Wave-absorbing material chemical coprecipitation reaction kettle

Abstract

The invention discloses a wave-absorbing material chemical coprecipitation reaction kettle, and relates to the technical field of chemical engineering equipment and functional material preparation, the reaction kettle comprises the following components: a three-stage gradient reaction cavity structure, an anti-pollution structure and a multi-physics field coupling control platform; through the design of a three-stage gradient reaction cavity structure, optimal distribution of a fluid field is realized, stable and efficient flowing of a reaction solution among different cavities is ensured, a multi-axis intelligent mixing system in a premixing area can quickly mix a precursor solution to achieve a molecular-level mixing state, an ideal initial condition is provided for subsequent reaction, and the reaction efficiency is improved. The nucleation area realizes uniform nucleation of nanocrystal nuclei through accurate temperature control and chemical environment control, the grain size distribution range is effectively reduced, the growth area controls preferred growth of crystal faces through adjustment of magnetic field intensity and solution supersaturation degree, particles with good monodispersity are obtained, and under the combined action of the designs, the particle size distribution range is effectively reduced. The reaction efficiency and the consistency of the product quality are obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical engineering equipment and functional material preparation, and specifically relates to a chemical co-precipitation reactor for absorbing materials. Background Art

[0002] As an important functional material, absorbing materials have broad application prospects in the fields of military stealth, electromagnetic shielding, microwave absorption, etc. The chemical co-precipitation method is an effective method for preparing absorbing materials. It mixes different metal ions in a solution and adjusts the reaction conditions to make the metal ions co-precipitate to form a precursor, and then obtains an absorbing material with specific electromagnetic properties through subsequent treatment. During the preparation process of the absorbing material, as a key device, the structure and performance of the reactor directly affect the quality of the product and the production efficiency.

[0003] There are deficiencies in the traditional technology. Poor mixing is a relatively prominent problem. The stirring method of the traditional reactor is difficult to achieve molecular-level mixing of the precursor solution, resulting in uneven distribution of each component in the reaction system and affecting the performance uniformity of the final absorbing material. Secondly, the problem of impurity pollution is serious. The sealing structure and internal material of the traditional reactor are prone to introducing metal impurities, and these impurities will interfere with the microstructure and electromagnetic properties of the absorbing material, reducing the quality of the material. Moreover, the grain size distribution is wide. Due to the insufficient control accuracy of nucleation and grain growth during the reaction process, the prepared absorbing material has different grain sizes, affecting the stability of the absorbing performance of the material. Finally, the reaction efficiency is low. The traditional reactor lacks effective multi-parameter collaborative control means, cannot make full use of the reaction conditions to promote the reaction, prolongs the production cycle, and increases the production cost. These problems limit the preparation of high-performance absorbing materials and urgently need to be solved through innovative reactor design.

[0004] In summary, the traditional chemical co-precipitation reactor for absorbing materials has problems such as poor mixing uniformity, inaccurate control of reaction parameters, and high pollution risk during the preparation process. These problems limit the improvement of the performance of absorbing materials and the expansion of the application scope. Therefore, it is particularly important to develop a chemical co-precipitation reactor for absorbing materials. Summary of the Invention

[0005] The purpose of the present invention is to make up for the deficiencies of the existing technology and provide a chemical co-precipitation reactor for absorbing materials. It can improve the mixing uniformity of the reaction solution, accurately control the reaction parameters, and introduce an effective anti-pollution structure by optimizing the design of the reactor, which can significantly improve the preparation efficiency and product quality of the absorbing material and meet the growing application requirements.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a chemical co-precipitation reactor for microwave absorption materials, which reactor comprises the following components: a three-stage gradient reaction cavity structure, an anti-pollution structure, and a multi-physical field coupling control platform;

[0007] The three-stage gradient reaction cavity structure: successively including a premixing zone, a nucleation zone, and a growth zone, and the zones are connected by a bionic fractal flow channel. The design of the bionic fractal flow channel can achieve an optimized distribution of the fluid field, ensuring a stable and efficient flow of the reaction solution between different chambers, avoiding the occurrence of fluid dead zones and turbulence phenomena, and providing a good fluid environment for subsequent reactions;

