Device system and method for controlling particle size of precursor in continuous crystallization process

Abstract

The invention provides a device system and method for controlling the particle size of a precursor in the continuous crystallization process, the device system comprises a flow controller, a sensor, a software prediction module and an error information optimization module, and the flow controller, the software prediction module and the sensor are electrically connected with one another. The error information optimization module is independently and electrically connected with the software prediction module, and a dynamic model is arranged in the software prediction module and used for storing correction growth factors k capable of being updated online. The device system disclosed by the invention can predict the particle size of the precursor particles at each stage and adjust the flow of the seed crystal slurry in time, so that the positive electrode precursor with uniform particle size and relatively low error can be prepared.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of battery material preparation, and relates to a device system and a method for controlling the particle size of a precursor in a continuous crystallization process. BACKGROUND

[0002] In the field of material preparation, continuous crystallization processes are widely used due to their efficiency and continuity. For example, in the preparation of lithium-ion battery cathode material precursors, ternary precursors are often prepared using controlled crystallization methods, in which nickel, cobalt, manganese, and other main metal elements are crystallized and precipitated from solution in the form of oxides or hydroxides. During this process, the control of the particle size of the precursor is crucial to the performance of the material.

[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 gaps between particles, which can result in high tap density of the cathode material, thereby achieving higher energy density. On the other hand, ternary precursors with a narrow particle size distribution have uniform particle size, which can result in higher power and better cycle performance of the battery. In addition, the particle size index also affects the stability, flowability of the cathode material, and the compaction of the electrode in the subsequent lithium-ion battery production process. Therefore, each cathode material manufacturer has specific requirements for the particle size distribution width of the precursor.

[0004] CN115072794A discloses a method for preparing ternary precursors with adjustable particle size distribution, which first prepares seeds in a seed tank and then grows them in a growth tank. By continuously adding seeds, the particle size distribution width of the product is adjusted to achieve the purpose of adjusting the tap density of the product. However, the method has limitations in seed addition process control. The setting of seed addition nodes, addition amount, and addition method mainly relies on human experience, lacks precise theoretical models and scientific calculation basis, and therefore cannot accurately predict particle size changes and achieve fine control of product particle size distribution.

[0005] CN116216792A discloses a method for preparing a wide-distribution fine-powder-free spherical high-nickel ternary precursor material by a seed-controlled precipitation method, which comprises: preparing mixed salt solution, complexing agent solution, precipitant solution and seed slurry; under the conditions of opening of protection gas and stirring, pumping the prepared solutions into a reaction kettle in parallel flow to realize NH4-Ni-Co-Mn-OH interaction through co-precipitation, and realizing complex coupling through the comprehensive action of stirring, air disturbance and liquid flow in a constant-temperature and constant-pressure atmosphere; and obtaining the wide-distribution fine-powder-free spherical high-nickel precursor material after slurry post-treatment at the reaction endpoint, with a particle size distribution not less than 1.35 and Dmin not less than 1.8 μm. In the method, the fixed-proportion seed slurry addition mode is difficult to adapt to complex and changeable reaction conditions, and cannot realize accurate dynamic regulation of particle size distribution.

[0006] The above scheme has too many human factors interference and poor controllability, and it is difficult to accurately regulate the particle size distribution of the precursor, which is difficult to apply in practice. SUMMARY

[0007] The purpose of the present application is to provide a device system and method for controlling the particle size of a precursor in a continuous crystallization process. The device system can predict the particle size of the precursor at each stage and adjust the flow rate of the seed slurry in time, thereby preparing a positive electrode precursor with uniform particle size and low error.

[0008] To achieve this purpose, the present application adopts the following technical solutions:

[0009] In a first aspect, the present application provides a device system for controlling the particle size of a precursor in a continuous crystallization process, which comprises 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 particle size of the precursor. The software prediction module is used to predict the particle size of the precursor and calculate the deviation between the predicted value and the measured value of the particle size of the precursor. The error information optimization module is used to correct the deviation between the predicted value and the measured value of the particle size of the precursor. 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. A dynamic model is provided in the software prediction module for storing an online-updatable correction growth factor k.

