A device system and method for controlling the particle size of precursors during continuous crystallization.

Abstract

This invention provides a device system and method for controlling the particle size of precursors during continuous crystallization. The device system includes a flow controller, a sensor, a software prediction module, and an error information optimization module. The flow controller, software prediction module, and sensor are electrically connected to each other. The error information optimization module is independently electrically connected to the software prediction module. The software prediction module contains a dynamic model for storing an online-updable correction growth factor k. The device system of this invention can predict the particle size of precursor particles at each stage and adjust the flow rate of the seed slurry in a timely manner, thus producing cathode precursors with uniform particle size and low error.

Description

Technical Field

[0001] This invention belongs to the field of battery material preparation technology, and relates to a device system and method for controlling the particle size of precursors during continuous crystallization. Background Technology

[0002] In the field of materials preparation, continuous crystallization processes are widely used due to their high efficiency and sustainability. Taking the preparation of lithium-ion battery cathode material precursors as an example, ternary precursors often employ controlled crystallization methods, where main metal elements such as nickel, cobalt, and manganese are crystallized from solution in the form of oxides or hydroxides. In this process, controlling the precursor particle size has a crucial impact on material properties.

[0003] Precursors with different particle size distributions exhibit different characteristics in applications. For example, in lithium-ion battery cathode materials, ternary precursors with a wide particle size distribution have small inter-particle gaps, resulting in high tap density and thus higher energy density. Conversely, ternary precursors with a narrow particle size distribution, while having limited tap density, have uniform particle size, enabling the battery to achieve higher power and better cycle performance. Furthermore, particle size also affects the stability, flowability, and electrode compaction of the cathode material during subsequent lithium-ion battery production. Therefore, cathode material manufacturers have specific requirements for the particle size distribution width of the precursors.

[0004] CN115072794A discloses a method for preparing a ternary precursor with controllable particle size distribution. The method first prepares seed crystals in a seeding vessel, followed by subsequent growth in a growth vessel. By continuously adding seed crystals, the particle size distribution width of the product can be controlled, thereby controlling the tap density. However, this method has limitations in controlling the seed crystal addition process. The setting of the seed crystal addition node, amount, and method relies mainly on human experience, lacking precise theoretical models and scientific calculations. Therefore, it cannot accurately predict particle size changes and achieves fine-grained control over the product's particle size distribution.

[0005] CN116216792A discloses a method for preparing a broad-distribution, fine-powder-free, spherical high-nickel ternary precursor material using a seed-controlled precipitation method. The method includes: preparing a mixed salt solution, a complexing agent solution, a precipitant solution, and a seed slurry; under protective gas conditions and with stirring, pumping the prepared solutions into a reactor in parallel flow to achieve co-precipitation and realize the interaction of NH4-Ni-Co-Mn-OH; and achieving complexation coupling in a constant temperature and pressure atmosphere through the combined effects of stirring, gas disturbance, and liquid flow. After post-treatment of the slurry at the reaction endpoint, a broad-distribution, fine-powder-free, spherical high-nickel precursor material is obtained, with a particle size distribution of not less than 1.35 μm and a Dmin of not less than 1.8 μm. However, the fixed proportion of seed slurry addition in this method is difficult to adapt to complex and variable reaction conditions and cannot achieve precise dynamic control of particle size distribution.

[0006] The above-mentioned schemes suffer from excessive human interference, poor controllability, and difficulty in accurately controlling the precursor particle size distribution, making them difficult to apply in practice. Summary of the Invention

[0007] The purpose of this invention is to provide a device system and method for controlling the particle size of precursors during continuous crystallization. The device system of this invention can predict the particle size of precursor particles at each stage and adjust the flow rate of seed slurry in a timely manner, thereby producing positive electrode precursors with uniform particle size and low error.

