Particle size adjustable intelligent calcium silicate processing device and system

Abstract

The invention relates to the technical field of intelligent calcium silicate processing and system control thereof, in particular to an intelligent calcium silicate processing device and system with adjustable granularity, the device comprises a physical device and a control device, the physical device comprises a tank reactor, a variable frequency motor driving stirring paddle and a multi-sensor monitoring unit; the control device comprises a data acquisition module for acquiring concentration distribution data, particle size distribution characteristic values, temperature data and rotating speed parameters in real time; the dynamic modeling module calls a crystal growth kinetic model library to output a theoretical stirring intensity interval; the optimization decision module generates a rotating speed adjusting instruction; the execution control module converts an instruction to drive the variable frequency motor and is linked with the temperature control system; and the closed-loop feedback module corrects the model parameters according to the new granularity data. The modeling precision is improved by synchronizing multi-source data, the stirring strength is dynamically adjusted to balance the crystal nucleation and growth rate, and uniform control over particle size distribution of the calcium silicate product is achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent calcium silicate processing and its system control technology, and in particular to an intelligent calcium silicate processing device and system with adjustable particle size. Background Technology

[0002] Existing intelligent calcium silicate processing equipment suffers from the following technical challenges in control: specifically, during the wet synthesis of calcium silicate, the stirring intensity is difficult to optimize precisely, resulting in uneven particle size distribution. This is because stirring intensity directly affects the uniformity of reactant mixing and crystal growth kinetics. The optimization process requires balancing nucleation and growth rates, but in actual production, reaction conditions such as temperature and concentration fluctuations cause dynamic changes, making it difficult to match ideal stirring parameters in real time. For example, insufficient stirring intensity leads to localized oversaturation within the reactor, causing calcium silicate crystals to rapidly nucleate and grow into coarse particles. Conversely, excessive stirring intensity introduces mechanical shear force, breaking up the formed crystals and producing fine powder, ultimately resulting in a wider product particle size range and affecting subsequent application performance. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an intelligent calcium silicate processing device and system with adjustable particle size, solving the problem of uneven particle size distribution of calcium silicate products during wet synthesis due to the difficulty in precisely optimizing stirring intensity.

[0004] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0005] In a first aspect, the present invention provides an intelligent calcium silicate processing device with adjustable particle size, comprising a physical device and a control device, wherein the control device and the physical device establish a communication connection.

[0006] The physical device includes:

[0007] The reaction vessel device is a batch reactor with a radial baffle array installed on the inner wall;

[0008] The mixing actuator is equipped with a three-bladed swept-back mixing impeller driven by a variable frequency motor.

[0009] Online monitoring device: The online monitoring device includes an immersion conductivity sensor array, a laser particle size probe, and a temperature sensor group;

[0010] The control device includes:

[0011] Data acquisition module: Real-time acquisition of concentration distribution data output by conductivity sensor, particle size distribution characteristic value collected by laser particle size probe, multi-point temperature data of temperature sensor group, and speed parameters of variable frequency motor, and transmission of concentration distribution data, temperature data, particle size distribution characteristic value and speed parameters to dynamic modeling module;

[0012] Dynamic modeling module: Receives concentration distribution data, temperature data, and particle size distribution characteristic values, calls the crystal growth kinetics model library, outputs the theoretical stirring intensity range corresponding to the target particle size, and transmits the theoretical stirring intensity range to the optimization decision module;

[0013] Optimization Decision Module: Receives the theoretical stirring intensity range and the current particle size distribution characteristic value, generates a variable frequency motor speed adjustment command, and transmits the speed adjustment command to the execution control module;

[0014] The execution control module converts speed adjustment commands into drive signals to control the variable frequency motor, synchronously links with the temperature control system, and triggers the laser particle size probe to generate updated particle size distribution characteristic values.

[0015] Closed-loop feedback module: Receives the updated particle size distribution characteristic value from the laser particle size probe, dynamically corrects the crystal growth kinetics model parameters, and updates the corrected model parameters to the crystal growth kinetics model library;

[0016] The dynamic modeling module calls the updated model parameters and re-outputs the theoretical stirring intensity range.

[0017] Furthermore, the intelligent calcium silicate processing device with adjustable particle size according to the present invention further includes:

[0018] The radial baffle array of the reaction vessel device improves the distribution of the reactant flow field, thereby reducing the spatial difference in the concentration distribution data collected by the conductivity sensor array.