[0010] The device system can predict the dynamic change of particle size through a mechanism model, update the correction growth factor of the model using real-time measurement data, and dynamically adjust the amount of seed input. This realizes accurate control of the particle size and distribution of the positive electrode precursor prepared by a continuous method, improves production efficiency, and is conducive to large-scale popularization and application.

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

[0012] Preferably, the online-updatable correction growth factor k is set in the dynamic model of particle size.

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

[0014] The precipitant, the complexing agent, the seed crystal and water are mixed to form a bottom liquid, and the metal salt solution, the precipitant solution and the complexing agent solution are injected into the bottom liquid in the reaction device to react;

[0015] When the slurry in the reaction device reaches the overflow port, the seed crystal slurry is introduced into the reaction device using a flow controller, the solid content of the slurry in the system and the average particle size of the particles are measured, a dynamic model of the continuous crystallization process is established using a software prediction module, and an online-updatable correction growth factor k1 is obtained;

[0016] The online slurry solid content and the online precursor average particle size in the reaction device are read using a sensor, and the actual measured values obtained by reading are transmitted to the software prediction module;

[0017] The software prediction module analyzes the deviation between the actual measured values and the predicted values, transmits the deviation to an error information optimization module, corrects a correction growth factor k2 using the error information optimization module, and transmits the correction growth factor k2 to the software prediction module;

[0018] The software prediction module sends a control signal to the flow controller to automatically adjust the flow of the seed crystal slurry.

[0019] In the method of the present application, k1 is continuously adjusted during the reaction process, the initial k1 is constructed according to the concentrations of various materials and the particle size in the system, and k2 is the correction growth factor adjusted by the error information optimization module, k2 is transmitted to the software prediction module as k1. For example, the correction growth factor in the first stage is k1, the deviation between the actual measured values and the predicted values is transmitted to the error information optimization module after the software prediction module analyzes the deviation, the correction growth factor k2 is obtained by correcting using the error information optimization module, and k2 is transmitted to the software prediction module as the correction growth factor k1 in the next stage. That is, k1 is calculated from the actual measured data at time t, and k2 is obtained by calculation, and k2 is the correction growth factor in the next stage, that is, at time t+1.

[0020] In the method, the correction growth factor k1 of the first stage is based on the in-pot particle number conservation, mass conservation and growth kinetics equation construction in the continuous method production process, k2 is the deviation between the actual measured value and the predicted value analyzed by the software prediction module, the deviation is transmitted to the error information optimization module, and the error information optimization module is corrected to obtain the factor, and the factor is recursively updated through real-time measurement data, so as to dynamically adjust the accuracy of model prediction.

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

[0022] (1) Particle number conservation:

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

[0024] (2) Mass conservation:

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

[0026] (3) Growth kinetics equation:

[0027] dD / dt=z(c-c*),Qsolute×Csolute=N(t)×dm / dt, all solutes are used for seed growth, and no additional nucleation;

[0028] (4) Get the change of particle size:

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

[0030] Growth factor correction:

[0031] On the hourly scale, assuming the flow rate is constant within each hour, the resulting size change equation dD / dt is discretized using Euler's method with a time step of Δt = 1 h. From Dreal(t+1) = Dreal(t) + (k1 x G1 - G2), it follows that k1 = (ΔD + G2) / G1, where Dreal(t+1) is the measured size at t+1 h, Dreal(t) is the measured size at t h, and ΔD = Dreal(t+1) - Dreal(t). The update of the correction factor k2 is performed using an exponentially weighted moving average method, which weights the historical data k2(t) and the current measurement k1(t) to ensure stability, i.e., k2(t+1) = k2(t) x a + k1(t) x (1-a).

[0032] In the above equation, N(t) is the number of particles in the reactor, Nseed(t) is the number of seed particles, C(t) is the solids concentration 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 a forgetting factor, z is a 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 aqueous ammonia.

[0035] Preferably, the ammonia concentration in the base solution is 0.1 M to 1 M, for example 0.1 M, 0.2 M, 0.3 M, 0.4 M, 0.5 M, 0.6 M, 0.7 M, 0.8 M, 0.9 M, or 1 M, and the like, not limited to the listed values, other unlisted values within this range are also applicable.

