Crystallizer control method and system based on proportional integral differential

Abstract

The invention relates to the technical field of continuous casting equipment, and discloses a crystallizer control method and system based on proportional integral differential, and the method comprises the steps: converting analog signals of liquid level height and wall surface temperature into analog control signals, and adjusting parameters of a cooling water inlet of a crystallizer; constructing a cooling water database according to the flow and temperature of the generated cooling water and the heat transfer information of each area in the crystallizer; calculating proportional control output which is in direct proportion to the difference value, calculating integral of past errors, predicting changes of the errors, combining the outputs of all the parts, and calculating a final control signal; according to the calculation result of the final control signal, the flow and the temperature of cooling water are adjusted, and the liquid level height and the wall surface temperature in the crystallizer reach set values. The system comprises a database construction module, an intelligent control module and a result evaluation module. According to the invention, the operation performance of the crystallizer is effectively maintained, and support is provided for further improvement.

Description

Technical Field

[0001] The present invention relates to the technical field of continuous casting equipment, and in particular to a crystallizer control method and system based on proportional integral differential. Background Art

[0002] The crystallizer is a bottomless steel ingot mold with forced water cooling. It is a very important component of the continuous casting machine and a key component in the continuous casting equipment. Its performance plays a very important role in the production capacity of the continuous casting machine, the quality of the casting, and the smoothness and stability of the process. The molten steel is cooled and initially solidified in the crystallizer, and a certain thickness of the shell is formed. The crystallizer with good thermal conductivity allows the ingot to form a shell in the crystallizer to a sufficient thickness. However, there is an inevitable air gap (air gap) between the copper wall and the shell, which increases the resistance to heat transfer. Poor thermal conductivity causes the shell of the ingot in the crystallizer to become thinner and leak, limiting the pulling speed. In addition, the uniform heat flux intensity of the crystallizer makes the shell of the ingot uniform. The thermal conductivity of the crystallizer not only affects the production efficiency of the continuous casting machine, but also further affects the quality of the ingot.

[0003] Prior art 1, Chinese patent, application number: 202311353859.6 discloses a method and device for measuring the temperature and heat flow of a continuous casting crystallizer of a steel billet. A device for measuring the temperature and heat flow of a continuous casting crystallizer of a steel billet is used, and a thin film thermocouple sensor with multiple thermal node characteristics is designed. Through reasonable layout and installation, the simultaneous measurement of the longitudinal and transverse temperatures of multiple temperature measuring points on the copper plate of the crystallizer can be achieved, and the temperature signal of the collected thin film thermocouple sensor can be further calculated, analyzed and processed to output the temperature and heat flow data interface of each temperature measuring point of the crystallizer, the real-time change curve of the temperature and heat flow, or the three-dimensional distribution cloud map of the temperature and heat flow; although it is convenient to accurately and real-timely grasp the local molten steel temperature changes in the crystallizer; however, it cannot be accurately controlled under unknown disturbances, and the control method cannot be intelligently adjusted, making the crystallizer unable to adapt to the complex and changeable operating environment.

[0004] Prior art 2, Chinese patent, application number: 202211142009.7 discloses a method for quickly determining the temperature field of a crystallizer based on the interpolation method, measuring the cross-sectional data of the crystallizer, and summarizing the temperature gradient in the direction of the crystallizer wall thickness according to the temperature field data of the finite element simulation and the actual test; pre-embed thermocouples at the four quadrants of the center line of the crystallizer section, and obtain the temperature value; interpolate the center line temperature through the piecewise sine function, obtain the temperature distribution of the center line represented by the piecewise sine function curve and output it by computer; fit the piecewise sine function curve by the interpolation method in the cylindrical coordinate system, obtain the temperature field of the section and output it by computer; use the same method to obtain the temperature field of another section on the crystallizer, then the surface between the two sections can be interpolated to obtain the temperature field between the two sections. Although it can handle the problem of quickly obtaining the temperature field demand, it realizes a method for quickly calculating the temperature field in the field environment, which improves the work efficiency; but the adaptability, work efficiency and stability of the crystallizer need to be further improved.

[0005] Prior art three, Chinese patent, application number: 202111003823.6 discloses a method for determining the critical cooling rate of a continuous casting process, including: heating a plurality of high-titanium steel samples with the same titanium content to melt them; cooling the melted plurality of samples at different cooling rates to the crystallizer temperature of the samples; quenching the cooled plurality of samples; obtaining the size of carbides precipitated from the plurality of samples during solidification, and obtaining the ferrite film thickness of the plurality of samples after quenching; determining the samples that meet the carbide size standard and the ferrite film thickness standard, and using the corresponding minimum cooling rate as the critical cooling rate of the high-titanium steel with the titanium content. Although the critical cooling rate of high-titanium steel entering the high thermoplastic range can be determined; however, there is a large room for improvement in the adaptability and stability of the crystallizer.

[0006] At present, the existing technologies 1, 2 and 3 have the problem that the system cannot be accurately controlled under unknown disturbances, and the control method cannot be intelligently adjusted, making the crystallizer unable to adapt to the complex and changeable operating environment. Therefore, the present invention provides a crystallizer control method and system based on proportional integral differential; an intelligent PID control system model is designed according to the input and output states of the crystallizer to enhance the adaptability of the crystallizer, ensure its working efficiency and stability, and achieve the adaptability and stability of the crystallizer.

[0007] at present, Summary of the invention

[0008] The main purpose of the present invention is to provide a crystallizer control method and system based on proportional-integral-differential to solve the problem in the prior art that the system cannot be accurately controlled under unknown disturbances, the control method cannot be intelligently adjusted, and the crystallizer cannot adapt to the complex and changeable operating environment.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A mold control method based on proportional-integral-differential, the layout method comprising:

[0011] Obtain the real-time liquid level and wall temperature in the crystallizer, convert the analog signals of the liquid level and wall temperature into analog control signals to adjust the crystallizer cooling water inlet parameters; construct a cooling water database based on the flow rate and temperature of the generated cooling water and the heat transfer information of each area in the crystallizer;

[0012] Set the corresponding target values ​​of the liquid level and wall temperature in the crystallizer; obtain the difference between the current liquid level and wall temperature and the set value; obtain the data from the cooling water database to provide parameters such as cooling water flow, temperature and heat transfer information in the crystallizer for the computational fluid dynamics model; calculate the proportional control output, which is proportional to the difference, calculate the integral of the past error, predict the change of the error, combine the outputs of each part, and calculate the final control signal;

[0013] According to the calculation results of the final control signal, adjust the flow and temperature of the cooling water to make the liquid level and wall temperature in the crystallizer reach the set values; continue to monitor the liquid level and wall temperature in the crystallizer, and record all monitored data and PID control operations in the database; analyze the crystallizer control data and evaluate the control effect of the PID parameters.

[0014] As a further improvement of the present invention, the process of converting the analog signals of the liquid level height and the wall temperature into analog control signals includes:

[0015] The crystallizer liquid level monitoring device and the crystallizer wall temperature monitoring device are used to measure the crystallizer liquid level and wall temperature respectively; the output signal is increased by the signal conditioning circuit;

[0016] Generate a control signal based on the set proportional factor and output signal, and obtain the reference of the control signal by calculating the difference between the set point and the real-time signal to form an additional adjustment;

[0017] The generated control signal is output in the form of an analog signal, the analog signal is connected to an actuator, and the flow rate and temperature of the cooling water are adjusted through the actuator.

[0018] As a further improvement of the present invention, the process of constructing a cooling water database includes:

[0019] The computational fluid dynamics model of the crystallizer is constructed in a 1:1 ratio, and the computational fluid dynamics model is meshed;

[0020] According to the selection of personalized numerical model of the crystallizer, the mass conservation equation is used to show the change of fluid density and its relationship with fluid velocity, the momentum conservation equation is used to describe the dynamic characteristics of fluid flow, the energy equation is used to perform mixed calculation based on the volume fraction of each phase, the turbulent characteristics of the flow are modeled, and the numerical simulation of the multiphase flow characteristics and thermodynamic behavior of the crystallizer is realized;

[0021] Given the density and viscosity physical parameters of fluid, air and other substances, and the fluid velocity boundary conditions, the results of numerical simulation are output.

