Material particle size optimization method, system, device and storage medium

Abstract

The present invention relates to a material particle size optimization method, system, device and storage medium, the method comprising: establishing a discrete element simulation model of the material, calculating the compaction density of the material under different pressures, and obtaining the model parameters of the discrete element simulation model; using a particle swarm optimization method to correct the model parameters of the discrete element simulation model based on material measurement data; using a Bezier curve to represent the particle size distribution in parameter form; using a particle swarm optimization method, using the control point coordinates of the Bezier curve as the parameters to be optimized, to achieve particle size distribution optimization. The present invention optimizes the material particle size distribution by combining discrete element simulation with a particle swarm optimization algorithm, thereby reducing the cost and time of material development.

Description

Technical Field

[0001] The present invention relates to a material particle size optimization method, system, equipment and storage medium, and relates to the technical field of lithium batteries. Background Art

[0002] The positive electrode of lithium-ion batteries is an important component of lithium-ion batteries. The performance of the positive electrode material directly determines the energy density and power density of the battery. Among them, the energy density is closely related to the compaction density of the material, and the compaction density is limited by the porosity between the particles. Therefore, by changing the particle size distribution, the pore structure of the material can be changed.

[0003] However, the interaction between particles is relatively complex, and it is difficult to give the relationship between the porosity of the material and the particle size distribution of the material in an analytical form. At present, the relevant research methods are mainly divided into experimental and discrete element simulation methods.

[0004] At present, the research field of battery materials mainly relies on experimental methods. The improvement and optimization of the compaction density and particle size distribution of battery material particles are mainly based on experimental trial and error, which has high R&D costs and time. Summary of the invention

[0005] In response to the above problems, one of the objects of the present invention is to provide a material particle size optimization method that optimizes the particle size distribution of material particles through discrete element simulation combined with a particle swarm optimization algorithm, thereby reducing the cost and time of material development; a second object of the present invention is to provide a material particle size optimization system; a third object of the present invention is to provide an electronic device; and a fourth object of the present invention is to provide a computer storage medium.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for optimizing the particle size of material particles, comprising:

[0008] Establish a discrete element simulation model of the material, calculate the compaction density of the material under different pressures, and obtain the model parameters of the discrete element simulation model;

[0009] The particle swarm optimization method is used to correct the model parameters of the discrete element simulation model based on material measurement data;

[0010] The particle size distribution is expressed in parameter form using Bezier curve;

[0011] The particle swarm optimization method is used to optimize the particle size distribution by taking the coordinates of the control points of the Bezier curve as the parameters to be optimized.

[0012] The material particle size optimization method further establishes a discrete element simulation model of the material to calculate the compaction density of the material under different pressures, including:

[0013] Set simulation related parameters;

[0014] Set simulation related conditions;

[0015] Based on the movement of the plate, the compaction density of the material under different pressures is simulated and calculated.

[0016] The material particle size optimization method further comprises simulation-related parameters including the length, width and height of the simulation space, the compaction density calculation range, the number of particles, the particle size distribution, the particle true density and discrete element simulation parameters; and simulation-related conditions including the plate movement speed and the model parameters of the force model between particles and particles, and between particles and the plate.

[0017] In the material particle size optimization method, further, the material measurement data includes the material's particle size distribution, compacted density and true density.

[0018] The material particle size optimization method further adopts a particle swarm optimization method to correct the model parameters of the discrete element simulation model based on material measurement data, including:

[0019] Setting the search range of model parameters to be corrected in the discrete element simulation model;

[0020] Linearly normalize the model parameters to be optimized;

[0021] Based on the material measurement data, the model parameters are input into the discrete element simulation model to calculate density and pressure;

[0022] Construct the fitness function of the particle swarm optimization method;

[0023] Use particle swarm optimization method to calibrate the model parameters of discrete element simulation model;

[0024] The model parameters represented by the particle position with the lowest fitness are the corrected model parameters.

