Electrode granulation simulation method and device, storage medium and product

Abstract

The invention discloses an electrode granulation simulation method and device, a storage medium and a product, and the method comprises the steps: carrying out the simulation of a dry mixing stage in an electrode granulation process, and determining the first position information of particles in the dry mixing stage based on a first mechanical model through a discrete element method; after the dry mixing stage simulation, determining a first standard deviation of the particle concentration according to the first position information, and if the first standard deviation is smaller than a first standard deviation threshold value, taking a first operation parameter set during the dry mixing stage simulation as a first operation parameter during actual operation; simulating a wet mixing stage in the electrode granulation process, and determining second position information of particles in the wet mixing stage based on a second mechanical model by adopting a discrete element method; after wet mixing stage simulation, a second standard deviation of the particle concentration is determined according to the second position information, and if the second standard deviation is smaller than a second standard deviation threshold value, a second operation parameter set during wet mixing stage simulation serves as a second operation parameter during actual operation.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of simulation technology, and in particular, to an electrode granulation simulation method, device, storage medium, and product. Background Art

[0002] Mixing granulation is a common manufacturing process in industries such as food, fertilizer, and medicine. In this process, with the help of adhesives, the same or multiple different types of fine powders are bonded to form dense and uniform particles to enhance properties such as fluidity, processability, dustiness, strength, appearance, dissolution rate, and anti-segregation.

[0003] As an important link in battery manufacturing, the quality control of the electrode mixing granulation process directly affects the performance and service life of the battery. However, in the actual production process, the electrode granulation process faces various challenges, including: (1) Difficulty in mixing fine powders: Due to their high specific surface area and easy agglomeration, fine powders are difficult to disperse evenly during the mixing process, which not only affects the performance of the electrode material but also may cause problems such as internal short circuits in the battery; (2) Uneven particle size distribution, thus affecting key parameters such as the packing density, conductivity, and ion diffusion rate of the electrode material; (3) Difficulty in controlling the particle morphology: Due to the complexity of the granulation process and the differences in raw material properties, it is often difficult to precisely control the particle morphology, etc. Summary of the Invention

[0004] The present invention provides an electrode granulation simulation method, device, storage medium, and product to determine the actual operation parameters through simulation, thereby achieving the purpose of improving the efficiency of electrode granulation and reducing the defective rate during the production process.

[0005] In a first aspect, an electrode granulation simulation method provided by the embodiments of the present invention includes:

[0006] Simulate the dry mixing stage in the electrode granulation process, use the discrete element method, and determine the first position information of the particles in the dry mixing stage based on the first mechanical model;

[0007] After the simulation of the dry mixing stage, determine the first standard deviation of the particle concentration according to the first position information. If the first standard deviation is less than the first standard deviation threshold, then use the first operation parameters set during the dry mixing stage simulation as the first operation parameters during actual operation;

[0008] Simulate the wet mixing stage in the electrode granulation process, use the discrete element method, and determine the second position information of the particles in the wet mixing stage based on the second mechanical model;

[0009] After the simulation of the wet mixing stage, determine the second standard deviation of the particle concentration according to the second position information. If the second standard deviation is less than the second standard deviation threshold, then use the second operation parameters set during the wet mixing stage simulation as the second operation parameters during actual operation.

[0010] Optionally, the first mechanical model adopts the Hertz-Mindlin with JKR model.

[0011] Optionally, the second mechanical model adopts a fusion model of the Hertz-Mindlin with JKR model and the Wet Mixing model.

[0012] Optionally, during the simulation process, the first position information and the second position information are updated every time a time step elapses.

[0013] Optionally, the time step is determined according to the following formula:

[0014] t = 20%T R

[0015]

[0016] In the formula, t represents the time step, T R represents the critical time step, r represents the particle radius of the particle, v represents the Poisson's ratio, ρ represents the particle solid density of the particle, and G represents the shear modulus.

[0017] Optionally, the first parameter includes the rotation speed of the stirrer.

[0018] Optionally, the second parameter includes the solid-liquid ratio of the active material, the conductive agent and the binder.

