Silicon steel strip quenching control method and system

Abstract

This application belongs to the field of silicon steel strip production control technology, and discloses a silicon steel strip rapid cooling control method and system. By acquiring motion parameters such as the instantaneous speed and speed change trend of the target segment of the silicon steel strip before entering the cooling zone in real time, and combining them with the physical location information of the cooling zone, the method accurately predicts the expected time when the target segment arrives at the cooling zone and the expected residence time in the zone. Based on this, the method dynamically determines the required target cooling intensity according to the preset ideal cooling process and the expected residence time. At the same time, considering the inherent response time of the cooling actuator, the method calculates and determines the triggering time of the cooling command in advance. This method can effectively solve the problem of cooling inaccuracy caused by production line speed fluctuations in the prior art, avoid the steel strip outlet temperature from deviating from the preset value, thereby ensuring the initial conditions of the subsequent cooling zone and maintaining the stability of the morphology and distribution of precipitates.

Description

Technical Field

[0001] This application relates to the field of silicon steel strip production control technology, and more specifically, to a method and system for controlling the rapid cooling of silicon steel strip. Background Technology

[0002] In the production of silicon steel strip, the quenching process after high-temperature normalization is crucial for controlling the internal grain structure and inhibitor precipitation morphology of the steel strip. Traditional quenching methods use water spraying and compressed air for cooling, focusing primarily on controlling the cooling rate. However, this method has several problems in actual operation. When the high-temperature steel strip comes into contact with cooling water, a vapor film generated by the Leidenfrost effect easily forms, hindering heat transfer and leading to uneven cooling in certain areas. This, in turn, causes oxidation and scale-like iron oxide scale to form on the steel strip surface. Furthermore, the traditional method lacks precise flow control in the width direction of the steel strip, resulting in differences in cooling rate and ultimately producing asymmetric edge waviness and other plate shape defects.

[0003] To address these issues, some new technologies have introduced segmented, precisely temperature-controlled quenching systems. These systems divide the quenching section into a "vapor film breaking zone," a "core precipitation strengthening zone," and a "final cooling zone," with independent control over the width of the steel strip. For example, in the "vapor film breaking zone," a high-pressure nozzle breaks down the vapor layer to ensure efficient heat transfer; in the "core precipitation strengthening zone," the cooling water flow rate and pressure are strictly controlled to achieve a specific cooling rate.

[0004] Existing technologies have effectively mitigated the problems of localized oxidation and lateral temperature unevenness caused by vapor film under stable operating conditions by dividing the quench section into multiple cooling zones and independently controlling the width direction of the steel strip (e.g., using segmented nozzles and staggered nozzle arrangements). However, in actual continuous production, the production line speed often experiences brief fluctuations. When the speed changes, the residence time of the steel strip in each cooling zone changes accordingly, causing the steel strip exit temperature to deviate from the preset value under a fixed cooling intensity, thus affecting the subsequent cooling effect and the final product performance. Therefore, how to maintain the accuracy and consistency of the cooling effect under dynamic speed conditions has become an urgent problem to be solved. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for controlling the rapid cooling of silicon steel strip, which aims to solve the problem that the control strategy of the existing rapid cooling system for silicon steel strip is limited when facing dynamic speed changes in the production line, resulting in the deterioration of the magnetic properties and strip shape of the same roll of steel strip in the speed fluctuation range.

[0006] In a first aspect, this application provides a method for rapid cooling control of silicon steel strip, used to control the cooling of silicon steel strip entering a cooling zone, the method comprising: A1. Obtain the real-time motion parameters of the target segment of the silicon steel strip before it enters the cooling zone; the real-time motion parameters include instantaneous velocity and velocity change trend; A2. Based on the real-time motion parameters and the physical location information of the cooling area, predict the expected time when the target segment arrives at the cooling area, and the expected time the target segment stays in the cooling area; A3. Based on the preset ideal cooling process and the expected residence time, determine the target cooling intensity required for the target segment within the cooling area; A4. Obtain the inherent response time of the cooling actuator from receiving the command to completing the state switch; A5. By combining the expected time and the inherent response time, determine the timing of the cooling command so that the cooling actuator adjusts the cooling intensity to the target cooling intensity when the target segment arrives at the cooling area.

[0007] Secondly, this application provides a rapid cooling control system for silicon steel strip, used for cooling control of silicon steel strip entering the cooling zone, the system comprising: The parameter acquisition module is used to acquire the real-time motion parameters of the target segment of the silicon steel strip before it enters the cooling zone; the real-time motion parameters include instantaneous speed and speed change trend. The prediction module is used to predict the expected time when the target segment arrives at the cooling area and the expected time the target segment stays in the cooling area, based on the real-time motion parameters and the physical location information of the cooling area. An intensity determination module is used to determine the target cooling intensity required by the target segment in the cooling area based on a preset ideal cooling process and the expected residence time. The response time acquisition module is used to acquire the inherent response time of the cooling actuator from receiving the instruction to completing the state switch; The instruction triggering module is used to combine the expected time and the inherent response time to determine the timing of the cooling instruction trigger, so that the cooling actuator adjusts the cooling intensity to the target cooling intensity when the target segment arrives at the cooling area.