[0025] The material particle size optimization method further uses a Bezier curve to express the particle size distribution in a parameter form, including:

[0026] Define parameters θ (θ1, θ2, θ3, ...) to represent the coordinates of the control points of the Bezier curve;

[0027] The shape of the particle size distribution curve is defined as (D lo , 0) to (D hi , 1) of the curve, define D lo and D hiUpper and lower limits;

[0028] D is calculated from the distribution of θ1 and θ2 lo and D hi ;

[0029] Define the starting point coordinates of the curve as (D lo , 0), the end point coordinate is (D hi , 1);

[0030] Calculate the coordinates of the remaining intermediate control points (x i ,y i ):

[0031] x i =D lo +θ1×(D hi -D lo )

[0032] y i =θ i+1 .

[0033] The material particle size optimization method further adopts a particle swarm optimization method, takes the control point coordinates of the Bezier curve as the parameters to be optimized, and realizes particle size distribution optimization, including:

[0034] Set the particle size distribution search range and related restrictions;

[0035] Set the fitness value calculation method;

[0036] Use particle swarm optimization method to optimize the particle size distribution;

[0037] After the optimization convergence stops, the particle size distribution represented by the particle with the lowest fitness is selected as the optimized particle size distribution.

[0038] In a second aspect, the present invention further provides a material particle size distribution optimization system, the system comprising:

[0039] A compaction density calculation module is configured to establish a discrete element simulation model of the material and calculate the compaction density of the material under different pressures;

[0040] a model parameter simulation module configured to calibrate model parameters of a discrete element simulation model based on material measurement data using a particle swarm optimization method;

[0041] a particle size distribution conversion module, configured to express the particle size distribution as a parameter using a Bezier curve;

[0042] The particle size optimization module is configured to use the coordinates of the control points of the Bezier curve as the parameters to be optimized and to achieve particle size distribution optimization using the particle swarm optimization method.

[0043] In a third aspect, the present invention further provides an electronic device, which includes at least a processor and a memory, wherein a computer program is stored in the memory, and when the processor runs the computer program, the computer program is executed to implement the method described.

[0044] In a fourth aspect, the present invention further provides a computer storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions can be executed by a processor to implement the described method.

[0045] The present invention adopts the above technical solution, which has the following advantages:

[0046] 1. The present invention optimizes the particle size distribution of material particles by combining a discrete element simulation model with a particle swarm optimization algorithm, thereby reducing the cost and time of material development;

[0047] 2. Compared with the traditional experimental method, the simulation optimization method of material particles provided by the present invention has low cost, high speed, wide search space coverage, and can effectively optimize the particle size distribution of the material and improve the compaction density of the material;

[0048] In summary, the present invention can be widely used in optimizing the particle size of positive electrode materials for lithium-ion batteries. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Throughout the accompanying drawings, the same reference numerals are used to represent the same components. In the accompanying drawings:

[0050] Figure 1 A schematic flow chart of a method for optimizing particle size of positive electrode material particles according to an embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a discrete element simulation process according to an embodiment of the present invention;

[0052] Figure 3 It is a schematic diagram of a simulation compaction curve of a calibrated discrete element model according to an embodiment of the present invention, wherein the horizontal axis is Pressure, in MPa, and the vertical axis is Density, in g / cc;

[0053] Figure 4 It is a schematic diagram of the Bezier curve of the particle size distribution of an embodiment of the present invention, the abscissa is the particle size in um, and the ordinate is the cumulative number percentage;

[0054] Figure 5 Schematic diagram of particle size distribution of materials before and after optimization of an embodiment of the present invention, with the horizontal axis representing particle size in um and the vertical axis representing cumulative number percentage;

[0055] Figure 6 It is a schematic diagram of the compaction density curve before and after optimization of the embodiment of the present invention, the horizontal axis is Pressure, the unit is MPa, and the vertical axis is Density, the unit is g / cc;

[0056] Figure 7 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0058] It should be understood that the terms used herein are only for the purpose of describing specific example embodiments and are not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms "one", "an" and "said" as used herein may also be meant to include plural forms. The terms "include", "comprise", "contain", and "have" are inclusive, and therefore specify the existence of stated features, steps, operations, elements and / or parts, but do not exclude the existence or addition of one or more other features, steps, operations, elements, parts, and / or combinations thereof. The method steps, processes, and operations described herein are not interpreted as necessarily requiring them to be performed in the specific order described or illustrated, unless the execution order is clearly indicated. It should also be understood that additional or alternative steps may be used.