[0019] In a second aspect, an embodiment of the present invention further provides an electronic device, including at least one processor, and a memory communicatively connected to the at least one processor;

[0020] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute any one of the electrode granulation simulation methods described in the embodiments of the present invention.

[0021] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, and the computer-readable storage medium stores computer instructions for causing a processor to execute any one of the electrode granulation simulation methods described in the embodiments of the present invention when executed.

[0022] In a fourth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, and the computer program implements any one of the electrode granulation simulation methods described in the embodiments of the present invention when executed by a processor.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention proposes an electrode granulation simulation method. In this method, by simulating the dry mixing stage and the wet mixing stage of electrode granulation respectively, the discrete element method is used in combination with the corresponding mechanical model to determine the particle position information, and the actual operating parameters are determined based on the comparison between the standard deviation of the particle concentration and the preset threshold. This way can accurately find the optimal operating parameters suitable for each stage, avoiding the blindness of relying on experience or a large number of trials and errors to determine parameters in the traditional method;

[0024] This method is analyzed based on the mechanical model and the actual particle motion situation, making the setting of operating parameters more scientific. The first mechanical model and the second mechanical model can accurately describe the mechanical behavior of particles in different stages, thus providing a reliable theoretical basis for the determination of parameters;

[0025] In this method, the standard deviation of the particle concentration is used to evaluate the standard deviation of the particle concentration in the dry mixing and wet mixing stages respectively, and then the appropriate operating parameters are determined. The appropriate operating parameters can promote the agglomeration and dispersion behavior of particles during the granulation process, forming an ideal particle size and shape distribution. Furthermore, it is possible to carry out actual production with the optimized operating parameters, which can improve the efficiency of electrode granulation and reduce the defective rate during the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is the flow chart of the electrode granulation simulation method in the embodiment;

[0027] Figure 2 is another flow chart of the electrode granulation simulation method in the embodiment;

[0028] Figure 3 is the schematic diagram of the relative standard deviation curve in the embodiment;

[0029] Figure 4 is the schematic diagram of the structure of the electronic device in the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0030] The present invention will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that for the sake of description, only parts related to the present invention rather than all structures are shown in the drawings.

[0031] Embodiment 1

[0032] Figure 1 is the flow chart of the electrode granulation simulation method in the embodiment. Refer to Figure 1 , the electrode granulation simulation method includes:

[0033] S101. Simulate the dry mixing stage in the electrode granulation process. Using the discrete element method, determine the first position information of the particles in the dry mixing stage based on the first mechanical model.

[0034] Exemplarily, in this solution, during the dry mixing stage, the particles or powders in the battery electrode material are mixed in a dry mixing device, and these different materials are evenly dispersed together to ensure that the proportion of various components in each tiny area meets the design requirements.

[0035] Since agglomerates may form during the storage and transportation of raw materials, the mechanical force during the dry mixing process can break these agglomerates, making the particles exist in a single or smaller agglomerated state, thereby increasing the contact area between the particles and improving the conductivity and ion transport performance of the electrode.

[0036] In addition, during the dry mixing process, some preliminary physical or chemical interactions may occur between different materials, laying a foundation for the subsequent granulation and electrode preparation processes.

[0037] Exemplarily, in this solution, the materials mixed in the dry mixing stage may include active substances and conductive agents.

[0038] Exemplarily, in this solution, the discrete element method (DEM) is a numerical calculation method used to analyze the dynamic problems of material systems, and it can be used to handle the complex mechanical behaviors of particle systems.

[0039] Exemplarily, in this solution, before simulating the dry mixing stage, establish a three-dimensional model of the mixing device and a particle model of the particles, and put the particles (particle model) into the mixing device (three-dimensional model).

[0040] During the simulation process, the preset DEM simulation algorithm calculates the force on each particle, and then determines the motion state (position) of the particles within each time step, thereby realizing the simulation of the dry mixing process of the materials.

[0041] Exemplarily, in this solution, the first mechanical model adopts a contact model, which is used to describe the interaction between particles in the dry mixing state. During the simulation process, the DEM simulation algorithm determines the force on the particles based on the mutual (mechanical) interaction between the particles described by this model.