[0008] To achieve this objective, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides a device system for controlling the precursor particle size during continuous crystallization. The device system includes a flow controller, a sensor, a software prediction module, and an error information optimization module. The flow controller is used to control the flow rate of the seed slurry. The sensor is used to read the solid content of the slurry and the precursor particle size. The software prediction module is used to predict the precursor particle size and calculate the deviation between the predicted and measured values ​​of the precursor particle size. The error information optimization module is used to correct the deviation between the predicted and measured values ​​of the precursor particle size. The flow controller, the software prediction module, and the sensor are electrically connected to each other. The error information optimization module is independently electrically connected to the software prediction module. The software prediction module has a dynamic model for storing an online-updable correction growth factor k.

[0010] The device system described in this invention can dynamically predict particle size changes through a mechanistic model and update the model's correction growth factor using real-time measurement data, thereby dynamically adjusting the amount of seed crystals added. This enables precise control of particle size and distribution during the continuous preparation of cathode precursors, improving production efficiency and facilitating large-scale application.

[0011] Preferably, the dynamic model includes a concentration dynamic model and a particle size dynamic model.

[0012] Preferably, the online-updable corrective growth factor k is set in the particle size dynamic model.

[0013] In a second aspect, the present invention provides a method for controlling the particle size of a precursor during continuous crystallization, the method using the apparatus system described in the first aspect, the method comprising the following steps:

[0014] A bottom solution is prepared by mixing precipitant, complexing agent, seed crystals and water. The metal salt solution, precipitant solution and complexing agent solution are injected into the bottom solution of the reaction apparatus in parallel to carry out the reaction.

[0015] When the slurry in the reaction device reaches the overflow port, the seed slurry is introduced into the reaction device using a flow controller. The solid content of the slurry and the average particle size in the system are measured. A dynamic model of the continuous crystallization process is established using a software prediction module to obtain the correction growth factor k1 that can be updated online.

[0016] Sensors are used to read the online slurry solids content and online precursor average particle size in the reaction device, and the actual measured values ​​are transmitted to the software prediction module.

[0017] The software prediction module analyzes the deviation between the actual measured value and the predicted value, transmits the deviation to the error information optimization module, uses the error information optimization module to correct and obtain the corrected growth factor k2, and transmits it to the software prediction module.

[0018] The software prediction module sends control signals to the flow controller to automatically adjust the seed slurry flow rate.

[0019] In the method described in this invention, k1 is continuously adjusted during the reaction process. Initially, k1 is constructed based on the concentration and particle size of various materials in the system. k2 is a corrected growth factor adjusted by the error information optimization module. k2 is then transmitted to the software prediction module as k1. For example, the corrected growth factor in the first stage is k1. After analyzing the deviation between the actual measured value and the predicted value, the software prediction module transmits the deviation to the error information optimization module. The error information optimization module corrects the deviation to obtain the corrected growth factor k2, which is then transmitted to the software prediction module as the corrected growth factor k1 for the next stage. In other words, k1 is calculated from the measured data at time t, and then k2 is calculated to obtain the corrected growth factor for the next stage, i.e., time t+1.

[0020] In the method described in this invention, the first-stage correction growth factor k1 is constructed based on the conservation of particle number, mass, and growth kinetic equations in the continuous production process. k2 is obtained by the software prediction module analyzing the deviation between the actual measured value and the predicted value, transmitting the deviation to the error information optimization module, and correcting it using the error information optimization module. This factor is updated recursively through real-time measurement data, thereby dynamically adjusting the accuracy of the model prediction.

[0021] The detailed derivation process of k1 and k2 is as follows:

[0022] (1) Particle number conservation:

[0023] dN / dt=Qseed×Nseed(t)-(Qseed+Qsolute)×N(t), where N(t)=C(t) / ρπ / (Dt) 3 ;

[0024] (2) Conservation of mass:

[0025] dC / dt=(Qseed×Cseed+Qsolute×Csolute)-(Qseed+Qsolute)×C+Rgrowth;

[0026] (3) Growth kinetic equation:

[0027] dD / dt=z(cc*), Qsolute×Csolute=N(t)×dm / dt, all solute is used for seed growth, no additional nucleation occurs;

[0028] (4) The particle size change is obtained by combining the following:

[0029] dD / dt=f(V,Qseed,Cseed,Dseed,Dt,Qsolute,Csolute)=k1×dD1 / dt(particle size growth term)+dD2 / dt(mixing effect due to the addition and outflow of new seed crystals)=k1×G1+G2, where G1 is the particle size increase term caused by growth, and G2 is the particle size decrease term caused by seed crystal mixing.

