Secondary cooling dynamic water distribution control method, system and equipment and storage medium

Abstract

The embodiment of the invention provides a secondary cooling dynamic water distribution control method, system and equipment and a storage medium, and relates to the technical field of steel continuous casting, and the method comprises the steps that an obtained target casting blank is divided into a plurality of slices according to a preset time period in the blank drawing direction; the position and growth time of each slice are recorded according to the obtained actual pulling speed, casting length and drawing length of the target casting blank, and the effective pulling speed of each slice is calculated according to the actual pulling speed; according to a preset database, target values, corresponding to the effective pulling speed, of the water quantity required by all the secondary cooling areas are inquired; and according to the obtained crystallizer wide and narrow face water amount of the target casting blank, the crystallizer wide and narrow face temperature difference, the secondary cooling water temperature and the tundish temperature, the target value of the water amount required by each secondary cooling area is regulated and controlled. Therefore, the effective pulling speed is comprehensively calculated through the actual pulling speed, the casting length and the drawing length of the target casting blank in the continuous casting process, the relation between the pulling speed and the water amount is enriched, the cooling effect is improved, and the quality problem that the corners and the edges of the blank crack in the steel grade continuous casting process is solved.

Description

Technical Field

[0001] This invention relates to the field of steel continuous casting technology, and more specifically, to a method, system, equipment, and storage medium for dynamic water distribution control in secondary cooling. Background Technology

[0002] Wide-width high-carbon chromium bearing steel is the raw material for producing stamped bearing rings. It needs to have good stamping performance. However, during the production process, wide-width high-carbon chromium bearing steel billets generally have problems such as corner and edge cracking. The most effective means to control the above quality defects is continuous casting with secondary cooling.

[0003] Secondary cooling in continuous casting involves pouring high-temperature molten steel into a mold, pulling it downwards while simultaneously spraying water to cool it and allow it to slowly solidify. Currently, the commonly used method for controlling secondary cooling is to adjust the water spraying speed based on the speed at which the billet is pulled out; for example, more water is sprayed if the billet is pulled out quickly, and less water is sprayed if it is pulled out slowly.

[0004] Commonly used secondary cooling methods for continuous casting can only predict the required water volume based on the current casting speed. During non-steady-state stages such as casting start-up, casting stop-up, ladle change, and speed reduction, the casting speed changes drastically. Relying solely on the relationship between casting speed and water volume results in poor cooling effect and can easily lead to quality problems such as cracking at the corners and edges of billets during the continuous casting of wide-width high-carbon chromium bearing steel. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a method, system, device, and storage medium for dynamic water distribution control in secondary cooling. During unsteady-state stages such as start-up casting, stop-casting, ladle changing, and speed reduction, when the casting speed changes drastically, the invention relies on the effective casting speed and water volume relationship calculated based on the actual casting speed, casting length, and drawing length. This enriches the relationship between casting speed and water volume, improves the cooling effect, and reduces quality problems such as corner and edge cracking of billets during continuous casting of steel grades.

[0006] To achieve the above objectives, in a first aspect, embodiments of the present invention provide a method for dynamic water distribution control in the secondary cooling zone. The method includes: acquiring continuous casting parameters of a target billet, the continuous casting parameters including actual casting speed, casting length, drawing length, water volume at the wide and narrow face of the crystallizer, temperature difference at the wide and narrow face of the crystallizer, secondary cooling water temperature, and tundish temperature; dividing the target billet into several slices along the casting direction according to a preset time period; recording slice data for each slice based on the actual casting speed, the casting length, and the drawing length, the slice data including the position of each slice and the growth time at that position; calculating the effective casting speed of each slice based on the position of each slice, the growth time at that position, and the actual casting speed; querying a preset database to find the target value of the water volume required in each secondary cooling zone corresponding to the effective casting speed of each slice; and adjusting the target value of the water volume required in each secondary cooling zone in real time based on the water volume at the wide and narrow face of the crystallizer, the temperature difference at the wide and narrow face of the crystallizer, the secondary cooling water temperature, and the tundish temperature.

[0007] In this embodiment, the actual casting speed, casting length, and drawing length of the target billet during continuous casting are incorporated into the data of several slices. The slice data for each slice is recorded to obtain the position and growth time of each slice. This allows for the calculation of the effective casting speed for each slice based on its position, the time at that position, and the actual casting speed. This, in turn, helps to find the target water volume required for each secondary cooling zone in the database based on the effective casting speed. Furthermore, based on the water volume across the wide and narrow face of the crystallizer, the temperature difference between the wide and narrow face of the crystallizer, the secondary cooling water temperature, and the tundish temperature, not only can the theoretical water volume required for each cooling zone under the current operating conditions be calculated, but also, combined with the slice tracking mechanism and effective casting speed feedback, the water volume setpoint can be continuously optimized. This achieves dynamic matching throughout the entire process from start to stop casting, effectively overcoming the problem of slow response to unsteady conditions in traditional single casting speed control modes. Especially in complex situations such as ladle changes, speed reduction, or abnormal temperature rises, the billet surface temperature can still maintain a stable evolution along the target curve. This significantly reduces corner and edge cracks in the billet, resulting in a marked decrease in the severity of internal defects such as center segregation and shrinkage cavities, thus comprehensively improving the overall internal quality. Furthermore, by comprehensively calculating the effective casting speed based on the actual casting speed, casting length, and drawing length of the target billet during continuous casting, the relationship between casting speed and water volume is enriched, improving cooling efficiency and reducing quality problems such as corner and edge cracking during continuous casting of steel grades.

[0008] In some embodiments, recording the slice data of each slice based on the actual drawing speed, the casting length, and the drawing length includes: calculating the moving distance of each slice within the preset time period based on the actual drawing speed and the casting length; recording the position of each slice based on the moving distance of each slice and the drawing length; and recording the growth time of the position of each slice based on the preset time period and the casting length.

[0009] This setup ensures accurate synchronization between the slice position and growth time, providing a reliable data foundation for subsequent age-based cooling control and enhancing the ability to respond to complex and variable operating conditions.

[0010] In some embodiments, calculating the effective pulling speed of each slice based on the position of each slice, the growth time at that position, and the actual pulling speed includes: calculating the average pulling speed of each slice based on the position of each slice and the growth time at that position; and calculating the effective pulling speed of each slice based on a weighted average of the average pulling speed and the actual pulling speed.

[0011] With this setting, the effective casting speed retains the instantaneous dynamic response capability and incorporates historical solidification background information, more realistically reflecting the cooling intensity required by the billet at present. This effectively alleviates the control fluctuation problem caused by relying solely on instantaneous casting speed and makes the water volume setting smoother and more reasonable.

[0012] In some embodiments, the target value for real-time adjustment of the required water volume in each zone of the secondary cooling system based on the water volume of the wide and narrow face of the crystallizer, the temperature difference between the wide and narrow face of the crystallizer, the temperature of the secondary cooling water, and the temperature of the tundish includes: calculating the surface temperature and billet shell thickness distribution of each slice based on the water volume of the wide and narrow face of the crystallizer, the temperature difference between the wide and narrow face of the crystallizer, the temperature of the secondary cooling water, and the temperature of the tundish; and real-time adjustment of the target value for the required water volume in each zone of the secondary cooling system based on the surface temperature and the billet shell thickness distribution of each slice.

[0013] This setup significantly improves the physical accuracy of water distribution control, enabling personalized cooling for specific heat load conditions, thereby suppressing internal defects such as center segregation and triangular cracks in the target billet.

[0014] In some embodiments, calculating the surface temperature and billet thickness distribution of each slice based on the water volume of the wide and narrow face of the crystallizer, the temperature difference between the wide and narrow face of the crystallizer, the temperature of the secondary cooling water, and the temperature of the tundish includes: calculating the surface temperature of each slice based on the temperature of the secondary cooling water and the temperature of the tundish; and calculating the billet thickness distribution of each slice based on the water volume of the wide and narrow face of the crystallizer and the temperature difference between the wide and narrow face of the crystallizer.

[0015] This setup allows for the acquisition of temperature gradients along the thickness direction, which in turn determines the thickness distribution of the blank at the current position for each slice. This facilitates the identification of insufficient cooling at the corners and prompts for enhanced spraying in the corresponding areas. It provides core data support for achieving high-precision closed-loop control and improves the ability to identify and intervene in local solidification anomalies at an early stage.