[0042] Exemplarily, in this solution, the first position information represents the position coordinates of the particles in a specified coordinate system, and it can represent the relative position of the particles in the mixing device (three-dimensional model).

[0043] Exemplarily, in this solution, the DEM simulation algorithm can determine the contact forces acting on the particles during the mixing process through the first mechanical model. By vectorially summing up the contact forces, the resultant force acting on the particles can be obtained.

[0044] Based on the resultant force acting on the particles, the acceleration of the particles can be calculated. Integrating the acceleration can obtain the velocity of the particles. Given the initial position of the particles, the current position of the particles can be obtained, that is, the first position information is determined.

[0045] S102. After the simulation of the dry mixing stage, determine the first standard deviation of the particle concentration according to the first position information. If the first standard deviation is less than the first standard deviation threshold, then use the first operating parameters set during the dry mixing stage simulation as the first operating parameters during actual operation.

[0046] Exemplarily, in this solution, the first standard deviation is the relative standard deviation (RSD) of the particle concentration, which is used to reflect the magnitude of the fluctuation of the particle concentration relative to its average value.

[0047] The first standard deviation is used to evaluate the uniformity of the mixing of various particles. The first standard deviation can be expressed by the following formula:

[0048]

[0049] In the formula, is the average value of the particle concentrations in all sub-regions, X i is the particle concentration in the i-th sub-region, and n is the number of sub-regions.

[0050] Exemplarily, in this solution, the DEM simulation algorithm calculates the contact forces between the particles, and then determines the first position information. The simulation area (the mixing area of the mixing equipment) is divided into several small sub-regions. Based on the first position information, the number of particles in each sub-region is counted. According to the number of particles and the volume of the sub-region, the particle concentration in each sub-region is calculated.

[0051] Exemplarily, in this solution, the first operating parameters can include the setting parameters of the mixing equipment. For example, if the mixing equipment is internally equipped with a stirrer, a shearer, and a scraper, then the first operating parameters can be one or more of the rotation speed of the stirrer, the rotation speed of the shearer, and the movement speed of the scraper.

[0052] S103. Simulate the wet mixing stage during the electrode granulation process. Using the discrete element method, determine the second position information of the particles in the wet mixing stage based on the second mechanical model.

[0053] Exemplarily, in this solution, the wet mixing stage is used to mix the active material, the conductive agent, and the binder to form a uniform and stable slurry.

[0054] Exemplarily, in this solution, the second mechanical model adopts a contact model, which is used to describe the interaction between particles in the wet mixing state. During the simulation process, the DEM simulation algorithm is used to determine the force conditions between particles based on the mutual (mechanical) interaction described by this model.

[0055] Exemplarily, in this solution, the second position information represents the position coordinates of the particles in a specified coordinate system, which can represent the relative positions of the particles in the mixing device (3D model).

[0056] Exemplarily, in this solution, the method for determining the second position information is the same as that for determining the first position information, and the specific content will not be elaborated here.

[0057] S104. After the simulation of the wet mixing stage, determine the second standard deviation of the particle concentration according to the second position information. If the second standard deviation is less than the second standard deviation threshold, then use the second operating parameters set during the wet mixing stage simulation as the second operating parameters during actual operation.

[0058] Exemplarily, in this solution, the definition and function of the second standard deviation are the same as those of the first standard deviation, and the corresponding calculation methods are also the same, and the specific content will not be elaborated here.

[0059] Exemplarily, in this solution, the second operating parameters may include the physical properties of the mixed slurry formed by the active material, conductive agent, and binder during the wet mixing stage. For example, viscosity, moisture content, etc.