[0030] Growth factor correction:

[0031] On an hourly scale, assuming the flow rate is constant within each hour, the resulting particle size variation formula dD / dt is discretized using Euler, with a time step Δt = 1 hour. From Dreal(t+1) = Dreal(t) + (k1 × G1 - G2), we derive k1 = (ΔD + G2) / G1, where Dreal(t+1) is the measured particle size at hour t+1, Dreal(t) is the measured particle size at hour t, and ΔD = Dreal(t+1) - Dreal(t). k2(t+1) = k2(t) × a + k1(t) × (1 - a). The update of the correction factor k2 uses an exponentially weighted moving average method, weighting the historical data k2(t) and the current measurement value k1(t) to ensure stability.

[0032] In the above formula, N(t) is the number of particles in the reactor, Nseed(t) is the number of seed slurry particles, C(t) is the solid content of the slurry in the reactor, Dt is the average particle size in the reactor, Dseed is the seed particle size, Qsolute is the solute flow rate, Qseed is the seed flow rate, Csolute is the solute concentration, Cseed is the seed concentration, Rgrowth is the solute conversion mass, m is the mass of a single particle, V is the volume of the reactor, k1 and k2 are correction growth factors, a is the forgetting factor, z is the growth rate constant, c is the solute concentration, and c* is the equilibrium concentration.

[0033] Preferably, the precipitant comprises sodium hydroxide.

[0034] Preferably, the complexing agent comprises ammonia.

[0035] Preferably, the ammonia concentration in the base liquid is [value missing].

[0036] Preferably, the pH of the base solution is 9 to 12, for example: 9, 9.5, 10, 11 or 12, etc., and is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0037] Preferably, the median grain size D50 of the seed crystal is 2μm to 5μm, for example: 2μm, 2.5μm, 3μm, 4μm or 5μm, etc., and is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0038] Preferably, the seed crystals are in the same proportion as the metal elements in the metal salt solution.

[0039] Preferably, the solute in the metal salt solution includes any one or a combination of at least two of nickel salts, cobalt salts, or manganese salts. Typical but non-limiting combinations include combinations of nickel salts, cobalt salts, and manganese salts, or combinations of nickel salts and manganese salts, etc.

[0040] Preferably, the mass concentration of the seed crystals in the substrate solution is 20 g / L to 60 g / L, for example: 20 g / L, 30 g / L, 40 g / L, 50 g / L or 60 g / L, etc., and is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0041] Preferably, the total mass concentration of metal ions in the metal salt solution is 50 g / L to 100 g / L, for example: 50 g / L, 60 g / L, 70 g / L, 80 g / L, 90 g / L or 100 g / L, etc., and is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0042] Preferably, the mass concentration of the precipitant solution is 300 g / L to 500 g / L, for example: 300 g / L, 350 g / L, 400 g / L, 450 g / L or 500 g / L, etc., and is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0043] Preferably, the molar concentration of the complexing agent solution is 5 mol / L to 15 mol / L, for example: 5 mol / L, 8 mol / L, 10 mol / L, 12 mol / L or 15 mol / L, etc., and is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0044] Preferably, stirring is performed during the reaction.