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

[0037] Preferably, the median particle size D50 of the seed is 2 μm to 5 μm, for example 2 μm, 2.5 μm, 3 μm, 4 μm, or 5 μm, and the like, not limited to the listed values, other unlisted values within this range are also applicable.

[0038] Preferably, the ratio of the seed to the metal element in the metal salt solution is the same.

[0039] Preferably, the solute of the metal salt solution comprises any one or a combination of at least two of a nickel salt, a cobalt salt, or a manganese salt, typical but non-limiting combinations include a combination of a nickel salt, a cobalt salt, and a manganese salt, or a combination of a nickel salt and a manganese salt, and the like.

[0040] Preferably, the mass concentration of the seed crystal in the base 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., not only limited to the listed values, other values not listed in the range of values 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., not only limited to the listed values, other values not listed in the range of values 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., not only limited to the listed values, other values not listed in the range of values 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., not only limited to the listed values, other values not listed in the range of values 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., not only limited to the listed values, other values not listed in the range of values are also applicable.

[0046] Preferably, the pH of the reaction is 9 to 12, for example, 9, 9.5, 10, 11, or 12, etc., not only limited to the listed values, other values not listed in the range of values 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., not only limited to the listed values, other values not listed in the range of values are also applicable.

[0048] Preferably, the mass concentration of the seed crystal 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., not only limited to the listed values, other values not listed in the range of values are also applicable.

[0049] Preferably, the injection of the seed slurry into the reaction device while maintaining the injection of the metal salt solution, the precipitant solution and the complexing agent solution ensures the continuous feeding and overflow of the reaction device.

[0050] The injection of the seed slurry into the reaction device while maintaining the continuous supply of other materials ensures that the process parameters such as pH, reaction temperature, rotation speed, etc. remain constant during the reaction.

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

[0052] The particle growth term G1 of the present application is the average particle size increase value caused by the continuous addition of solutes to promote the growth of particles in the kettle. The mixing term G2 is the average particle size decrease value caused by the continuous addition of smaller particle size seeds.

[0053] Preferably, the correction growth factor k2(t+1) = a x k2(t) + (1-a) x k1(1+t), wherein a is a 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 of the present application is essentially a weighting formula, which is an empirical value determined according to the results of multiple experiments and can be between 0.7 and 0.9. It is used to weight the historical data and the current measurement value to ensure stability.

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

[0056] (1) The device system and method for controlling the particle size of the precursor in the continuous crystallization process of the present application can predict the particle size change through the mechanism model to adjust the seed input amount, and dynamically update the growth factor of the model using real-time measurement data to improve the prediction accuracy, thereby realizing accurate control of the particle size and distribution of the positive electrode precursor prepared by the continuous method, and facilitating large-scale popularization and application.

[0057] (2) The device system and method for controlling the particle size of the precursor in the continuous crystallization process of the present application are suitable for various proportions of ternary positive electrode precursors and have universality. The particle uniformity of the prepared positive electrode precursor is greatly improved, and the error with the predicted target particle size is small, within 1.42%. BRIEF DESCRIPTION OF DRAWINGS

[0058] Figure 1 is a structural schematic diagram of the device system for controlling the particle size of the precursor in the continuous crystallization process provided by the embodiments of the present application, 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 is a schematic diagram of the working process of the device system in the method for controlling the particle size of a precursor in a continuous crystallization process provided by the embodiment of the present application.

[0060] Figure 3 is a SEM image of the positive electrode precursor prepared in the embodiment 1 of the present application.

[0061] Figure 4 is a comparison diagram of the predicted value and the actual value of the precursor at each stage in the embodiment 1 of the present application.

[0062] Figure 5 is a predicted error curve diagram of the precursor at each stage in the embodiment 1 of the present application. DETAILED DESCRIPTION

[0063] The technical solutions of the present application will be further described by specific embodiments. Those skilled in the art should understand that the embodiments are only used to help understand the present application and should not be regarded as specific limitations to the present application.