[0060] This embodiment proposes an electrode granulation simulation method. In this method, by simulating the dry mixing stage and the wet mixing stage of electrode granulation respectively, the discrete element method is used to combine with the corresponding mechanical model to determine the particle position information, and based on the comparison between the standard deviation of the particle concentration and the preset threshold, the actual operating parameters are determined. This way can accurately find the best operating parameters suitable for each stage, avoiding the blindness of relying on experience or a large number of trials and errors to determine parameters in the traditional method;

[0061] This method is analyzed based on the mechanical model and the actual particle motion conditions, making the setting of the operating parameters more scientific. The first mechanical model and the second mechanical model can accurately describe the mechanical behavior of particles in different stages, thus providing a reliable theoretical basis for the determination of parameters;

[0062] In this method, the standard deviation of the particle concentration is evaluated respectively in the dry mixing and wet mixing stages, and then appropriate operating parameters are determined. The appropriate operating parameters can promote the agglomeration and dispersion behavior of particles during the granulation process, forming an ideal particle size and shape distribution. Furthermore, it is possible to carry out actual production using the optimized operating parameters, which can improve the efficiency of electrode granulation and reduce the defective rate during the production process.

[0063] On the basis of any of the foregoing solutions, in an implementable solution, the first mechanical model adopts the Hertz-Mindlin with JKR model.

[0064] In this solution, the first mechanical model adopts the Hertz-Mindlin with JKR model, which combines the Hertz-Mindlin contact model and the JKR (Johnson-Kendall-Roberts) adhesion model;

[0065] Among them, the Hertz-Mindlin contact model is mainly used to calculate the elastic contact force and frictional force between particles. It is based on the Hertz elastic contact theory, considering the elastic deformation of particles during contact, and can describe the normal elastic force between particles;

[0066] In addition, combined with the Mindlin theory, the tangential force and frictional force between particles are processed, which is suitable for describing the non-adhesive contact behavior between rigid particles;

[0067] The JKR adhesion model is used to consider the adhesion force between particles. It is based on the concept of surface energy and believes that when two particles are in contact, due to the action of surface energy, an adhesion force will be generated in the contact area, and the particles may remain in contact even without an external load;

[0068] Based on the Hertz elastic contact theory, the normal elastic force is expressed by the following formula:

[0069]

[0070] Combined with the JKR adhesion model, the normal elastic force is updated to be expressed by the following formula:

[0071]

[0072] In the formula, E represents the (particle) equivalent elastic modulus, R represents the (particle) equivalent radius, δ n represents the normal overlap of particles, and γ represents the surface energy;

[0073] Based on the Mindlin theory, when two particles are in contact and under the normal elastic force F n_JKRUnder the action, when the tangential relative displacement starts, when the tangential displacement is small, the contact area is in an elastic state, and the tangential force is expressed by the following formula:

[0074]

[0075] In the formula, G represents the equivalent shear modulus, δ t represents the tangential relative displacement (of the particles), G 1 and G 2 respectively represent the shear moduli of two (contacting) particles, v 1 and v 2 respectively represent the Poisson's ratios of two (contacting) particles;

[0076] When the tangential relative displacement continues to increase and the entire contact area enters the slip state, the tangential force reaches its maximum value. At this time, the tangential force is the static friction force F f , F f is expressed by the following formula:

[0077] F f = μ s F n_JKR

[0078] When relative sliding occurs between the particles, the tangential force will change from the static friction force to the dynamic friction force, and the dynamic friction force is expressed by the following formula:

[0079] F fd = μ d F n_JKR

[0080] In the formula, μ s represents the static friction coefficient, and μ d represents the dynamic friction coefficient.

[0081] Exemplarily, in this solution, when using the first mechanical model, the parameters in the first mechanical model are calibrated, and the parameters include particle properties and contact parameters;

[0082] Among them, the particle properties include the physical properties of various particles used in the granulation process, such as particle size distribution, shape, density, elastic modulus, Poisson's ratio, etc.;

[0083] The contact parameters include the contact parameters between particles and between particles and the equipment wall surface, such as static friction coefficient, rolling friction coefficient, recovery coefficient, surface energy, etc.

[0084] Exemplarily, in this solution, for the particle size distribution, it can be measured by equipment such as a laser particle size analyzer, the elastic modulus and Poisson's ratio can be obtained through material mechanics experiments, and the contact parameters can be measured through special friction experiments and collision experiments.