[0008] Beneficial Effects: This application provides a method and system for rapid cooling control of silicon steel strip. By acquiring motion parameters such as the instantaneous velocity and velocity change trend of the target segment of the silicon steel strip before entering the cooling zone in real time, and combining this with the physical location information of the cooling zone, the system accurately predicts the expected arrival time of the target segment in the cooling zone and the expected residence time within that zone. Based on this, the required target cooling intensity is dynamically determined according to the preset ideal cooling process and expected residence time. Simultaneously, considering the inherent response time of the cooling actuator, the triggering timing of the cooling command is calculated and determined in advance. This method effectively solves the problem of cooling inaccuracy caused by production line speed fluctuations in existing technologies, avoiding deviations of the steel strip outlet temperature from the preset value, thereby ensuring the initial conditions of the subsequent cooling zone and maintaining the stability of the morphology and distribution of precipitates. Through this forward-looking, real-time motion parameter-based dynamic control strategy, this application can significantly improve the accuracy and stability of the rapid cooling process of silicon steel strip, reduce surface oxide scale, improve plate shape defects, and ultimately enhance the magnetic properties of the silicon steel strip, overcoming the limitations of traditional rapid cooling systems under dynamic production conditions. Attached Figure Description

[0009] Figure 1 A flowchart of a method for controlling the rapid cooling of silicon steel strip provided in this application.

[0010] Figure 2 A schematic diagram of a rapid cooling control system for silicon steel strip provided in this application.

[0011] Labeling Explanation: 1. Parameter Acquisition Module; 2. Prediction Module; 3. Intensity Determination Module; 4. Response Time Acquisition Module; 5. Command Trigger Module. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0013] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0014] Please refer to Figure 1 This application discloses a method for rapid cooling control of silicon steel strip in some embodiments, used to control the cooling of silicon steel strip entering a cooling zone. The method includes: A1. Obtain the real-time motion parameters of the target segment of the silicon steel strip before it enters the cooling zone; the real-time motion parameters include instantaneous velocity and velocity change trend; A2. Based on the real-time motion parameters and the physical location information of the cooling area, predict the expected time when the target segment arrives at the cooling area, and the expected time the target segment stays in the cooling area; A3. Based on the preset ideal cooling process and the expected residence time, determine the target cooling intensity required for the target segment within the cooling area; A4. Obtain the inherent response time of the cooling actuator from receiving the command to completing the state switch; A5. By combining the expected time and the inherent response time, determine the timing of the cooling command so that the cooling actuator adjusts the cooling intensity to the target cooling intensity when the target segment arrives at the cooling area.

[0015] This application acquires the motion parameters of the silicon steel strip in real time and combines them with the physical information of the cooling zone to accurately predict the residence time of the steel strip in the cooling zone. This allows for dynamic adjustment of the cooling intensity and command triggering timing, effectively addressing the challenges posed by production line speed fluctuations and ensuring the cooling quality and product performance of the silicon steel strip under dynamic production conditions.

[0016] "Target segment of silicon steel strip" refers to a specific area selected for cooling control on a continuous silicon steel strip production line. Generally, the system periodically issues cooling commands (i.e., periodically executes the steps of the above method), and the target segment is the segment of the sensor detection area that moves to the cooling area at the beginning of each control cycle, where the position of the sensor detection area is fixed and known.

[0017] "Real-time motion parameters" refer to the instantaneous data of the motion state of the target segment of the silicon steel strip before it enters the cooling zone, mainly including instantaneous speed and speed change trend. Instantaneous speed reflects the instantaneous moving rate of the target segment at a certain moment, while speed change trend describes whether its speed is accelerating, decelerating, or remaining stable.

[0018] The “cooling zone” refers to the specific physical space on the production line where silicon steel strip undergoes rapid cooling, and where cooling actuators are deployed to cool the silicon steel strip.

[0019] The "ideal cooling process" refers to the preset ideal cooling process, which is a temperature-time curve or a set of target cooling rates determined in advance through experiments or thermodynamic simulations based on the steel grade, specifications, and target magnetic properties. This process aims to ensure that the silicon steel strip achieves the ideal grain structure and precipitate morphology during the cooling process.

[0020] "Cooling actuators" refer to equipment responsible for performing cooling operations within the cooling area, such as array-type water spray devices or array-type air spray devices. Preferably, the flow rate, pressure, and other parameters of each nozzle in the nozzle array of these devices can be independently adjusted. These mechanisms adjust the flow rate, pressure, and other parameters of the cooling medium by receiving control commands, thereby changing the cooling intensity on the silicon steel strip.

[0021] "Inherent response time" refers to the time required for a cooling actuator to fully switch its internal state and reach the required operating state from the moment it receives a control command. This time is determined by the physical characteristics of the actuator itself and is usually fixed or fluctuates within a certain range.

[0022] This application provides a method for controlling the rapid cooling of silicon steel strip, the core of which lies in using precise prediction and dynamic adjustment to address the impact of production line speed fluctuations on the cooling effect.

[0023] In step A1, it is necessary to acquire the real-time motion parameters of the target segment of the silicon steel strip before it enters the cooling zone. These parameters include instantaneous velocity and velocity change trend. There are various ways to acquire real-time motion parameters. For example, a vision-based recognition technique can be used, where a high-speed camera continuously captures images of the silicon steel strip. Then, image processing algorithms are used to analyze the displacement of specific marker points or texture features in the image sequence to calculate the instantaneous velocity. The velocity change trend can be obtained by differentiating or fitting continuous instantaneous velocity data. For example, if the velocity measured at time t0 is v0, its downward movement is t1, and the velocity measured at time t1 is v1, then the current acceleration a≈(v1-v0) / (t1-t0) is the velocity change trend. Another method is to use a Doppler velocimeter to measure the velocity and then differentiate or fit the measured velocity data to obtain the velocity change trend. Alternatively, velocity and acceleration data can be directly read from the production line control system; this acceleration data represents the velocity change trend.

