi-autogenous mill breaking capacity is obtained according to the remaining mass of unbroken ore particles in the system and the removal rate of the ore particles; when the remaining mass of unbroken ore particles in the system changes to be stable, it indicates that the system breaking program tends to be stable; when the system breaking program tends to be stable, the greater the removal rate of the ore particles, the stronger the semi-autogenous mill breaking capacity.

9. A system for assessing the breakage capacity of a semi-autogenous mill based on DEM simulation, characterised in that, The application relates to a computer program product, which comprises a memory and a processor, the memory storing a computer program, and the processor being connected to the memory and used for executing the computer program to realize the semi-mill breaking capacity evaluation method based on DEM simulation as claimed in any one of claims 1 to 8.

10. A computer program product, characterised in that, The application relates to a computer program product, which comprises a computer program / instruction, the computer program / instruction being executed by a processor to realize the semi-mill breaking capacity evaluation method based on DEM simulation as claimed in any one of claims 1 to 8.

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