[0022] As a further improvement of the present invention, the selected numerical model is as follows:

[0023]

[0024] Where ρ is the fluid density, V and u are the volume and velocity of the fluid phase, p and μ represent the pressure and molecular viscosity of the fluid, respectively. The instantaneous time of the numerical simulation and the gravitational acceleration vector of the fluid are represented by symbols t and g, respectively. The surface tension between the phases in each control volume is considered and expressed by F ST express;

[0025] The energy equation for a single phase is used to estimate the energy transfer in each cell while sharing energy between phases:

[0026]

[0027] The effective thermal conductivity and temperature of the fluid are represented by T f and k eff Energy is represented by the symbol E and is considered as a mass-averaged variable:

[0028]

[0029] Where E i is the energy of the ith phase, calculated from its specific heat and shared temperature. In present usage, the volume fraction of the fluid is denoted by the symbol α, the subscript g denotes the gas phase, and the subscript l denotes the liquid phase;

[0030] The explicit time equation is used to solve the volume fraction equation to track the interface position in the gas-liquid two-phase system. The equation is expressed as:

[0031]

[0032] For the volume fraction model, the gas phase and liquid phase coexist in each computational cell, where the volume fractions of the two phases vary from 0 to 1, and the sum of the volume fractions of the two phases is 1; the density and viscosity fluid properties are also shared by all phases and represent volume averages. The volume average density and viscosity are calculated based on the constraint functions of the participating phases:

[0033] α g +α l =1

[0034] ρ=α g ρ g +α l ρ l

[0035] μ=α g μ g +α l μ l

[0036] Multiphase turbulence simulation using:

[0037]

[0038] In the formula, β * represents the closure coefficient, which is taken as a constant of 0.09. The generation of turbulent Prandtl number and k is represented by the symbols μ and k and P k , and the ω generated by the turbulent Prandtl number is represented by μ ω and P ω express;

[0039] By solving and calculating the numerical simulation system, a scenario close to the actual work can be simulated;

[0040] The crystallizer cooling water heat flux function is constructed based on deep learning, and its formula is expressed as:

[0041] Q=f(u,L,∈,T,b1,b2,h)

[0042] Where u, L,∈, T, b1, b2, and h represent the cooling water flow rate, the length of the crystallizer, the inverted taper of the crystallizer per meter, the fluid temperature, the lower opening width of the crystallizer, the upper opening width of the crystallizer, and the liquid level, respectively.

[0043] As a further improvement of the present invention, meshing the computational fluid dynamics model includes:

[0044] Collect the design drawings of the crystallizer, including size, shape, material properties and inlet and outlet locations; understand the working conditions, build a three-dimensional geometric model of the crystallizer, and input all geometric parameters of the crystallizer in a 1:1 ratio;

[0045] Save the geometric model in a format supported by the computational fluid dynamics software, determine the size and type of the grid, and import the geometric model of the crystallizer into the grid generation software; make preliminary divisions on the surface of the geometric body and set the grid density; generate a volume grid based on the surface grid and use the orthogonality of the grid for evaluation;

[0046] Preliminary simulation is performed to verify the performance of the grid, monitor convergence and calculation operation; based on the results of the preliminary simulation, the grid density and partitioning strategy are adjusted.

[0047] As a further improvement of the present invention, the process of calculating the final control signal includes:

[0048] Set the target values ​​of liquid level and wall temperature, monitor the liquid level and wall temperature of the crystallizer in real time; calculate the liquid level error and wall temperature error; obtain the cooling water flow, inlet and outlet temperature difference, and effective cooling area of ​​the crystallizer from the cooling water database to calculate the heat flux;

[0049] According to the principle of proportional control, the control signal is calculated, which is proportional to the liquid level error and the temperature error. The past errors are integrated according to the formula, the differential term is calculated according to the formula, the rate of change of the error is predicted, and the proportional, integral and differential terms are combined to calculate the control signal;

[0050] The calculated control signal is converted into an analog control signal and output to the cooling water flow and temperature control device to adjust the flow and temperature of the cooling water to adjust the liquid level and wall temperature inside the crystallizer; the heat flux formula is used to assist in evaluating the cooling effect; the formula is:

[0051]

[0052] Among them, c W is the heat capacity of cooling water, Q W is the cooling water flow rate, ΔT is the cooling water inlet and outlet temperature difference, S eff is the effective cooling area of ​​the crystallizer.

[0053] As a further improvement of the present invention, the formula for calculating the proportional control output is:

[0054] P H =K p ·E H ,P T =K p ·E T

[0055] Among them, K p is the proportional gain, E H Indicates the liquid level error, E T represents the wall temperature error;

[0056] The formula for calculating the integral term is:

[0057] Integrate the past errors and sum them over time period t:

[0058] I H =I H +Ki ·E H Δt, I T =I T +K i ·E T ·Δt

[0059] Among them, K i represents the integral gain;

[0060] The formula for calculating the differential term is:

[0061] Rate of change of forecast error:

[0062]

[0063] Among them, K d is the differential gain;

[0064] Combining the proportional, integral, and derivative terms, the control signal is calculated:

[0065] C H =P H +I H +D H ,C T =P T +I T +D T

[0066] Among them, C H and C T They are the final output signals of liquid level control and wall temperature control respectively.

[0067] As a further improvement of the present invention, the process of combining proportional, integral and differential terms to calculate the control signal includes:

[0068] Define the saturation function and define a saturation function S(x) to implement, which limits the input signal:

[0069] Combine the saturation function with the PID control signal to ensure that the final control output does not exceed the set limit; set the total calculation formula of the PID control signal, and process the total control signal U(t) through the saturation function S;

[0070] Output and implementation of the control signal, calculate the liquid level error, calculate the proportional part, perform integral calculation, perform differential calculation, and combine the output to calculate the total control signal; input U(t) into the saturation function to obtain the saturated control signal, and output the control signal to the actuator.

[0071] As a further improvement of the present invention, outputting the control signal to the actuator includes:

[0072] Identify the actuator and design a standardized interface for the actuator to receive signals from the control system and convert them into specific physical actions; the interface includes: digital input signal, a digital signal channel for receiving control instructions; an analog signal channel, connected through an analog-to-digital converter and a digital-to-analog converter to achieve conversion between digital signals and analog signals to control the actuator power output;

[0073] According to the control signal output by the saturation function, it is converted into the final control input that matches the actuator capacity; through dynamic linearization processing, the delay and amplitude change caused by nonlinear characteristics are eliminated; after the control signal is output, the dynamic linearization function is used to achieve fine-tuning of the control signal;

[0074] At the output end of the actuator, a flow meter, pressure sensor and position sensor are installed to monitor the real-time status of the actuator and send feedback signals back to the control system. The original control signal is adjusted according to the feedback signal, the feedback value is compared with the set value in real time, and the error signal is calculated to adjust the future control signal to form a closed-loop control.

[0075] To achieve the above object, the present invention also provides the following technical solutions:

[0076] A crystallizer control device based on proportional-integral-differential is applied to the crystallizer control method based on proportional-integral-differential. The crystallizer control device based on proportional-integral-differential includes:

[0077] The database construction module is used to obtain the real-time liquid level and wall temperature in the crystallizer, convert the analog signals of the liquid level and wall temperature into analog control signals to adjust the crystallizer cooling water inlet parameters; construct a cooling water database based on the flow rate and temperature of the generated cooling water and the heat transfer information of each area in the crystallizer;

[0078] The intelligent control module is used to set the corresponding target values ​​of the liquid level and wall temperature in the crystallizer; obtain the difference between the current liquid level and wall temperature and the set value; obtain the data of the cooling water database, and provide parameters such as cooling water flow, temperature and heat transfer information in the crystallizer for the computational fluid dynamics model; calculate the proportional control output, which is proportional to the difference, calculate the integral of the past error, predict the change of the error, combine the outputs of each part, and calculate the final control signal;

[0079] The result evaluation module is used to adjust the flow and temperature of the cooling water according to the calculation results of the final control signal, so that the liquid level and wall temperature in the crystallizer reach the set values; continue to monitor the liquid level and wall temperature in the crystallizer, and record all monitored data and PID control operations in the database; analyze the crystallizer control data and evaluate the control effect of the PID parameters.

[0080] The present invention obtains real-time data and constructs a cooling water database. By obtaining the liquid level and wall temperature in the crystallizer, the current system status can be timely understood; the analog signals of the liquid level and wall temperature are converted into analog control signals, so that dynamic adjustment of the cooling water inlet parameters can be achieved; by generating cooling water flow, temperature and heat transfer information of each area of ​​the crystallizer, a data repository is constructed to provide a basis for future decision-making. Significance: Real-time monitoring and signal conversion enable the system to respond quickly to changes and ensure the stability and effectiveness of the crystallization process; the construction of a cooling water database provides necessary data support for subsequent control algorithms and optimizations, and improves the intelligence level of the system. Set target values ​​and control algorithm calculations, set target values ​​for the liquid level and wall temperature, and ensure that the control system has a clear adjustment benchmark; calculate the difference (error) between the current state and the set value to provide input for the PID algorithm; calculate the proportional control output, integral output and differential output, and merge them into the final control signal. Significance: Clear target setting ensures that the system is always adjusted in the expected direction, improving control accuracy; by analyzing the error between the current state and the target state, the PID control algorithm can comprehensively consider the immediate, historical and trend information, so as to adjust more flexibly and efficiently to achieve smooth control. Perform control and data recording analysis, adjust the cooling water flow and temperature according to the calculated final control signal, and ensure that the various operating parameters of the crystallizer reach the set value; monitor the real-time feedback of the system, and record the monitoring data and control operations in the database to provide a basis for subsequent analysis; analyze the collected control data to evaluate the control effect of PID parameters. Significance: Through the combination of actual control and monitoring, an effective closed-loop feedback control system is formed to ensure the stability and efficiency of the crystallization process; data recording and analysis provide a basis for subsequent optimization. Through the study of historical data, potential problems and optimization opportunities can be discovered, thereby continuously improving system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a schematic diagram of the steps of an embodiment of a crystallizer control method based on proportional integral differential of the present invention;