[0024] In step A2, based on real-time motion parameters and the physical location information of the cooling zone, the estimated arrival time of the target segment in the cooling zone and the estimated dwell time of the target segment within the cooling zone are predicted. For example, a prediction model based on kinematic principles can be established. This model takes the instantaneous velocity and velocity change trend of the silicon steel strip as input, combines it with the known entrance position of the cooling zone, and predicts when the target segment will arrive at the entrance of the cooling zone through integration or iterative calculation. Once the estimated time is predicted, the estimated dwell time of the target segment within the cooling zone is further predicted by combining the physical length of the cooling zone and the possible velocity changes of the silicon steel strip during the estimated dwell period.

[0025] In step A3, based on a preset ideal cooling history and the expected residence time, the target cooling intensity required for the target segment within the cooling area is determined. For example, a cooling history database can be pre-established, storing ideal cooling curves for different silicon steel strip types, different initial temperatures, and different target cooling effects. Once the expected residence time is obtained, the system retrieves the ideal cooling history that best matches the current conditions from the database. Then, based on this ideal cooling history and the expected residence time, the system calculates the amount of heat that needs to be removed per unit time during the entire residence period to achieve the ideal temperature drop curve, thereby determining the required heat exchange intensity as the target cooling intensity.

[0026] In step A4, it is necessary to obtain the inherent response time of the cooling actuator from receiving the command to completing the state switch. This inherent response time can be obtained through experimental calibration or by consulting the equipment manual. For example, during the equipment installation and commissioning phase, the cooling actuator can be tested multiple times, and the time from sending the control command to the actual cooling intensity reaching the set value can be recorded. The average value of these tests can then be taken as the inherent response time. Alternatively, the equipment manufacturer will typically provide this parameter in its technical specifications.

[0027] In step A5, the timing of the cooling command is determined by combining the expected time and the inherent response time, so that the cooling actuator adjusts the cooling intensity to the target cooling intensity when the target segment arrives at the cooling area. For example, a simple countdown mechanism can be set. When it is predicted that the target segment will arrive at the cooling area at a certain expected time, the system will backtrack one inherent response time from that expected time and use the backtracked time point as the triggering time for the cooling command. In this way, when the system's current time reaches the triggering time, the cooling command is immediately issued, ensuring that the cooling actuator has precisely adjusted its cooling intensity to the required target cooling intensity as soon as the target segment enters the cooling area.

[0028] Compared with existing technologies, the silicon steel strip quenching control method of this application has significant advantages and innovations. Traditional quenching systems reveal significant limitations in their control strategies when faced with production line speed fluctuations, leading to deterioration in the magnetic properties and shape of the same coil of steel strip in the speed fluctuation range. This is mainly because traditional methods lack the ability to accurately perceive and predict the real-time motion parameters of the silicon steel strip, cannot dynamically adjust the cooling intensity based on the actual residence time of the steel strip in the cooling zone, and fail to effectively consider the response delay of the cooling actuator.

[0029] The core innovation of this application lies in its establishment of a prediction-adjustment-compensation mechanism based on real-time motion parameters. By acquiring the instantaneous speed and speed change trend of the silicon steel strip in real time, this application can accurately predict the expected residence time of the steel strip in the cooling zone, which is not possible with traditional methods. Based on this accurate prediction, the system can dynamically determine the required target cooling intensity, thereby achieving adaptive adjustment of the cooling process and effectively avoiding over- or under-cooling caused by speed fluctuations. In addition, this application also considers the inherent response time of the cooling actuator. By triggering the cooling command in advance, it ensures that the cooling intensity is accurately delivered when the target segment arrives at the cooling zone, further improving the accuracy of control. Therefore, the method of this application can significantly improve the control accuracy and stability of the rapid cooling process of silicon steel strip, effectively solving the problems of uneven cooling, plate shape defects, and magnetic property degradation that exist in traditional methods under dynamic production conditions, providing a more reliable and efficient quality assurance for the production of silicon steel strip.

[0030] In some implementations, step A1 includes: A101. The raw speed data of the target segment is collected in real time by a sensor; A102. Perform digital filtering on the original velocity data to obtain smooth velocity data, which is used as the instantaneous velocity; A103. Perform differential calculation on the smoothed speed data to obtain the speed change trend.

[0031] Specifically, the sensor can be understood as a device capable of measuring the speed of a silicon steel strip in a non-contact or contact manner, such as a laser velocimeter, encoder, or vision measurement system. Its purpose is to acquire the motion information of the target segment of the silicon steel strip in real time. The raw speed data refers to the initial, unprocessed speed measurement value directly acquired by the sensor, which may contain noise or fluctuations.

[0032] The purpose of digital filtering on the raw speed data is to eliminate or reduce random noise, measurement errors, or high-frequency fluctuations, thereby obtaining more accurate and stable speed information. Various algorithms can be used for digital filtering, such as moving average filtering, Kalman filtering, and median filtering, to adapt to different noise characteristics and real-time requirements. The smoothed speed data obtained after filtering more accurately reflects the instantaneous speed of the silicon steel strip.

[0033] In practical applications, differential calculations are performed on the smoothed speed data to quantify the rate of change of the instantaneous speed of the silicon steel strip, thereby obtaining the speed change trend. Differential calculations can be achieved by calculating the speed difference between adjacent time points or adjacent sampling points, such as first-order or higher-order differentials. The speed change trend reflects whether the silicon steel strip is accelerating, decelerating, or moving at a constant speed, which is crucial for subsequent motion prediction and cooling control.