[0019] The three-bladed swept-back agitator of the agitation actuator operates under the drive of a variable frequency motor, so that the rotational speed parameter output by the variable frequency motor is correlated with the shear strength of the agitator blades.

[0020] Furthermore, in the intelligent calcium silicate processing device with adjustable particle size described in this invention, the data acquisition module includes:

[0021] Signal synchronization unit: Aligns the timestamp data of the conductivity sensor array, laser particle size probe, temperature sensor group, and variable frequency motor;

[0022] Preprocessing unit: Performs spatial interpolation on concentration data to generate a three-dimensional concentration field, extracts D10 / D50 / D90 values ​​from particle size distribution feature values, and calculates the maximum temperature difference.

[0023] Furthermore, in the intelligent calcium silicate processing device with adjustable particle size described in this invention, the dynamic modeling module includes:

[0024] Operating condition identification unit: Identifies local oversaturated areas based on concentration field cloud map, and determines temperature distribution status based on maximum temperature difference;

[0025] Real-time simulation unit: Simulates the trend of crystal size distribution change using concentration gradient and temperature gradient as boundary conditions.

[0026] Furthermore, in the intelligent calcium silicate processing device with adjustable particle size described in this invention, the optimization decision module includes:

[0027] Deviation analysis unit: Compares the current D50 value with the preset target value to determine the particle size distribution width;

[0028] PID parameter tuning unit: Automatically adjusts the proportional coefficient according to the deviation and outputs the speed compensation amount.

[0029] Furthermore, in the intelligent calcium silicate processing device with adjustable particle size described in this invention, the execution control module includes:

[0030] Signal conversion unit: converts the speed compensation amount into an analog signal to drive the frequency converter;

[0031] Safety interlock unit: Executes a step-down speed reduction when the stirring torque suddenly changes.

[0032] Furthermore, in the intelligent calcium silicate processing device with adjustable particle size described in this invention, the closed-loop feedback module includes:

[0033] Performance evaluation unit: Calculates the absolute error between the new granular data and the target value, as well as the rate of change of the distribution width;

[0034] Model calibration unit: When the distribution width continuously exceeds the limit, collect the current working condition data and refit the crystal growth model parameters.

[0035] Furthermore, in the intelligent calcium silicate processing device with adjustable particle size described in this invention, the control device executes in a 30-second cycle.

[0036] Furthermore, in the intelligent calcium silicate processing device with adjustable particle size described in this invention, the laser particle size probe outputs a particle size distribution characteristic value every 30 seconds.

[0037] When the distribution width exceeds the limit three times consecutively, the closed-loop feedback module refits the exponential term parameters of the crystal growth rate equation.

[0038] Secondly, the present invention provides an intelligent calcium silicate processing system with adjustable particle size, applied to the aforementioned intelligent calcium silicate processing device with adjustable particle size, comprising:

[0039] Physical execution unit: includes a batch reactor, a three-bladed swept-back impeller driven by a variable frequency motor, an immersion conductivity sensor array, a laser particle size probe, and a temperature sensor group;

[0040] Intelligent control unit: Communicatively connected to the physical execution unit, including:

[0041] Data acquisition subunit: synchronously acquires concentration distribution data output by conductivity sensor, particle size distribution characteristic value of laser particle size probe, multi-point temperature data of temperature sensor group, and speed parameters of variable frequency motor;

[0042] Dynamic modeling subunit: Receives concentration distribution data, temperature data, and particle size distribution characteristic values, and calls the crystal growth kinetics model library to output the theoretical stirring intensity range;

[0043] Optimization decision subunit: Receives theoretical stirring intensity range and particle size distribution characteristic values, and generates variable frequency motor speed adjustment commands;

[0044] The execution control subunit converts the speed regulation command into a drive signal to control the variable frequency motor, links the temperature control system, and triggers the laser particle size probe to update data.

[0045] Closed-loop feedback subunit: Receives updated particle size distribution characteristic values, corrects crystal growth kinetic model parameters, and updates the model library.