[0008] The premixing zone is equipped with a multi-axis intelligent mixing system, which is the coupled action of a main stirring paddle and a microfluidic field agitator. The main stirring paddle is responsible for macroscopically stirring the precursor solution and quickly mixing the components in the solution. The microfluidic field agitator generates tiny flow field disturbances at the microscopic level, further refining the mixing scale, enabling the precursor solution to reach molecular-level mixing, ensuring the uniformity of the reaction system components, and providing ideal starting conditions for subsequent reactions;

[0009] Among them, the microfluidic field agitator generates local velocity changes and direction changes in the solution by the rapid rotation of small blades with a certain shape and a size much smaller than that of the main stirring paddle, thereby forming tiny flow field disturbances. After the main stirring paddle is started, after a certain time delay, the microfluidic field agitator is started. This time delay can be determined according to the properties of the reaction solution and the size of the premixing zone.

[0010] The nucleation zone integrates a distributed microchannel temperature control network, and dynamically adjusts the coolant flow rate through a PID algorithm. At the same time, a closed-loop feedback system is constructed in combination with pH / concentration on-line sensors. The pH / concentration on-line sensors real-time monitor the changes in the pH value and concentration of the solution in the nucleation zone, and transmit the data to the control system. The control system adjusts the coolant flow rate through the PID algorithm according to the preset parameters, thereby precisely controlling the temperature of the nucleation zone. This precise temperature and chemical environment control can achieve uniform nucleation of nanocrystals and effectively narrow the grain size distribution range;

[0011] The growth zone adopts a spiral flow guiding structure design, and controls the preferential growth of crystal planes by adjusting the magnetic field strength and solution supersaturation. Under the action of the spiral flow guiding structure, the solution forms a stable spiral flow in the growth zone, providing a uniform environment for grain growth. At the same time, the magnetic field strength is adjusted by an external magnetic field generator, and the influence of the magnetic field on crystal growth is utilized. Combined with the control of solution supersaturation, the selection of the crystal plane growth direction is realized, and particles with good monodispersity are obtained, thereby improving the performance stability of the microwave absorption material;

[0012] The anti-pollution structure: The device introduces a magnetic drive seal anti-pollution structure, which adopts a PFA lining and a non-contact drive system. The magnetic drive seal structure uses magnetic force to achieve sealing, avoiding the contamination of the reaction system by debris generated by friction in traditional mechanical seals. The PFA lining has good chemical stability and low adsorption, which can prevent the inner wall of the reaction kettle from contaminating the reaction solution. The non-contact drive system further reduces the risk of introducing impurities due to mechanical contact, significantly reducing the content of metal impurities in the reaction kettle and improving the purity of the microwave absorbing material.

[0013] The multi-physical field coupling control platform: Through the multi-physical field coupling control platform, the reaction temperature, pH value, and redox potential are synchronously optimized. This platform integrates a temperature control module, a pH adjustment module, and a redox potential adjustment module, which can monitor and adjust multiple physicochemical parameters during the reaction process in real time. When preparing ferrite and core-shell composite materials, by precisely controlling these parameters, the directional growth of crystal phases and the modification of surface hydroxyl functional groups are realized, thereby obtaining microwave absorbing materials with specific microstructures and electromagnetic properties.

[0014] Furthermore, in the multi-axis intelligent mixing system of the premixing zone, the rotational speed control of the main stirring paddle adopts an adaptive fuzzy control algorithm. This algorithm is based on fuzzy logic and dynamically adjusts the rotational speed of the main stirring paddle according to parameters such as the initial concentration, viscosity of the precursor solution, and the loading capacity of the reaction kettle. First, the input variables are defined. The initial concentration of the precursor solution is divided into three fuzzy sets: low, medium, and high, represented by the membership function μ C (x), where C1 and C2 are concentration thresholds determined based on a large number of experiments. The solution viscosity is divided into three fuzzy sets: low, medium, and high, represented by the membership function μ v (y). where V1 and V2 are viscosity thresholds determined based on experiments. The loading capacity of the reaction kettle is divided into three fuzzy sets: less, medium, and more, represented by the membership function μ L (z). where L1 and L2 are loading capacity thresholds determined according to the specifications of the reaction kettle. The output variable is the rotational speed adjustment amount Δn of the main stirring paddle, which is divided into five fuzzy sets: negative large, negative small, zero, positive small, and positive large. According to the fuzzy rule base, the fuzzy value of Δn is calculated through fuzzy reasoning, and then the actual rotational speed adjustment amount is obtained through defuzzification. After being verified by a large number of experiments, this algorithm can make the precursor solution reach the molecular-level mixing state faster in the premixing zone. Compared with the traditional stirring method, the mixing time is shortened by 30%-50%, greatly improving the mixing efficiency and providing more uniform starting conditions for the subsequent reaction.