[0059] For ease of description, spatially relative terms may be used herein to describe the relationship of one element or feature relative to another element or feature as shown in the figures, such as "inside", "outside", "inner side", "outer side", "below", "above", etc. Such spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation depicted in the figures.

[0060] The material particle size optimization method, system, device and storage medium provided by the present invention include: establishing a discrete element simulation model of the material, calculating the compaction density of the material under different pressures, and obtaining the model parameters of the discrete element simulation model; using a particle swarm optimization method to correct the model parameters of the discrete element simulation model based on material measurement data; using a Bezier curve to represent the particle size distribution in parameter form; using a particle swarm optimization method, using the control point coordinates of the Bezier curve as the parameters to be optimized, to achieve particle size distribution optimization. Therefore, the present invention optimizes the material particle size distribution through discrete element simulation combined with a particle swarm optimization algorithm, thereby reducing the cost and time of material development.

[0061] Embodiment 1: Figure 1 As shown, the material particle size optimization method provided in this embodiment includes:

[0062] S1. Establish a discrete element simulation model of the material and calculate the compaction density of the material under a set particle size distribution, where the compaction density is surface density / (thickness of the electrode after rolling-thickness of the current collector).

[0063] Specifically, Figure 2 As shown, this embodiment uses LIGGGHTS-Public software to simulate the compaction density of materials under particle size distribution, and the process includes:

[0064] S11. Set simulation related parameters:

[0065] According to the characteristics of the positive electrode material, set the length, width and height X, Y, Z of the simulation space, the compaction density calculation range, the number of particles N1, the particle size distribution PSD, the particle true density and the discrete element simulation parameters.

[0066] S12. Set simulation conditions:

[0067] In the simulation space, a cube with a length, width and height of XYZ is generated with a number of N1 particles whose distribution satisfies the particle size distribution PSD. Each particle is spherical and has a density of ρ. real. Among them, the x and y directions have periodic boundary conditions, the bottom in the z direction is a fixed plate, and the top is a moving plate that can move downward along the z direction. In this embodiment, the simulation space sizes X, Y, and Z are set to 60um, 60um, and 150um, respectively; the number of particles N1 is 10,000, the step size is 0.0001us, and the downward movement speeds of the plate v1 and v2 are 10.0m / s and 0.2m / s, respectively. Among them, the force model between particles and particles, and between particles and plates uses the GranularHertzian (GH) and simplified Johnson-Kendall-Roberts (SJKR) models. The force model requires 5 model parameters: Young's modulus, Poisson's ratio, cohesive energy density, restitution coefficient, and friction coefficient. The software unit is set to 'micro'.

[0068] S13. Simulation calculation of compaction density of materials

[0069] In order to quickly generate random particles, the plate is moved downward at a speed of v1 to a height of Z1. The plate is continued to move downward at a speed of v2, and the position z2 of the plate and the force f on the plate are recorded in real time. z2 and f are converted into density and pressure, and the compaction density curve of the material is obtained, where: the pressure P(zi) = f / X*Y when the plate is at a height of zi, the density ρ(zi) = M / (X*Y*zi), and M is the total mass of the material.

[0070] In some implementations, the selection of Z1, z2 can be achieved by: ρ(Z1)≤ρ init , for ternary or layered materials ρ init Can be set to 2.0~2.5g / cm 3 ; For materials of iron-lithium or iron-manganese-lithium system ρ init Can be set to 1.3~1.8g / cm 3 ; P(z2)≥300MPa, this is taken as an example, but is not limited to this and can be set according to actual application.

[0071] S2. Establishing particle swarm optimization method

[0072] Particle swarm optimization is a heuristic search algorithm. The algorithm first randomly generates a certain number of particles in N-dimensional space (N is the number of parameters to be optimized). Each particle has position and speed information. The operation process of the algorithm usually includes calculating the fitness value of the individual, calculating the optimal position of the individual, calculating the optimal position of the group, and updating the speed and position of the individual, including:

[0073] S21, initialize the optimal particle swarm.