[0082] Figure 2 A schematic flow chart of the steps of converting analog signals of liquid level height and wall temperature into analog control signals according to an embodiment of the crystallizer control method based on proportional integral differential of the present invention;

[0083] Figure 3 A schematic diagram of building a cooling water database for an embodiment of a crystallizer control method based on proportional integral differential of the present invention;

[0084] Figure 4 A schematic flow chart of the steps of calculating the final control signal in one embodiment of the crystallizer control method based on proportional integral differential of the present invention;

[0085] Figure 5 A schematic diagram of calculating a final control signal according to an embodiment of a crystallizer control method based on proportional-integral-differential of the present invention;

[0086] Figure 6 A schematic flow chart of steps for evaluating the control effect of PID parameters in an embodiment of a crystallizer control method based on proportional-integral-differential of the present invention;

[0087] Figure 7 It is a functional module schematic diagram of an embodiment of a crystallizer control system based on proportional-integral-differential of the present invention;

[0088] Figure 8 It is a structural schematic diagram of an embodiment of an electronic device of the present invention;

[0089] Fig. 9 It is a schematic structural diagram of an embodiment of the storage medium of the present invention. DETAILED DESCRIPTION

[0090] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] The terms "first", "second" and "third" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0092] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0093] like Figure 1 As shown, this embodiment provides an embodiment of a crystallizer control method based on proportional integral differential. In this embodiment, the crystallizer control method based on proportional integral differential specifically includes the following steps:

[0094] Step S1: obtaining the liquid level and wall temperature in the real-time crystallizer, converting the analog signals of the liquid level and wall temperature into analog control signals to adjust the crystallizer cooling water inlet parameters; constructing a cooling water database according to the flow rate and temperature of the generated cooling water and the heat transfer information of each area in the crystallizer;

[0095] Step S2: Set the corresponding target values ​​of the liquid level and wall temperature in the crystallizer; obtain the difference between the current liquid level and wall temperature and the set value; obtain the data of the cooling water database, and provide parameters such as cooling water flow, temperature and heat transfer information in the crystallizer for the computational fluid dynamics model; calculate the proportional control output, which is proportional to the difference, calculate the integral of the past error, predict the change of the error, combine the outputs of each part, and calculate the final control signal;

[0096] Step S3: According to the calculation result of the final control signal, adjust the flow rate and temperature of the cooling water so that the liquid level and wall temperature in the crystallizer reach the set value; continue to monitor the liquid level and wall temperature in the crystallizer, and record all monitored data and PID control operations in the database; analyze the crystallizer control data and evaluate the control effect of the PID parameters.

[0097] Preferably, step S1 of this embodiment obtains real-time data and constructs a cooling water database. By obtaining the liquid level and wall temperature in the crystallizer, the current system status can be timely understood; the analog signals of the liquid level and wall temperature are converted into analog control signals, so that dynamic adjustment of the cooling water inlet parameters can be achieved; by generating cooling water flow, temperature and heat transfer information of each area of ​​the crystallizer, a data repository is constructed to provide a basis for future decision-making. Significance: Real-time monitoring and signal conversion enable the system to respond quickly to changes and ensure the stability and effectiveness of the crystallization process; the construction of a cooling water database provides necessary data support for subsequent control algorithms and optimizations, and improves the intelligence level of the system. Step S2 sets the target value and control algorithm calculation, sets the target value of the liquid level and wall temperature, and ensures that the control system has a clear adjustment benchmark; calculates the difference (error) between the current state and the set value to provide input for the PID algorithm; calculates the proportional control output, integral output and differential output, and merges them into the final control signal. Significance: Clear target setting ensures that the system is always adjusted in the expected direction, improving control accuracy; by analyzing the error between the current state and the target state, the PID control algorithm can comprehensively consider the immediate, historical and trend information, so as to adjust more flexibly and efficiently to achieve smooth control. Step S3 performs control and data recording analysis, adjusts the cooling water flow and temperature according to the calculated final control signal, and ensures that the various operating parameters of the crystallizer reach the set values; monitors the real-time feedback of the system, and records the monitoring data and control operations in the database to provide a basis for subsequent analysis; analyzes the collected control data and evaluates the control effect of the PID parameters. Significance: Through the combination of actual control and monitoring, an effective closed-loop feedback control system is formed to ensure the stability and efficiency of the crystallization process; data recording and analysis provide a basis for subsequent optimization. Through the study of historical data, potential problems and optimization opportunities can be discovered, thereby continuously improving system performance.

[0098] In summary, this embodiment can achieve an efficient and stable production process through three steps of the PID-based crystallizer control method. Step S1 ensures that the system grasps the current state, step S2 sets clear goals and implements scientific control strategies, and step S3 maintains the stability of the system through execution control and continuous monitoring, while laying the foundation for data analysis and optimization. A series of processes effectively maintain the operating performance of the crystallizer and provide support for further improvements.

[0099] This embodiment improves the monitoring and control system of the traditional crystallizer to achieve real-time control of operating parameters such as cooling water flow and stability according to the crystallizer parameters and detection values, thereby improving the stability and efficiency of the crystallizer. At the same time, it solves the problem that the system cannot be accurately controlled under unknown disturbances in the traditional method, and the control method cannot be intelligently adjusted, making the crystallizer unable to adapt to the complex and changeable operating environment; design an intelligent PID control system model according to the input and output status of the crystallizer, enhance the adaptability of the crystallizer, and ensure its working efficiency and stability; realize the self-adaptation and stability of the crystallizer, and compare the actual output with the expected output in real time through the system, and feedback control enables the system to achieve the expected goal more accurately.

[0100] Furthermore, if Figure 2 As shown, the process of converting the analog signals of the liquid level height and the wall temperature into the analog control signal in step S1 specifically includes the following steps:

[0101] Step S11: using a crystallizer liquid level monitoring device and a crystallizer wall temperature monitoring device to measure the crystallizer liquid level and wall temperature respectively; and increasing the output signal through a signal conditioning circuit;

[0102] Step S12: Generate a control signal according to the set proportional factor and the output signal, and obtain a reference of the control signal by calculating the difference between the set point and the real-time signal to form an additional adjustment;

[0103] Step S13: Output the generated control signal in the form of an analog signal, connect the analog signal to the actuator, and adjust the flow rate and temperature of the cooling water through the actuator.

[0104] Preferably, in step S11 of this embodiment, the signal measurement and conditioning is performed by using a crystallizer liquid level monitoring device (such as an ultrasonic sensor) and a wall temperature monitoring device (such as a thermocouple) to obtain the real-time liquid level and wall temperature, respectively; ensuring that the system has an accurate understanding of the current state and can promptly reflect the process changes of the crystallizer; using a signal conditioning circuit (such as a low-pass filter, an amplifier) ​​to process the output of the sensor, it is possible to reduce noise interference, enhance signal stability, and thus improve the accuracy and reliability of the signal. Significance: High-quality measurement data provides a solid foundation for subsequent control decisions and helps to achieve efficient process control; through signal conditioning, the system can effectively reduce errors introduced by environmental interference and achieve a more stable control process. Step S12: Control signal generation. The control signal is generated by the set proportional factor and the measured signal. The difference (deviation) between the real-time signal and the set point is calculated to form the reference of the control signal. The core algorithm of the control logic is implemented. The actual process is compared with the target value through this process. After the reference control signal is generated, the signal can be further adjusted according to different control strategies (such as proportional, integral, differential, etc.) so that the control output can balance the gap between the real-time state and the target state, and enhance the response sensitivity of the system. Significance: It ensures that the system can perform adjustments based on real-time feedback, realize goal-oriented adaptive control, and help the system steadily reach the set point. Through real-time feedback and necessary adjustments, the control system can respond quickly in a rapidly changing production environment and improve the accuracy of overall process control. Step S13 controls the output and execution of the control signal, outputs the generated control signal in the form of an analog signal (such as 0-10V or 4-20mA) and connects it to the actuator; ensures that the control signal can be received and executed by the physical device to form an actual operation; the actuator (such as a valve or pump) instantly adjusts the flow and temperature of the cooling water according to the input control signal, directly affecting the operating conditions in the crystallizer, such as the liquid surface and wall temperature, and can achieve dynamic and precise cooling control. Significance: This step converts the results of the control algorithm into actual operations, ensures the stability and efficiency of the crystallization process, and is reflected in the quality of the crystallized product through appropriate adjustment of the cooling water flow and temperature; through the flow and temperature information fed back by the actuator, the system can instantly maintain and adjust the cooling effect, thereby optimizing the entire crystallization process and ensuring the sustainability and economy of production.