[0046] Beneficial effects of this invention;

[0047] This invention improves the accuracy of state perception through the synergistic optimization of physical devices and data acquisition. Closed-loop decision-making in the control device enables dynamic parameter matching, and system-level cyclic optimization maintains particle size stability. A radial baffle array optimizes the reactive material field distribution, significantly reducing spatial differences in concentration distribution data collected by the conductivity sensor array. A three-bladed swept-back agitator operates in conjunction with a variable-frequency motor, ensuring that the rotational speed parameter accurately represents shear strength. Multi-source data from the online monitoring device is synchronously processed by the data acquisition module, improving the quality of the operating data input to the dynamic modeling module. The dynamic modeling module calls a crystal growth kinetics model library to analyze concentration and temperature gradients, outputting a theoretical stirring intensity range. The optimization decision module generates rotational speed adjustment commands based on particle size deviation. The execution control module drives the variable-frequency motor to adjust its speed and links it with the temperature control system, triggering the generation of new particle size data. The closed-loop feedback module refits model parameters and updates the model library based on the new particle size distribution characteristic values, while the dynamic modeling module calls the new parameters and cyclically outputs optimization commands. This closed-loop control chain executes every 30 seconds, continuously balancing the crystal nucleation rate and growth rate, stabilizing the particle size distribution width within the target threshold, and improving the application performance of calcium silicate products. Attached Figure Description

[0048] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0049] Figure 1 The system architecture diagram of the intelligent calcium silicate processing system with adjustable particle size provided in the embodiments of the present invention is shown. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings. To better understand the objectives of this invention, it will be described in further detail below.

[0051] In a first aspect, the present invention provides an intelligent calcium silicate processing device with adjustable particle size, comprising a physical device and a control device, wherein the control device and the physical device establish a communication connection.

[0052] The physical device includes:

[0053] The reaction vessel device is a batch reactor with a radial baffle array installed on the inner wall;

[0054] The mixing actuator is equipped with a three-bladed swept-back mixing impeller driven by a variable frequency motor.

[0055] Online monitoring device: The online monitoring device includes an immersion conductivity sensor array, a laser particle size probe, and a temperature sensor group;

[0056] The control device includes:

[0057] Data acquisition module: Real-time acquisition of concentration distribution data output by conductivity sensor, particle size distribution characteristic value collected by laser particle size probe, multi-point temperature data of temperature sensor group, and speed parameters of variable frequency motor, and transmission of concentration distribution data, temperature data, particle size distribution characteristic value and speed parameters to dynamic modeling module;

[0058] Dynamic modeling module: Receives concentration distribution data, temperature data, and particle size distribution characteristic values, calls the crystal growth kinetics model library, outputs the theoretical stirring intensity range corresponding to the target particle size, and transmits the theoretical stirring intensity range to the optimization decision module;

[0059] Optimization Decision Module: Receives the theoretical stirring intensity range and the current particle size distribution characteristic value, generates a variable frequency motor speed adjustment command, and transmits the speed adjustment command to the execution control module;

[0060] The execution control module converts speed adjustment commands into drive signals to control the variable frequency motor, synchronously links with the temperature control system, and triggers the laser particle size probe to generate updated particle size distribution characteristic values.

[0061] Closed-loop feedback module: Receives the updated particle size distribution characteristic value from the laser particle size probe, dynamically corrects the crystal growth kinetics model parameters, and updates the corrected model parameters to the crystal growth kinetics model library;

[0062] The dynamic modeling module calls the updated model parameters and re-outputs the theoretical stirring intensity range.

[0063] The reaction vessel in the physical apparatus adopts a kettle-type reactor structure, with a radial baffle array installed on the inner wall to optimize the reaction flow field distribution. The stirring actuator is equipped with a three-bladed swept-back impeller driven by a variable frequency motor, and the stirring shear intensity is adjusted by changing the motor speed. The online monitoring device includes an immersion conductivity sensor array to collect reactant concentration distribution, a laser particle size probe to periodically output crystal particle size distribution characteristic values, and a temperature sensor group to monitor temperature data at multiple points inside the reactor.

[0064] The control device's data acquisition module acquires in real time the concentration distribution data output by the conductivity sensor, the particle size distribution characteristic values ​​collected by the laser particle size probe, the multi-point temperature data from the temperature sensor group, and the real-time speed parameters of the variable frequency motor. The data acquisition module performs timestamp alignment processing on the above data, integrating the concentration distribution data, temperature data, particle size distribution characteristic values, and speed parameters into a unified time-series dataset, which is then transmitted to the dynamic modeling module.