[0045] Preferably, the stirring speed is 400 rpm to 800 rpm, for example: 400 rpm, 500 rpm, 600 rpm, 700 rpm or 800 rpm, etc., and is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0046] Preferably, the pH of the reaction is 9 to 12, for example: 9, 9.5, 10, 11 or 12, etc., and is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0047] Preferably, the ammonia concentration during the reaction is 3 g / L to 8 g / L, for example: 3 g / L, 4 g / L, 5 g / L, 6 g / L, 7 g / L or 8 g / L, etc., and is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0048] Preferably, the mass concentration of the seed slurry is 20 g / L to 150 g / L, for example: 20 g / L, 50 g / L, 80 g / L, 100 g / L or 150 g / L, etc., and is not limited to the listed values. Other unlisted values ​​within this range are also applicable.

[0049] Preferably, the process of introducing seed slurry into the reaction device is accompanied by the injection of metal salt solution, precipitant solution and complexing agent solution, ensuring that the reaction device is continuously fed and overflowed simultaneously.

[0050] The present invention requires that while the seed slurry is introduced into the reaction device, other materials must be continuously supplied to ensure that process parameters such as pH, reaction temperature, and rotation speed remain constant during the reaction.

[0051] Preferably, the growth factor k1 = (dD / dt - G2) / G1, where dD / dt is the growth rate of particles in the reaction device, G1 is the particle size growth term, and G2 is the mixing term.

[0052] The particle growth term G1 in this invention refers to the increase in average particle size resulting from the continuous addition of solute to promote particle growth within the reactor. The mixing term G2 refers to the decrease in average particle size within the reactor caused by the continuous addition of smaller-diameter seed crystals.

[0053] Preferably, the corrected growth factor k2(t+1) = a×k2(t) + (1-a)×k1(1+t), where a is the forgetting factor and t is the number of test stages, for example, t is the first stage test and 1+t is the second stage test.

[0054] The forgetting factor 'a' described in this invention is essentially a weighted formula, an empirical value determined based on multiple experimental results. It can take values ​​between 0.7 and 0.9 and is used to weight historical data and current measurements to ensure stability.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] (1) The device system and method for controlling the precursor particle size during continuous crystallization described in this invention can predict particle size changes through a mechanism model to adjust the amount of seed crystals fed in, and dynamically update the growth factor of the model using real-time measurement data to improve prediction accuracy. It can achieve precise control of particle size and distribution during the continuous preparation of cathode precursors, which is conducive to large-scale promotion and application.

[0057] (2) The device system and method for controlling the particle size of precursors during continuous crystallization described in this invention are applicable to ternary cathode precursors of various proportions and have universality. The uniformity of the cathode precursor particles is greatly improved and the error with the predicted target particle size is small, reaching within 1.42%. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of the device system for controlling the particle size of precursors during continuous crystallization provided in an embodiment of the present invention. 1 is a flow controller, 2 is a sensor, 3 is a software prediction module, and 4 is an error information optimization module.

[0059] Figure 2 This is a schematic diagram of the working process of the apparatus system in the method for controlling the particle size of precursors during continuous crystallization provided in the embodiments of the present invention.

[0060] Figure 3 This is a SEM image of the positive electrode precursor prepared in Example 1 of the present invention.

[0061] Figure 4 This is a comparison chart of the predicted and actual values ​​of the precursors at each stage in Embodiment 1 of the present invention.

[0062] Figure 5 This is a prediction error curve of the precursor at each stage in Embodiment 1 of the present invention. Detailed Implementation

[0063] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention in any way.

[0064] The structural schematic diagrams of the device system for controlling the precursor particle size during continuous crystallization used in various embodiments of the present invention are shown below. Figure 1 As shown, the device system includes a flow controller 1, a sensor 2, a software prediction module 3, and an error information optimization module 4;

[0065] The flow controller 1, the software prediction module 3, and the sensor 2 are electrically connected to each other.

[0066] The error information optimization module 4 is independently electrically connected to the software prediction module 3;

[0067] The software prediction module 3 is equipped with a dynamic model, which includes a concentration dynamic model and a particle size dynamic model. The particle size dynamic model generates a corrected growth factor k that can be updated online.