[0064] The structural schematic diagram of the device system for controlling the particle size of a precursor in a continuous crystallization process used by each embodiment of the present application is shown in Figure 1 , which comprises 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 with each other.

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

[0067] A dynamic model is arranged in the software prediction module 3, which comprises a concentration dynamic model and a particle size dynamic model, and the particle size dynamic model generates a correction growth factor k which can be updated online.

[0068] Embodiment 1

[0069] The present embodiment provides a method for controlling the particle size of a precursor in a continuous crystallization process, and the schematic diagram of the working process of the device system in the method is shown in Figure 2 , which comprises the following steps:

[0070] Preparation of the bottom solution: 400 g / L sodium hydroxide solution, 10 mol / L ammonia, Ni:Co:Mn molar ratio of 95:3:2, median particle size D50 of 4 μm seed crystal, 50% of the volume of the reaction kettle is pure water, the above substances are added into the reaction kettle as the growth bottom solution, and the pH of the bottom solution is 11. After nitrogen is introduced and the dissolved oxygen in the reaction bottom solution is discharged, the same element proportion as the seed crystal is added into the reaction kettle through the feeding device, and the mass concentration of the salt solution is 80 g / L, the mass concentration of the ammonia is 10 mol / L, and the mass concentration of the sodium hydroxide solution is 400 g / L to carry out the reaction. The stirring speed of the reaction kettle is set to 600 rpm, the reaction pH is controlled at 10, and the ammonia is controlled at 5 g / L;

[0071] When the slurry liquid level in the reaction kettle reaches the overflow port, the continuous addition of the seed slurry with a fixed mass concentration of 50 g / L is started, the solid content of the slurry in the system and the average particle size of the particles are measured, the dynamic model of the continuous crystallization process is established by using the software prediction module, and the corrected growth factor k1 that can be updated online is obtained to predict the solid content of the slurry in the system and the average particle size of the particles in the next stage;

[0072] The online solid content of the slurry in the reaction device and the online precursor average particle size are read by using a sensor, and the actual measured values obtained by reading are transmitted to the software prediction module;

[0073] 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, corrects the corrected growth factor k2 by using the error information optimization module, and transmits the corrected growth factor k2 to the software prediction module, so that the cumulative error is reduced. The seed crystal flow value required to meet the target particle size is solved, and it is ensured that the model always adapts to the latest data distribution;

[0074] The software prediction module sends a control signal to the flow controller, automatically adjusts the seed slurry flow, and dynamically adjusts the process model parameters according to the real-time sensor feedback.

[0075] The SEM diagram of the positive electrode precursor prepared in this embodiment is shown in Figure 3 , the comparison diagram of the predicted value and the actual value of the precursor at each stage is shown in Figure 4 , and the predicted error curve diagram of the precursor at each stage is shown in Figure 5 , and Figures 3-5 It can be seen that the particle size of the precursor at each stage can be strictly controlled by prediction, the sphericity of the prepared precursor is high, the particle size distribution is uniform, and the actual error is significantly reduced.

[0076] Example 2

[0077] The method for controlling the particle size of the precursor in the continuous crystallization process provided in this embodiment is shown in Figure 2 , and the method comprises the following steps:

[0078] Preparation of the bottom solution: 300 g / L sodium hydroxide solution, 5 mol / L ammonia, Ni:Co:Mn molar ratio of 60:20:20, median particle size D50 of 2 μm seed crystal, 50% of the volume of the reaction kettle of pure water, the above substances are added into the reaction kettle as the growth bottom solution, the pH of the bottom solution is 9. After purging nitrogen to remove dissolved oxygen in the reaction bottom solution, start adding the same element proportion as the seed crystal to the reaction kettle through the feeding device, the mass concentration of the salt solution is 50 g / L, the mass concentration of ammonia is 5 mol / L, and the mass concentration of sodium hydroxide solution is 300 g / L for reaction. The stirring speed of the reaction kettle is set to 400 rpm, the reaction pH is controlled at 9, and the ammonia is controlled at 3 g / L;