[0085] Exemplarily, as an implementable method, the calibration test selects the shear box, the loose density, the internal friction angle and the flow factor as the response values, and combines the DEM simulation to calibrate the particle properties (parameters);

[0086] Among them, the bulk density refers to the mass per unit volume of bulk material in a loose state. By adjusting the particle density parameters in the model, the bulk density obtained by DEM simulation is made consistent with the measured value in the calibration test, thereby completing the calibration of particle density.

[0087] The shape and size distribution of particles will affect the stacking mode and porosity between particles, and thus affect the bulk density. By adjusting the parameters of particle shape and size distribution in the model, the stacking state of particles is optimized, so that the bulk density obtained by simulation is close to the experimental value.

[0088] The internal friction angle reflects the ability of particles in bulk materials to resist relative sliding and is closely related to the friction between particles.

[0089] In the Hertz-Mindlin model, the static friction coefficient is the key parameter for calculating the tangential force between particles. The larger the static friction coefficient, the greater the friction between particles, the stronger the material's ability to resist shear deformation, and the greater the internal friction angle.

[0090] By changing the static friction coefficient in the model and observing the performance of the material during the shearing process in the DEM simulation, the static friction coefficient can be calibrated so that the internal friction angle obtained by the simulation is consistent with the measured value of the calibration test.

[0091] The rolling friction coefficient affects the rolling resistance of the particles, and also affects the overall shear performance and internal friction angle of the material. When the rolling friction coefficient is large, the rolling of the particles is restricted and more sliding occurs, which changes the mechanical response of the material during the shear process and thus affects the internal friction angle.

[0092] By adjusting the rolling friction coefficient and comparing the simulation results with the test data, the parameter can be calibrated.

[0093] The flow factor reflects the fluidity of the granular material, and the elastic deformation characteristics of the material will affect its fluidity. In the Hertz-Mindlin model, the elastic modulus determines the degree of elastic deformation of the particles when in contact, and the Poisson's ratio reflects the relationship between the lateral strain and the longitudinal strain of the material when it is subjected to force;

[0094] The appropriate elastic modulus and Poisson's ratio can make the deformation behavior of particles under stress conform to the actual situation, thereby affecting the flow properties of the material. By adjusting these two parameters, the flow factor of the material in the DEM simulation can match the calibration test results.

[0095] If there is adhesion between the particles of the granular material, the surface energy will affect the agglomeration and separation behavior of the particles, and thus affect the fluidity of the material;

[0096] In the JKR model, the surface energy parameter describes the adhesion characteristics between particles. The greater the surface energy, the stronger the adhesion force between particles, and the worse the fluidity of the material. By adjusting the surface energy parameter, the flow factor in the simulation is made consistent with the experimental measurement value to complete the calibration of the surface energy.

[0097] Exemplarily, in this solution, EDEM (software) is used to simulate and determine parameters such as the above-mentioned loose bulk density, internal friction angle, and flow factor, including:

[0098] In EDEM, a simulated shear box model is established, and the particles are placed in the simulated shear box according to a certain initial distribution. Simulate the natural packing process of the particles, let the particles freely fall under the action of gravity and accumulate in the shear box until a stable state is reached;

[0099] After the simulation is stable, the total mass of all particles in the shear box is statistically calculated. At the same time, according to the size of the shear box and the packing range of the particles in the simulation, the volume occupied by the particles is determined, and the simulated loose bulk density is calculated according to the calculation formula of the loose bulk density;

[0100] In EDEM, different normal stress boundary conditions are set, and by applying a vertical pressure to the simulated shear box, the particle system reaches equilibrium under the corresponding normal stress;

[0101] Then, a horizontal displacement is applied to the lower box to simulate the action of the horizontal shear force, and the shear stress-shear displacement curve of the particle system under different normal stresses is recorded;

[0102] Extract the shear stress values when the particle system starts to undergo shear failure under different normal stresses from the simulated shear stress-shear displacement curve, draw a scatter plot with the normal stress as the abscissa and the shear stress as the ordinate, and perform linear fitting. Calculate the simulated internal friction angle according to the slope of the fitting line;

[0103] During the simulated shear process, the shear stress and shear displacement data of the particle system are recorded in real time to generate a shear stress-shear displacement curve. At the same time, observe the movement trajectory and distribution of the particles, and analyze the flow characteristics of the particle system;

[0104] According to the pre-determined calculation method of the flow factor, extract the required characteristic parameters from the simulated shear stress-shear displacement curve, such as the shear displacement at a specific shear stress, the slope of the curve, etc., and calculate the simulated flow factor.