[0065] The dynamic modeling module receives concentration distribution data, temperature data, and particle size distribution characteristic values ​​transmitted from the data acquisition module, and calls upon a pre-defined crystal growth kinetics model library. Based on the current concentration gradient, the dynamic modeling module identifies local supersaturation regions and, combined with the temperature gradient trend, simulates the future evolution of crystal size distribution, outputting the theoretical stirring intensity range corresponding to the target particle size. The dynamic modeling module then transmits this theoretical stirring intensity range to the optimization decision module.

[0066] The optimization decision module receives the theoretical stirring intensity range output by the dynamic modeling module and the latest particle size distribution characteristic values ​​collected by the laser particle size probe. It compares the deviation between the current median crystal particle size and the preset target particle size, analyzes whether the particle size distribution width exceeds a threshold, and automatically adjusts the proportional control coefficient based on the deviation to generate a variable frequency motor speed adjustment compensation command. The optimization decision module then transmits the speed adjustment command to the execution control module.

[0067] The execution control module receives the speed adjustment command transmitted from the optimization decision module, converts the digital command signal into a pulse width modulation waveform to drive the frequency converter. The execution control module controls the variable frequency motor to adjust the agitator speed and synchronously adjusts the cooling water valve opening to maintain a constant reaction temperature. The execution control module triggers the laser particle size probe to start a new round of particle size detection, generating updated particle size distribution characteristic values.

[0068] The closed-loop feedback module receives the updated particle size distribution characteristic values ​​from the laser particle size probe and calculates the absolute error between the actual median particle size and the target value, as well as the rate of change of the distribution width. When the particle size distribution width continuously exceeds a set threshold, the closed-loop feedback module collects current operating condition data samples and refits the exponential term parameters of the rate equation in the crystal growth kinetics model. The closed-loop feedback module then updates the corrected model parameters to the crystal growth kinetics model library.

[0069] The dynamic modeling module calls upon the updated crystal growth kinetics model library parameters and, combined with the latest operating data, re-outputs the theoretical stirring intensity range, forming a continuously optimized closed-loop control chain. By cyclically executing the above process, the stirring intensity and reaction condition fluctuations are dynamically matched to maintain a balance between the crystal nucleation rate and growth rate.

[0070] Specifically, the intelligent calcium silicate processing device with adjustable particle size according to the present invention further includes:

[0071] The radial baffle array of the reaction vessel device improves the distribution of the reactant flow field, thereby reducing the spatial difference in the concentration distribution data collected by the conductivity sensor array.

[0072] The three-bladed swept-back agitator of the agitation actuator operates under the drive of a variable frequency motor, so that the rotational speed parameter output by the variable frequency motor is correlated with the shear strength of the agitator blades.

[0073] The radial baffle array of the reaction vessel optimizes the reaction material flow distribution, reducing spatial differences in the concentration distribution data collected by the conductivity sensor array. The three-bladed swept-back agitator of the stirring actuator operates under the drive of a variable frequency motor, allowing the motor's output rotational speed parameter to directly reflect the shear intensity applied by the agitator blades. Reduced spatial differences in concentration distribution data improve the accuracy of the dynamic modeling module in identifying localized oversaturation regions, and the correlation between rotational speed parameter and shear intensity provides a key control variable for the optimization decision-making module.

[0074] Specifically, the intelligent calcium silicate processing device with adjustable particle size according to the present invention includes a data acquisition module comprising:

[0075] Signal synchronization unit: Aligns the timestamp data of the conductivity sensor array, laser particle size probe, temperature sensor group, and variable frequency motor;

[0076] Preprocessing unit: Performs spatial interpolation on concentration data to generate a three-dimensional concentration field, extracts D10 / D50 / D90 values ​​from particle size distribution feature values, and calculates the maximum temperature difference.

[0077] The signal synchronization unit of the data acquisition module aligns the timestamp data of the conductivity sensor array, laser particle size probe, temperature sensor group, and variable frequency motor to generate a synchronized time-series dataset. The preprocessing unit performs spatial interpolation calculations on the synchronized concentration data to generate a three-dimensional concentration field cloud map, extracts D10, D50, and D90 values ​​from the particle size distribution characteristic values, and calculates the maximum axial and radial temperature difference of the temperature sensor group's monitoring data. The preprocessed three-dimensional concentration field cloud map, particle size characteristic values, and maximum temperature difference are then transmitted to the dynamic modeling module.