[0068] Example 1

[0069] This embodiment provides a method for controlling the particle size of precursors during continuous crystallization. The flowchart of the apparatus system in this method is shown below. Figure 2 As shown, the method includes the following steps:

[0070] The substrate solution was prepared as follows: 400 g / L sodium hydroxide solution, 10 mol / L ammonia water, seed crystals with a median particle size D50 of 4 μm (Ni:Co:Mn molar ratio of 95:3:2), and 50% of the reactor volume of pure water. All of these substances were added to the reactor as the growth substrate solution, with a pH of 11. Nitrogen gas was introduced to purge dissolved oxygen from the substrate solution. Then, a salt solution with a mass concentration of 80 g / L (equal to the seed crystals), 10 mol / L ammonia water, and 400 g / L sodium hydroxide solution were added to the reactor via a feeding device. The stirring speed of the reactor was set to 600 rpm, the reaction pH was controlled at 10, and the ammonia concentration was controlled at 5 g / L.

[0071] When the slurry level in the reactor reaches the overflow port, a fixed mass concentration of 50 g / L of seed slurry is continuously added. The solid content of the slurry and the average particle size in the system are measured. A dynamic model of the continuous crystallization process is established using the software prediction module to obtain an online-updable correction growth factor k1 for predicting the solid content of the slurry and the average particle size in the next stage of the system.

[0072] Sensors are used to read the online slurry solids content and online precursor average particle size in the reaction device, and the actual measured values ​​are transmitted to the software prediction module.

[0073] The software prediction module analyzes the deviation between the actual measured value and the predicted value, and transmits the deviation to the error information optimization module. The error information optimization module corrects the deviation to obtain the correction growth factor k2, which is then transmitted back to the software prediction module to reduce accumulated errors. It also calculates the seed flow rate required to meet the target particle size, ensuring the model always adapts to the latest data distribution.

[0074] The software prediction module sends control signals to the flow controller to automatically adjust the seed slurry flow rate and dynamically adjusts the process model parameters based on real-time sensor feedback.

[0075] The SEM image of the positive electrode precursor obtained in this embodiment is shown below. Figure 3 As shown in the figure, the predicted and actual values ​​of precursors at each stage are compared. Figure 4 As shown in the figure, the prediction error curves of precursors at each stage are as follows: Figure 5 As shown, by Figure 3-5 As can be seen, this application can strictly control the particle size of the precursor at each stage through prediction, resulting in precursors with high sphericity and uniform particle size distribution, and significantly reducing the error compared to the actual values.

[0076] Example 2

[0077] This embodiment provides a method for controlling the particle size of precursors during continuous crystallization. The flowchart of the apparatus system in this method is shown below. Figure 2 As shown, the method includes the following steps:

[0078] The substrate solution was prepared as follows: 300 g / L sodium hydroxide solution, 5 mol / L ammonia water, seed crystals with a median particle size D50 of 2 μm (Ni:Co:Mn molar ratio of 60:20:20), and 50% of the reactor volume of pure water. All of these substances were added to the reactor as the growth substrate solution, with a pH of 9. After purging the substrate solution with nitrogen gas to remove dissolved oxygen, a salt solution with a mass concentration of 50 g / L (equal to the seed crystals), 5 mol / L ammonia water, and a 300 g / L sodium hydroxide solution were added to the reactor via a feeding device. The reactor stirring speed was set to 400 rpm, the reaction pH was controlled at 9, and the ammonia concentration was controlled at 3 g / L.

[0079] When the slurry level in the reactor reaches the overflow port, a fixed mass concentration of 150 g / L of seed slurry is continuously added. The solid content of the slurry and the average particle size in the system are measured. A dynamic model of the continuous crystallization process is established using the software prediction module to obtain an online-updable correction growth factor k1 for predicting the solid content of the slurry and the average particle size in the next stage of the system.

[0080] Sensors are used to read the online slurry solids content and online precursor average particle size in the reaction device, and the actual measured values ​​are transmitted to the software prediction module.