[0016] In some embodiments, the step of adjusting the target value of the water volume required for each secondary cooling zone in real time based on the surface temperature of each slice and the thickness distribution of the billet shell includes: generating a real-time temperature curve based on the surface temperature of each slice; comparing the real-time temperature curve with a preset target temperature curve to determine whether there is a deviation between the real-time temperature curve and the preset target temperature curve; and adjusting the target value of the water volume required for each secondary cooling zone according to the real-time temperature curve and the thickness distribution of the billet shell when there is a deviation between the real-time temperature curve and the preset target temperature curve.

[0017] This configuration enables self-correction, maintaining a stable cooling path under disturbed conditions, significantly reducing the frequency of human intervention and improving the level of automation.

[0018] In some embodiments, the required water volume for each secondary cooling zone includes the water volume sprayed by the nozzles in each secondary cooling zone. The step of adjusting the target value of the required water volume for each secondary cooling zone according to the real-time temperature curve and the billet thickness distribution includes: obtaining the operating parameters of the nozzles in each secondary cooling zone; comparing the operating parameters of the nozzles in each secondary cooling zone with preset standard parameters to determine whether there are any blocked nozzles in the nozzles in each secondary cooling zone; and, if there are blocked nozzles in the nozzles in the secondary cooling zone, adjusting the target value of the water volume sprayed by the remaining unblocked nozzles in the nozzles in each secondary cooling zone according to the real-time temperature curve and the billet thickness distribution.

[0019] This setup upgrades the system from passive alarm to active compensation, ensuring high-quality billet production even when equipment is not in ideal condition, and improving the continuity and stability of continuous casting operations.

[0020] Secondly, embodiments of the present invention provide a dynamic water distribution control system for secondary cooling, the system comprising: an acquisition module for acquiring continuous casting parameters of a target billet, the continuous casting parameters including actual casting speed, casting length, drawing length, water volume at the wide and narrow face of the crystallizer, temperature difference at the wide and narrow face of the crystallizer, secondary cooling water temperature, and tundish temperature; a tracking module for dividing the target billet into several slices along the casting direction according to a preset time period; recording slice data for each slice according to the actual casting speed, the casting length, and the drawing length, the slice data including the current slice position and the growth time at that position; and a calculation module for calculating the effective casting speed of each slice based on the position of each slice, the growth time at that position, and the actual casting speed; querying a preset database for the target value of the water volume required for each zone of secondary cooling corresponding to the effective casting speed of each slice; and real-time adjusting the target value of the water volume required for each zone of secondary cooling according to the water volume at the wide and narrow face of the crystallizer, the temperature difference at the wide and narrow face of the crystallizer, the secondary cooling water temperature, and the tundish temperature.

[0021] Thirdly, embodiments of the present invention provide an electronic device, including a processor and a memory, wherein the memory stores a computer program executable by the processor, and the processor can execute the computer program to implement the dual-cooling dynamic water distribution control method as described in the first aspect.

[0022] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the dynamic water distribution control method for secondary cooling as described in the first aspect.

[0023] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart of a dynamic water distribution control method for a secondary cooling system provided in an embodiment of the present invention; Figure 2 for Figure 1 Flowchart of sub-steps S301~S303 in step S300; Figure 3 for Figure 1 Flowcharts of sub-steps S401~S402 of step S400; Figure 4 A flowchart of another dynamic water distribution control method for secondary cooling provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of a solidified micro-element provided in an embodiment of the present invention; Figure 6 for Figure 4 Flowcharts of sub-steps S601~S603 of step S600; Figure 7 for Figure 4 Flowcharts of sub-steps S701~S703 in step S700; Figure 8 for Figure 6 Flowchart of sub-steps S7031~S7033 in step S703; Figure 9 This is a schematic diagram of the functional modules of the dual-cooling dynamic water distribution control system provided in an embodiment of the present invention; Figure 10 This is a schematic diagram of the system framework of the dual-cooling dynamic water distribution control system provided in an embodiment of the present invention; Figure 11 This is a schematic diagram of the steel grade selection and modification interface provided in an embodiment of the present invention; Figure 12 The diagram illustrates the selection and modification of casting machine parameters and control system parameters provided in this embodiment of the invention. Figure 13 A block diagram of an electronic device provided in an embodiment of the present invention.

[0026] Icons: 1000 - Secondary cooling dynamic water distribution control system; 1100 - Acquisition module; 1200 - Tracking module; 1300 - Calculation module; 2000 - Electronic equipment; 2100 - Processor; 2200 - Memory; 2300 - Bus; 2400 - Communication interface. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0028] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0029] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0030] As described in the background section, the commonly used secondary cooling method for continuous casting can only predict the required water volume based on the current casting speed. During non-steady-state stages such as casting start-up, casting stop-up, ladle change, and speed reduction, the casting speed changes drastically. Relying solely on the relationship between casting speed and water volume cannot provide optimal cooling, which can easily lead to quality problems such as cracking at the corners and edges of billets during the continuous casting of wide-width high-carbon chromium bearing steel.

[0031] To address this, this invention provides a dynamic water distribution control method for secondary cooling. During unsteady-state stages such as casting start-up, casting stop-up, ladle change, and speed reduction, when casting speed changes drastically, the method relies on the effective casting speed and water volume relationship calculated based on the actual casting speed, casting length, and drawing length. This enriches the relationship between casting speed and water volume, improves the cooling effect, and reduces quality problems such as corner and edge cracking during continuous casting of steel. (See also...) Figure 1 , Figure 1 This invention provides a flowchart of a dynamic water distribution control method for a secondary cooling system, comprising steps S100 to S500: S100. Obtain the continuous casting parameters of the target billet, including the actual casting speed, casting length, drawing length, water volume of the wide and narrow faces of the crystallizer, temperature difference of the wide and narrow faces of the crystallizer, secondary cooling water temperature and tundish temperature.

[0032] In this embodiment, the actual drawing speed reflects the speed at which the billet is pulled out of the crystallizer during the current casting process, and its dynamic changes directly affect the solidification behavior and cooling requirements of the billet. The casting length refers to the actual travel distance from the meniscus of the crystallizer to the front end of the billet at the current moment, used to characterize the spatial position evolution of the billet within the continuous casting machine. The drawing length refers to the physical metallurgical length of the entire continuous casting machine from the crystallizer outlet to the last pair of straightening rolls, representing the maximum effective travel range that the billet can cover to complete complete solidification. These three types of parameters are collected in real time by the PLC process control system and transmitted to the secondary cooling dynamic water distribution model computer via industrial Ethernet to ensure the timeliness and accuracy of the data. Furthermore, when the casting length is detected to exceed the drawing length, a zeroing mechanism is triggered to prevent data from invalid regions from participating in subsequent calculations, thereby ensuring the rationality and logical consistency of the model's operational boundary conditions.

[0033] Furthermore, the water flow rate on both the wide and narrow sides of the crystallizer is precisely measured using flow meters installed on the water supply pipe. The actual spray volume of each loop on both the wide and narrow sides is recorded to assess the symmetry and adequacy of the cooling distribution within the crystallizer. Simultaneously, the temperature difference between the inlet and outlet water on both sides is collected in real time by paired temperature sensors. Combined with the water flow data, the heat carried away by each side per unit time can be calculated, thus revealing the change in the crystallizer's heat transfer efficiency and providing a basis for judging abnormal conditions such as copper plate scaling and water flow blockage. The secondary cooling water temperature is monitored online at a point before entering the main spray pipe, continuously monitoring the inlet temperature of the cooling medium. Since this directly affects the spray evaporation cooling capacity, it is a key input for correcting the nozzle heat transfer coefficient, especially during seasons with significant ambient temperature changes or during day-night cycles. The tundish temperature represents the thermodynamic state of the molten steel before it is injected into the crystallizer. Under normal circumstances, a stable reading is provided by a continuous temperature measuring device. When signal interruption or abnormal fluctuations occur, the system automatically switches to point measurements from a manual insertion temperature gun as a supplement, ensuring continuous and reliable data. All the above parameters are centrally collected through the PLC process control system and transmitted at high speed to the secondary cooling dynamic water distribution model computer via industrial Ethernet. Data synchronization is completed based on timestamp alignment to ensure the timeliness and consistency of model calculations.

[0034] S200: Divide the target billet into several slices along the billet pulling direction according to a preset time period.

[0035] In this embodiment, to achieve refined tracking and control of the billet solidification process, the target billet is divided into multiple virtual slices along the casting direction. This division method is dynamically updated based on a preset time period. Within each time period, the number and spatial interval of newly added slices are determined according to the current casting speed and mesh discretization accuracy, so that the slice distribution can accurately reflect the instantaneous state of the billet in continuous motion. This slice division method not only meets the convergence and stability requirements of the one-dimensional finite difference method in numerical calculation, but also establishes a continuous state transfer relationship in the time dimension, providing discretized computational units for subsequent temperature field simulation and water volume control. As the billet continues to descend, new slices are continuously generated at the head, while slices at the end are removed after completing their solidification process, forming a dynamically rolling computational structure that ensures that the focus during continuous casting is always on the effective billet segment currently in the secondary cooling zone.