[0078] Specifically, the intelligent calcium silicate processing device with adjustable particle size according to the present invention includes a dynamic modeling module comprising:

[0079] Operating condition identification unit: Identifies local oversaturated areas based on concentration field cloud map, and determines temperature distribution status based on maximum temperature difference;

[0080] Real-time simulation unit: Simulates the trend of crystal size distribution change using concentration gradient and temperature gradient as boundary conditions.

[0081] The dynamic modeling module's operating condition identification unit identifies the area ratio of local oversaturated regions based on the three-dimensional concentration field cloud map and determines abnormal temperature distribution states based on the maximum temperature difference. The real-time simulation unit uses the concentration gradient change trend and temperature gradient as boundary conditions, calls the nucleation rate equation and growth rate equation parameter sets in the crystal growth kinetic model library, simulates the evolution trend of crystal size distribution within a preset time period, and outputs the theoretical stirring intensity range.

[0082] Specifically, the intelligent calcium silicate processing device with adjustable particle size according to the present invention includes an optimization decision module comprising:

[0083] Deviation analysis unit: Compares the current D50 value with the preset target value to determine the particle size distribution width;

[0084] PID parameter tuning unit: Automatically adjusts the proportional coefficient according to the deviation and outputs the speed compensation amount.

[0085] The deviation analysis unit of the optimization decision module compares the current D50 value with the preset target value to determine whether the granularity distribution width exceeds the threshold. The PID parameter tuning unit automatically adjusts the proportional control coefficient based on the magnitude of the D50 deviation and outputs the speed compensation amount by combining the preset compensation strategy in the fuzzy rule base. The speed compensation amount serves as the core parameter of the variable frequency motor speed regulation command.

[0086] Specifically, the intelligent calcium silicate processing device with adjustable particle size according to the present invention includes an execution control module comprising:

[0087] Signal conversion unit: converts the speed compensation amount into an analog signal to drive the frequency converter;

[0088] Safety interlock unit: Executes a step-down speed reduction when the stirring torque suddenly changes.

[0089] The signal conversion unit of the execution control module converts the speed compensation amount into an analog voltage signal to drive the frequency converter and control the variable frequency motor to adjust its speed. The safety interlock unit monitors the change in stirring torque in real time. When a sudden change in torque is detected to exceed a preset ratio, a phased, stepped speed reduction program is initiated to prevent blade damage. After the speed is adjusted, the laser particle size probe is triggered to start a new detection cycle.

[0090] Specifically, in the intelligent calcium silicate processing device with adjustable particle size described in this invention, the closed-loop feedback module includes:

[0091] Performance evaluation unit: Calculates the absolute error between the new granular data and the target value, as well as the rate of change of the distribution width;

[0092] Model calibration unit: When the distribution width continuously exceeds the limit, collect the current working condition data and refit the crystal growth model parameters.

[0093] The closed-loop feedback module's performance evaluation unit calculates the absolute error between the D50 value and the target value in the newly generated particle size data, and analyzes the rate of change in particle size distribution width. When the particle size distribution width continuously exceeds a set threshold, the model calibration unit collects samples of the current concentration gradient, temperature gradient, and particle size characteristic values, and refits the exponential term parameters of the growth rate equation in the crystal growth kinetics model. The fitting results are then updated to the crystal growth kinetics model library.

[0094] Specifically, in the intelligent calcium silicate processing device with adjustable particle size described in this invention, the control device executes in a 30-second cycle.

[0095] The control device cyclically executes data acquisition, dynamic modeling, optimization decision-making, execution control, and closed-loop feedback processes at fixed time intervals. This cyclical execution mechanism ensures continuous monitoring of the crystal growth state and dynamic adjustment of the stirring intensity.

[0096] Specifically, in the intelligent calcium silicate processing device with adjustable particle size described in this invention, the laser particle size probe outputs a particle size distribution characteristic value every 30 seconds.

[0097] When the distribution width exceeds the limit three times consecutively, the closed-loop feedback module refits the exponential term parameters of the crystal growth rate equation.

[0098] The laser particle size probe outputs particle size distribution characteristic data at fixed time intervals. When the particle size distribution width exceeds a set number of consecutive values, the closed-loop feedback module triggers a refitting procedure for the crystal growth kinetics model parameters. This refitting operation updates the model library parameters, improving the accuracy of subsequent theoretical stirring intensity range outputs.