[0015] In this application, the adaptive fuzzy control algorithm is an intelligent control method based on fuzzy logic, aiming to adjust the rotation speed of the main stirring paddle in real time and flexibly according to the dynamic changes during the reaction process to achieve efficient mixing of the precursor solution. This algorithm fuzzifies some factors that are difficult to precisely quantify (such as the initial concentration and viscosity of the precursor solution and the loading amount of the reaction kettle), and through a series of fuzzy rules and reasoning processes, obtains an appropriate rotation speed adjustment amount.

[0016] Furthermore, in the distributed microchannel temperature control network of the nucleation zone, the PID control algorithm for the coolant flow rate is improved. Adaptive weight factors α, β, and γ are introduced to dynamically adjust the proportional, integral, and differential links. The formula is:

[0017]

[0018] Among them, the determination of α, β, and γ is based on the real-time process of the nucleation reaction and the dynamic changes of the solution. By setting multiple temperature monitoring points in the nucleation zone, the non-uniformity U of the temperature distribution is obtained. According to the formula:

[0019]

[0020] where U max is the maximum allowable temperature non-uniformity determined by experiments. The more uneven the temperature distribution, the larger α, enhancing the role of the proportional link to quickly adjust the temperature deviation. According to the change rate v of the ion concentration in the solution, v max is the maximum ion concentration change rate determined by experiments. The faster the ion concentration changes, the smaller β, appropriately weakening the integral link to avoid integral saturation;

[0021] According to the fluctuation amplitude A of the pH value, A max is the maximum pH fluctuation amplitude determined by experiments. The larger the pH fluctuation, the larger γ, strengthening the differential link to suppress the rapid fluctuation of temperature change. Through experimental verification, the improved algorithm can make the temperature control accuracy of the nucleation zone reach ±0.5°C. Compared with the traditional PID control, the uniformity of nanocrystallites is increased by 40%-60%, effectively narrowing the range of grain size distribution.

[0022] Furthermore, the adjustment of the magnetic field strength in the growth zone adopts an intelligent algorithm based on particle swarm optimization. In the growth zone, the grain growth is significantly affected by the magnetic field strength. In the particle swarm optimization algorithm;

[0023] Each particle represents a candidate solution for the magnetic field strength. The position X of the particle i =[x i1 ,x i2 ,…,x idRepresents the values of magnetic field strength in different dimensions, velocity V i = [v i1 , v i2 , …, v id represents the adjustment direction and step size of the magnetic field strength;

[0024] When the algorithm is initialized, the initial positions and velocities of N particles are randomly generated, and the fitness function F(X i ) is determined according to the monodispersity index of the grains. The monodispersity index is measured by calculating the reciprocal of the coefficient of variation of the grain size , where σ is the standard deviation of the grain size, is the average grain size, The particles update their velocities and positions according to their own historical best positions P i and the global best position G;

[0025] The velocity update formula is v ij (t + 1) = ωv ij (t) + c1r 1j (t)(p ij ―x ij (t)) + c2r 2j (t)(g j ―x ij (t));

[0026] The position update formula is x ij (t + 1) = x ij (t) + v ij (t + 1), where ω is the inertia weight, c1 and c2 are learning factors, r 1j 、r 2j are random numbers between [0, 1]. After multiple iterations, the particle swarm gradually converges to the optimal solution, that is, the magnetic field strength most suitable for grain growth. Through this algorithm, the monodispersity of the grains prepared in the growth area has been increased by 50% - 70%, effectively improving the performance stability of the microwave absorbing material.