[0036] S300. Record the slice data for each slice based on the actual drawing speed, casting length, and drawing length. The slice data includes the position of each slice and the growth time at that position.

[0037] In this embodiment, the position information of each slice is determined by its spatial coordinates relative to the meniscus, and is precisely calculated by combining real-time casting speed and time step. The growth time refers to the accumulated time experienced by the slice since entering the solidification process, that is, the billet age during the gradual transition from the initial high-temperature liquid state to the solid state. The introduction of this parameter breaks through the limitation of traditional methods that rely solely on instantaneous casting speed to control cooling intensity, and instead focuses on the actual thermal history of the individual billet along the cooling path. By synchronously recording the changing trends of actual casting speed, casting length, and drawing length, the positional evolution trajectory of each slice and its corresponding growth time can be dynamically corrected, thereby constructing a spatiotemporal mapping relationship that is continuously updated over time, providing a key basis for subsequent water volume adjustment strategies based on unsteady-state heat transfer laws.

[0038] S400: Calculate the effective pulling speed of each slice based on its position, growth time at that position, and actual pulling speed.

[0039] In this embodiment, based on the obtained slice position and growth time information, combined with the real-time monitored actual casting speed, the effective casting speed corresponding to each slice is calculated. This parameter is not a simple instantaneous speed reading, but a weighted result that comprehensively considers the average speed of the slice during its movement and its current instantaneous speed, aiming to more realistically reflect the overall motion characteristics and thermal response inertia of the billet in a specific cooling section. Since the billet may experience drastic speed fluctuations under unsteady conditions (such as the stages of drastic changes in casting speed, such as starting pouring, stopping pouring, changing ladles, and speed reduction), simply adjusting the water volume based on the instantaneous casting speed can easily lead to uneven cooling or even an increased risk of cracking. Therefore, using the effective casting speed as an intermediate variable can smooth the control response to a certain extent and improve the robustness of the system. This calculation process fully integrates the concept of the billet's growth process, so that cooling control is no longer limited to the linear mapping of the static water distribution table, but establishes a dynamic correlation mechanism that matches the internal thermodynamic evolution of the billet.

[0040] S500: Query the target value of the water volume required for each secondary cooling zone corresponding to the effective pulling speed of each slice from the preset database; adjust the target value of the water volume required for each secondary cooling zone in real time according to the water volume of the wide and narrow face of the crystallizer, the temperature difference of the wide and narrow face of the crystallizer, the temperature of the secondary cooling water and the temperature of the tundish.

[0041] In this embodiment, based on the effective casting speed of each slice calculated above, the target values ​​of the required water volume for each secondary cooling zone are retrieved and interpolated from a pre-built secondary cooling database. The database is a multi-dimensional mapping set built upon extensive production data and solidification heat transfer model simulations, covering optimal water distribution strategies for different steel grades, cross-sectional specifications, casting speed ranges, and thermal conditions. The query process not only considers the direct impact of the effective casting speed but also incorporates steel grade physical property parameters, target surface temperature curves, and boundary constraints of the cooling capacity of each zone, ensuring that the output water volume setting conforms to process specifications and is engineering-feasible. Specifically, the database is organized in the form of relational data tables, containing multiple interrelated data tables, such as steel grade physical property parameter tables, billet specification configuration tables, target temperature curve tables, cooling circuit characteristic tables, and water volume setting benchmark tables. The steel grade physical property parameter table records key physical properties such as thermal conductivity, specific heat capacity, density, liquidus temperature, and high-temperature mechanical properties for various steel grades, including wide-width high-carbon chromium bearing steel. The billet specification configuration table defines the nozzle arrangement and cooling zone division corresponding to different cross-sectional dimensions. The target temperature curve table stores the ideal surface temperature evolution path of each steel grade under specific working conditions, obtained based on metallurgical criteria and solidification behavior analysis. Based on this, the water volume setting benchmark table establishes a functional relationship between the effective casting speed and the required water volume in each secondary cooling zone through historically accumulated optimization data or offline simulation results. This relationship is not a simple linear correspondence but rather a comprehensive empirical model output value that combines the influence of multiple variables such as casting length, crystallizer heat flow state, and tundish superheat.

[0042] For example, after calculating the effective casting speed of a certain slab slice, this value is used as the query key for matching and retrieval in the database. Based on the current steel grade, cross-sectional width, and process status, the corresponding interpolation algorithm is invoked to extract the target cooling water volume values ​​for each zone that best match the operating conditions from the pre-stored parameter matrix. The entire database supports a dynamic update mechanism, allowing technicians to write new process parameters into the system after verification, thereby continuously improving the adaptability and accuracy of the water distribution strategy. Furthermore, the database has version control functionality, ensuring parameter traceability between different production batches and providing data support for quality analysis and process improvement. This achieves a shift from experience-driven to model-driven approaches, enabling the cooling control strategy to adapt to complex and ever-changing actual production environments. Especially when dealing with materials such as wide-width high-carbon chromium bearing steel, which have extremely high requirements for cooling uniformity, it demonstrates significantly better adaptability and accuracy than traditional feedforward control methods.

[0043] Furthermore, by utilizing the water volume on the wide and narrow sides of the crystallizer and the corresponding inlet and outlet water temperature differences, the actual heat transfer on each side of the crystallizer is calculated. Based on this, the asymmetric characteristics of the surface temperature distribution and initial shell thickness of the billet when exiting the crystallizer are deduced. This information is directly used as the boundary condition input for the solidification heat transfer model to correct the initial thermal state value of the billet in the initial stage of entering the secondary cooling zone. If insufficient cooling on the narrow side is found to cause local thinning of the billet shell, the spray intensity on the outer arc side is automatically increased in the front section of the secondary cooling zone to compensate for the solidification lag area; conversely, the water volume is appropriately reduced to avoid excessive cooling that could cause thermal stress cracks. The tundish temperature, as a direct reflection of the superheat of the molten steel, affects the total latent heat release and liquid core length during the entire solidification process. By incorporating it into the model, the total cooling demand can be dynamically adjusted. When the molten steel temperature is higher than the standard range, the water volume benchmark value of each cooling circuit is increased to ensure sufficient heat dissipation capacity to match the additional heat carried by the molten steel. The secondary cooling water temperature is used to correct the nozzle's heat transfer efficiency in real time. Under the same flow rate, an increase in cooling water temperature leads to a decrease in evaporative heat absorption capacity, thereby weakening the actual cooling effect. Therefore, the heat transfer coefficient is dynamically compensated based on the measured water temperature. The performance degradation caused by the increase in medium temperature is offset by increasing the spray volume, maintaining the expected cooling rate of the billet surface. The above parameters work together on the solidification heat transfer model constructed by the one-dimensional finite difference method. The model divides the billet into several virtual slices along the billet pulling direction. Combined with the billet age tracking mechanism, the time experienced by each slice in different cooling zones and its corresponding surface temperature evolution curve are calculated. The calculation results are then compared point by point with the preset target temperature curve. Once a deviation is detected that exceeds the allowable threshold, a feedback adjustment algorithm is activated to solve the heat flux density required to meet the target temperature. This is then further converted into an increment or decrement of the water volume setting for each cooling zone, forming a closed-loop control logic from temperature error, heat flux correction, and water volume adjustment. The final target value of water volume not only takes into account the steady-state characteristics of the current working conditions, but also incorporates historical evolution information in the unsteady-state process, such as temperature fluctuations caused by ladle changes or prolonged residence time caused by speed reduction, so as to ensure that the billet can still maintain a uniform and stable solidification process under complex and variable working conditions.

[0044] In some embodiments, to record the slice data of each slice more clearly and accurately, refer to Figure 2 , Figure 2 for Figure 1 The flowchart of sub-steps S301~S303 of step S300, wherein steps S301~S303 include: S301. Calculate the moving distance of each slice within a preset time period based on the actual casting speed and casting length.