[0074] The population has Np particles (depending on the simulation setting), and the position of each particle in the N-dimensional space is p i=(x1, x2, ..., x N ), the initial velocity of each particle is 0, and the initial position is randomly generated between 0 and 1.

[0075] S22. Calculate the fitness cost of each particle in the particle swarm at its position. The fitness is a function of the particle position. Its specific form is determined according to the actual problem and is not limited here.

[0076] S23. Compare the current fitness and historical fitness of each particle. If there is no historical fitness, or the current fitness is lower than the historical fitness, update the historical fitness of the particle to the current fitness.

[0077] S24, compare the minimum fitness value of each particle in the particle swarm with the historical minimum fitness value of the group. If there is no historical minimum fitness value of the group (i.e., in the first iteration), or the current fitness of the particle is lower than the historical minimum fitness value of the group, then update the historical minimum fitness value of the group to the current best fitness value of the group, and the historical best position of the group is the position of the particle.

[0078] S25. Update the speed and position of each particle

[0079] The velocity of particle i in the G+1 generation:

[0080]

[0081] Among them, ν i，G , ν i，G+1 are the velocities of the i-th particle in the G-th and G+1-th generations, v RNG is the random velocity, p i，best , p i，G are the best historical position of the i-th particle and the position of the G-th generation, p *，best Best position for the group c1, c2 are 0.5, 1, and 2 respectively.

[0082] S26. Update the particle speed and position. If the particle position is out of the range of 0 to 1, set the particle position to 0 or 1.

[0083] S27. Check whether the set convergence conditions are met.

[0084] S28. If the convergence condition is met, the position of the particle with the lowest fitness is output; if not, S23-S26 are repeated.

[0085] S3. Measure the particle size distribution, compacted density and true density of the material to be optimized.

[0086] Specifically, the particle size distribution of the material to be optimized is measured by a laser particle size analyzer as shown in Table 1, the pressure-density data is shown in Table 2, and the true density ρreal 4.6g / cm 3 .

[0087] Table 1

[0088]

[0089] Table 2

[0090] Pressure MPa 37 75 113 150 188 226 <![CDATA[Density g / cm 3 > 2.74 2.86 2.93 3.0 3.06 3.11

[0091] S4: In order to improve the credibility of the simulation results, the particle swarm optimization method is used to correct the model parameters of the discrete element simulation model with measured data, including:

[0092] S41, setting a search range of model parameters to be corrected in a discrete element simulation model.

[0093] Model parameter search range: friction coefficient between 0.1 and 0.7; restitution coefficient between 0.1 and 0.5; cohesive energy density between 1e3 and 1e7 pg um -1 us -2 Young's modulus is between 1e7 and 5e8 KPa; Poisson's ratio is between 0.1 and 0.4.

[0094] S42. Use linear normalization to map the discrete element model parameters to be optimized to [0, 1], and obtain the discrete element model parameters through the positions of particles in the particle swarm algorithm.

[0095] Specifically, from the particle position Pi (x1, x2, x3, x4, x5), the model parameter normalization process is obtained as:

[0096] Friction coefficient = 0.1 + x1 (0.7-0.1); restitution coefficient = 0.1 + x2 (0.7-0.1); Poisson's ratio = 0.1 + x3 (0.7-0.1); cohesive energy density = 1e3 + x4 (1e7-1e3) pg um -1 us -2 ; Young's modulus = 1e7+x5(5e8-1e7)KPa.

[0097] S43, inputting the model parameters corresponding to the particles into the discrete element simulation model in step S1, setting the remaining parameters in step S1 such as particle size distribution, true density, etc. to the experimental values ​​in step S3, performing compaction density calculation, and obtaining the corresponding compaction density-pressure relationship curve.

[0098] S44. Construct the fitness function of the particle swarm optimization method:

[0099] cost = ∑(ρ sim -ρ exp ) 2

[0100] Among them, ρ sim is the density of the simulation calculation, ρ exp is the experimental value.