[0081] The software prediction module analyzes the deviation between the actual measured value and the predicted value, and transmits the deviation to the error information optimization module. The error information optimization module corrects the deviation to obtain the correction growth factor k2, which is then transmitted back to the software prediction module to reduce accumulated errors. It also calculates the seed flow rate required to meet the target particle size, ensuring the model always adapts to the latest data distribution.

[0082] The software prediction module sends control signals to the flow controller to automatically adjust the seed slurry flow rate and dynamically adjusts the process model parameters based on real-time sensor feedback.

[0083] Example 3

[0084] This embodiment provides a method for controlling the particle size of precursors during continuous crystallization. The flowchart of the apparatus system in this method is shown below. Figure 2 As shown, the method includes the following steps:

[0085] The substrate solution was prepared as follows: 500 g / L sodium hydroxide solution, 15 mol / L ammonia water, Ni:Co:Mn seed crystals with a median particle size D50 of 5 μm (Ni:Co:Mn molar ratio of 80:10:10), and 50% of the reactor volume of pure water. All of these substances were added to the reactor as the growth substrate solution, with a pH of 12. Nitrogen gas was introduced to purge dissolved oxygen from the substrate solution. Then, a salt solution with a mass concentration of 100 g / L, ammonia water with a mass concentration of 10 mol / L, and a sodium hydroxide solution with a mass concentration of 500 g / L were added to the reactor via a feeding device. The stirring speed of the reactor was set to 800 rpm, the reaction pH was controlled at 12, and the ammonia concentration was controlled at 8 g / L.

[0086] When the slurry level in the reactor reaches the overflow port, a fixed mass concentration of 20 g / L of seed slurry is continuously added. The solid content of the slurry and the average particle size in the system are measured. A dynamic model of the continuous crystallization process is established using the software prediction module to obtain an online-updable correction growth factor k1 for predicting the solid content of the slurry and the average particle size in the next stage of the system.

[0087] Sensors are used to read the online slurry solids content and online precursor average particle size in the reaction device, and the actual measured values ​​are transmitted to the software prediction module.

[0088] The software prediction module analyzes the deviation between the actual measured value and the predicted value, and transmits the deviation to the error information optimization module. The error information optimization module corrects the deviation to obtain the correction growth factor k2, which is then transmitted back to the software prediction module to reduce accumulated errors. It also calculates the seed flow rate required to meet the target particle size, ensuring the model always adapts to the latest data distribution.

[0089] The software prediction module sends control signals to the flow controller to automatically adjust the seed slurry flow rate and dynamically adjusts the process model parameters based on real-time sensor feedback.

[0090] Comparative Example 1

[0091] The only difference between this comparative example and Example 1 is that the device system for controlling the precursor particle size during the continuous crystallization process described above is not used to control the precursor particle size.

[0092] Performance testing:

[0093] The particle size of the precursors obtained in Examples 1-3 and Comparative Example 1 was measured, and the D10, D50, and D90 of the particles, as well as the error between the average particle size and the predicted particle size, were recorded. The test results are shown in Table 1.

[0094] Table 1

[0095] Example 1 1.21 5.96 10.05 15.87 Example 2 1.38 4.89 10.00 14.72 Example 3 1.42 6.01 10.03 16.33 Comparative Example 1 4.12 3.55 10.00 19.87

[0096] As can be seen from Table 1, and from Examples 1-3, the device system and method for controlling the precursor particle size during continuous crystallization described in this invention are applicable to ternary cathode precursors of various proportions, have universality, and significantly improve the particle uniformity of the cathode precursors, with a small error compared to the predicted target particle size, reaching within 1.42%.

[0097] As can be seen from the comparison between Example 1 and Comparative Example 1, the present invention uses a device system to control the precursor particle size during continuous crystallization to prepare the cathode precursor. The dynamic changes in particle size are predicted through a mechanistic model, and the correction growth factor of the model is updated using real-time measurement data to dynamically adjust the seed crystal input. This achieves precise control over the particle size and distribution during the continuous preparation of the cathode precursor, improving production efficiency and facilitating large-scale application.