[0105] In summary, through these three steps, the analog signals of liquid level height and wall temperature are successfully converted into analog control signals to form a complete control system. Accurate measurement, effective signal processing, reasonable control logic and direct physical execution work together to ensure the efficiency, stability and automation of the process, providing reliable technical support for crystallization production.

[0106] Furthermore, the process of constructing the cooling water database in step S1 specifically includes the following steps:

[0107] Step S14: constructing a computational fluid dynamics model of the crystallizer in a 1:1 ratio, and meshing the computational fluid dynamics model;

[0108] Step S15: selecting a personalized numerical model according to the crystallizer, using the mass conservation equation to display the change of fluid density and its relationship with the fluid velocity, using the momentum conservation equation to describe the dynamic characteristics of the fluid flow, using the energy equation to perform a mixed calculation based on the volume fraction of each phase, modeling the turbulent characteristics of the flow, and realizing the numerical simulation of the multiphase flow characteristics and thermodynamic behavior of the crystallizer;

[0109] Step S16: Given physical parameters such as density and viscosity of fluid, air and other materials, and boundary conditions such as fluid velocity, the numerical simulation results are output.

[0110] Preferably, step S14 of this embodiment constructs a computational fluid dynamics model and performs grid division, and models the crystallizer at a 1:1 ratio to ensure that the model is physically representative; the computational fluid dynamics (CFD) model is grid divided to discretize the model, so that the flow characteristics and heat transfer of the fluid can be calculated in different control volumes. The significance achieved: through fine grid division, more accurate numerical calculations can be guaranteed in key areas; the constructed model can be adjusted according to actual operating conditions to provide a basis for subsequent fluid dynamic analysis; a reasonable grid can help effectively capture the multi-scale characteristics of flow and heat transfer during the calculation process. Step S15 selects a personalized numerical model and performs numerical simulation. According to the characteristics and needs of the crystallizer, appropriate mass conservation, momentum conservation and energy equations are selected to establish a numerical model that conforms to physical laws; in view of the complexity of multiphase fluids, a suitable turbulence model is established to describe the instability and random characteristics of the flow. Significance achieved: By using a suitable mathematical model, the fluid behavior can be predicted more accurately, ensuring that the simulation results are consistent with the actual working conditions; the flow, heat transfer, phase change and other processes inside the crystallizer are fully simulated, so as to better understand the flow mechanism and thermodynamic behavior, which is convenient for subsequent optimization; through numerical simulation, potential performance problems of the crystallizer can be identified early and improvement plans can be provided. Step S16 sets physical parameters and boundary conditions, outputs numerical results, and accurately gives physical parameters such as density and viscosity of fluids, air and other materials, which helps to truly reproduce the behavior of materials in the simulation; according to the actual working conditions, boundary conditions such as fluid velocity and temperature are defined to guide the numerical simulation; simulation results are generated, including data of key physical quantities such as velocity field, pressure field, temperature field, volume fraction, etc. Significance achieved: By accurately setting physical parameters and boundary conditions, the stability and credibility of the simulation results are ensured, which is convenient for subsequent data analysis; the output numerical simulation results provide basic data for the construction of the cooling water database, which will be used for further analysis, optimization design and decision support; the simulation results can be used as an important basis for evaluating the performance of the crystallizer and the efficiency of the cooling water system, laying the foundation for more in-depth research (for specific principles, refer to the attached Figure 3 ).

[0111] In this embodiment, a numerical model and a numerical method suitable for the crystallizer are selected, and the specific numerical model selected is as follows:

[0112]

[0113] Where ρ is the fluid density, V and u are the volume and velocity of the fluid phase, p and μ represent the pressure and molecular viscosity of the fluid, respectively. The instantaneous time of the numerical simulation and the gravitational acceleration vector of the fluid are represented by symbols t and g, respectively. The surface tension between the phases in each control volume is considered and expressed by F ST express.

[0114] The energy equation for a single phase is used to estimate the energy transfer in each cell while sharing energy between phases as follows:

[0115]

[0116] The effective thermal conductivity and temperature of the fluid are represented by T f and k eff Energy is represented by the symbol E and is considered as a mass-averaged variable:

[0117]

[0118] Where E i is the energy of the ith phase, calculated from its specific heat and shared temperature. In present usage, the volume fraction of the fluid is denoted by the symbol α, the subscript g denotes the gas phase, and the subscript l denotes the liquid phase;

[0119] The volume fraction equation is solved using an explicit time equation to track the interface position in a gas-liquid two-phase system, which can be expressed as:

[0120]

[0121] For the volume fraction VOF model, the gas phase and liquid phase can coexist in each computational unit, where the volume fractions of the two phases vary from 0 to 1, and the sum of the volume fractions of the two phases is 1 (∑ i α i =1). Fluid properties such as density and viscosity are also shared by all phases and are expressed as volume averages, meaning that these properties in a single cell either represent purely one of the fluid phases or a mixture of the phases. The volume average density and viscosity are calculated from the constraint functions of the participating phases as shown below:

[0122] α g +α l =1

[0123] ρ=α g ρ g +α l ρ l

[0124] μ=α g μ g +α l μ l

[0125] Due to the three-dimensional, multi-scale and non-steady characteristics of the flow, the simulation of multiphase turbulence is very complex.

[0126]

[0127] In the above formula, β* represents the closure coefficient, which is taken as a constant of 0.09. The generation of turbulent Prandtl number and k is represented by the symbol σ k and P k , and the ω generated by the turbulent Prandtl number is represented by σ ω and P ω express;

[0128] By solving and calculating the numerical simulation system, a scenario close to the actual work can be simulated;

[0129] The crystallizer cooling water heat flux function is constructed based on deep learning, and its formula can be expressed as follows:

[0130] Q=f(u,L,∈,T,b1,b2,h)

[0131] Where u, L,∈, T, b1, b2, and h represent the cooling water flow rate, the length of the crystallizer, the inverted taper of the crystallizer per meter, the fluid temperature, the lower opening width of the crystallizer, the upper opening width of the crystallizer, and the liquid level, respectively.

[0132] In summary, each step of this embodiment is interrelated and together constructs a complete cooling water database process, ensuring the effectiveness of the data finally generated in terms of scientificity and practicality.

[0133] Furthermore, the process of meshing the computational fluid dynamics model in step S14 specifically includes the following steps:

[0134] Step S141: Collect the design drawings of the crystallizer, including size, shape, material properties and inlet and outlet locations; understand the working conditions, such as temperature, pressure, fluid type (gas, liquid) and operating frequency; establish a three-dimensional geometric model of the crystallizer, and input all geometric parameters of the crystallizer in a 1:1 ratio;

[0135] Step S142: Save the geometric model in a format supported by the computational fluid dynamics software, determine the size and type of the grid, and import the geometric model of the crystallizer into the grid generation software; perform preliminary division on the surface of the geometric body and set the grid density; generate a volume grid based on the surface grid, and use the orthogonality of the grid for evaluation to ensure the accuracy of the fluid dynamics calculation;

[0136] Step S143: Perform a preliminary simulation to verify the performance of the grid, monitor convergence and computational performance; adjust the grid density and partitioning strategy based on the results of the preliminary simulation.

[0137] Preferably, in step S141 of this embodiment, the model is established and the design parameters are collected to comprehensively collect the design parameters of the crystallizer, including size, shape, material properties, inlet and outlet positions, and working conditions (such as temperature, pressure, fluid type, etc.), to ensure that the model is built based on accurate actual information; based on the collected data, a three-dimensional geometric model of the crystallizer is established using CAD software to ensure that all geometric parameters are input in a 1:1 ratio to reflect the real physical structure. The significance achieved: An accurate geometric model can ensure the true reproduction of fluid flow and heat transfer phenomena, laying the foundation for subsequent simulations; considering the influence of different working conditions during the modeling process helps to foresee the fluid behavior in actual operation and improve the reliability of the model; by understanding the working conditions and geometric parameters, reasonable strategies and indicators can be formulated for subsequent meshing. Step S142: Mesh generation and quality assessment: save the geometric model in a format supported by the CFD software to ensure that the model can be smoothly imported into the mesh generation software; select the appropriate mesh type (structured or unstructured) according to the specific flow field requirements, and determine the size of the mesh to adapt to different flow characteristics; perform preliminary mesh division on the surface of the geometric body, and set the necessary mesh fineness to capture important flow characteristics; generate volume meshes based on the surface meshes to ensure that the meshes have appropriate density in key areas (such as boundary layers, flow turning points, etc.); use quality indicators such as the orthogonality of the meshes for evaluation, which ensures that the generated meshes have appropriate geometric quality, thereby reducing errors in numerical operations. Significance achieved: Reasonable mesh division enables the simulation to more accurately express the flow and heat transfer process and reduce numerical errors; on the premise of ensuring accuracy, by selecting the appropriate mesh type and size, the effective use of computing resources is achieved; provide a basis for subsequent optimization to ensure that the required simulation accuracy is achieved while maintaining computing efficiency. Step S143: Preliminary simulation and mesh optimization: Perform preliminary simulation to verify the performance of the current mesh, monitor the convergence and computational stability of the simulation results, and lay the foundation for subsequent optimization; timely discover potential problems (such as poor convergence, unstable flow, etc.) by observing the preliminary simulation results (such as flow field, pressure field, etc.). Significance achieved: Dynamic monitoring of simulation operation enables early identification and handling of problems, improving the credibility of the overall model; based on the feedback of preliminary results, further adjust the mesh density and division strategy to achieve higher simulation accuracy; through continuous iteration and optimization of the mesh generation process, gradually form an efficient and accurate CFD research framework to improve work efficiency.