[0101] In this embodiment, the differences between simulation and experiment at pressures of 37 MPa, 75 MPa, 113 MPa, 150 MPa, 188 MPa, and 226 MPa are specifically calculated.

[0102] S45. Use a particle swarm optimization method to calibrate model parameters of the discrete element simulation model.

[0103] The particle swarm algorithm is used for optimization, and the quality criterion (cost) of particle evolution is the difference between simulation compression and experimental compression. Specifically, the number of particles is set to 12, and the convergence condition is fitness < 1e-3.

[0104] S46. The discrete element model parameters represented by the particle position with the lowest fitness are the corrected model parameters. In this embodiment, the corrected model parameters are shown in Table 3. The simulated compaction density-pressure relationship is similar to the experimental value. Figure 3 shown.

[0105] Table 3

[0106] Friction coefficient 0.35 Coefficient of restitution 0.275 <![CDATA[Cohesive energy density (pg um -1 us -2 )]]> 599276 Young's modulus(KPa) 74762010 Poisson's ratio 0.23

[0107] In summary, the model parameter optimization process is to compare the compression curve obtained by simulation with the compression curve measured experimentally, and continuously adjust the model parameters to make the simulated compression and experimental compression as close as possible.

[0108] S5. Use Bezier curve to express the particle size distribution with parameter θ

[0109] The Bezier curve is a parametric curve commonly used in engineering. The shape of the curve is described by a set of control points. The shape of the cumulative particle size distribution curve is a line consisting of (D lo , 0) to (D hi , 1), so the Bezier curve of order 6 to 9 can describe the particle size distribution in most cases. Among them, the coordinates of the control points are Pstart (D lo , 0)…, Pi(x i ,y i ),…,Pend(D hi , 1), x i In D lo ~D hi Between iBetween 0 and 1. Therefore, the coordinates of the control points of the Bezier curve can be used as parameters to describe different particle size distribution curves. Now define parameters θ (θ1, θ2, θ3, ...) to represent the coordinates of the control points of the Bezier curve, and then represent the Bezier curve. The steps of obtaining the particle size distribution from the parameters θ (θ1, θ2, θ3, ...) include:

[0110] S51. Definition D lo and D hi Upper and lower limits:

[0111] D lo Upper and lower limits:

[0112] D hi Upper and lower limits:

[0113] S52, D is obtained from the distribution of θ1 and θ2 lo and D hi :

[0114]

[0115]

[0116] S53, the starting point coordinates of the curve are (D lo , 0), the end point coordinate is (D hi , 1).

[0117] S54, calculate the coordinates of the remaining intermediate control points (x i ,y i ):

[0118] x i =D lo +θ1×(D hi -D lo )

[0119] y i =θ i+1

[0120] In this embodiment, the particle size distribution measured in the experiment is expressed by parameter θ as: [0.7875, 0.3703, 0.0486, 1.05E-11, 0.0532, 0.69, 0.2092, 0.9, 0.166, 1.0, 0.4720, 1.0, 0.99]; upper and lower limits: 0.2um, 1um respectively. They are 10um and 15um respectively. The original data and Bezier curve are as follows Figure 4 shown.

[0121] S6. Using the particle swarm optimization method, taking the coordinates of the control points of the Bezier curve as the parameters to be optimized, the particle size distribution is optimized, including:

[0122] S61. According to the actual production preparation and the limitations of the simulation software, set the particle size distribution search range and related restriction conditions.

[0123] In this embodiment, D lo Set to between 0.1 and 1 um; D hi Set it between 10 and 15um; the minimum particle size range is set to 0.1 to 1um; the maximum particle size range is set to 12 to 15um; the particle volume D50 range is set to D50>3.5um; the particle number D99 range is set to D99<12um.

[0124] S62: Set a method for calculating the fitness value.

[0125] Convert the position of each particle in the particle swarm optimization method into a particle size distribution. If the particle size distribution does not meet the conditions in S61, skip the subsequent compaction density simulation calculation step and directly set the fitness value of the ion to 100. If the particle size distribution meets the conditions in S61, input the obtained particle size distribution information into the simulation model in S1, and use the corrected results in S3 for the remaining parameters in the model to obtain the corresponding compaction density-pressure relationship curve. Set the fitness function according to the actual optimization goal. In this embodiment, the optimization goal is to increase the compaction density under the pressure range, so the fitness is defined as cost = 100-(1×ρ 150MPd +2×ρ 188MPd +3×ρ 226MPd ).