[0034] The proposed solution first acquires raw velocity data in real time, ensuring immediate perception of the silicon steel strip's motion state. Then, digital filtering of the raw velocity data effectively removes measurement noise and interference, resulting in smoother and more accurate instantaneous velocity data, avoiding misjudgments caused by data fluctuations. Furthermore, differential calculation of the smoothed velocity data accurately captures the velocity change trend of the silicon steel strip, providing crucial dynamic information for predicting its future trajectory and residence time within the cooling zone. Thus, steps A101 to A103 work together to provide reliable and refined real-time motion parameters for subsequent predictions of the estimated time and residence time (step A2), thereby improving the accuracy and responsiveness of the entire rapid cooling control method.

[0035] In some implementations, step A2 includes: A201. Based on the instantaneous velocity and the velocity change trend, establish a displacement prediction model for the target segment; A202. Using the displacement prediction model, calculate the time required for the target segment to move to the entrance boundary of the cooling area based on the physical location information of the cooling area; A203. Add the current system time to the duration to obtain the estimated time; A204. Obtain the physical length of the cooling zone in the running direction of the silicon steel strip; A205. Based on the displacement prediction model, the estimated time, and the physical length, calculate the duration of the target segment passing through the cooling area, which is taken as the estimated dwell time.

[0036] Specifically, a displacement prediction model refers to a mathematical or algorithmic model that can dynamically predict the positional changes of a target segment of a silicon steel strip over a future period of time based on its instantaneous velocity and velocity variation trend. For example, this model can be a kinematic model based on Kalman filtering or polynomial fitting, aiming to provide a more accurate displacement prediction capability than simple linear extrapolation, in order to cope with non-uniform motion states such as acceleration and deceleration that may occur during the operation of the silicon steel strip.

[0037] Specifically, the displacement prediction model is used to calculate the required time. This involves taking the distance from the entrance boundary of the cooling zone to the current position of the target segment (which can be taken as the position of the sensor detection area) as the target displacement, and using the model to calculate backwards or iteratively to determine the time required for the target segment to move from its current position to the entrance boundary. The purpose is to accurately predict when the target segment will begin to enter the cooling zone.

[0038] In practical applications, the estimated time refers to the absolute time when the target segment arrives at the entrance boundary of the cooling area. It is obtained by adding the current system time to the duration calculated above, ensuring the accuracy of the timing of subsequent cooling command triggering.

[0039] Furthermore, the physical length of the cooling zone in the direction of silicon steel strip operation is a fixed parameter that is known in advance or obtained by measurement, the purpose of which is to provide basic data for calculating the dwell time of the target segment in the cooling zone.

[0040] Therefore, the estimated dwell time refers to the total time taken for the target segment to travel from the entrance boundary of the cooling zone to the exit boundary. This calculation uses a displacement prediction model, combining the estimated time and the physical length of the cooling zone, to simulate the trajectory and velocity changes of the target segment within the cooling zone, thus obtaining a more accurate dwell time. Its purpose is to provide a precise time window for subsequent determination of the target cooling intensity.

[0041] This application's solution, by introducing a displacement prediction model and combining it with the instantaneous velocity and velocity variation trend of the silicon steel strip, can dynamically and accurately predict the future position of the target segment. This overcomes the inaccuracy problem caused by the velocity fluctuation of the silicon steel strip in traditional methods. Specifically, the displacement prediction model can capture the acceleration or deceleration state of the silicon steel strip, thereby more accurately calculating the time required for the target segment to reach the inlet boundary of the cooling zone, and further combining it with the physical length of the cooling zone to accurately estimate the expected residence time of the target segment within the cooling zone. This prediction method based on a dynamic kinematic model significantly improves the ability to predict the motion state of the silicon steel strip, providing reliable time parameters for the subsequent precise adjustment of cooling intensity.

[0042] The following is a concrete example. Assume a silicon steel strip enters the sensor detection area with an initial velocity V0 and moves with acceleration a. The sensor collects instantaneous velocity data in real time and calculates the velocity change trend. Based on this data, a simple uniformly accelerated linear motion model can be established as a displacement prediction model: S = V0 * t + 0.5 * a * t^2, where S is the distance traveled and t is time. The distance from the inlet boundary of the cooling zone to the current position of the target segment is L_e, and the physical length is L_c. First, using the displacement prediction model, by solving L_e = V0 * t_e + 0.5 * a * t_e^2, the time t_e required for the target segment to move to the inlet boundary of the cooling zone is calculated. Then, the current system time T_c is added to t_e to obtain the predicted time T_ex = T_c + t_e. Next, to calculate the expected residence time, we can first calculate the total time t_exit required for the target segment to move to the cooling zone exit boundary (i.e., L_e + L_c), and then solve for L_e + L_c = V0 * t_exit + 0.5 * a * t_exit^2 using the displacement prediction model. Finally, the expected residence time T_d = t_exit - t_e. In this way, even if the silicon steel strip experiences velocity changes before and after the cooling zone, its arrival and residence times can be accurately predicted, thus providing accurate timing information for cooling control.

[0043] In some implementations, step A3 includes: A301. Based on the preset ideal cooling process, calculate the total heat required to remove from the target segment within the expected residence time to achieve the target temperature drop; A302. Calculate the heat exchange intensity per unit time based on the total heat and the expected residence time, and use it as the target cooling intensity.

[0044] The ideal cooling process, as preset here, is the temperature drop curve or temperature change trajectory that the silicon steel strip should follow within the cooling zone. This process is typically pre-set based on the material properties of the silicon steel strip, final product requirements, and production process parameters, aiming to ensure that the silicon steel strip obtains the required metallographic structure and mechanical properties. The target temperature drop refers to the amount of temperature decrease that the target segment needs to achieve within the expected residence time, which is directly derived from the ideal cooling process. Total heat refers to all the heat energy that needs to be removed from the target segment to achieve the target temperature drop. This total heat can be calculated using parameters such as the specific heat capacity, density, dimensions, and target temperature drop of the silicon steel strip.