[0138] In summary, through the above steps, this embodiment can ensure that the grid division of the computational fluid dynamics model not only meets the actual needs, but also provides a basis for subsequent complex numerical simulations. It aims to maximize the accuracy and reliability of the model to achieve efficient fluid mechanics research and engineering applications.

[0139] Furthermore, if Figure 4As shown, the process of calculating the final control signal in step S2 specifically includes the following steps:

[0140] Step S21: setting target values ​​of liquid level and wall temperature, monitoring the liquid level and wall temperature of the crystallizer in real time; calculating the liquid level error and wall temperature error; obtaining the cooling water flow rate, inlet and outlet temperature difference, and effective cooling area of ​​the crystallizer from the cooling water database for calculating the heat flux;

[0141] Step S22: According to the principle of proportional control, the control signal is calculated, which is proportional to the liquid level error and the temperature error, the past error is integrated according to the formula, the differential term is calculated according to the formula, the rate of change of the error is predicted, and the proportional, integral and differential terms are combined to calculate the control signal;

[0142] Among them, the formula for calculating the proportional control output is:

[0143] P H =K p ·E H ,P T =K p ·E T

[0144] Among them, K p is the proportional gain, E H Indicates the liquid level error, E T represents the wall temperature error;

[0145] The formula for calculating the integral term is:

[0146] Integrate the past errors (sum over time period (t)):

[0147] I H =I H +K i ·E H Δt, I T =I T +K i ·E T ·Δt

[0148] Among them, K i represents the integral gain;

[0149] The formula for calculating the differential term is:

[0150] Rate of change of forecast error:

[0151]

[0152] Among them, K d is the differential gain;

[0153] Combining the proportional, integral, and derivative terms, the control signal is calculated:

[0154] C H =P H +I H +D H ,C T =P T +I T +D T

[0155] Among them, C H and C T They are the final output signals of liquid level control and wall temperature control respectively;

[0156] Step S23: convert the calculated control signal into an analog control signal, output it to the cooling water flow and temperature regulating device, adjust the flow and temperature of the cooling water to adjust the liquid level and wall temperature inside the crystallizer; use the heat flux formula to assist in evaluating the cooling effect; the formula is:

[0157]

[0158] Among them, c W is the heat capacity of cooling water, Q W is the cooling water flow rate, ΔT is the cooling water inlet and outlet temperature difference, S eff It is the effective cooling area of ​​the crystallizer; it provides a reference for PID control to confirm the effectiveness of the cooling system.

[0159] Preferably, step S21 of this embodiment sets the target value and real-time monitoring, sets specific target values ​​for the liquid level and wall temperature, so that the system has clear operating standards during operation; continuously obtains the actual liquid level and wall temperature through sensors to provide real-time data support; calculates the error between the current state and the set target, and provides necessary input parameters for the subsequent control algorithm; retrieves parameters such as cooling water flow, temperature difference and effective cooling area from the cooling water database to prepare for heat flux calculation. Significance: Ensure that the operation of the crystallizer can be carried out around the set target, reduce operating deviations, and improve product quality; provide feedback on the actual state of process variables to improve the responsiveness and timeliness of the system; accurate cooling water parameters support fluid mechanics analysis and provide a scientific basis for control decisions. Step S22 calculates the control signal, and ensures that the system has a quick initial response to the error by calculating the proportional control signal; compensates for the static error by integrating the past errors, and ensures that the error continues to approach zero when the system runs for a long time; predicts the change trend of the error, provides dynamic response information, reduces system overshoot and oscillation, and improves stability; integrates proportional, integral and differential terms, calculates the control signal, and forms a comprehensive control strategy for the system. Significance: By combining proportional and differential control, the system's response speed to transient changes is improved, and the oscillation of transient processes is effectively suppressed; the steady-state error is eliminated through the integral term, so that the system maintains precise control under continuous operation; the three-part control signal is combined to enhance the effect of the control strategy, making the system performance more accurate and reliable. Step S23 outputs the control signal and evaluation, converts the calculated control signal into an adjustable physical control signal, and controls the flow and temperature of the cooling water;

[0160] The effectiveness of the cooling system is evaluated by the heat flux formula to check whether the current cooling conditions meet the requirements. Significance: Accurate adjustment of cooling water flow and temperature can effectively control the process environment in the crystallizer, ensuring the stability of operation and the quality of the product;

[0161] Through heat flux assessment, the cooling capacity and heat dissipation performance of the system are confirmed to ensure long-term stable operation; through monitoring and data analysis, PID parameters and control strategies are adjusted in time to continuously optimize system performance and efficiency.

[0162] In summary, each step of this embodiment constitutes a key part of the overall control strategy, from setting goals to real-time monitoring, to signal calculation and output adjustment, all of which are working to improve the stability, efficiency and product quality of the crystallization process. The structural design ensures rapid and effective response under different working conditions, and is an example of the practical application of advanced control theory in industry.

[0163] Furthermore, the process of combining the proportional, integral and differential terms in step S22 to calculate the control signal specifically includes the following steps:

[0164] Step S221: define a saturation function, define a saturation function S(x) to implement, which limits the input signal:

[0165] S(x)={α(t)·maxoutput if x>α(t)·maxoutputx ifα(t)·minoutput≤x≤α(t)·maxoutputα(t)·min o output if x<α(t)·minoutput

[0166] α(t)=1+k·|E H |

[0167] Where: maxoutput represents the maximum output limit of the control signal, minoutput represents the minimum output limit of the control signal; k is an adjustable parameter used to control the response speed of the dynamic gain to the error size;

[0168] Step S222: Combine the saturation function with the PID control signal to ensure that the final control output does not exceed the set limit; the total calculation formula of the PID control signal is set as follows:

[0169] U(t)=C H =P H +I H +D H

[0170] The total control signal U(t) is processed by the saturation function S:

[0171] C H =S(U(t))=S(P H +I H +D H )

[0172] Step S223: output and implementation of the control signal, calculate the liquid level error, calculate the proportional part, perform integral calculation, perform differential calculation, combine and output the total control signal; input U(t) into the saturation function to obtain the saturated control signal, and output the control signal to the actuator.

[0173] Preferably, step S221 of this embodiment defines a saturation function to limit the range of the input signal; introduces a dynamic adjustment coefficient to dynamically adjust the saturation limit of the output according to the liquid level error (E_H). By defining the maximum and minimum output limits, excessive fluctuations of the control signal are prevented; the dynamic adjustment coefficient can flexibly adjust the saturation value as the error changes, so that the control system can better adapt to different error conditions. Practical significance: It effectively suppresses the extreme changes of the control signal caused by drastic fluctuations in the error (such as emergencies), thereby improving the stability and reliability of the system; by limiting the maximum and minimum outputs through the saturation function, it is ensured that the actuator will not be damaged due to excessive control signals, thereby extending the life of the equipment. Step S222 combines the saturation function with the PID control signal, and inputs the total control signal calculated by the PID algorithm into the saturation function to obtain the final control signal; integrates different control modules (proportional, integral, differential) together to achieve a more comprehensive control strategy; ensures that the control output meets the physical limitations: through the saturation function, it is ensured that the calculated control signal is within the range allowed by the actuator. Practical significance: The combination of multiple control items helps to improve control accuracy and optimize system response time; by limiting the output, the control effect under different working conditions is kept consistent, which enhances the robustness of the system. Step S223 outputs and implements the control signal, calculates the liquid level error, calculates the proportional part, performs integral calculation, performs differential calculation, and combines the output to form a total control signal; finally, the final control signal is output to the actuator and the operation is executed; the results of each calculation module are summarized and output as an executable control signal, and the conversion between the system state and the control instruction is processed; through the real-time feedback control of the actuator, the system state can be reacted and the output can be improved. Practical significance: The control signal can be calculated and adjusted in real time according to the input error to adapt to different working conditions, which is particularly important for control in a dynamic environment; the theoretical control strategy is effectively implemented in the equipment execution to ensure the stable operation of the system and achieve the set goals, such as maintaining the liquid level and wall temperature in an ideal state.

[0174] In summary, this embodiment not only clarifies the role of each technical point, but also plays a key role in improving the performance of human-computer interaction and automated control. Through systematic and dynamic processing methods, the accuracy and responsiveness of crystallizer control can be significantly improved, providing a more reliable solution for industrial production processes. The optimization and integration of each step ultimately contributes to improving system safety, stability and efficiency.