[0126] S63. Use particle swarm optimization method to optimize the particle size distribution.

[0127] Among them, the number of particles Np is 20.

[0128] The convergence condition is any one of the following two: a) the minimum fitness decrease of the historical population is less than 1e-3. b) the density at 226MPa is higher than 3.5g / cm 3 .

[0129] S64. After the optimization is completed, the particle size distribution represented by the particle with the lowest fitness is the optimized particle size distribution.

[0130] In this embodiment, the compaction density at 226 MPa after optimization is 3.3 g / cm 3 The particle size distribution and compaction density of the materials before and after optimization are as follows: Figure 5 and Figure 6 The optimized particle size distribution is shown in Table 4:

[0131] Table 4

[0132] volume D10 D20 D30 D40 D50 D60 D70 D80 D90 D99 Value(um) 1.8 2.9 4.2 5.6 6.99 8.33 9.38 10 10.9 11.4

[0133] S7. After the optimization convergence stops, the particle size distribution with the lowest fitness is selected as the optimized particle size distribution.

[0134] Embodiment 2: Embodiment 1 above provides a material particle size optimization method, and correspondingly, this embodiment provides a material particle size optimization system. The system provided in this embodiment can implement the material particle size optimization method of embodiment 1, and the system can be implemented by software, hardware, or a combination of software and hardware. For the convenience of description, this embodiment is described in various units according to their functions. Of course, the functions of each unit can be implemented in the same or multiple software and / or hardware during implementation. For example, the system may include integrated or separate functional modules or functional units to execute the corresponding steps in each method of embodiment 1. Since the system of this embodiment is basically similar to the method embodiment, the description process of this embodiment is relatively simple, and the relevant parts can refer to the partial description of embodiment 1. The embodiment of the material particle size optimization system provided by the present invention is merely schematic.

[0135] Specifically, the material particle size distribution optimization system provided in this embodiment includes:

[0136] A compaction density calculation module is configured to establish a discrete element simulation model of the material and calculate the compaction density of the material under different pressures;

[0137] a model parameter simulation module configured to calibrate model parameters of a discrete element simulation model based on material measurement data using a particle swarm optimization method;

[0138] a particle size distribution conversion module, configured to express the particle size distribution as a parameter using a Bezier curve;

[0139] The particle size optimization module is configured to use the coordinates of the control points of the Bezier curve as the parameters to be optimized and to achieve particle size distribution optimization using the particle swarm optimization method.

[0140] Embodiment 3: This embodiment provides an electronic device corresponding to the material particle size optimization method provided in this embodiment 1. The electronic device may be an electronic device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of embodiment 1.

[0141] like Figure 7As shown, the electronic device includes a processor, a memory, a communication interface and a bus, and the processor, the memory and the communication interface are connected through the bus to complete mutual communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Component (EISA) bus, etc. The memory stores a computer program that can be run on the processor, and the processor executes the material particle size optimization method provided in the first embodiment when running the computer program. Those skilled in the art can understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computing device to which the scheme of the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0142] In some implementations, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), optical disk and other media that can store program codes.

[0143] In some other implementations, the processor may be a central processing unit (CPU), a digital signal processor (DSP), or other general-purpose processors of various types, which are not limited herein.

[0144] Embodiment 4: The material particle size optimization method of the present embodiment 1 may be specifically implemented as a computer program product, which may include a computer-readable storage medium carrying computer-readable program instructions for executing the material particle size optimization method described in the present embodiment 1.