[0045] In this embodiment, the distance moved by each slab segment is dynamically calculated within each preset time period using real-time data of the actual casting speed and casting length. This process relies on high-frequency sampling of the casting speed by the PLC process control system to ensure that speed changes within the time period can be accurately captured. Since the slab may experience acceleration and deceleration fluctuations during the casting straightening process, especially during pouring, ladle changes, or abnormal operating conditions, the distance integration is performed by combining the effective casting speed weighted by the instantaneous casting speed and historical speed, which improves the accuracy of position estimation. When calculating the movement distance of each segment, within each time step, the current effective casting speed is multiplied by the time interval to obtain the displacement increment within that period, and this increment is accumulated to the position value of the previous moment, thereby achieving continuous tracking of the spatial displacement of each segment. This calculation mechanism ensures that even under non-steady-state operating conditions, the model can still accurately reflect the actual motion trajectory of each micro-segment of the slab, providing a reliable spatial reference for subsequent temperature field simulation and cooling control.

[0046] S302. Record the position of each slice based on the moving distance and pull-out length of each slice.

[0047] In this embodiment, based on the distance each slice moves per unit time calculated in the previous step, and combined with the physical boundary parameter of the drawing length, the absolute position of each slice within the continuous casting machine is determined and recorded. The drawing length, as a fixed distance from the crystallizer outlet to the last pair of straightening rolls, defines the maximum effective path that the billet can travel in the entire secondary cooling zone. When a virtual slice gradually moves forward with the billet drawing process and approaches or reaches this length limit, it is determined that it has left the critical cooling zone or completed the main solidification stage, and thus it is decided whether to retain it for water volume control calculation. The position record includes not only the axial distance of the slice from the meniscus, but also its location in the secondary cooling zone (such as the foot roll section, section one, section two, etc.), so that differentiated water distribution strategies can be implemented according to the region in the future, ensuring that the cooling command can accurately correspond to the actual spatial distribution of the billet.

[0048] S303. Record the growth time of each slice position based on the preset time period and casting length.

[0049] In this embodiment, a preset time period and continuously updated casting length information are further utilized to synchronously record the accumulated growth time, i.e., billet age, of each slice at its current position. This parameter is not a simple clock time, but rather the actual solidification time experienced by each slice from the formation of the initial billet shell at the meniscus of the crystallizer to its current position. By accumulating the step time of each time period and combining it with the movement state of the slice within the corresponding interval (such as whether there is a brief pause or variable speed operation), dynamic correction of the growth time is achieved. The introduction of growth time makes cooling control no longer limited to the response of instantaneous process parameters, but establishes a time-dimensional benchmark that matches the thermodynamic evolution process inside the billet. Especially for steel grades such as wide-width high-carbon chromium bearing steel, which are sensitive to the cooling path, accurately grasping the growth time of each part helps to identify its phase transformation critical point and prevent defects such as corner cracks or triangular cracks caused by local overcooling or reheating, thereby laying the foundation for realizing refined water volume regulation based on thermal history.

[0050] In some embodiments, for a clear understanding of the calculation process for effective pulling speed, refer to... Figure 3 , Figure 3 for Figure 1 The flowchart of sub-steps S401~S402 of step S400, wherein steps S401~S402 include: S401. Calculate the average pulling speed of each slice based on its position and growth time.

[0051] In this embodiment, the average casting speed of each slice from its formation to the current moment is calculated by relating its spatial position to its corresponding growth time. This parameter reflects the overall motion characteristics of the slice along the casting direction throughout the solidification process, and its calculation is based on the ratio of the total displacement from the meniscus to the current position to the elapsed time. Since the casting billet often experiences unsteady conditions during actual operation, such as initial acceleration during casting, deceleration during ladle changes, or abnormal interruptions, the instantaneous casting speed fluctuates significantly. If only the current speed is used as the control basis, it can easily lead to a mismatch in the cooling strategy. Therefore, introducing the average casting speed can effectively characterize the overall movement rhythm of the slice along the cooling path, reflecting its cumulative thermal history effect. This calculation process combines the slice movement trajectory and time series data established in the previous steps, ensuring that the motion state of each virtual micro-element is taken into consideration, thereby providing a more representative speed benchmark for subsequent water volume setting.

[0052] For example, in the initialization phase, the average casting speed calculation divides the billet into multiple equidistant or variable-distance virtual slices along the casting direction. Each slice is assigned a unique identifier and its initial generation time and starting position are recorded, i.e., the spatial coordinates at the meniscus. As the casting process continues, the control system tracks the actual movement distance of each segment of the billet in real time through high-precision encoders and position sensors, and updates the current position of each slice in conjunction with timestamps. For any slice participating in the calculation, the system reads the difference between its current position and its initial position as the total displacement of the slice since its generation, and simultaneously obtains the growth time it has experienced from generation to the current moment, i.e., the difference between the current time and the slice generation time. Then, the average casting speed of the slice is calculated using the ratio of displacement to time, i.e., total displacement minus total growth time.

[0053] S402. Calculate the effective pulling speed of each slice based on the weighted average of the average pulling speed and the actual pulling speed.

[0054] In this embodiment, the average casting speed obtained in the previous step is further weighted and averaged with the actual casting speed collected in real time to obtain the final effective casting speed for each slice. This weighting mechanism is not a simple arithmetic average, but dynamically adjusts the weight allocation based on the process stability judgment: in the steady-state casting stage, the actual casting speed has a higher weight to ensure that the control system has a rapid response capability to the current working condition; while in the non-steady-state stage where the casting speed changes drastically, the weight of the average casting speed is appropriately increased to suppress control oscillations caused by instantaneous disturbances and improve the robustness of the system. This speed synthesis method that integrates historical behavior and current state makes the effective casting speed not only physically interpretable, but also closer to the actual heat transfer requirements of the billet. As the core intermediate variable connecting the dynamic water distribution model and multi-source input data, the introduction of the effective casting speed breaks through the technical limitations of traditional single-reliance on instantaneous casting speed to regulate the cooling water volume, so that the water volume setting of each secondary cooling zone can more accurately match the heat load changes of the billet at different positions and different growth times, thereby significantly improving the surface and internal quality uniformity of wide-width high-carbon chromium bearing steel continuous casting billets.

[0055] For example, the required amount of water is determined by calculating the location of the slab slice and the time taken to form the shell. Theoretically, since the heat conducted from the shell to the surface is carried away by water droplets sprayed onto the slab surface, we have: (1) In the formula ,and Cooling water volume The function is given by: (2) Therefore, time is used as a parameter to control the cooling water volume. In this embodiment, the effective casting speed is used to represent the growth process of the billet slices to determine the water volume in each secondary cooling zone. The formula is as follows: (3) (4) in Average speed; Since the instantaneous velocity is given, it can be seen that the effective pull speed of the secondary cooling water distribution is a weighted sum of the average velocity of the slice and the instantaneous velocity. The water volume of each loop can be obtained by searching the database or by using interpolation based on the effective velocity of the secondary cooling water distribution.

[0056] In some embodiments, when finding the target values ​​of the required water volume for each zone of the secondary cooling system based on the effective casting speed, the continuous casting parameters also include the water volume at the wide and narrow face of the mold, the temperature difference between the wide and narrow face of the mold, the temperature of the secondary cooling water, and the temperature of the tundish. The dynamic water distribution control method for the secondary cooling system further includes steps S600~S700, see reference. Figure 4 , Figure 4 This is a flowchart of another dynamic water distribution control method for secondary cooling provided in an embodiment of the present invention. Steps S600 to S700 include: S600 calculates the surface temperature and shell thickness distribution of each slice based on the water volume of the wide and narrow face of the crystallizer, the temperature difference between the wide and narrow face of the crystallizer, the temperature of the secondary cooling water and the temperature of the tundish.

[0057] In this embodiment, based on multi-source measured parameters such as the cooling water volume and inlet / outlet temperature difference of the wide and narrow faces of the crystallizer, the temperature of the secondary cooling water, and the temperature of the molten steel in the tundish, combined with the location and growth time information of the billet slices, the surface temperature and billet shell thickness distribution of each slice are calculated online using a solidification heat transfer model. This process relies on a mathematical model constructed using the one-dimensional finite difference method, discretizing the billet along the thickness direction into several nodes, and using the heat balance equation to simulate the heat conduction process from the liquid core to the surface. Among them, the water volume and temperature difference of the wide and narrow faces of the crystallizer directly reflect the initial cooling intensity and affect the quality of the initial billet shell formation when exiting the crystallizer; while the tundish temperature determines the superheat when the molten steel enters the crystallizer, which is a key input for determining the overall solidification initiation conditions. The secondary cooling water temperature, as the basic thermal state of the spray cooling medium, participates in the calculation of the convective heat transfer coefficient, thus affecting the estimation accuracy of the heat flux density on the surface of the billet in the secondary cooling zone. These parameters are collectively input into the heat transfer model as boundary conditions, enabling the system to dynamically update the temperature field evolution process on each slice cross-section within each time step, and thereby derive the corresponding billet shell growth. Because wide-width high-carbon chromium bearing steel has extremely high requirements for solidification uniformity, this refined simulation can accurately identify potential weak areas, such as corners or edges where cracks are prone to occur, thus providing high-confidence technical support for subsequent closed-loop control.