[0027] Furthermore, the design of the bionic fractal flow channel is based on the fractal geometry principle and fluid dynamics simulation. The structural parameters of the fractal flow channel are determined by the iterative formula D n+1 = D n ×λ, D n$D_n$ is the characteristic dimension of the nth-level flow channel, and $\lambda$ is the scaling factor related to the fractal dimension, with a value range of 1.1 - 1.3, which is determined by simulation optimization according to the viscosity and flow rate of different reaction solutions. During the design process, computational fluid dynamics software is used to simulate bionic fractal flow channels with different structures. Taking the velocity distribution uniformity, pressure loss, and mixing effect of the fluid as evaluation indicators, the simulation results show that the optimized bionic fractal flow channel can improve the velocity distribution uniformity by 60% - 80% when the fluid flows between chambers, reduce the pressure loss by 30% - 50%, avoid fluid dead zones and turbulence phenomena, ensure the smooth and efficient flow of the reaction solution between different chambers, provide a good fluid environment for reactions at each stage, and thus improve the reaction efficiency and the consistency of product quality.

[0028] Furthermore, in the multi-physical field coupling control platform, the coordinated control of reaction temperature, pH value, and redox potential adopts a multi-variable predictive control algorithm. A three-layer neural network is constructed. The input layer nodes are the temperature $T$ at the current moment t , pH value $pH$ t , redox potential $ORP$ t and time $t$. The number of hidden layer nodes is determined according to the empirical formula , where $n$ i is the number of input layer nodes, $n$ o is the number of output layer nodes, and $a$ is a constant with a value range of 1 - 10, which is determined to be 5 through multiple experiments. The output layer nodes are the predicted values $T$ t+1 , $pH$ t+1 , $ORP$ t+1 of the temperature, pH value, and redox potential at the next moment. The training data of the neural network comes from a large amount of reaction experimental data. The network weights are adjusted through the backpropagation algorithm. After the prediction model is established, according to the deviation between the predicted value and the target value, the model predictive control algorithm is used to calculate the temperature adjustment amount $\Delta T$, pH adjustment amount $\Delta pH$, and redox potential adjustment amount $\Delta ORP$ to achieve the coordinated control of the multi-physical field. This algorithm can control the fluctuation ranges of temperature, pH value, and redox potential during the reaction process within $\pm1^{\circ}C$, $\pm0.2$, and $\pm5mV$ respectively, effectively promoting the oriented growth of crystal phases and surface hydroxyl functional group modification of ferrite and core-shell composite absorbing materials, and improving the microstructure and electromagnetic properties of the materials.

[0029] Furthermore, the magnetic drive seal anti-pollution structure uses permanent magnetic materials and an optimized magnetic circuit design. Neodymium iron boron permanent magnets are selected as the power source for the magnetic drive seal, and its remanence $B$ r is greater than 1.2T, and the coercivity $H$ cGreater than 800 kA / m, the magnetic circuit design is optimized through finite element analysis software to form a uniform and stable magnetic field distribution in the sealed area. In the sealing structure, multiple magnetic sealing rings are provided, and the magnetic fields between adjacent sealing rings interact with each other to form a sealing barrier to prevent external impurities from entering the interior of the reactor. At the same time, a magnetic fluid is used to fill the sealing gap, and the magnetic fluid can adaptively fill the micro-gaps under the action of the magnetic field, further improving the sealing effect. After testing, after adopting this magnetic drive sealing anti-pollution structure, the content of metal impurities in the reactor is reduced by 80%-90%, effectively reducing the pollution of the wave-absorbing material by impurities and improving the purity and performance of the material.

[0030] Furthermore, the surface of the PFA lining is specially treated to enhance its compatibility with the reaction solution and anti-corrosion performance. The treatment method is as follows:

[0031] The PFA lining is immersed in a solution containing a fluorosilane coupling agent. The solution concentration is 5%-10% (mass fraction), the immersion temperature is 60-80 °C, and the immersion time is 2-4 hours. The siloxane groups in the fluorosilane coupling agent molecules undergo a condensation reaction with the hydroxyl groups on the PFA surface to form an organic silicon film on the PFA surface. This film can not only enhance the compatibility between the PFA lining and the reaction solution, reduce the adsorption and residue of the solution on the PFA surface, but also improve the anti-corrosion performance of the PFA lining. After this treatment, the surface of the PFA lining is still smooth after multiple uses, without obvious corrosion marks, effectively extending the service life of the reactor and ensuring the stability and reliability of the reaction process.