[0175] Furthermore, the process of outputting the control signal to the actuator in step S223 specifically includes the following steps:

[0176] Step S2231: Confirm the actuator and design a standardized interface for the actuator to receive signals from the control system and convert them into specific physical actions; the interface includes: a digital input signal, a digital signal channel for receiving control instructions; an analog signal channel, connected through an analog-to-digital converter and a digital-to-analog converter, to achieve conversion between digital signals and analog signals to control the actuator power output;

[0177] Step S2232: according to the control signal output by the saturation function, convert it into a final control input that matches the actuator capacity; eliminate the delay and amplitude change caused by the nonlinear characteristics through dynamic linearization processing; after the control signal is output, fine-tune the control signal through the dynamic linearization function;

[0178] Step S2233: At the output end of the actuator, install a flow meter, a pressure sensor and a position sensor to monitor the real-time status of the actuator and send feedback signals back to the control system. The original control signal is adjusted by the feedback signal, the feedback value is compared with the set value in real time, and the error signal is calculated to adjust the future control signal to form a closed-loop control.

[0179] Preferably, step S2231 of this embodiment confirms the actuator and designs a standardized interface. By designing a standardized input interface, compatibility is provided for different types of actuators, so that the control system can flexibly adapt to various actuators, simplifying the system integration process; achieving compatibility between digital signals (such as PWM, serial communication) and analog signals (such as voltage and current control), allowing flexible control strategies to be applied to different types of actuators; standardized interfaces reduce the complexity of debugging and maintenance, and reduce the probability of equipment failure caused by interface mismatch. Practical significance: Through standardized interfaces, the integration of multiple actuators can be quickly realized, greatly shortening the project development cycle; it can be easier to switch between different control strategies or actuators, enhancing the scalability and adaptability of the system. Step S2232 controls signal conversion and dynamic linearization processing, converting the output control signal into an input that matches the actuator capacity, which helps to improve the accuracy of the actuator response and ensure that it can output on demand; through dynamic linearization processing, the control signal can be finely adjusted to eliminate the delay and amplitude changes caused by the nonlinear characteristics of the actuator, making the control process more stable; according to the real-time actuator performance, the output signal is adjusted to adapt to the real-time changes in the operating state. Practical significance: Dynamic linearization processing enables each output signal to accurately reflect the control intention and reduce the uncertainty of the actuator response; by effectively suppressing the influence of nonlinear factors, the robustness and stability of the system under different working conditions are increased, and the possibility of failure is reduced. Step S2233 Feedback mechanism and closed-loop control, through feedback devices (such as flow meters, pressure sensors and position sensors) to monitor the output state of the actuator in real time, so that the control system can obtain the operation status of the actuator in time; calculate the error according to the feedback information, adjust the original control signal through feedback, so that the control process forms a closed loop, which helps to quickly respond to system disturbances; the error signal will be used to adjust the control strategy, so that even in the face of external disturbances and internal uncertainties, the control system can still maintain the set control target. Practical significance: Closed-loop control enables the system to automatically adjust when facing changing environmental and load conditions, thereby maintaining stable operation; through real-time feedback, it reduces the energy waste that may be caused by excessive or insufficient control, and ensures the effective use of resources.

[0180] In summary, in the process of outputting the overall control signal to the actuator, this embodiment is interconnected to jointly construct an efficient, accurate and flexible control system. Through standardized interfaces, dynamic linearization processing is used to improve signal quality, and closed-loop control is formed in combination with real-time feedback, ensuring high reliability and high responsiveness of the system under changing and complex conditions. The performance of the control system is improved, which is conducive to its wide application in industries, robots, automation and other fields, and improves the safety and economy of actual operations.

[0181] Furthermore, the process of forming a closed-loop control in step S2233 specifically includes the following steps:

[0182] Step S22331: Install a flow meter, a pressure sensor and a position sensor at the output end of the actuator to capture the working status of the actuator in real time; initialize and debug the sensor, calibrate the sensor through known reference input and corresponding output; and clearly set the control target value;

[0183] Step S22332: Real-time data acquisition and comparison, converting the flow, pressure and position data into digital signals through an analog-to-digital converter, and using a high-speed sampling cycle to monitor the changing signals; comparing the state value monitored in real time with the preset target value;

[0184] Step S22333: After the error signal is generated, analyze it to distinguish the error type of the system: such as steady-state error and dynamic error; set different processing strategies for each type of error, such as the steady-state error may need to adjust the gain, while the dynamic error shortens the response time; calculate based on the error signal to adjust the control signal;

[0185] The control signal can be updated in the following ways: Among them, (K p )、(K i )、(K d ) are proportional, integral and differential coefficients respectively; after calculating the control signal U(t), the signal output is limited by the aforementioned saturation function to ensure that the control signal does not exceed the capacity range of the actuator (maximum and minimum output limits).

[0186] Preferably, in step S22331 of this embodiment, sensor installation and debugging is performed by installing a flow meter, a pressure sensor and a position sensor at the output end of the actuator to continuously monitor the actuator status; the sensor can provide real-time data feedback to ensure that the control system can respond to the working status of the actuator in a timely manner; by initializing the debugging and calibrating the reference input, the data output by the sensor is ensured to accurately reflect the actual physical quantity, reducing the error caused by insufficient sensor accuracy, which is crucial to the control effect of the system; determining the control target value lays the foundation for subsequent feedback control, allowing the system to self-adjust according to the established goals, thereby improving the ability to achieve the goals. Practical significance: timely understanding of the actual status of the actuator can effectively avoid potential problems (such as exceeding the safety threshold), thereby increasing the safety and stability of the system; by real-time monitoring and determining the target value, the control system can dynamically adjust according to real-time data, improving the level of intelligent control. Step S22332 Real-time data acquisition and comparison: The real-time flow, pressure and position data are converted into digital signals through analog-to-digital converters to achieve high-speed and accurate data collection; high-frequency sampling can more accurately capture the dynamic changes of the actuator and effectively track the system status; the collected state value is compared with the preset target value to form a preliminary error signal, and the comparison provides a data basis for control decisions. Practical significance: With the acquisition of real-time data, the control system can quickly detect changes and ensure that the system can be adjusted in time to cope with external interference or internal changes; accurate real-time comparison makes it possible to achieve control goals, optimizes the performance of the actuator, and improves the efficiency and accuracy of operation. Step S22333 Error signal analysis and control signal adjustment: Analyze the generated error signal, distinguish between steady-state error and dynamic error, and use different methods for targeted processing; enable the system to allocate control resources more reasonably; adjust the control signal according to the error signal, which may include adjusting the gain to improve the steady-state response, or using high-frequency control strategies to shorten the dynamic response time. The adaptive capability can ensure that the system's performance under different working conditions always reaches the expected goal. Practical significance: By analyzing and specifically processing different types of errors, the control accuracy of the system is increased, the error of the actuator output is reduced, and the operational reliability is effectively improved; the rapid response capability to different error types ensures faster feedback and adjustment of the system under dynamic conditions, enhancing the overall response efficiency of the system.

[0187] In summary, this embodiment forms a closed-loop control process through the effective combination of these three steps, which not only improves the overall performance of the system, but also enhances the flexibility and adaptability of the control. Each step is closely connected and mutually dependent, so that the entire control system can maintain efficient operation in a complex and changing environment. This precise and efficient control process has broad application prospects and important practical significance in industrial automation, robot control and other intelligent control systems.

[0188] Furthermore, if Figure 6 As shown, the process of evaluating the control effect of the PID parameters in step S3 specifically includes the following steps:

[0189] Step S31: extract real-time data such as liquid level, wall temperature, cooling water flow and temperature, record them periodically through sensors, and store them in a database; each monitoring data record should include a timestamp, the values ​​of various parameters and the corresponding PID control signal; pre-process the acquired data, including removing noise, eliminating missing data and outliers (such as large-scale deviations introduced by sensor failures);

[0190] Step S32: for each time point, calculate the difference between the actual liquid level and the target value, the actual wall temperature and the target value, record the error change curve over time, and form an error trend graph; apply statistical analysis methods to evaluate the error; by comparing with the target value, judge the performance of the PID control system in a stable state, and analyze whether the system has overshoot, oscillation or steady-state error;

[0191] Step S33: Perform sensitivity analysis on the PID parameters, observe the changes in system response by changing the values ​​of proportional gain, integral gain and differential gain; record the error and response time under different parameter settings, and dynamically adjust the PID parameters to obtain the best output effect.