[0079] When the slurry level in the reaction kettle reaches the overflow port, start continuously adding the seed slurry with a fixed mass concentration of 150 g / L. Measure the slurry solid content and the average particle size of the particles in the system, and use the software prediction module to establish a dynamic model of the continuous crystallization process to obtain the online updated correction growth factor k1 for predicting the slurry solid content and the average particle size of the particles in the system in the next stage;

[0080] Use a sensor to read the online slurry solid content and the online precursor average particle size in the reaction device, and transmit the actual measured values obtained by reading to the software prediction module;

[0081] 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, corrects the correction growth factor k2 using the error information optimization module, and transmits it to the software prediction module to reduce the cumulative error. And solve the required seed flow value that meets the target particle size, ensure that the model always adapts to the latest data distribution;

[0082] The software prediction module sends a control signal to the flow controller to automatically adjust the seed slurry flow. According to the real-time sensor feedback, dynamically adjust the process model parameters.

[0083] Example 3

[0084] The embodiment provides a method for controlling the particle size of a precursor in a continuous crystallization process, wherein the working flow diagram of the device system is as shown in Figure 2 The method comprises the following steps:

[0085] Preparation of the bottom solution: 500 g / L sodium hydroxide solution, 15 mol / L ammonia, Ni:Co:Mn molar ratio of 80:10:10, median particle size D50 of 5 μm seed crystal, 50% of the volume of the reaction kettle of pure water, the above substances are added into the reaction kettle as the growth of the bottom solution, the pH of the bottom solution is 12. After the nitrogen is introduced and the dissolved oxygen in the reaction bottom solution is discharged, the reaction is started by adding the same element proportion as the salt solution to the reaction kettle through the feeding device, the mass concentration of the salt solution is 100 g / L, the mass concentration of the ammonia is 10 mol / L, and the mass concentration of the sodium hydroxide solution is 500 g / L. The stirring speed of the reaction kettle is set to 800 rpm, the reaction pH is controlled at 12, and the ammonia is controlled at 8 g / L;

[0086] When the slurry level in the reaction kettle reaches the overflow port, the continuous addition of the seed slurry with a fixed mass concentration of 20 g / L is started, the solid content of the slurry in the system and the average particle size of the particles are measured, the dynamic model of the continuous crystallization process is established using the software prediction module, and the corrected growth factor k1 for online updating is obtained for predicting the solid content of the slurry in the system and the average particle size of the particles in the next stage;

[0087] The online slurry solid content and the online precursor average particle size in the reaction device are read by the sensor, and the actual measured values read are transmitted to the software prediction module;

[0088] 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, corrects the corrected growth factor k2 using the error information optimization module, and transmits it to the software prediction module to reduce the cumulative error. The required seed flow value that meets the target particle size is solved, and it is ensured that the model always adapts to the latest data distribution;

[0089] The software prediction module sends a control signal to the flow controller to automatically adjust the seed slurry flow, and dynamically adjusts the process model parameters according to the 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 particle size of the precursor in the continuous crystallization process is not used to control the particle size of the precursor.

[0092] Performance test:

[0093] The particle size of the precursor obtained in Examples 1-3 and Comparative Example 1 is measured respectively, the D10, D50 and D90 of the particles and the error between the average particle size and the predicted particle size are recorded, and the test results are shown in Table 1:

[0094] Table 1

[0095] Error (%) D10 (pm) D50 (pm) D90 (pm) 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, it can be obtained from Examples 1-3 that the device system and method for controlling the particle size of the precursor in the continuous crystallization process according to the present application is suitable for ternary positive electrode precursors of various proportions, has universality, the particle uniformity of the prepared positive electrode precursors is greatly improved, and the error with the predicted target particle size is small, reaching within 1.42%.

[0097] As can be obtained from the comparison of Example 1 and Comparative Example 1, the positive electrode precursor is prepared by using the device system for controlling the particle size of the precursor in the continuous crystallization process according to the present application, the particle size change is dynamically predicted by the mechanism model, the correction growth factor of the model is updated by using the real-time measurement data, and the seed input amount is dynamically adjusted. The particle size and distribution in the process of preparing the positive electrode precursor by the continuous method are precisely controlled, the production efficiency is improved, and large-scale popularization and application are facilitated.