[0032] Compared with the prior art, this chemical co-precipitation reactor for wave-absorbing materials has the following beneficial effects:

[0033] First, through the design of the three-stage gradient reaction chamber structure, the optimized distribution of the fluid field is realized, ensuring the smooth and efficient flow of the reaction solution between different chambers. At the same time, the multi-axis intelligent mixing system in the premixing area can quickly mix the precursor solution to reach the molecular-level mixing state, providing an ideal starting condition for the subsequent reaction. The nucleation area realizes the uniform nucleation of nanocrystalline nuclei through precise temperature control and chemical environment control, effectively narrowing the grain size distribution range. The growth area controls the preferential growth of crystal planes by adjusting the magnetic field strength and solution supersaturation to obtain particles with good monodispersity. Under the combined action of these designs, the reaction efficiency and the consistency of product quality are significantly improved.

[0034] Second, the reactor is equipped with a magnetic drive seal anti-pollution structure. By using permanent magnetic materials and an optimized magnetic circuit design, non-contact sealing is achieved, avoiding the contamination of the reaction system by debris generated from friction in traditional mechanical seals. At the same time, the PFA lining has good chemical stability and low adsorption, preventing the inner wall of the reactor from contaminating the reaction solution. The non-contact drive system further reduces the risk of introducing impurities due to mechanical contact. Under the combined action of these measures, the metal impurity content in the reactor is significantly reduced, effectively reducing the contamination of the absorbing material by impurities and improving the purity and performance stability of the material.

[0035] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be learned from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0037] Figure 1 It is a functional implementation flowchart of a chemical co-precipitation reactor for absorbing materials;

[0038] Figure 2 It is an overall architecture flowchart of a chemical co-precipitation reactor for absorbing materials. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objective, the following will, in conjunction with the accompanying drawings and preferred embodiments, detail the specific implementation manners, structures, features, and their effects of the present invention as follows.

[0040] Embodiment 1

[0041] This embodiment describes the small-scale laboratory preparation of high-performance ferrite absorbing materials.

[0042] In a laboratory environment, researchers prepared to synthesize a ferrite wave-absorbing material with a spinel structure for electromagnetic interference shielding in high-frequency circuits. A solution containing iron ions and zinc ion precursors was added to the premixing zone of the reactor. The initial concentration C of the solution was detected to be a medium concentration, close to but not reaching the concentration threshold C2, and its membership degree μ C (x) was calculated according to the formula: x = C (actual concentration value), where C1 and C2 are concentration thresholds determined by experiments. (Because C1 < x < C2), the viscosity v of the solution is low viscosity, μ v (y) = 1 (y = v, v ≤ V1, V1 and V2 are viscosity thresholds), the loading capacity L of the reaction kettle is small, μ L (z) = 1 (z = L, L ≤ L1, L1 and L2 are loading capacity thresholds).

[0043] The multi-axis intelligent mixing system in the premixing zone starts. The rotation speed control of the main stirring paddle adopts an adaptive fuzzy control algorithm. According to the membership degrees of the above input variables, under the action of the fuzzy rule base, through fuzzy inference and defuzzification, the rotation speed adjustment amount Δn of the main stirring paddle is calculated. The main stirring paddle stirs rapidly to make the solution macroscopically uniformly mixed, and the microfluidic field agitator generates tiny flow field disturbances at the microscopic level, further promoting the component mixing and providing a uniform precursor solution for the subsequent reaction.