[0105] Exemplarily, in this solution, the mixing device is made of stainless steel. The calibration results are shown in Tables 1 and 2. Among them, the particle sizes of the active material and the conductive agent are set to 2 mm and 1.5 mm respectively, and the particle shape is replaced by a spherical shape.

[0106] Table 1 (Properties of Granules in Dry-Mixing Stage) (characteristic parameter)

[0107] Material Poisson's Ratio Shear Modulus (GPa) Density (kg / m3) Mass Fraction (%) Stainless Steel 0.3 78 7800 / Active Material 0.25 0.01 4500 93 Conductive Agent 0.25 0.01 600 6

[0108] Table 2 (Contact parameters of particles in the dry mixing stage (restitution coefficient / static friction / dynamic friction / JKR surface energy))

[0109]

[0110]

[0111] On the basis of any of the foregoing solutions, in an implementable solution, the second mechanical model adopts a fusion model of the Hertz-Mindlin with JKR model and the Wet Mixing model.

[0112] Exemplarily, in this solution, the Wet Mixing model, that is, the wet mixing model, is mainly used to describe the interaction between particles and the mixing process in the presence of a liquid medium. The liquid bridge force and the viscous force can be determined through the Wet Mixing model;

[0113] Among them, the liquid bridge force is an important part of the interaction between particles in the wet mixing process, and it is related to factors such as the surface tension of the liquid, the contact angle, the liquid volume, and the particle spacing. When the surface tension is larger, the liquid volume is appropriate, and the particle spacing is smaller, the liquid bridge force is stronger, which will cause the particles to agglomerate together;

[0114] The viscous force of the liquid will generate resistance to the movement of the particles, affecting the relative movement speed and trajectory of the particles. The greater the viscosity, the more restricted the movement of the particles, and the mixing process may become slower.

[0115] Exemplarily, in this solution, on the basis of the content described in the foregoing second mechanical model, based on the fusion model, the total interaction force between particles can be expressed by the following formula:

[0116] F total = F ff + F lb + F v

[0117] In the formula, F total represents the total interaction force between particles, F ff represents the static friction force F f or the dynamic friction force F fd, F lb represents the liquid bridge force, F v represents the viscous force.

[0118] Exemplarily, in this solution, the liquid bridge force F lb is expressed by the following formula:

[0119] F lb = F r + F p

[0120] F r = 2πR eq γ 1 cosθ

[0121]

[0122] In the formula, γ 1 represents the surface tension of the liquid, R 1 , R 2 respectively represent the radii of two spherical particles, θ represents the contact angle between the liquid and the particle surface, and F p represents the additional pressure in the liquid bridge, which can be calculated by combining the Laplace equation with the geometry of the liquid bridge.

[0123] Exemplarily, in this solution, the viscous force F v is expressed by the following formula:

[0124] F v = 6πηRv

[0125] In the formula, η represents the viscosity of the fluid, v represents the movement speed of the particle in the fluid, and R represents the radius of the spherical particle.

[0126] Exemplarily, in this solution, when using the second mechanical model, due to the addition of the binder, parameters such as the interaction force and cohesive force between the active material and the conductive agent have changed. Therefore, the contact parameters need to be calibrated again;

[0127] Among them, the calibration method is the same as the calibration method of the contact parameters when using the first mechanical model described above, and the specific content will not be elaborated here.

[0128] On the basis of any of the above solutions, in an implementable solution, during the simulation process, the first position information and the second position information are updated once every time step, and the time step is determined according to the following formula:

[0129] t = 20%T R

[0130]

[0131] where \(t\) represents the time step, \(T\) R represents the critical time step, \(r\) represents the particle radius of the particle, \(v\) represents the Poisson's ratio, \(\rho\) represents the particle solid density of the particle, and \(G\) represents the shear modulus.