[0099] Secondly, please refer to Figure 1 This invention provides an intelligent calcium silicate processing system with adjustable particle size, applied to the aforementioned intelligent calcium silicate processing device with adjustable particle size, comprising:

[0100] Physical execution unit: includes a batch reactor, a three-bladed swept-back impeller driven by a variable frequency motor, an immersion conductivity sensor array, a laser particle size probe, and a temperature sensor group;

[0101] Intelligent control unit: Communicatively connected to the physical execution unit, including:

[0102] Data acquisition subunit: synchronously acquires concentration distribution data output by conductivity sensor, particle size distribution characteristic value of laser particle size probe, multi-point temperature data of temperature sensor group, and speed parameters of variable frequency motor;

[0103] Dynamic modeling subunit: Receives concentration distribution data, temperature data, and particle size distribution characteristic values, and calls the crystal growth kinetics model library to output the theoretical stirring intensity range;

[0104] Optimization decision subunit: Receives theoretical stirring intensity range and particle size distribution characteristic values, and generates variable frequency motor speed adjustment commands;

[0105] The execution control subunit converts the speed regulation command into a drive signal to control the variable frequency motor, links the temperature control system, and triggers the laser particle size probe to update data.

[0106] Closed-loop feedback subunit: Receives updated particle size distribution characteristic values, corrects crystal growth kinetic model parameters, and updates the model library.

[0107] This invention solves the problem of precisely optimizing stirring intensity by establishing a dynamic closed-loop control chain. The radial baffle array of the reaction vessel optimizes the reaction material flow distribution, reducing the spatial non-uniformity of the concentration distribution data collected by the conductivity sensor array. The laser particle size probe of the online monitoring device periodically outputs particle size distribution characteristic values, the temperature sensor group monitors multi-point temperature data in real time, and the data acquisition module simultaneously collects the above data and the variable frequency motor speed parameters.

[0108] The dynamic modeling module receives concentration distribution data, temperature data, and particle size distribution characteristic values. It then calls upon the crystal growth kinetics model library to analyze the current concentration and temperature gradients, simulates the evolution trend of crystal size distribution, and outputs the theoretical stirring intensity range corresponding to the target particle size. The optimization decision module compares the deviation between the current median particle size and the preset target, and automatically generates a speed adjustment command based on the theoretical stirring intensity range.

[0109] The execution control module converts the speed command into a drive signal to control the variable frequency motor, and links with the temperature control system to maintain stable reaction conditions. After the stirring intensity is adjusted, the laser particle size probe is triggered to generate new particle size distribution characteristic values. The closed-loop feedback module receives the new data and calculates the actual particle size deviation and the rate of change of distribution width. When the distribution width continuously exceeds the standard, the crystal growth kinetic model parameters are refitted, and the model library is updated for use by the dynamic modeling module. The system cycles through data acquisition, modeling, decision-making, execution, and feedback every 30 seconds, dynamically matching the fluctuations in stirring intensity and reaction conditions to maintain a balance between crystal nucleation rate and growth rate.

Claims

1. An intelligent calcium silicate processing device with adjustable particle size, characterized in that, It includes physical devices and control devices, and the control devices establish a communication connection with the physical devices; The physical device includes: The reaction vessel device is a batch reactor with a radial baffle array installed on the inner wall; The mixing actuator is equipped with a three-bladed swept-back mixing impeller driven by a variable frequency motor. Online monitoring device: The online monitoring device includes an immersion conductivity sensor array, a laser particle size probe, and a temperature sensor group; The control device includes: Data acquisition module: Real-time acquisition of concentration distribution data output by conductivity sensor, particle size distribution characteristic value collected by laser particle size probe, multi-point temperature data of temperature sensor group, and speed parameters of variable frequency motor, and transmission of concentration distribution data, temperature data, particle size distribution characteristic value and speed parameters to dynamic modeling module; Dynamic modeling module: Receives concentration distribution data, temperature data, and particle size distribution characteristic values, calls the crystal growth kinetics model library, outputs the theoretical stirring intensity range corresponding to the target particle size, and transmits the theoretical stirring intensity range to the optimization decision module; Optimization Decision Module: Receives the theoretical stirring intensity range and the current particle size distribution characteristic value, generates a variable frequency motor speed adjustment command, and transmits the speed adjustment command to the execution control module; The execution control module converts speed adjustment commands into drive signals to control the variable frequency motor, synchronously links with the temperature control system, and triggers the laser particle size probe to generate updated particle size distribution characteristic values. Closed-loop feedback module: Receives the updated particle size distribution characteristic value from the laser particle size probe, dynamically corrects the crystal growth kinetics model parameters, and updates the corrected model parameters to the crystal growth kinetics model library; The dynamic modeling module calls the updated model parameters and re-outputs the theoretical stirring intensity range.