[0058] For example, the solidification heat transfer model is based on the one-dimensional finite difference method, using real-time data to perform online simulation calculations of the solidification process of each billet slice, predicting its surface temperature and shell thickness distribution in real time. To derive the mathematical model of the billet temperature distribution, it is assumed that a height of [missing information] is taken along the center of the billet from the meniscus of the crystallizer. Thick as Width is The infinitesimal elements move downwards together with the cast billet, such as... Figure 5 As shown, Figure 5 This is a schematic diagram of a solidified micro-element provided in an embodiment of the present invention. Figure 5 It can be seen that for a infinitesimal element to achieve thermal equilibrium: the heat stored in the infinitesimal element = the heat received - the heat expended, and its one-dimensional equilibrium equation is: (5) When transforming the partial differential equation of thermal conduction into a difference equation, a difference grid is established. Imagine a thin slice located below the meniscus of the crystallizer, in a region half the thickness of the cast billet. This slice is divided into many equal grids, each grid center representing a node with a uniform temperature. The distance between two nodes is... .set up Half the thickness of the cast billet, If there are nodes, then: (6) Meanwhile, the time it takes for the thin sheet to move downwards from the crystallizer with the billet to the cutting point is divided into equal time increments. Let the pulling speed be... Then the height of each cell is: (7) This creates a rectangular grid, which is used to calculate the temperature of each node at different times. , These are the spatial step and the time step, respectively. The center temperature of each cell represents the temperature of the entire cell. The method of dividing the cells into center and boundary cells should ensure that the center temperature of the cell is exactly located at the center and surface of the billet.

[0059] Then, the Taylor series expansion is performed: (8) (9) Adding the two equations above together and omitting the higher-order terms, we get: (10) (11) Substituting the above two equations into (1), we get: (12) For points on the surface of the cast billet: (13) For the center point of the cast billet: (14) Equations (8) to (10) constitute a set of difference equations for the one-dimensional partial differential equation of thermal conduction. The temperature distribution of the slab cross section can be calculated using the above set of difference equations. Replacing the differential equation with the difference equation requires discarding the higher-order derivative terms in the Taylor expansion, which will cause errors. However, when the time step is small enough, approximate substitution is feasible. In this case, the convergence and stability conditions of the difference equation must be met: (15) Furthermore, the establishment of the solidification heat transfer model also relies on boundary conditions, which serve as a crucial bridge connecting theoretical calculations with the actual continuous casting process. Boundary conditions consist of two parts: initial conditions and boundary conditions. Initial conditions are used to set the temperature distribution inside the billet at the start of the calculation. Typically, the pouring temperature of the molten steel at the meniscus of the crystallizer is used as the starting point, assigning corresponding initial values ​​to each calculation node, allowing the simulation to evolve from a state consistent with actual conditions. Boundary conditions reflect the heat exchange methods between the billet and the external environment in different areas. For example, heat is conducted through copper plates on the inner wall of the crystallizer, heat is removed by water atomization in the secondary cooling zone, and heat is dissipated through radiation in the air cooling section. The initial conditions include the start time... hour, , Temperature of molten steel in micro-element at the meniscus inside the crystallizer (Pouring temperature); In the boundary conditions, heat transfer is symmetrical on both sides of the billet centerline, that is: That is, central heat flow (16) The surface of the cast billet has: (17) φ represents the surface heat flux, and its expression is: crystallizer (18) Second cooling zone (19) Air-cooled area (20) S700 adjusts the target value of the water volume required in each secondary cooling zone in real time based on the surface temperature and shell thickness distribution of each slice.

[0060] In this embodiment, based on the surface temperature and billet thickness distribution of each slice calculated in the previous step, the deviation between the current cooling effect and the preset target curve is evaluated in real time, and the target value of the water volume required in each secondary cooling zone is dynamically adjusted accordingly. This control mechanism constitutes the closed-loop control core of the entire dynamic water distribution method. When the actual surface temperature of a certain slice is detected to be higher than the set target, it indicates insufficient cooling, and the water spray volume in the corresponding area will be appropriately increased to enhance heat dissipation; conversely, if the temperature is too low, the water supply will be reduced to avoid crack propagation caused by thermal stress concentration due to excessive cooling. During the control process, not only single-point temperature error is considered, but also the growth trend and uniformity index of billet thickness are taken into account to ensure that the billet achieves stable solidification in both the longitudinal and transverse directions. In addition, differentiated response sensitivities are set according to the functional characteristics of different cooling zones (such as the foot roll section focusing on rapid cooling and the end section focusing on uniform slow cooling) to prevent overshoot or lag in water volume adjustment. Through continuous iterative optimization, the thermal history of the billet throughout the secondary cooling path is made to closely approximate the optimal cooling trajectory, significantly reducing the incidence of defects such as corner cracks, triangular cracks, and center segregation. In particular, it exhibits superior adaptability and stability compared to traditional feedforward control methods under unsteady casting conditions.

[0061] In some embodiments, the heat transfer mode in the secondary cooling zone is that the heat dissipated by the molten steel in the crystallizer accounts for only about 20% of the heat dissipated when the molten steel is completely solidified. After the billet shell with liquid core exits the crystallizer, it enters the secondary cooling zone and is cooled by water spray under the support and guidance of the rollers, and the billet reaches complete solidification. In the secondary cooling zone, the heat of the molten steel in the center of the billet is conducted to the surface through the billet shell. The spray water droplets hit the surface of the billet and take away the heat, and the surface temperature drops suddenly, so that a large temperature gradient is formed between the center and the surface, which forms the driving force for heat transfer of the billet. The heat transfer on the surface of the billet in the secondary cooling zone is carried out in the following ways: (1) pure radiation heat transfer from the surface of the billet to the air, accounting for 25%; (2) evaporation of spray water droplets, accounting for 33%; (3) heating of spray water, accounting for 25%; (4) contact heat transfer between the rollers and the billet, accounting for 17%.

[0062] For small billets, the heat transfer in the secondary cooling zone mainly involves two methods: (1) and (2), while for slabs and large billets, there are the four methods mentioned above. When equipment and process conditions are constant, the radiative heat transfer and support roller heat transfer of the cast billet do not change significantly; the dominant method is the heat exchange between the sprayed water droplets and the surface of the cast billet. In this embodiment, the following convective heat transfer equation is used to describe this heat transfer process: (twenty one) (twenty two) In the formula: - Heat flow, ; - Heat transfer coefficient, ; - Surface temperature of the cast billet ; - Cooling water temperature, ; - Water flow density, .

[0063] It is understandable that secondary cooling is closely related to casting machine output and billet quality. When other process conditions remain unchanged, increased secondary cooling intensity and casting speed lead to improved casting machine productivity. Simultaneously, secondary cooling also significantly impacts billet quality. Defects such as internal cracks, surface cracks, bulging, and center segregation in continuously cast billets are closely linked to secondary cooling. Therefore, optimizing and controlling secondary cooling is crucial.

[0064] In some embodiments, the surface temperature and shell thickness distribution of each slice are calculated separately, see [reference]. Figure 6 , Figure 6 for Figure 4 The flowchart of sub-steps S601~S603 of step S600, steps S601~S602 include: S601. The surface temperature of each slice is calculated based on the temperature of the secondary cooling water and the temperature of the tundish.

[0065] In this embodiment, the secondary cooling water temperature and the molten steel temperature in the tundish are used as key boundary conditions. Combined with the current position of the billet slice, growth time, and dynamically updated heat transfer model parameters, the surface temperature of each slice at the current moment is calculated. The tundish temperature reflects the initial thermal state of the molten steel when injected into the crystallizer, directly affecting the overall superheat level of the billet and the initial solidification energy input. The secondary cooling water temperature determines the basic cooling capacity of the spray medium and is an important variable for evaluating the spray heat transfer efficiency. These two temperature parameters jointly participate in the boundary heat flux calculation in the unsteady-state heat transfer equation. Especially in the secondary cooling zone, a dynamic mapping is established between water flow density, water temperature, and the heat flux on the billet surface using empirical formulas or field-calibrated heat transfer coefficient relationships. Within each time step, the corresponding heat transfer sub-model is invoked according to the cooling zone in which the slice is located, considering the combined contributions of various heat dissipation mechanisms such as radiation, evaporation, convection, and roll contact, thereby solving for the instantaneous temperature value of the outer surface of the slice. This process not only relies on offline calibrated database support, but also integrates online data feedback for real-time correction, ensuring that the surface temperature calculation results still have sufficient engineering accuracy even under interference factors such as water quality fluctuations or environmental temperature changes, providing a reliable thermal state basis for subsequent closed-loop water control.