[0098] The applicant declares that the above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. It should be understood by those skilled in the art that any changes or replacements within the technical scope disclosed by the present application can be easily thought of by any person skilled in the art, and all fall within the protection scope and disclosure scope of the present application.

Claims

1. An apparatus system for controlling the size of precursor particles in a continuous crystallization process, characterized by, The device system comprises a flow controller, a sensor, a software prediction module and an error information optimization module; The flow controller is used to control the flow of the seed slurry, the sensor is used to read the solid content of the slurry and the particle size of the precursor, the software prediction module is used to predict the particle size of the precursor and calculate the deviation between the predicted value and the measured value of the particle size of the precursor, and the error information optimization module is used to correct the deviation between the predicted value and the measured value of the particle size of the precursor. The flow controller, the software prediction module and the sensor are electrically connected with each other. The error information optimization module is independently electrically connected with the software prediction module. A dynamic model is arranged in the software prediction module to store an online-updatable correction growth factor k.

2. The device system of claim 1, wherein, The dynamic model comprises a concentration dynamic model and a particle size dynamic model.

3. The apparatus system of claim 2, wherein, The online-updatable correction growth factor k is arranged in the particle size dynamic model.

4. A method of controlling the size of precursor particles in a continuous crystallization process, characterized by, The method uses the device system according to any one of claims 1-3, and the method comprises the following steps: A precipitant, a complexing agent, seed crystals and water are mixed to prepare a bottom liquid, a metal salt solution, a precipitant solution and a complexing agent solution are injected into the bottom liquid in the reaction device to react; When the slurry in the reaction device reaches the overflow port, the flow controller is used to pass the seed slurry into the reaction device, the solid content of the slurry in the system and the average particle size of the particles are measured, the dynamic model of the continuous crystallization process is established by using the software prediction module, and an online-updatable correction growth factor k1 is obtained; The sensor is used to read the online solid content of the slurry and the online average particle size of the precursor in the reaction device, and the actual measured value obtained by reading is 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, corrects the correction growth factor k2 by using the error information optimization module and transmits the correction growth factor k2 to the software prediction module; the software prediction module sends a control signal to the flow controller to automatically adjust the flow of the seed slurry.

5. The method of claim 4, wherein, The precipitant comprises sodium hydroxide; Preferably, the complexing agent comprises ammonia water; Preferably, the ammonia concentration in the bottom liquid is 3g / L-8g / L; Preferably, the pH of the bottom liquid is 9-12; Preferably, the median particle size D50 of the seed crystals is 2μm-5μm; Preferably, the proportion of the seed crystals to the metal elements in the metal salt solution is the same; Preferably, the mass concentration of the seed crystals in the bottom liquid is 20g / L-60g / L.

6. The method of claim 4 or 5, wherein, The total mass concentration of metal ions in the metal salt solution is 50g / L-100g / L; Preferably, the mass concentration of the precipitant solution is 300g / L-500g / L; Preferably, the molar concentration of the complexing agent solution is 5mol / L-10mol / L.

7. The method according to any one of claims 4 to 6, wherein, The reaction is carried out under stirring; Preferably, the stirring speed is 400rpm-800rpm; Preferably, the pH of the reaction is 9-12; Preferably, the ammonia concentration during the reaction is 3g / L-8g / L.

8. The method according to any one of claims 4 to 7, wherein, The mass concentration of the seed slurry is 20g / L-150g / L; Preferably, the injection of the seed slurry into the reaction device is performed while maintaining the injection of the metal salt solution, the precipitant solution and the complexing agent solution, ensuring a continuous feed and overflow of the reaction device.

9. The method according to any one of claims 4 to 8, wherein, The growth factor k1 = (dD / dt - G2) / G1, where dD / dt is the growth rate of the particles in the reaction device, G1 is the particle size growth term and G2 is the mixing term.

10. The method according to any one of claims 4 to 9, characterized in that, The correction growth factor k2(t+1) = a x k2(t) + (1-a) x k1(1+t), where a is a forgetting factor and t is the number of test stages.

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