2. The intelligent calcium silicate processing device with adjustable particle size according to claim 1, characterized in that, Also includes: The radial baffle array of the reaction vessel device improves the distribution of the reactant flow field, thereby reducing the spatial difference in the concentration distribution data collected by the conductivity sensor array. The three-bladed swept-back agitator of the agitation actuator operates under the drive of a variable frequency motor, so that the rotational speed parameter output by the variable frequency motor is correlated with the shear strength of the agitator blades.

3. The intelligent calcium silicate processing device with adjustable particle size according to claim 2, characterized in that, The data acquisition module includes: Signal synchronization unit: Aligns the timestamp data of the conductivity sensor array, laser particle size probe, temperature sensor group, and variable frequency motor; Preprocessing unit: Performs spatial interpolation on concentration data to generate a three-dimensional concentration field, extracts D10 / D50 / D90 values ​​from particle size distribution feature values, and calculates the maximum temperature difference.

4. The intelligent calcium silicate processing device with adjustable particle size according to claim 3, characterized in that, The dynamic modeling module includes: Operating condition identification unit: Identifies local oversaturated areas based on concentration field cloud map, and determines temperature distribution status based on maximum temperature difference; Real-time simulation unit: Simulates the trend of crystal size distribution change using concentration gradient and temperature gradient as boundary conditions.

5. The intelligent calcium silicate processing device with adjustable particle size according to claim 4, characterized in that, The optimization decision module includes: Deviation analysis unit: Compares the current D50 value with the preset target value to determine the particle size distribution width; PID parameter tuning unit: Automatically adjusts the proportional coefficient according to the deviation and outputs the speed compensation amount.

6. The intelligent calcium silicate processing device with adjustable particle size according to claim 5, characterized in that, The execution control module includes: Signal conversion unit: converts the speed compensation amount into an analog signal to drive the frequency converter; Safety interlock unit: Executes a step-down speed reduction when the stirring torque suddenly changes.

7. The intelligent calcium silicate processing device with adjustable particle size according to claim 6, characterized in that, The closed-loop feedback module includes: Performance evaluation unit: Calculates the absolute error between the new granular data and the target value, as well as the rate of change of the distribution width; Model calibration unit: When the distribution width continuously exceeds the limit, collect the current working condition data and refit the crystal growth model parameters.

8. The intelligent calcium silicate processing device with adjustable particle size according to claim 7, characterized in that, The control device executes in a cycle every 30 seconds.

9. The intelligent calcium silicate processing device with adjustable particle size according to claim 8, characterized in that, The laser particle size probe outputs a particle size distribution characteristic value every 30 seconds. When the distribution width exceeds the limit three times consecutively, the closed-loop feedback module refits the exponential term parameters of the crystal growth rate equation.

10. An intelligent calcium silicate processing system with adjustable particle size, applied to the intelligent calcium silicate processing apparatus with adjustable particle size as described in any one of claims 1 to 9, characterized in that, include: Physical execution unit: includes a batch reactor, a three-bladed swept-back impeller driven by a variable frequency motor, an immersion conductivity sensor array, a laser particle size probe, and a temperature sensor group; Intelligent control unit: Communicatively connected to the physical execution unit, including: Data acquisition subunit: synchronously acquires concentration distribution data output by conductivity sensor, particle size distribution characteristic value of laser particle size probe, multi-point temperature data of temperature sensor group, and speed parameters of variable frequency motor; Dynamic modeling subunit: Receives concentration distribution data, temperature data, and particle size distribution characteristic values, and calls the crystal growth kinetics model library to output the theoretical stirring intensity range; Optimization decision subunit: Receives theoretical stirring intensity range and particle size distribution characteristic values, and generates variable frequency motor speed adjustment commands; The execution control subunit converts the speed regulation command into a drive signal to control the variable frequency motor, links the temperature control system, and triggers the laser particle size probe to update data. Closed-loop feedback subunit: Receives updated particle size distribution characteristic values, corrects crystal growth kinetic model parameters, and updates the model library.

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