[0066] For example, the cross section of the billet is discretized based on the established solidification heat transfer model, and the half-thickness direction is divided into several temperature nodes. A set of difference equations is constructed using the initial and boundary conditions obtained in the previous steps. For each virtual slice, the evolution of its surface temperature is solved step by step in the moving coordinate system. The temperature of the secondary cooling water, as the core parameter of the heat transfer environment in the secondary cooling zone, directly affects the calculation of the surface heat flux density. Specifically, when the solidification heat transfer model is calculated, after the current slice enters a certain cooling zone, the water flow density of the nozzle in that zone and the actual inlet temperature of the secondary cooling water are called. Combined with the initial value of the billet surface temperature, they are substituted into the convection heat transfer equation to calculate the instantaneous surface heat flux. As shown in formulas (21) and (22), the surface heat flux is proportional to the difference between the billet surface temperature and the cooling water temperature. The proportionality coefficient is the heat transfer coefficient, which is related to the water flow density, thus forming a nonlinear coupling relationship. The solidification heat transfer model iteratively solves the equation to determine the heat loss rate per unit area, which is then applied as a boundary condition to the outer nodes of the differential mesh. Simultaneously, the tundish temperature, reflecting the superheat of the molten steel, indirectly affects the casting temperature, i.e., the initial internal temperature at the moment of slice generation. The system corrects the initial conditions based on the difference between the measured tundish temperature and the liquidus temperature of the steel grade, ensuring that the initial temperature of each newly generated slice more closely approximates the actual state of the molten steel, thereby improving the accuracy of the subsequent cooling process simulation.

[0067] The solidification heat transfer model can be understood as integrating the heat transfer process step by step over time: within each calculation cycle, based on the cooling intensity corresponding to the current slice's location, the temperature of the surrounding medium, and its own thermophysical parameters, a system of difference equations is solved to update the temperature distribution of each node, ultimately obtaining the temperature value of the node on the slice's surface. This process continues until the slice is completely solidified or leaves the secondary cooling zone. The frequency of the entire calculation is synchronized with the PLC data refresh, ensuring the continuity and real-time nature of temperature prediction. Notably, due to the temperature rise along the cooling water path in the secondary cooling water, a water temperature compensation algorithm is also introduced to dynamically correct the effective cooling water temperature of each section based on the supply water pressure, flow rate, and return water monitoring data, further improving the accuracy of the thermal boundary conditions. Therefore, the obtained surface temperature is a result of the combined effect of multiple factors, including the initial state of the material, the characteristics of the external cooling medium, and the evolution of the spatial location, demonstrating the refined expressive capability of the mechanistic model in the control of complex industrial processes.

[0068] S602. The thickness distribution of the blank shell for each slice is calculated based on the water volume and temperature difference between the wide and narrow sides of the crystallizer.

[0069] In this embodiment, based on the cooling water flow rate and the temperature difference between the inlet and outlet water on the wide and narrow sides of the crystallizer, the shell thickness distribution of each slice is further derived and calculated. The crystallizer, as the first cooling zone in the continuous casting process, directly determines the quality of the initial shell formation. The wide and narrow sides correspond to the two sides in the width direction and the two ends in the thickness direction of the slab, respectively. Differences in water distribution and heat exchange efficiency between the two sides lead to uneven shell growth, especially for large-section slabs such as wide-width high-carbon chromium bearing steel. By monitoring the inlet and outlet water temperature difference and flow rate data of the wide and narrow sides, the heat carried away per unit time is deduced, and the local heat flux density distribution on each side is estimated. These heat flux data serve as important input boundary conditions for the solidification heat transfer model, used to correct the temperature field evolution rate in the initial stage. Combined with physical properties such as the liquidus temperature, thermal conductivity, and specific heat capacity of the steel grade, the solid fraction change process is solved iteratively layer by layer in a one-dimensional finite difference grid, ultimately obtaining the shell thickness growth curve along the slab cross-section direction. Because the slices at different locations experience different cooling histories, the cumulative solidification process from the crystallizer outlet to the current point is dynamically updated based on their actual trajectory along the billet pulling path. This allows for accurate modeling of the continuous longitudinal and differentiated transverse distribution of the billet shell thickness for each slice. This calculation result is not only used to monitor the safety of the primary billet shell but also provides crucial feedback information on structural strength for subsequent secondary cooling zone water volume adjustment.

[0070] For example, the cooling water flow rate and inlet / outlet temperature difference of the wide and narrow sides of the crystallizer are collected in real time by PLC, and the heat transfer of the wide and narrow sides is calculated separately. According to the principle of energy conservation, the heat absorbed by the cooling water is equal to the sum of the latent heat and sensible heat released by the molten steel in the region below the meniscus. The calculation formula is: Heat transfer = Specific heat capacity of water × Mass flow rate of water × Inlet / outlet temperature difference. The difference in heat transfer between the wide and narrow sides reflects the degree of uneven cooling around the billet. Especially for wide slabs, insufficient cooling capacity of the narrow side often leads to an excessively thin initial shell at the corners, becoming a hidden danger for subsequent crack initiation. Subsequently, these heat transfer data are used as boundary conditions input into the solidification heat transfer model to correct the surface heat transfer coefficient distribution of the crystallizer section. The solidification heat transfer model divides the cross-section of the billet into multiple calculation nodes and sets the pouring temperature as the initial condition in the initial stage. During the time progression, one-dimensional or simplified two-dimensional thermal conductivity difference equations are solved based on the equivalent heat transfer coefficients of different regions to simulate the process of molten steel transforming from liquid to solid phase. When the temperature at a certain node drops below the solidus temperature and meets the local solidification criterion, it is determined that a solid shell has formed at that location, and its growth thickness is recorded. Due to the different cooling capacities of the wide and narrow faces, the model automatically calculates the difference in shell growth rate between the wide and narrow faces, thereby generating an asymmetric shell thickness distribution along the circumference of the cast billet.

[0071] Furthermore, by combining the casting speed information, the residence time of each slice within the crystallizer is determined, i.e., the time required for it to descend from the meniscus to the crystallizer outlet. During this period, heat conduction calculations are continuously performed, ultimately outputting the overall shell morphology of the slice when it leaves the crystallizer. This shell thickness distribution not only includes the minimum thickness value (usually located at the corner or the middle of the narrow face) but also provides a complete profile along the width direction, providing an important basis for the subsequent water distribution strategy in the secondary cooling zone. For example, if the temperature difference or water volume of the narrow face corresponding to a certain slice is small, resulting in the calculated shell thickness of the narrow face being lower than the safety threshold, the solidification heat transfer model will appropriately reduce the cooling intensity of that area in the downstream cooling zone to avoid longitudinal cracks or bulging defects caused by an imbalance in internal and external cooling rates.

[0072] In some embodiments, the target values ​​for the water volume required in each secondary cooling zone can be adjusted in real time based on the surface temperature and shell thickness of each slice after calculation. (See [reference]) Figure 7 , Figure 7 for Figure 4 The flowchart of sub-steps S701~S703 of step S700, wherein steps S701~S703 include: S701 generates a real-time temperature curve based on the surface temperature of each slice.

[0073] In this embodiment, surface temperature data calculated at different locations for each billet slice are arranged in an orderly manner according to time series and spatial coordinates to generate a continuous real-time temperature curve. This curve reflects the dynamic evolution of the billet's surface temperature along the entire cooling path from the crystallizer outlet to the end of the secondary cooling stage. The horizontal axis typically represents the distance along the casting direction or the casting length, while the vertical axis corresponds to the surface temperature value at each point. Because the billet is virtually sliced ​​at a preset time period and the thermal state of each segment is continuously tracked, the resulting temperature curve has high spatiotemporal resolution, accurately capturing local temperature anomalies caused by factors such as casting speed fluctuations, superheat changes, or nozzle blockage. This curve not only reflects the overall cooling trend but also identifies temperature deviations in specific areas such as corners, edges, or transition zones of fan-shaped sections, providing operators and control systems with intuitive and accurate thermal history visualization information. It also serves as one of the core bases for subsequent closed-loop control.