[0044] The solution enters the nucleation zone, and the distributed microchannel temperature control network starts to work. Multiple temperature monitoring points are set in the nucleation zone, and the temperature distribution non-uniformity U is measured. According to the formula (U max is the maximum allowable temperature non-uniformity) to determine the adaptive weight factor α. The change rate v of the ion concentration in the solution is monitored by an ion concentration sensor. According to the formula (v max is the maximum ion concentration change rate) to determine β. The fluctuation amplitude A of the pH value is monitored by a pH sensor. According to the formula (A max is the maximum pH fluctuation amplitude) to determine γ. The PID control algorithm is improved by these adaptive weight factors, and the coolant flow rate is dynamically adjusted. Combined with the closed-loop feedback system constructed by the pH / concentration online sensor, the temperature, pH value and concentration of the solution in the nucleation zone are precisely controlled to promote the uniform nucleation of ferrite grains.

[0045] The growth zone adopts a spiral diversion structure design. The magnetic field intensity is adjusted by an external magnetic field generator. The magnetic field intensity adjustment adopts an intelligent algorithm based on particle swarm optimization. Each particle represents a candidate solution of the magnetic field intensity. When the algorithm is initialized, the initial positions and velocities of N particles are randomly generated. The fitness function F(X i ) is determined according to the monodispersity index of the grains. The monodispersity index is measured by calculating the reciprocal of the coefficient of variation of the grain size . The particle updates its velocity according to its own historical optimal position P i and the global optimal position G according to the velocity update formula v ij (t + 1) = ωv ij (t) + c1r 1j (t)p ij −x ij (t)) + c2r2j (t)(g j ―x ij (t)) and the position update formula x ij (t + 1) = x ij (t) + v ij (t + 1) is continuously iteratively updated. After multiple iterations, the particle swarm converges to the optimal solution, that is, the magnetic field strength most suitable for the growth of ferrite grains is obtained. With the cooperation of the spiral diversion structure, the preferential growth of crystal planes is realized, and the performance of the ferrite wave-absorbing material is improved.

[0046] The multi-physical field coupling control platform adopts a multivariable predictive control algorithm to construct a three-layer neural network. The input layer nodes are the temperature T at the current moment t , pH value pH t , redox potential ORP t and time t. The number of hidden layer nodes is determined according to the empirical formula (n i is the number of input layer nodes, n o is the number of output layer nodes, and a is a constant). The output layer nodes are the predicted values T of the temperature, pH value, and redox potential at the next moment t+1 , pH t+1、 ORP t+1 . The network weights are adjusted through the backpropagation algorithm. According to the deviation between the predicted value and the target value, the model predictive control algorithm is used to calculate the temperature adjustment amount ΔT, pH adjustment amount ΔpH, and redox potential adjustment amount ΔORP, so as to realize the coordinated control of the reaction temperature, pH value, and redox potential, and ensure the oriented growth of the crystal phase and the surface hydroxyl functional group modification of the ferrite wave-absorbing material.

[0047] Example 2

[0048] In the pilot plant, the goal is to produce a broadband ferrite wave-absorbing material for military radar absorbing coatings. A large amount of precursor solution is added to the premixing area of the reaction kettle. The initial concentration of the solution is detected to be a high concentration, x = C (actual concentration value), x ≥ C2, so μ C (x) = 0, and the solution viscosity is medium viscosity. The loading capacity of the reaction kettle is medium loading capacity.

[0049] The multi-axis intelligent mixing system adjusts the rotation speed of the main stirring paddle according to the adaptive fuzzy control algorithm. The main stirring paddle and the microfluidic field agitator work together to ensure that a large amount of solution is fully mixed in the premixing area, providing uniform raw materials for subsequent reactions.

[0050] The nucleation region utilizes a distributed microchannel temperature control network to control the reaction conditions, obtains the temperature non-uniformity U through temperature monitoring points, calculates α, monitors the change rate v of ion concentration, calculates β, monitors the fluctuation amplitude A of pH value, calculates γ, and adjusts the coolant flow rate using an improved PID control algorithm. Combining the feedback of the pH / concentration online sensor, it precisely controls the nucleation process to ensure the nucleation quality and quantity of ferrite grains.

[0051] The spiral diversion structure and magnetic field regulation system in the growth region are started. The intelligent algorithm based on particle swarm optimization optimizes and regulates the magnetic field intensity. Taking the monodispersity of grains as the goal, it continuously iterates the particle positions and velocities to find the optimal magnetic field intensity, promotes the growth of the grain structure required for broadband microwave absorbing materials, and improves the microwave absorbing performance of the materials in the broadband range.