[0192] Preferably, the data extraction and preprocessing in step S31 of this embodiment uses sensors to periodically record key parameters such as liquid level, wall temperature, cooling water flow and temperature, providing a real-time data basis to ensure that the control system has sufficient information for evaluation and adjustment; data quality is ensured by denoising the data, eliminating missing data and outliers; high-quality data is the basis for subsequent analysis and helps to improve the reliability and accuracy of the analysis results; the timestamp, parameter value and PID control signal of each monitoring data are recorded to form a traceable data set for subsequent auditing and analysis. Practical significance: Good data quality and recording methods enhance the control system's ability to respond to abnormal conditions and reduce misjudgments caused by data errors; the cleaned consistency and accuracy of the data set provides a reliable basis for subsequent error analysis, sensitivity analysis and parameter optimization. Step S32 Error analysis and evaluation, calculate the difference between the actual liquid level and wall temperature at each time point and the target value, form a time series of system errors, and record to help the system judge the current control effect in real time; the generated error trend chart provides an intuitive view for subsequent analysis, which is convenient for identifying potential problems (such as overshoot, oscillation, steady-state error, etc.), and helps engineers to deeply understand the operating status of PID control; apply statistical methods to evaluate system performance (such as mean square error, maximum error, etc.), quantify the characteristics of the error, and help judge the control performance. Practical significance: By understanding the control performance under stable conditions, it can help engineers make targeted adjustments and optimize the control system; identify problems such as overshoot and oscillation in the system, which provides a basis for the next improvement and truly improves the control accuracy. Step S33 Sensitivity analysis and parameter optimization, by changing PID parameters (proportional gain, integral gain and differential gain), observe the changes in system response, and identify the sensitivity of the system to different parameters. This analysis can reveal which parameter has the greatest impact on system performance; based on the recorded errors and response times under different parameter settings, dynamic adjustments and optimizations can be made to help obtain the best control output effect; through sensitivity analysis and dynamic adjustment, the best combination of PID controller parameters can be achieved, thereby achieving faster response time and lower steady-state error. Practical significance: By adjusting PID parameters, the control system can adapt to different operating conditions, thereby improving overall performance and control effects; the sensitivity analysis process is not just a one-time process, but can form a feedback mechanism that enables the control system to achieve optimal performance under different conditions, which helps to achieve continuous performance optimization.

[0193] In summary, each step of this embodiment plays an indispensable role in the process of evaluating the control effect of PID parameters. Data extraction and preprocessing ensure the reliability of basic information, error analysis and evaluation reveal the performance and improvement space of the control system, and sensitivity analysis and parameter optimization make the controller more adaptable and efficient. On the whole, the systematic and scientific nature of this series of steps lays a solid foundation for the efficient control of the crystallizer and helps technicians achieve better control effects in practical applications.

[0194] like Figure 7 As shown, this embodiment also provides an embodiment of a crystallizer control device based on proportional integral differential. In this embodiment, the crystallizer control device based on proportional integral differential is applied to the crystallizer control method based on proportional integral differential in the above embodiment. The crystallizer control device based on proportional integral differential includes a database construction module 1, an intelligent control module 2, and a result evaluation module 3 which are electrically connected in sequence;

[0195] Among them, the database construction module 1 is used to obtain the real-time liquid level and wall temperature in the crystallizer, and convert the analog signals of the liquid level and wall temperature into analog control signals to adjust the cooling water inlet parameters of the crystallizer; construct a cooling water database according to the flow rate, temperature and heat transfer information of each area in the crystallizer generated by the cooling water; the intelligent control module 2 is used to set the corresponding target values ​​of the liquid level and wall temperature in the crystallizer; obtain the difference between the current liquid level and wall temperature and the set value; obtain the data of the cooling water database, and provide parameters such as cooling water flow, temperature and heat transfer information in the crystallizer for the computational fluid dynamics model; calculate the proportional control output, which is proportional to the difference, calculate the integral of the past error, predict the change of the error, merge the outputs of each part, and calculate the final control signal; the result evaluation module 3 is used to adjust the flow rate and temperature of the cooling water according to the calculation result of the final control signal, so that the liquid level and wall temperature in the crystallizer reach the set value; continue to monitor the liquid level and wall temperature in the crystallizer, and record all monitored data and PID control operations in the database; analyze the crystallizer control data and evaluate the control effect of the PID parameters.

[0196] Preferably, the database construction module 1 of this embodiment can continuously monitor and obtain the liquid level and wall temperature inside the crystallizer, and the real-time performance enables the system to respond to changes quickly and adjust the control strategy in time; the analog signals of the liquid level and wall temperature obtained are converted into analog control signals; by collecting the flow rate, temperature and heat transfer information of each area in the crystallizer, a cooling water database is established to provide rich historical and real-time data support to assist subsequent control and optimization. Practical significance: Real-time data gives the system good feedback capability, which helps to accurately control the environmental conditions of the crystallizer; building a cooling water database not only supports the decision-making of the current operation, but also provides valuable data resources for subsequent performance optimization, troubleshooting and historical analysis. The intelligent control module 2 sets the target value of the liquid level and wall temperature, and monitors the difference between the current actual value and the target value in real time, so that deviations can be found and responded in time; using the data of the cooling water database, the cooling water flow rate, temperature and heat transfer parameters required by the fluid mechanics model are supplied, which supports and helps to accurately evaluate and calculate the required control output; the control output is calculated by the proportional control method, and the final control signal is generated in combination with the integral and change prediction of the past errors, ensuring the optimal performance of the control system under different environmental conditions. Practical significance: The intelligent control module introduces elements of automation and intelligence, making the control of the crystallizer more accurate and efficient, and helping to maintain the required operating conditions; accurate error calculation and prediction mechanisms can speed up system response, reduce fluctuations and instability in operation, and improve product consistency and quality during the crystallization process. The result evaluation module 3 adjusts the flow and temperature of the cooling water according to the final control signal to ensure that the liquid level and wall temperature reach the preset target values ​​and ensure that the system remains in the best working state; the module not only adjusts the system, but also continuously monitors the liquid level and wall temperature, and records all monitoring data and PID operations in the database to form a comprehensive record; by analyzing the control data obtained from the monitoring, the control effect of the PID parameters is evaluated, and potential problems in the system are discovered, so as to optimize and adjust the PID parameters. Practical significance: Through continuous monitoring of results and recording of historical data, a dynamic optimization process can be achieved to improve the stability and efficiency of the long-term operation of the system; the evaluation of the control effect not only guarantees the performance of the current operation, but also provides data basis for future adjustments and improvements, and enhances the self-regulation ability of the system.

[0197] In summary, this embodiment forms a complete crystallizer control system. The database construction module provides real-time and accurate data support for the system, the intelligent control module uses these data to efficiently complete the control logic, and the result evaluation module ensures the optimization and improvement of the entire system through continuous monitoring and data analysis. Overall, this modular design improves the flexibility, intelligence level and reliability of the system, making the crystallizer operation more efficient and stable, and achieving the production goal of high-quality products.

[0198] like Figure 8 As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device 4 includes a processor 41 and a memory 42 coupled to the processor 41.

[0199] The memory 42 stores program instructions for implementing the mold control method based on proportional-integral-differential in any of the above embodiments.

[0200] The processor 41 is used to execute program instructions stored in the memory 42 to perform crystallizer control.

[0201] The processor 41 may also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip having signal processing capabilities. The processor 41 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0202] Further, Fig. 9 The schematic diagram of the structure of the storage medium of an embodiment of the present application is that the storage medium 5 of the embodiment of the present application stores program instructions 51 that can implement all the above methods, wherein the program instructions 51 can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0203] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0204] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units. The above is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present invention.

[0205] The specific implementation methods of the invention are described in detail above, but they are only examples, and the invention is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the invention, therefore, the equalization, modification, improvement, etc. made without departing from the spirit and principle of the invention should be included in the scope of the invention.

Claims

1. A crystallizer control method based on proportional integral differential, It is characterized in that The layout method comprises: Obtain the real-time liquid level and wall temperature in the crystallizer, convert the analog signals of the liquid level and wall temperature into analog control signals to adjust the crystallizer cooling water inlet parameters; construct a cooling water database based on the flow rate and temperature of the generated cooling water and the heat transfer information of each area in the crystallizer; Set the corresponding target values ​​of the liquid level and wall temperature in the crystallizer; obtain the difference between the current liquid level and wall temperature and the set value; obtain the data from the cooling water database to provide the cooling water flow, temperature and heat transfer information parameters in the crystallizer for the computational fluid dynamics model; calculate the proportional control output, which is proportional to the difference, calculate the integral of the past error, predict the change of the error, combine the outputs of each part, and calculate the final control signal; According to the calculation results of the final control signal, adjust the flow and temperature of the cooling water to make the liquid level and wall temperature in the crystallizer reach the set values; continue to monitor the liquid level and wall temperature in the crystallizer, and record all monitored data and PID control operations in the database; analyze the crystallizer control data and evaluate the control effect of the PID parameters.