[0074] S702. Compare the real-time temperature curve with the preset target temperature curve to determine whether there is a deviation between the real-time temperature curve and the preset target temperature curve.

[0075] In this embodiment, the generated real-time temperature curve is compared point-by-point with the pre-set target temperature curve to determine whether there is a significant deviation between the two. The target temperature curve is an ideal cooling path formulated based on the high-temperature mechanical properties, solidification characteristics, and metallurgical principles of wide-width high-carbon chromium bearing steel. It aims to ensure that the billet does not develop thermal stress cracks due to excessive cooling during solidification, nor does it suffer from insufficient shell strength or center segregation due to insufficient cooling. The comparison process not only focuses on the consistency of the overall trend but also performs refined analysis of key sections (such as the initial billet shell area, liquid core shrinkage area, and pre-straightening area) by setting reasonable tolerance thresholds. When the real-time temperature is higher or lower than the target value by more than the allowable range, a deviation is determined, and the area where the deviation occurs and its duration are further located. This judgment mechanism combines mathematical interpolation, sliding window comparison, and trend prediction algorithms to effectively eliminate misjudgments caused by measurement noise, ensuring the stability and accuracy of the decision.

[0076] S703. In the event of a deviation between the real-time temperature curve and the preset target temperature curve, adjust the target value of the water volume required in each secondary cooling zone according to the real-time temperature curve and the thickness distribution of the billet shell.

[0077] In this embodiment, once a deviation exceeding the tolerance between the real-time temperature curve and the preset target curve is confirmed, the water volume adjustment mechanism is immediately activated. This mechanism dynamically adjusts the target water volume required for each secondary cooling zone based on the current surface temperature distribution of each slice and the evolution of the billet shell thickness. The adjustment is not simply a proportional increase or decrease in water volume, but rather a multi-parameter collaborative optimization based on feedback from the solidification heat transfer model: if the temperature in a certain cooling zone is too high and the billet shell growth is slow, the water volume in that zone is appropriately increased to enhance heat dissipation; conversely, if the temperature is too low or the billet shell is already thick enough, the water supply is reduced to avoid the risk of overcooling and cracking. Especially for wide slabs, the system also considers the uniformity of the transverse water flow density distribution to prevent stress concentration caused by overcooling at the edges and corners. Furthermore, under unsteady-state conditions, priority is given to ensuring temperature consistency in key sections (such as before the straightening point), and the cooling load between the preceding and following sections is rationally allocated. This closed-loop control strategy, driven by both real-time thermal state and structural development, has enabled a technological leap from experience-based water distribution to precise temperature control, significantly improving the stability and consistency of the internal quality of wide-width high-carbon chromium bearing steel continuous casting billets.

[0078] In some embodiments, when water spray cooling is performed in each secondary cooling zone, the water spray nozzles may be clogged or not. For details regarding the nozzle situation, please refer to [link / reference needed]. Figure 8 , Figure 8 for Figure 7 The flowchart of sub-steps S7031~S7033 in step S703, steps S7031~S7033 include: S7031. Obtain the working parameters of the nozzles in each zone of the secondary cooling system.

[0079] In this embodiment, the operating parameters of the nozzles in each secondary cooling zone are collected in real time by a process control system (PLC). These parameters include key operational data such as the water supply pressure, actual flow rate, valve opening, and inlet / outlet water temperature difference of each cooling circuit. Since the nozzles are the final execution units for precise distribution of cooling water, their operating status directly affects the uniformity and controllability of the surface cooling of the billet. Therefore, comprehensive monitoring of these parameters forms an important foundation for intelligent water distribution control. Especially for steel grades such as wide-width high-carbon chromium bearing steel, which have extremely high requirements for cooling consistency, even slight blockage or poor atomization in local nozzles can lead to temperature field imbalance at the edges or corners, thereby inducing crack defects. The system continuously acquires data through pressure and flow sensors distributed at the front end of each cooling section and converts this data into an actual spraying capacity assessment value by combining it with the nozzle design characteristic curve, thus providing a quantitative basis for subsequent assessment of the nozzle health status.

[0080] S7032. Compare the working parameters of the nozzles in each secondary cooling zone with the preset standard parameters to determine whether there are any blocked nozzles in each secondary cooling zone.

[0081] In this embodiment, the collected operating parameters of each nozzle are compared and analyzed with their corresponding preset standard parameters to identify whether there is clogging under abnormal operating conditions. The preset standard parameters are an ideal operating threshold database established before the equipment is put into normal operation, based on nozzle type, arrangement density, water flow characteristics, and on-site calibration results. It covers the pressure-flow relationship range that each circuit should have under different drawing speeds and steel grades. When the actual flow rate of a certain section is significantly lower than the theoretical value and the pressure does not drop synchronously, or the valve opening has reached the set output but the flow response is lagging, it is determined that there may be partial nozzle clogging in that area. Furthermore, by combining historical trend analysis and spatial distribution pattern recognition, it is possible to distinguish whether it is a single nozzle failure or a failure of the entire spray system, and to locate the specific cooling zone. This judgment process not only relies on static threshold comparison, but also introduces a dynamic change rate detection mechanism to avoid false alarms caused by short-term process fluctuations, ensuring the reliability and timeliness of the diagnostic results.

[0082] S7033. In the case of blocked nozzles in each of the secondary cooling zones, the target value of the water volume sprayed by the remaining unblocked nozzles in each of the secondary cooling zones is adjusted according to the real-time temperature curve and the billet thickness distribution.

[0083] In this embodiment, once a clogged nozzle is confirmed in any of the secondary cooling zones, the entire cooling circuit is not simply shut down. Instead, based on the current distribution of still-effective nozzles, the water distribution strategy for the remaining unclogged nozzles is re-optimized. At this point, the control logic, based on the real-time temperature curve and billet thickness distribution, further introduces a nozzle availability weighting factor: that is, while maintaining the total cooling intensity matching the target thermal history, the water supply of adjacent functional nozzles is adjusted to compensate for the cooling capacity loss caused by the clog. For example, in the case of clogged edge nozzles, the spray intensity of adjacent inner nozzles is moderately increased, and the compensation effect is evaluated using a billet transverse heat transfer model to prevent local overheating or excessive temperature gradients. Simultaneously, considering the risk of uneven cooling often associated with nozzle clogging, the tolerance window of the target temperature curve is actively adjusted to appropriately slow down the cooling rate in that area, avoiding thermal stress concentration caused by forced temperature control. This strategy achieves a high level of cooling control accuracy even under non-ideal equipment conditions, significantly improving the robustness and quality stability of the continuous casting process, and is particularly suitable for aging unit environments with long-term continuous casting and nozzle scaling.

[0084] Based on the above method, embodiments of the present invention also provide a system corresponding to the above method, such as... Figure 9 As shown, Figure 9 This is a functional module diagram of the dual-cooling dynamic water distribution control system 1000 provided in an embodiment of the present invention. It should be noted that the basic principle and technical effects of the dual-cooling dynamic water distribution control system 1000 provided in this embodiment are the same as those in the above method embodiments. For the sake of brevity, parts not mentioned in this embodiment can be referred to the corresponding content in the method embodiments.

[0085] In this embodiment, the secondary cooling dynamic water distribution control system 1000 includes an acquisition module 1100, a tracking module 1200, and a calculation module 1300. The acquisition module 1100 is used to acquire the continuous casting parameters of the target billet, including the actual casting speed, casting length, drawing length, water flow rate at the wide and narrow faces of the crystallizer, temperature difference between the wide and narrow faces of the crystallizer, secondary cooling water temperature, and tundish temperature. It can be understood that the acquisition module 1100 is used to execute the above step S100.

[0086] The tracking module 1200 is used to divide the target billet into several slices along the casting direction according to a preset time period; and to record the slice data of each slice according to the actual casting speed, casting length, and drawing length. The slice data includes the current position of the slice and the growth time at that position. It can be understood that the tracking module 1200 is used to perform the above steps S200~S300.

[0087] The calculation module 1300 is used to calculate the effective pulling speed of each slice based on its position, growth time at that position, and actual pulling speed; to query the target value of the water volume required for each secondary cooling zone corresponding to the effective pulling speed of each slice from a preset database; and to adjust the target value of the water volume required for each secondary cooling zone in real time based on the water volume on the wide and narrow sides of the crystallizer, the temperature difference between the wide and narrow sides of the crystallizer, the temperature of the secondary cooling water, and the temperature of the tundish. It can be understood that the calculation module 1300 is used to execute the above steps S400~S500.