[0098] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method of controlling the size of precursor particles in a continuous crystallization process, characterized by, The method uses a device system for controlling the precursor particle size during continuous crystallization, the device system including a flow controller, a sensor, a software prediction module, and an error information optimization module; The flow controller is used to control the flow rate of the seed slurry, the sensor is used to read the solid content of the slurry and the precursor particle size, the software prediction module is used to predict the precursor particle size and calculate the deviation between the predicted value and the measured value of the precursor particle size, and the error information optimization module is used to correct the deviation between the predicted value and the measured value of the precursor particle size. The flow controller, software prediction module, and sensor are electrically connected to each other. The error information optimization module is independently electrically connected to the software prediction module; The software prediction module is equipped with a dynamic model to store the corrected growth factor k, which can be updated online. The method includes the following steps: A bottom solution is prepared by mixing precipitant, complexing agent, seed crystals and water. The metal salt solution, precipitant solution and complexing agent solution are injected into the bottom solution of the reaction apparatus in parallel to carry out the reaction. When the slurry in the reaction device reaches the overflow port, the seed slurry is introduced into the reaction device using a flow controller. The solid content of the slurry and the average particle size in the system are measured. A dynamic model of the continuous crystallization process is established using a software prediction module to obtain the correction growth factor k1 that can be updated online. Sensors are used to read the online slurry solids content and online precursor average particle size in the reaction device, and the actual measured values ​​are transmitted to the software prediction module. The software prediction module analyzes the deviation between the actual measured value and the predicted value, transmits the deviation to the error information optimization module, uses the error information optimization module to correct the deviation and obtain the corrected growth factor k2, and transmits it to the software prediction module; the software prediction module sends a control signal to the flow controller to automatically adjust the flow rate of the seed slurry. The growth factor k1 = (dD / dt - G2) / G1, where dD / dt is the growth rate of particles in the reaction device, G1 is the particle size growth term, and G2 is the mixing term. The corrected growth factor k2(t+1) = a × k2(t) + (1-a) × k1(1+t), where a is the forgetting factor and t is the number of test stages.

2. The method of claim 1, wherein, The dynamic model includes a concentration dynamic model and a particle size dynamic model.

3. The method of claim 2, wherein, The online-updable corrective growth factor k is set in the particle size dynamic model.

4. The method of claim 1, wherein, The precipitant includes sodium hydroxide.

5. The method of claim 1, wherein, The complexing agent includes ammonia.

6. The method of claim 1, wherein, The ammonia concentration in the substrate is 3 g / L to 8 g / L.

7. The method of claim 1, wherein, The pH of the base solution is 9-12.

8. The method of claim 1, wherein, The median grain size D50 of the seed crystals is 2μm~5μm.

9. The method of claim 1, wherein, The seed crystals are in the same proportion as the metal elements in the metal salt solution.

10. The method of claim 1, wherein, The mass concentration of seed crystals in the substrate solution is 20 g / L to 60 g / L.

11. The method of claim 1, wherein, The total mass concentration of metal ions in the metal salt solution is 50 g / L to 100 g / L.

12. The method of claim 1, wherein, The mass concentration of the precipitant solution is 300 g / L to 500 g / L.

13. The method of claim 1, wherein, The molar concentration of the complexing agent solution is 5 mol / L to 10 mol / L.

14. The method of claim 1, wherein, Stirring is performed during the reaction.

15. The method of claim 14, wherein, The stirring speed is 400 rpm to 800 rpm.

16. The method of claim 1, wherein, The pH of the reaction is 9-12.

17. The method of claim 1, wherein, The ammonia concentration during the reaction is 3 g / L to 8 g / L.

18. The method of claim 1, wherein, The mass concentration of the seed slurry is 20 g / L to 150 g / L.

19. The method of claim 1, wherein, The process involves simultaneously introducing seed slurry into the reaction device while also injecting metal salt solution, precipitant solution, and complexing agent solution, ensuring continuous feeding and overflow of the reaction device.

Images