[0045] Heat exchange intensity can be understood as the amount of heat removed from a target section of silicon steel strip per unit time, reflecting the cooling capacity of the cooling system within a specific time period. The target cooling intensity is this heat exchange intensity, which is the cooling power or efficiency indicator that the cooling actuator needs to achieve.

[0046] The proposed solution refines the determination of the target cooling intensity by calculating the total heat to be removed and the heat exchange intensity per unit time, making the calculation of cooling intensity more accurate and quantifiable. First, based on the preset ideal cooling process and expected residence time, the total heat that must be removed from the target section to achieve the target temperature drop can be accurately calculated. Then, dividing this total heat by the expected residence time yields the heat exchange intensity required per unit time, thus directly determining the target cooling intensity required by the cooling actuator. This step-by-step calculation method ensures a close match between the cooling intensity and the actual cooling requirements of the silicon steel strip.

[0047] In some implementations, step A5 includes: A501. The target cooling intensity is mapped to control parameters of the cooling actuator, and a cooling command is generated based on the control parameters; the control parameters include the flow rate and pressure parameters of the cooling medium; A502. Using the estimated time as the base time, backtrack the inherent response time to obtain the instruction issuance time, which is then used as the triggering opportunity; A503. When the current system time reaches the instruction issuance time, the cooling instruction is issued to the cooling actuator so that the cooling actuator adjusts the cooling intensity to the target cooling intensity when the target segment reaches the cooling area.

[0048] The control parameters of the cooling actuator, such as the flow rate and pressure of the cooling medium, are physical quantities that directly affect the cooling intensity. Mapping the target cooling intensity to these control parameters can typically be achieved through a pre-established cooling model, lookup table, or empirical formula. For example, a functional relationship between the target cooling intensity and the flow rate and pressure of the cooling medium can be established based on experimental data or simulation results, thereby calculating the corresponding flow rate and pressure setpoints for a given target cooling intensity. The cooling command is generated based on these control parameters and is used to drive the cooling actuator to perform the corresponding actions.

[0049] Furthermore, to ensure that the cooling actuator reaches the target cooling intensity as soon as the target segment enters the cooling zone, the command issuance time needs to be advanced. Therefore, by using the expected time as the baseline time and tracing back the inherent response time, the command issuance time can be obtained. This command issuance time is the triggering point for the cooling command.

[0050] Specifically, the system's current time refers to the time monitored in real time by the control system. When the system's current time matches the calculated command issuance time, the cooling command is sent to the cooling actuator. Upon receiving the command, the cooling actuator, after its inherent response time, adjusts the cooling intensity precisely when the target segment reaches the cooling area, achieving the preset target cooling intensity.

[0051] The solution proposed in this application ensures the timeliness and accuracy of the cooling process by transforming the abstract target cooling intensity into control parameters that the cooling actuator can directly execute, and by accurately calculating the triggering timing of the commands. Specifically, step A501 establishes a correspondence between the target cooling intensity and the physical control quantities (such as flow rate and pressure) of the cooling actuator, enabling the system to generate operable commands based on cooling requirements. Step A502 proactively determines the command issuance time by considering the inherent response delay of the cooling actuator, effectively compensating for the lag in system response. Step A503 ensures that the command is executed at the precisely calculated time, allowing the cooling actuator to immediately provide the required target cooling intensity the moment the target section of the silicon steel strip enters the cooling zone.

[0052] In the actual production process of silicon steel strip, due to factors such as uneven heating, edge effects, or defects in the strip itself, silicon steel strip often suffers from uneven transverse temperature distribution before entering the cooling zone. If cooling control is based solely on a single overall target cooling intensity, it may not effectively address the temperature differences along the width of the silicon steel strip, resulting in uneven cooling and affecting product quality and performance.

[0053] Therefore, in some preferred embodiments, step A501 includes: Obtain the lateral temperature distribution data of the target segment before it enters the cooling zone; Based on the lateral temperature distribution data, determine the heat load distribution data of different sub-regions in the width direction of the target segment; Based on the heat load distribution data, the target cooling intensity is decomposed into cooling intensity components of each of the sub-regions; Each of the cooling intensity components is mapped to the control parameters of the execution loop of the cooling actuator corresponding to each of the sub-regions; Sub-cooling commands are generated for each of the execution loops based on the control parameters; In step A503, when the current system time reaches the instruction issuance time, each of the sub-cooling instructions is issued to each of the execution loops.

[0054] Specifically, acquiring lateral temperature distribution data refers to using non-contact temperature measurement devices, such as infrared thermal imagers or multi-point infrared thermometers, to scan and collect temperature data in real time along the entire width of the target section of the silicon steel strip before it enters the cooling zone. This data accurately reflects the temperature differences of the silicon steel strip in the lateral direction. The lateral temperature distribution data can be understood as a series of temperature measurements at different points along the width of the silicon steel strip, aiming to provide refined temperature information for regional cooling control.

[0055] Furthermore, determining the heat load distribution data for different sub-regions along the width direction based on the transverse temperature distribution data refers to calculating the amount of heat that the silicon steel strip needs to remove per unit time in each preset sub-region along the width direction, based on the collected transverse temperature distribution data and parameters such as the material properties, thickness, and movement speed of the silicon steel strip. For example, the width of the silicon steel strip can be divided into several independent sub-regions, and the corresponding heat load for each sub-region can be calculated.