[0132] Based on any of the above solutions, in an implementable solution, the first parameter includes the rotation speed of the stirrer.

[0133] Based on any of the above solutions, in an implementable solution, the second parameter includes the solid-liquid ratio of the active material, the conductive agent and the binder.

[0134] Figure 2 is another flowchart of the electrode granulation simulation method in the embodiment. Refer to Figure 2 , based on any of the above solutions, in an implementable solution, the electrode granulation simulation method may include:

[0135] Build a model of the mixer by Solidworks and convert the model into STEP format and import it into the EDEM software; combined with the actual situation, set the material of the mixing device to stainless steel (density: 7800 kg / m³, shear modulus 70 Mpa, Poisson's ratio 0.30);

[0136] In the study of the dry mixing stage (mixing of the active material and the conductive agent), in order to improve the calculation efficiency of the EDEM software and realize the use on an industrial scale, linearly scale up the particle model, and set the particle sizes of the active material and the conductive agent to 2 mm and 1.5 mm respectively, and replace the particle shape with a sphere;

[0137] Select a shear box for calibration tests, use the loose bulk density, the internal friction angle and the flow factor as response values, and use DEM simulation to calibrate the parameters such as the loose bulk density, the internal friction angle and the flow factor used in the Hertz-Mindlin with JKR contact model, as shown in Table 1 and Table 2;

[0138] Use Solidworks to build a particle factory, select the generation method and speed of the particles, construct a virtual geometric body inside the mixer as the particle generation area, generate particles in the order of the active material, the binder and the conductive agent, select the dynamic generation method, and the feeding amounts are 15 kg and 0.97 kg respectively;

[0139] Set the moving parts of the mixer to the bottom stirrer, the side shearer and the upper scraper, and all three rotate around their respective central axes;

[0140] Set the operating speeds of the stirrer to 150 rpm, 180 rpm, and 210 rpm respectively; set the rotational speed of the shearer to 3500 rpm; set the speed of the scraper to 10 rpm, and compare the uniformity of the distribution of the active material and the conductive agent at different stirring speeds;

[0141] After the simulation runs for the set duration, through the DEM simulation algorithm, calculate the resultant external force of the particles based on the Hertz-Mindlin with JKR contact model, and then determine the position information of the particles;

[0142] Divide the simulation area (the mixing area of the mixing equipment) into several small sub-areas. Based on the position information, count the number of particles in each sub-area, and calculate the particle concentration in each sub-area according to the number of particles and the volume of the sub-area;

[0143] Calculate the relative standard deviation (RSD) of the particle concentration of one of the powdery substances in the sample;

[0144] Divide the mixing area into four directions: middle, edge, upper, and lower. Take samples for conductivity testing respectively, and conduct 3 tests on the samples in each direction;

[0145] Figure 3 It is a schematic diagram of the relative standard deviation curve in the embodiment. Refer to Figure 3 , based on the relative standard deviation and the conductivity test results, the optimal solution in the dry mixing stage is that the operating speed of the stirrer is 180 rpm;

[0146] In the wet mixing stage, study the influence of different liquid-solid ratios on the particle size distribution. Set the liquid contents to 15%, 16%, and 17% respectively, and set the operating speeds of the stirrer of the mixer to 30 rpm; set the rotational speed of the shearer to 300 rpm;

[0147] Use DEM simulation to calibrate the surface tension, contact angle, and new surface energy used in the fusion model of the Hertz-Mindlin with JKR contact model and the Wet Mixing model, as shown in Table 3;

[0148] Table 3 (Contact parameters of particles in the wet mixing stage)

[0149]

[0150] After the simulation runs for the set duration, through the DEM simulation algorithm, calculate the resultant external force of the particles based on the fusion model, and then determine the position information of the particles;

[0151] Divide the simulation area (the mixing area of the mixing device) into several small sub-areas. Based on the position information, count the number of particles in each sub-area, and calculate the particle concentration in each sub-area according to the number of particles and the volume of the sub-area.