2. The mold control method based on proportional-integral-differential according to claim 1, It is characterized in that The process of converting the analog signals of liquid level height and wall temperature into analog control signals includes: The crystallizer liquid level monitoring device and the crystallizer wall temperature monitoring device are used to measure the crystallizer liquid level and wall temperature respectively; the output signal is increased by the signal conditioning circuit; Generate a control signal based on the set proportional factor and output signal, and obtain the reference of the control signal by calculating the difference between the set point and the real-time signal to form an additional adjustment; The generated control signal is output in the form of an analog signal, the analog signal is connected to an actuator, and the flow rate and temperature of the cooling water are adjusted through the actuator.

3. The mold control method based on proportional-integral-differential according to claim 1, It is characterized in that The process of building a cooling water database includes: The computational fluid dynamics model of the crystallizer is constructed in a 1:1 ratio, and the computational fluid dynamics model is meshed; According to the selection of personalized numerical model of the crystallizer, the mass conservation equation is used to show the change of fluid density and its relationship with fluid velocity, the momentum conservation equation is used to describe the dynamic characteristics of fluid flow, the energy equation is used to perform mixed calculation based on the volume fraction of each phase, the turbulent characteristics of the flow are modeled, and the numerical simulation of the multiphase flow characteristics and thermodynamic behavior of the crystallizer is realized; Given the density and viscosity physical parameters of the fluid and air material, and the fluid velocity boundary conditions, the results of the numerical simulation are output.

4. The mold control method based on proportional-integral-differential according to claim 3, It is characterized in that The numerical models chosen are as follows: Where ρ is the fluid density, V and u are the volume and velocity of the fluid phase, p and μ represent the pressure and molecular viscosity of the fluid, respectively. The instantaneous time of the numerical simulation and the gravitational acceleration vector of the fluid are represented by symbols t and g, respectively. The surface tension between the phases in each control volume is considered and expressed by F ST express; The energy equation for a single phase is used to estimate the energy transfer in each cell while sharing energy between phases: The effective thermal conductivity and temperature of the fluid are represented by T f and k eff Energy is represented by the symbol E and is considered as a mass-averaged variable: Where E i is the energy of the ith phase, calculated from its specific heat and shared temperature. In present usage, the volume fraction of the fluid is denoted by the symbol α, the subscript g denotes the gas phase, and the subscript l denotes the liquid phase; The explicit time equation is used to solve the volume fraction equation to track the interface position in the gas-liquid two-phase system. The equation is expressed as: For the volume fraction model, the gas phase and liquid phase coexist in each computational cell, where the volume fractions of the two phases vary from 0 to 1, and the sum of the volume fractions of the two phases is 1; the density and viscosity fluid properties are also shared by all phases and represent volume averages. The volume average density and viscosity are calculated based on the constraint functions of the participating phases: α g +α l ＝1 p=a g r g +a l r l μ = a g m g +a l m l Multiphase turbulence simulation using: In the formula, β * represents the closure coefficient, which is taken as a constant of 0.

09. The generation of turbulent Prandtl number and k is represented by the symbol σ k and P k , and the ω generated by the turbulent Prandtl number is represented by σ ω and P ω express; By solving and calculating the numerical simulation system, a scenario close to the actual work can be simulated; The crystallizer cooling water heat flux function is constructed based on deep learning, and its formula is expressed as: Q=f(u,L,∈,T,b 1 ,b 2 ,h) where u,L,∈,T,b 1 ,b 2 , h represent the cooling water flow rate, crystallizer length, inverted taper per meter of crystallizer length, fluid temperature, crystallizer lower opening width, crystallizer upper opening width and liquid level height respectively.

5. The mold control method based on proportional-integral-differential according to claim 3, It is characterized in that Meshing of computational fluid dynamics models, including: Collect the design drawings of the crystallizer, including size, shape, material properties and inlet and outlet locations; understand the working conditions, build a three-dimensional geometric model of the crystallizer, and input all geometric parameters of the crystallizer in a 1:1 ratio; Save the geometric model in a format supported by the computational fluid dynamics software, determine the size and type of the grid, and import the geometric model of the crystallizer into the grid generation software; make preliminary divisions on the surface of the geometric body and set the grid density; generate a volume grid based on the surface grid and use the orthogonality of the grid for evaluation; Preliminary simulation is performed to verify the performance of the grid, monitor convergence and calculation operation; based on the results of the preliminary simulation, the grid density and partitioning strategy are adjusted.

6. The mold control method based on proportional-integral-differential according to claim 1, It is characterized in that The process of calculating the final control signal includes: Set the target values ​​of liquid level and wall temperature, monitor the liquid level and wall temperature of the crystallizer in real time; calculate the liquid level error and wall temperature error; obtain the cooling water flow, inlet and outlet temperature difference, and effective cooling area of ​​the crystallizer from the cooling water database to calculate the heat flux; According to the principle of proportional control, the control signal is calculated, which is proportional to the liquid level error and the temperature error. The past errors are integrated according to the formula, the differential term is calculated according to the formula, the rate of change of the error is predicted, and the proportional, integral and differential terms are combined to calculate the control signal; The calculated control signal is converted into an analog control signal and output to the cooling water flow and temperature control device to adjust the flow and temperature of the cooling water to adjust the liquid level and wall temperature inside the crystallizer; the heat flux formula is used to assist in evaluating the cooling effect; the formula is: Among them, c W is the heat capacity of cooling water, Q W is the cooling water flow rate, ΔT is the cooling water inlet and outlet temperature difference, S eff is the effective cooling area of ​​the crystallizer.

7. The mold control method based on proportional-integral-differential according to claim 6, It is characterized in that The formula for calculating the proportional control output is: P H =K p ·E H ,P T =K p ·E T Among them, K p is the proportional gain, E H Indicates the liquid level error, E T represents the wall temperature error; The formula for calculating the integral term is: Integrate the past errors and sum them over time period t: I H =I H +K i ·E H ·Δt,I T =I T +K i ·E T ·Δt Among them, K i represents the integral gain; The formula for calculating the differential term is: Rate of change of forecast error: Among them, K d is the differential gain; Combining the proportional, integral, and derivative terms, the control signal is calculated: C H =P H +I H +D H ,C T =P T +I T +D T Among them, C H and C T They are the final output signals of liquid level control and wall temperature control respectively.

8. The mold control method based on proportional-integral-differential according to claim 6, It is characterized in that The process of combining the proportional, integral, and derivative terms to calculate the control signal includes: Define the saturation function and define a saturation function S(x) to implement, which limits the input signal: Combine the saturation function with the PID control signal to ensure that the final control output does not exceed the set limit; set the total calculation formula of the PID control signal, and process the total control signal U(t) through the saturation function S; Output and implementation of the control signal, calculate the liquid level error, calculate the proportional part, perform integral calculation, perform differential calculation, and combine the output to calculate the total control signal; input U(t) into the saturation function to obtain the saturated control signal, and output the control signal to the actuator.

9. The mold control method based on proportional-integral-differential according to claim 8, It is characterized in that Output control signals to actuators, including: Identify the actuator and design a standardized interface for the actuator to receive signals from the control system and convert them into specific physical actions; the interface includes: digital input signal, a digital signal channel for receiving control instructions; an analog signal channel, connected through an analog-to-digital converter and a digital-to-analog converter to achieve conversion between digital signals and analog signals to control the actuator power output; According to the control signal output by the saturation function, it is converted into the final control input that matches the actuator capacity; through dynamic linearization processing, the delay and amplitude change caused by nonlinear characteristics are eliminated; after the control signal is output, the dynamic linearization function is used to achieve fine-tuning of the control signal; At the output end of the actuator, a flow meter, pressure sensor and position sensor are installed to monitor the real-time status of the actuator and send feedback signals back to the control system. The original control signal is adjusted according to the feedback signal, the feedback value is compared with the set value in real time, and the error signal is calculated to adjust the future control signal to form a closed-loop control.

10. A crystallizer control device based on proportional-integral-differential, which is applied to the crystallizer control method based on proportional-integral-differential as claimed in any one of claims 1 to 9, It is characterized in that The mold control device based on proportional-integral-differential includes: The database construction module is used to obtain the real-time liquid level and wall temperature in the crystallizer, convert the analog signals of the liquid level and wall temperature into analog control signals to adjust the crystallizer cooling water inlet parameters; construct a cooling water database based on the flow rate and temperature of the generated cooling water and the heat transfer information of each area in the crystallizer; The intelligent control module is used to set the corresponding target values ​​of the liquid level and wall temperature in the crystallizer; obtain the difference between the current liquid level and wall temperature and the set value; obtain the data of the cooling water database, and provide the cooling water flow, temperature and heat transfer information parameters in the crystallizer for the computational fluid dynamics model; calculate the proportional control output, which is proportional to the difference, calculate the integral of the past error, predict the change of the error, combine the outputs of each part, and calculate the final control signal; The result evaluation module is used to adjust the flow and temperature of the cooling water according to the calculation results of the final control signal, so that the liquid level and wall temperature in the crystallizer reach the set values; continue to monitor the liquid level and wall temperature in the crystallizer, and record all monitored data and PID control operations in the database; analyze the crystallizer control data and evaluate the control effect of the PID parameters.

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