[0056] Therefore, based on the heat load distribution data, the target cooling intensity is decomposed into cooling intensity components for each sub-region. This means that instead of a single overall cooling intensity, the specific cooling intensity required for each sub-region is calculated based on its actual heat load demand. For example, for sub-regions with higher temperatures, the corresponding cooling intensity component will be higher; while for sub-regions with lower temperatures, the cooling intensity component will be correspondingly lower (for example, the cooling intensity component of each sub-region can be calculated using the following formula: w_i=W*r_i / r_z, where w_i is the cooling intensity component of the i-th sub-region, W is the target cooling intensity, r_i is the heat load of the i-th sub-region, and r_z is the sum of the heat loads of all i-th sub-regions; or, the optimal cooling intensity allocation ratio under different typical heat load distribution data can be pre-determined through experiments, and then the similarity between the actual heat load distribution data and various typical heat load distribution data can be calculated to match the most similar typical heat load distribution data, and the target cooling intensity is decomposed into cooling intensity components for each sub-region based on the cooling intensity allocation ratio of the most similar typical heat load distribution data).

[0057] The cooling actuator corresponds to the execution loop of each sub-region. It can be understood as having a segmented or region-controllable structure, for example, composed of multiple independent nozzle groups or cooling units arranged along the width of the silicon steel strip. Each nozzle group or cooling unit can independently receive commands and adjust the flow rate and pressure of its cooling medium, thereby achieving precise cooling of different sub-regions along the width of the silicon steel strip. Its purpose is to provide refined cooling control capabilities.

[0058] In practical applications, generating sub-cooling commands for each execution loop based on control parameters means converting the cooling intensity component of each sub-region into the specific control signal required by the corresponding execution loop, such as adjusting the valve opening or pump speed, to ensure that each sub-region can obtain its required target cooling intensity.

[0059] This application's solution first acquires the transverse temperature distribution data of the target silicon steel strip before it enters the cooling zone, thereby accurately determining the temperature difference along the width of the silicon steel strip. Based on this acquired transverse temperature distribution data, the heat load distribution data of different sub-regions along the width of the silicon steel strip can be further determined. This allows the system to identify which regions require more cooling and which require less. It is precisely because of this refined heat load information that it becomes possible to decompose the overall target cooling intensity into cooling intensity components for each sub-region, thus enabling a response to the differentiated cooling needs of different transverse positions of the silicon steel strip. On this basis, by mapping each cooling intensity component to the control parameters of the execution loop of the cooling actuator corresponding to each sub-region, and generating corresponding sub-cooling commands, it is ensured that the cooling actuator can apply precise and independent cooling to different sub-regions according to the actual transverse temperature distribution of the silicon steel strip. Finally, when the system reaches the command issuance time, each sub-cooling command is issued to each execution loop, enabling the cooling actuator to effectively compensate for the transverse temperature unevenness of the silicon steel strip when the target segment arrives at the cooling zone, achieving refined regional cooling control.

[0060] Through the above technical solution, this application overcomes the limitations of traditional single cooling intensity control schemes in handling transverse temperature unevenness in silicon steel strips. By introducing transverse temperature distribution data and heat load distribution data, and decomposing the target cooling intensity into cooling intensity components for each sub-region, differentiated and precise cooling control of different regions along the width of the silicon steel strip is achieved. This significantly improves the uniformity of cooling and effectively avoids problems such as internal stress, warping deformation, and inconsistent microstructure properties in the silicon steel strip caused by local overcooling or undercooling. Compared with the basic scheme, this application has higher control precision and adaptability, and can better meet the requirements of silicon steel strip production for high-quality, highly uniform products, thereby improving the overall performance and added value of the product.

[0061] The following is a specific example. Suppose that during a rapid cooling process of silicon steel strip, an infrared thermal imager detects that the temperature of the target section of the silicon steel strip before entering the cooling zone is approximately 20°C higher at the edge than at the center. According to the above scheme, firstly, the lateral temperature distribution data of this target section is acquired, and its width is divided into three sub-regions: the left edge region, the center region, and the right edge region. Based on this temperature data, the system calculates that the heat load distribution data of the left and right edge regions are higher than that of the center region. Subsequently, based on the preset ideal cooling process and the expected residence time, the overall target cooling intensity is determined, and combined with the heat load distribution data of each sub-region, the target cooling intensity is decomposed into three cooling intensity components: the cooling intensity component of the left edge region, the cooling intensity component of the center region, and the cooling intensity component of the right edge region. The cooling intensity component of the edge region is set to be higher than that of the center region. Next, these cooling intensity components are mapped to control parameters of the execution loops of the cooling actuators (e.g., a cooling device consisting of multiple independently controlled nozzle groups) corresponding to each sub-region. For example, the flow rate and pressure of the cooling medium in the left and right edge nozzle groups are adjusted to be higher than those in the center nozzle group. Finally, at the moment the command is issued, the system sends the sub-cooling commands generated for these three sub-regions to the corresponding execution loops. This ensures that when the target section of the silicon steel strip arrives at the cooling area, the edge areas receive stronger cooling, while the center area receives relatively weaker cooling. As a result, after cooling, the temperature distribution along the width of the entire silicon steel strip tends to be uniform, effectively avoiding the problem of uneven cooling caused by uneven initial temperature.

[0062] In actual industrial production environments, the inherent response time of cooling actuators is not constant and may be affected by factors such as fluctuations in the pressure of the power supply system. If these external factors are not considered, the actual response time of the cooling actuator may deviate from the preset inherent response time, resulting in the cooling intensity failing to be precisely adjusted to the target cooling intensity when the target section reaches the cooling area, thus affecting the cooling effect and product quality of the silicon steel strip.

[0063] Therefore, in some preferred embodiments, after step A4 and before step A5, the following step is also included: A6. Based on the medium pressure data of the power supply system of the cooling actuator, the inherent response time is corrected to compensate for the response time deviation caused by pressure fluctuations.