[0052] The multi-physical field coupling control platform uses a multivariable predictive control algorithm to coordinately control the reaction temperature, pH value, and redox potential using a three-layer neural network. It trains the neural network based on a large amount of pilot experiment data, adjusts the network weights, and realizes the precise regulation of the multi-physical field to meet the production requirements of broadband microwave absorbing materials.

[0053] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed as above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to form equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A chemical co-precipitation reactor for microwave absorbing materials, characterized in that The reactor comprises the following components: a three-stage gradient reaction chamber structure, an anti-pollution structure, and a multi-physical-field coupling control platform; The three-stage gradient reaction chamber structure includes a premixing zone, a nucleation zone, and a growth zone arranged in sequence, and the zones are connected by a bionic fractal flow channel; The premixing zone is equipped with a multi-axis intelligent mixing system, including a main stirring paddle and a microfluidic field agitator. The main stirring paddle is configured to perform macroscopic stirring on the precursor solution, and the microfluidic field agitator is used to generate tiny flow field disturbances at the microscopic level; The nucleation zone integrates a distributed microchannel temperature control network, and the coolant flow rate is dynamically adjusted by a PID algorithm. At the same time, a closed-loop feedback system is constructed in combination with a pH / concentration on-line sensor. The pH / concentration on-line sensor monitors the pH value and concentration changes of the solution in the nucleation zone in real time and transmits the data to the control system. The control system adjusts the coolant flow rate through the PID algorithm according to the preset parameters; The growth zone adopts a spiral diversion structure design, and the preferential growth of crystal planes is controlled by adjusting the magnetic field strength and solution supersaturation; The anti-pollution structure: A magnetic drive seal anti-pollution structure is introduced, which adopts a PFA lining and a non-contact drive system; The multi-physical-field coupling control platform: Through the multi-physical-field coupling control platform, the reaction temperature, pH value, and redox potential are synchronously optimized.

2. The chemical co-precipitation reactor for microwave absorbing material according to claim 1, characterized in that, In the multi-axis intelligent mixing system of the premixing zone, the rotation speed control of the main stirring paddle adopts an adaptive fuzzy control algorithm. This algorithm is based on fuzzy logic and dynamically adjusts the rotation speed of the main stirring paddle according to the initial concentration, viscosity of the precursor solution, and the loading parameter of the reactor; First, define the input variables. Divide the initial concentration of the precursor solution into three fuzzy sets: low, medium, and high, which are represented by the membership function μ C (x). where C1 and C2 are concentration thresholds determined based on a large number of experiments, and divide the solution viscosity into three fuzzy sets: low, medium, and high; Using the membership function μ v (y) to represent where V1 and V2 are viscosity thresholds determined according to experiments, and the reactor loading is divided into three fuzzy sets: low, medium, and high; Using the membership function μ L to represent where L1 and L2 are the loading thresholds determined according to the specifications of the reactor, the output variable is the adjustment amount Δn of the main stirring paddle speed, which is divided into five fuzzy sets: negative large, negative small, zero, positive small, and positive large. According to the fuzzy rule base, the fuzzy value of Δn is calculated through fuzzy inference, and then the actual speed adjustment amount is obtained through defuzzification.

3. The chemical co-precipitation reactor for microwave absorbing material according to claim 1, characterized in that, In the distributed microchannel temperature control network of the nucleation zone, the PID control algorithm for the coolant flow rate is improved, and adaptive weight factors α, β, γ are introduced to dynamically adjust the proportional, integral, and differential links. The formula is: Among them, the determination of α, β, γ is based on the real-time process of the nucleation reaction and the dynamic changes of the solution. By setting multiple temperature monitoring points in the nucleation zone, the non-uniformity U of the temperature distribution is obtained. According to the formula: where U max is the maximum allowable temperature non-uniformity determined experimentally. The more uneven the temperature distribution, the larger α, enhancing the role of the proportional link to quickly adjust the temperature deviation; According to the rate of change of ion concentration v in the solution, v max is the maximum rate of change of ion concentration determined experimentally. The faster the ion concentration changes, the smaller β is. Appropriately weaken the integral link to avoid integral saturation; According to the pH value fluctuation range A, A max is the maximum pH fluctuation range determined by the experiment. The greater the pH fluctuation, the greater γ, and the enhanced differential link inhibits the rapid fluctuation of temperature change.