[0152] Calculate the relative standard deviation (RSD) of the particle concentration of one of the powdery substances in the sample.

[0153] The mixing area is divided into four directions: middle, edge, upper, and lower. Sampling is carried out separately for conductivity testing, and the samples in each direction are tested 3 times.

[0154] From the test results, it can be obtained that increasing the liquid-solid ratio will increase the number of contacts generated between particles (the result of particle vibration and collision). The increasing trend of the contact number leads to an increase in the corresponding bond formation, which means that the particle size of the produced particles will tend to increase.

[0155] In the actual granulation process, the appropriate liquid content can be selected according to the actual requirements of the particle size.

[0156] Example Two

[0157] Figure 4 FIG. shows a schematic structural diagram of an electronic device 10 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0158] As Figure 4 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can execute various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0159] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0160] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the electrode granulation simulation method.

[0161] In some embodiments, the electrode granulation simulation method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the electrode granulation simulation method described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the electrode granulation simulation method by any other suitable means (e.g., by means of firmware).

[0162] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), system-on-chip systems (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0163] A computer program for implementing the method of the present invention may be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs may be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0164] In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium may be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0165] In order to provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0166] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0167] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is created by computer programs that run on respective computers and have a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0168] Embodiment III

[0169] This embodiment provides a computer program product, including a computer program, which when executed by a processor, implements any one of the electrode granulation simulation methods described in Embodiment I. The implementation process and beneficial effects of the simulation method are the same as the corresponding content described in Embodiment I, and the specific content will not be elaborated herein.

[0170] Note that the above is only the preferred embodiment of the present invention and the applied technical principle. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An electrode granulation simulation method, characterized in that: include: The dry mixing stage in the electrode granulation process is simulated, and the first position information of the particles in the dry mixing stage is determined based on the first mechanical model using the discrete element method; After the dry mixing stage simulation, determining a first standard deviation of the concentration of the particles according to the first position information, and if the first standard deviation is less than a first standard deviation threshold, using the first operating parameter set during the dry mixing stage simulation as the first operating parameter during the actual operation; The wet mixing stage in the electrode granulation process is simulated, and the second position information of the particles in the wet mixing stage is determined based on the second mechanical model using the discrete element method; After the wet mixing stage simulation, the second standard deviation of the particle concentration is determined according to the second position information. If the second standard deviation is less than the second standard deviation threshold, the second operating parameter set during the wet mixing stage simulation is used as the second operating parameter during the actual operation.

2. The electrode granulation simulation method according to claim 1, characterized in that: The first mechanical model adopts the Hertz-Mindlin with JKR model.

3. The electrode granulation simulation method according to claim 1, characterized in that: The second mechanical model adopts a fusion model of the Hertz-Mindlin with JKR model and the Wet Mixing model.

4. The electrode granulation simulation method according to claim 1, characterized in that: During the simulation process, the first position information and the second position information are updated once every time step, and the time step is determined according to the following formula: t=20%T R Where t represents the time step, T R represents the critical time step, r represents the particle radius of the particle, v represents the Poisson's ratio, ρ represents the particle solid density of the particle, and G represents the shear modulus.

5. The electrode granulation simulation method according to claim 1, characterized in that: In the dry mixing stage and the wet mixing stage, the diameter of the configured particles is larger than the diameter of the actual particles, and the flow characteristics of the configured particles are the same as the flow characteristics of the actual particles.

6. The electrode granulation simulation method according to claim 1, characterized in that: The first parameter includes the rotation speed of the stirrer.

7. The electrode granulation simulation method according to claim 1, characterized in that: The second parameter includes the solid-to-liquid ratio of the active material, the conductive agent and the binder.

8. An electronic device, characterized in that: comprising at least one processor, and a memory communicatively connected to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the electrode granulation simulation method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the electrode granulation simulation method according to any one of claims 1 to 7 when executed.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the electrode granulation simulation method according to any one of claims 1 to 7.

Images