[0064] Specifically, the power supply system of the cooling actuator typically refers to a system that provides the cooling actuator with a cooling medium (e.g., water, oil, gas) or driving energy (e.g., hydraulic pressure, pneumatic pressure). The medium pressure data refers to the real-time pressure value of the medium within the power supply system, which can be collected in real-time by pressure sensors installed on the power supply pipeline or energy storage device. The inherent response time refers to the time required for the cooling actuator to completely switch its state (e.g., valve fully open or close, nozzle reaches the set flow rate) after receiving a control command. In practical applications, fluctuations in medium pressure directly affect the flow rate of the cooling medium, pressure transmission efficiency, and the motion resistance of the internal mechanical components of the actuator, thus causing changes in its response time. For example, when the medium pressure is lower than the rated value, the actuator's movement may slow down, and the response time will be longer; conversely, when the medium pressure is too high, the response time may be shorter. Therefore, correcting the inherent response time aims to dynamically adjust the preset inherent response time based on real-time medium pressure data, making it more accurately reflect the actual response characteristics of the cooling actuator under current operating conditions. The correction process can be implemented based on a pre-established pressure-response time deviation model or a lookup table method. This model or table is obtained through experimental or historical data analysis and is used to quantify the offset of response time under different medium pressures.

[0065] This application's solution effectively solves the problem of inaccurate response time of the cooling actuator caused by fluctuations in the power supply system medium pressure by introducing a dynamic correction mechanism for the inherent response time. Specifically, when the power supply system medium pressure of the cooling actuator fluctuates, this fluctuation directly affects the transmission characteristics of the cooling medium and the operating speed of the actuator, resulting in a deviation between the actual time from receiving the command to completing the state switch and the preset inherent response time. Without correction, the triggering timing of the cooling command determined based on the inaccurate inherent response time cannot ensure that the cooling actuator adjusts the cooling intensity to the target value at the precise moment the target segment arrives at the cooling area. By acquiring medium pressure data in real time and using this data to correct the inherent response time, the response time offset caused by pressure fluctuations can be dynamically compensated. Therefore, the corrected inherent response time can more accurately reflect the actual response characteristics of the cooling actuator under the current operating conditions, thereby enabling the triggering timing of the cooling command to be determined more precisely.

[0066] In some implementations, step A6 includes: A601. Real-time acquisition of medium pressure data from the power supply system to which the cooling actuator belongs; A602. Input the medium pressure data into a preset response deviation model to calculate the response time offset caused by pressure fluctuations; A603. The inherent response time is corrected using the response time offset.

[0067] In step A601, acquiring the medium pressure data of the power supply system to which the cooling actuator belongs in real time refers to continuously monitoring and collecting the current pressure value of the cooling medium (such as water, oil, gas, etc.) through pressure sensors installed on the power supply system (such as cooling medium pump stations, valve pipelines, etc.). This pressure data is dynamically changing and can reflect the working status of the power supply system.

[0068] In step A602, the medium pressure data is input into a preset response deviation model to calculate the response time offset caused by pressure fluctuations. This involves using a pre-established mathematical model or lookup table, taking the real-time collected medium pressure data as input, and outputting a corresponding response time offset. This response deviation model can be established based on historical data, experimental tests, or theoretical analysis to describe the nonlinear or linear relationship between medium pressure and the response time of the cooling actuator. For example, when the medium pressure is lower than the set value, the response time may be prolonged; when the medium pressure is higher than the set value, the response time may be shortened or remain stable. This model can quantify this effect, thus obtaining an accurate offset.

[0069] In step A603, correcting the inherent response time using the response time offset involves superimposing the inherent response time obtained in step A4 with the response time offset calculated in step A602 to obtain a corrected, more accurate inherent response time. The corrected inherent response time will be used to determine the timing of subsequent cooling command triggers.

[0070] This application's solution acquires real-time medium pressure data from the power supply system to which the cooling actuator belongs, and inputs this data into a preset response deviation model. This allows for precise calculation of the response time offset caused by pressure fluctuations. Since medium pressure is a key factor affecting the actual response speed of the cooling actuator, its fluctuations can cause delays or advances in the actuator's action, thus rendering the preset inherent response time inaccurate. By introducing a response deviation model, this application quantifies the impact of pressure fluctuations on the response time, thereby obtaining a precise offset. Based on this offset, the inherent response time is corrected, allowing the inherent response time used to determine the timing of the cooling command to dynamically adapt to the actual operating conditions of the power supply system. This ensures that the cooling command accurately adjusts the cooling intensity to the target cooling intensity when the target segment reaches the cooling area.

[0071] refer to Figure 2 This application provides a rapid cooling control system for silicon steel strip, used for cooling control of silicon steel strip entering the cooling zone, the system comprising: The parameter acquisition module 1 is used to acquire the real-time motion parameters of the target segment of the silicon steel strip before it enters the cooling zone; the real-time motion parameters include instantaneous speed and speed change trend (the specific process can be referred to step A1 above). Prediction module 2 is used to predict the expected time when the target segment arrives at the cooling area and the expected time the target segment stays in the cooling area based on the real-time motion parameters and the physical location information of the cooling area (for details, please refer to step A2 above). Intensity determination module 3 is used to determine the target cooling intensity required by the target segment in the cooling area based on the preset ideal cooling process and the expected residence time (the specific process can be referred to step A3 above). The response time acquisition module 4 is used to acquire the inherent response time of the cooling actuator from receiving the instruction to completing the state switch (for details, please refer to step A4 above). The instruction triggering module 5 is used to combine the expected time and the inherent response time to determine the timing of the cooling instruction trigger, so that the cooling actuator adjusts the cooling intensity to the target cooling intensity when the target segment arrives at the cooling area (the specific process can be referred to step A5 above).