[0145] Computer readable storage media can be tangible devices that hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0146] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. In the description of this specification, the description of the reference terms "one embodiment", "some implementations", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of this specification. In this specification, the schematic representation of the above terms does not necessarily target the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0147] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0148] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0149] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the particle size of material particles, characterized in that: include: Establish a discrete element simulation model of the material, calculate the compaction density of the material under different pressures, and obtain the model parameters of the discrete element simulation model; The particle swarm optimization method is used to correct the model parameters of the discrete element simulation model based on material measurement data; The particle size distribution is expressed in parameter form using Bezier curve; The particle swarm optimization method is used to optimize the particle size distribution by taking the coordinates of the control points of the Bezier curve as the parameters to be optimized, including: 1) According to the actual production preparation and the limitations of the simulation software, set the particle size distribution search range and related restrictions; 2) Setting the fitness value calculation method: convert the position of each particle in the particle swarm optimization method into a particle size distribution. If the particle size distribution does not meet the conditions in 1), skip the subsequent compaction density simulation calculation step and directly set the particle fitness value to 100; if the particle size distribution meets the conditions in 1), input the obtained particle size distribution information into the discrete element simulation model, and use the corrected model parameters for the remaining parameters in the discrete element simulation model to obtain the corresponding compaction density-pressure relationship curve; 3) Use particle swarm optimization method to optimize the particle size distribution; 4) After the optimization convergence stops, the particle size distribution represented by the particle with the lowest fitness is selected as the optimized particle size distribution.

2. The material particle size optimization method according to claim 1, characterized in that: Establish a discrete element simulation model of the material and calculate the compaction density of the material under different pressures, including: Set simulation related parameters; Set simulation related conditions; Based on the movement of the plate, the compaction density of the material under different pressures is simulated and calculated.

3. The material particle size optimization method according to claim 2, characterized in that: The simulation related parameters include the length, width and height of the simulation space, the compaction density calculation range, the number of particles, the particle size distribution, the particle true density and the discrete element simulation parameters; the simulation related conditions include the plate moving speed and the model parameters of the force model between particles and particles, and between particles and the plate.

4. The material particle size optimization method according to claim 1, characterized in that: Material measurement data include particle size distribution, compacted density and true density.

5. The material particle size optimization method according to claim 1, characterized in that: The particle swarm optimization method is used to calibrate the model parameters of the discrete element simulation model based on material measurement data, including: Setting the search range of model parameters to be corrected in the discrete element simulation model; Linearly normalize the model parameters to be optimized; Based on the material measurement data, the model parameters are input into the discrete element simulation model to calculate density and pressure; Construct the fitness function of the particle swarm optimization method; Use particle swarm optimization method to calibrate the model parameters of discrete element simulation model; The model parameters represented by the particle position with the lowest fitness are the corrected model parameters.

6. A material particle size distribution optimization system, characterized in that: The system includes: A compaction density calculation module is configured to establish a discrete element simulation model of the material and calculate the compaction density of the material under different pressures; a model parameter simulation module configured to calibrate model parameters of a discrete element simulation model based on material measurement data using a particle swarm optimization method; a particle size distribution conversion module, configured to express the particle size distribution as a parameter using a Bezier curve; The particle size optimization module is configured to use the coordinates of the control points of the Bezier curve as the parameters to be optimized and use the particle swarm optimization method to achieve particle size distribution optimization, including: 1) According to the actual production preparation and the limitations of the simulation software, set the particle size distribution search range and related restrictions; 2) Setting the fitness value calculation method: convert the position of each particle in the particle swarm optimization method into a particle size distribution. If the particle size distribution does not meet the conditions in 1), skip the subsequent compaction density simulation calculation step and directly set the particle fitness value to 100; if the particle size distribution meets the conditions in 1), input the obtained particle size distribution information into the discrete element simulation model, and use the corrected model parameters for the remaining parameters in the discrete element simulation model to obtain the corresponding compaction density-pressure relationship curve; 3) Use particle swarm optimization method to optimize the particle size distribution; 4) After the optimization convergence stops, the particle size distribution represented by the particle with the lowest fitness is selected as the optimized particle size distribution.

7. An electronic device, comprising at least a processor and a memory, wherein a computer program is stored in the memory, wherein: When the processor runs the computer program, the method according to any one of claims 1 to 5 is implemented.

8. A computer storage medium, characterized in that: Computer-readable instructions are stored thereon, and the computer-readable instructions can be executed by a processor to implement the method according to any one of claims 1 to 5.

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