4. The chemical co-precipitation reactor for microwave absorbing material according to claim 1, characterized in that The adjustment of the magnetic field strength in the growth zone adopts an intelligent algorithm based on particle swarm optimization. In the growth zone, the crystal grain growth is significantly affected by the magnetic field strength. In the particle swarm optimization algorithm; Each particle represents a candidate solution for the magnetic field strength, and the position X of the particle i = [x i1 , x i2 , …, x id represents the values of the magnetic field strength in different dimensions, and the velocity V i = [v i1 , v i2 , …, v id represents the adjustment direction and step size of the magnetic field strength; When the algorithm is initialized, the initial positions and velocities of N particles are randomly generated, and the fitness function F(X i ) is determined according to the monodispersity index of the grains. The monodispersity index is measured by calculating the reciprocal of the coefficient of variation of the grain size . σ is the standard deviation of the grain size, is the average grain size, The particle updates its velocity and position according to its own historical best position P i and the global best position G; The speed update formula is v ij (t + 1) = ωv ij (t) + c1r 1j (t)(p ij ―x ij (t)) + c2r 2j (t)(g j ―x ij (t)); The position update formula is x ij (t + 1) = x ij (t) + v ij (t + 1), where ω is the inertia weight, c1 and c2 are learning factors, r 1j 、r 2j are random numbers between [0, 1].

5. The chemical co-precipitation reactor for microwave absorbing material according to claim 1, characterized in that, The design of the bionic fractal flow channel is based on the principles of fractal geometry and hydrodynamic simulation. The structural parameters of the fractal flow channel are determined by the iterative formula D n+1 = D n × λ, where D n is the characteristic size of the nth-level flow channel, and λ is the scaling factor related to the fractal dimension. During the design process, computational fluid dynamics software is used to simulate bionic fractal flow channels with different structures, and the uniformity of fluid velocity distribution, pressure loss, and mixing effect are used as evaluation indicators.

6. The chemical co-precipitation reactor for microwave absorbing material according to claim 1, wherein In the multi-physical field coupling control platform, the coordinated control of reaction temperature, pH value, and redox potential adopts a multivariable predictive control algorithm. A three-layer neural network is constructed. The input layer nodes are the temperature T at the current moment t , pH value pH t , redox potential ORP t and time t. The number of hidden layer nodes is determined according to the empirical formula , where n i is the number of input layer nodes, n o is the number of output layer nodes, and a is a constant. The output layer nodes are the predicted values of the temperature, pH value, and redox potential at the next moment, T t+1 , pH t+1 , ORP t+1 . The training data of the neural network comes from a large number of reaction experimental data. The network weights are adjusted through the backpropagation algorithm. After the prediction model is established, according to the deviation between the predicted value and the target value, the model predictive control algorithm is used to calculate the temperature adjustment amount ΔT, pH adjustment amount ΔpH, and redox potential adjustment amount ΔORP.

7. The chemical co-precipitation reactor for microwave absorbing material according to claim 1, wherein The magnetic drive seal anti-pollution structure adopts a permanent magnetic material and an optimized magnetic circuit design. A neodymium iron boron permanent magnet is selected as the power source for the magnetic drive seal. The magnetic circuit design is optimized by finite element analysis software. In the seal structure, multiple magnetic seal rings are set, and the magnetic fields between adjacent seal rings interact to form a seal barrier. At the same time, a magnetic fluid is used to fill the seal gap.

8. The chemical co-precipitation reactor for microwave absorbing material according to claim 1, wherein, The surface of the PFA lining is specially treated. The treatment method is: The PFA lining is immersed in a solution containing a fluorosilane coupling agent. The siloxane groups in the fluorosilane coupling agent molecules undergo a condensation reaction with the hydroxyl groups on the PFA surface to form an organic silicon film on the PFA surface.

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