[0072] In some implementations, the system further includes: The correction module is used to correct the inherent response time based on the medium pressure data of the power supply system of the cooling actuator, so as to compensate for the response time deviation caused by pressure fluctuations (the specific process can be referred to step A6 above).

[0073] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for rapid cooling control of silicon steel strip, used to control the cooling of silicon steel strip entering the cooling zone, characterized in that, The method includes: A1. Obtain the real-time motion parameters of the target segment of the silicon steel strip before it enters the cooling zone; the real-time motion parameters include instantaneous velocity and velocity change trend; A2. Based on the real-time motion parameters and the physical location information of the cooling area, predict the expected time when the target segment arrives at the cooling area, and the expected time the target segment stays in the cooling area; A3. Based on the preset ideal cooling process and the expected residence time, determine the target cooling intensity required for the target segment within the cooling area; A4. Obtain the inherent response time of the cooling actuator from receiving the command to completing the state switch; A5. By combining the expected time and the inherent response time, determine the timing of the cooling command so that the cooling actuator adjusts the cooling intensity to the target cooling intensity when the target segment arrives at the cooling area.

2. The method for controlling the rapid cooling of silicon steel strip according to claim 1, characterized in that, Step A1 includes: A101. The raw speed data of the target segment is collected in real time by a sensor; A102. Perform digital filtering on the original velocity data to obtain smooth velocity data, which is used as the instantaneous velocity; A103. Perform differential calculation on the smoothed speed data to obtain the speed change trend.

3. The method for controlling the rapid cooling of silicon steel strip according to claim 2, characterized in that, The sensor is a laser velocimeter, encoder, or vision measurement system.

4. The method for controlling the rapid cooling of silicon steel strip according to claim 1, characterized in that, Step A2 includes: A201. Based on the instantaneous velocity and the velocity change trend, establish a displacement prediction model for the target segment; A202. Using the displacement prediction model, calculate the time required for the target segment to move to the entrance boundary of the cooling area based on the physical location information of the cooling area; A203. Add the current system time to the duration to obtain the estimated time; A204. Obtain the physical length of the cooling zone in the running direction of the silicon steel strip; A205. Based on the displacement prediction model, the estimated time, and the physical length, calculate the duration of the target segment passing through the cooling area, which is taken as the estimated dwell time.

5. The method for controlling the rapid cooling of silicon steel strip according to claim 1, characterized in that, Step A3 includes: A301. Based on the preset ideal cooling process, calculate the total heat required to remove from the target segment within the expected residence time to achieve the target temperature drop; A302. Calculate the heat exchange intensity per unit time based on the total heat and the expected residence time, and use it as the target cooling intensity.

6. The method for controlling the rapid cooling of silicon steel strip according to claim 1, characterized in that, Step A5 includes: A501. The target cooling intensity is mapped to control parameters of the cooling actuator, and a cooling command is generated based on the control parameters; the control parameters include the flow rate and pressure parameters of the cooling medium; A502. Using the estimated time as the base time, backtrack the inherent response time to obtain the instruction issuance time, which is then used as the triggering opportunity; A503. When the current system time reaches the instruction issuance time, the cooling instruction is issued to the cooling actuator so that the cooling actuator adjusts the cooling intensity to the target cooling intensity when the target segment reaches the cooling area.

7. The method for controlling the rapid cooling of silicon steel strip according to claim 6, characterized in that, Step A501 includes: Obtain the lateral temperature distribution data of the target segment before it enters the cooling zone; Based on the lateral temperature distribution data, determine the heat load distribution data of different sub-regions in the width direction of the target segment; Based on the heat load distribution data, the target cooling intensity is decomposed into cooling intensity components of each of the sub-regions; Each of the cooling intensity components is mapped to the control parameters of the execution loop of the cooling actuator corresponding to each of the sub-regions; Sub-cooling commands are generated for each of the execution loops based on the control parameters; In step A503, when the current system time reaches the instruction issuance time, each of the sub-cooling instructions is issued to each of the execution loops.

8. The method for controlling the rapid cooling of silicon steel strip according to claim 1, characterized in that, The steps following step A4 and before step A5 include: A6. Based on the medium pressure data of the power supply system of the cooling actuator, the inherent response time is corrected to compensate for the response time deviation caused by pressure fluctuations.

9. The method for controlling the rapid cooling of silicon steel strip according to claim 8, characterized in that, Step A6 includes: A601. Real-time acquisition of medium pressure data from the power supply system to which the cooling actuator belongs; A602. Input the medium pressure data into a preset response deviation model to calculate the response time offset caused by pressure fluctuations; A603. The inherent response time is corrected using the response time offset.

10. A rapid cooling control system for silicon steel strip, used for cooling control of silicon steel strip entering the cooling zone, characterized in that, The system includes: The parameter acquisition module is used to acquire the real-time motion parameters of the target segment of the silicon steel strip before it enters the cooling zone; the real-time motion parameters include instantaneous speed and speed change trend. The prediction module is used to predict the expected time when the target segment arrives at the cooling area and the expected time the target segment stays in the cooling area, based on the real-time motion parameters and the physical location information of the cooling area. An intensity determination module is used to determine the target cooling intensity required by the target segment in the cooling area based on a preset ideal cooling process and the expected residence time. The response time acquisition module is used to acquire the inherent response time of the cooling actuator from receiving the instruction to completing the state switch; The instruction triggering module is used to combine the expected time and the inherent response time to determine the timing of the cooling instruction trigger, so that the cooling actuator adjusts the cooling intensity to the target cooling intensity when the target segment arrives at the cooling area.

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