[0088] In some embodiments, the tracking module 1200 is further configured to calculate the moving distance of each slice within a preset time period based on the actual drawing speed and casting length; record the position of each slice based on the moving distance and drawing length of each slice; and record the growth time of each slice's position based on the preset time period and casting length. It can be understood that the tracking module 1200 is used to perform the above steps S301 to S303.

[0089] In some embodiments, the calculation module 1300 is used to calculate the average pulling speed of each slice based on the position of each slice and the growth time at that position; and to calculate the effective pulling speed of each slice based on a weighted average of the average pulling speed and the actual pulling speed. It can be understood that the calculation module 1300 is used to perform the above steps S401~S402.

[0090] In some embodiments, the continuous casting parameters further include the water flow rate across the wide and narrow face of the mold, the temperature difference across the wide and narrow face of the mold, the secondary cooling water temperature, and the tundish temperature. The calculation module 1300 is used to calculate the surface temperature and billet shell thickness distribution of each slice based on the water flow rate across the wide and narrow face of the mold, the temperature difference across the wide and narrow face of the mold, the secondary cooling water temperature, and the tundish temperature; and to adjust the target value of the required water flow rate in each zone of the secondary cooling system in real time based on the surface temperature and billet shell thickness distribution of each slice. It can be understood that the calculation module 1300 is used to perform the above steps S600~S700.

[0091] In some embodiments, the calculation module 1300 is used to generate a real-time temperature curve based on the surface temperature of each slice; compare the real-time temperature curve with a preset target temperature curve to determine whether there is a deviation between the real-time temperature curve and the preset target temperature curve; and, if there is a deviation between the real-time temperature curve and the preset target temperature curve, adjust the target value of the water volume required in each secondary cooling zone according to the real-time temperature curve and the thickness distribution of the billet shell. It can be understood that the calculation module 1300 is used to perform the above steps S601 to S603.

[0092] In some embodiments, the required water volume for each secondary cooling zone includes the water volume sprayed by the nozzles in each secondary cooling zone. The calculation module 1300 is used to obtain the operating parameters of the nozzles in each secondary cooling zone; compare the operating parameters of the nozzles in each secondary cooling zone with preset standard parameters to determine whether there are any blocked nozzles in each secondary cooling zone; if there are blocked nozzles in each secondary cooling zone, adjust the target value of the water volume sprayed by the remaining unblocked nozzles in each secondary cooling zone according to the real-time temperature curve and the billet thickness distribution. It can be understood that the calculation module 1300 is used to perform the above steps S701~S703.

[0093] In some embodiments, to improve the phenomenon of corner and edge cracking in wide-width high-carbon chromium bearing steel billets during continuous casting production, a billet solidification heat transfer model, a billet age model, unsteady-state casting control technology, complex boundary condition determination technology, and a new generation of continuous casting secondary cooling database have been developed. (See also...) Figure 10 , Figure 10 This is a schematic diagram of the system framework of the secondary cooling dynamic water distribution control system 1000 provided in an embodiment of the present invention. The secondary cooling dynamic water distribution control system 1000 also includes functional modules, class modules, and forms. The functional modules include a solidification heat transfer calculation module 1300 and a system initialization module. The class modules include a database module, a data update module, a post-processing chart processing module, and a dynamic button control module. The forms include a steel property form, a casting parameter form, a main interface form, a software animation form, and an exit form. Through the various modules of the above-mentioned secondary cooling dynamic water distribution control system 1000, problems such as surface transverse cracks, corner transverse cracks, center segregation, and center transport of steel bodies can be improved. The specific improvement process is as follows: The implementation of the dynamic water distribution control system involves operators switching to L2 dynamic water distribution before casting begins. The system automatically selects a matching cooling curve based on the steel grade and billet specifications in the production plan issued by the ERP system. Then, all necessary casting process parameters and real-time measurement data are integrated through a PLC workstation. Next, the PLC process workstation is connected to the secondary cooling dynamic water distribution model computer via an industrial Ethernet network. The model uses multi-source data, including actual casting speed, water volume and temperature difference across the crystallizer width and width, secondary cooling water temperature and tundish temperature, casting length, drawing length, and actual water volume in each secondary cooling zone, to calculate the temperature change of the billet surface and the billet shell growth state online using a solidification heat transfer model. It dynamically optimizes and outputs the water volume setpoints for each cooling zone. Finally, the PLC receives the results and directly drives the actuator to achieve precise closed-loop control of the secondary cooling water volume, ensuring that the billet cools at the predetermined cooling rate while preventing billet temperature fluctuations, reducing the probability of billet cracks, and improving billet quality.

[0094] For example, precise closed-loop control of the secondary cooling water volume is achieved, reducing the deviation between the actual surface temperature of the billet and the set target temperature with an accuracy rate of over 95%. Cooling efficiency is improved by 50%, significantly mitigating corner and edge cracking problems on the surface of wide-width high-carbon chromium bearing steel continuously cast billets. Simultaneously, it significantly improves center segregation and shrinkage cavities in medium-carbon (alloy) steel and high-carbon steel. The defect severity of unsteady-state center porosity, intermediate cracks, and corner cracks in continuously cast billets is reduced from an average grade of 1.5 to 0.5, and the defect severity of triangular cracks is reduced from an average grade of 1.0 to 0.5. See Table 1 for the defect level assessment of wide-width high-carbon chromium bearing steel continuously cast slabs.

[0095] Table 1

[0096] For example, see Figure 11 and Figure 12 , Figure 11 This is a schematic diagram of the steel grade selection and modification interface provided in an embodiment of the present invention. Figure 12 This diagram illustrates the selection and modification of casting machine parameters and control system parameters provided in this embodiment of the invention. After starting the secondary cooling dynamic water distribution control system 1000, the main interface leads to the steel grade selection window, where the selected steel grade can be selected and modified, and the physical property parameters of the selected steel grade are displayed. The parameter setting window allows for setting and modifying the casting machine operating parameters and control system parameters. The main interface displays the selected steel grade and the set casting machine operating parameters, while simultaneously displaying real-time collected data, such as casting speed, tundish temperature (continuous temperature measurement values ​​are used within the normal range; point measurement values ​​are used if abnormal), cooling water temperature, cooling water volume and temperature difference across the wide and narrow faces of the crystallizer, casting length, drawing length, and actual water volume in the secondary cooling zone. The liquidus temperature of the steel grade is obtained through two methods: database input and on-site calculation based on the steel grade composition.

[0097] It is understandable that by setting the steel grade to be cast and collecting real-time casting parameters, along with the associated database, solidification heat transfer calculations are performed to obtain key data such as the liquid core length, the surface temperature of the billet at the crystallizer outlet, the billet shell thickness, and the casting machine outlet temperature. Simultaneously, the surface and center temperature curves of the billet along its length, as well as the billet shell thickness curve, are displayed. The set and actual water volumes for each zone of the secondary cooling system are presented numerically and graphically on the same interface. To intuitively understand the calculation results of the dynamic water distribution model for the secondary cooling system, an animation of the solid-liquid phase solidification during billet casting is displayed on the main interface, along with temperature readings at different locations on the casting machine. To prevent accidents, an emergency restart button is provided for immediate restart of the control system.

[0098] Furthermore, determining the target surface temperature profile of the billet is a prerequisite for ensuring good billet quality and a stable surface temperature distribution. The target surface temperature distribution of the billet is related to the physical properties and high-temperature mechanical properties of the steel grade. It is also constrained by the cooling conditions of the casting machine's cooling section. Therefore, determining the target surface temperature of the billet should consider multiple factors. First, it must meet the minimum shell thickness requirement for the billet exiting the crystallizer. The target temperature trend should conform to the general laws of heat transfer during billet solidification, and it should also comply with the cooling regime and metallurgical principles determined by the high-temperature mechanical properties of the steel grade.

[0099] Based on the same inventive concept disclosed above, the present invention also provides a block diagram of an electronic device 2000 performing the above method. Please refer to... Figure 13 , Figure 13 This is a block diagram of an electronic device 2000 provided in an embodiment of the present invention. The electronic device 2000 includes a processor 2100, a memory 2200, a bus 2300, and a communication interface 2400. The processor 2100 and the memory 2200 are connected via the bus 2300, and the processor 2100 communicates with external devices via the communication interface 2400.

[0100] Processor 2100 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed through integrated logic circuits in the hardware of processor 2100 or through software instructions. The processor 2100 may be a general-purpose processor 2100, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0101] The memory 2200 is used to store computer programs. For example, the dual-cooling dynamic water distribution control system 1000 in this embodiment of the invention includes at least one software function module that can be stored in the memory 2200 in the form of software or firmware. After receiving the execution instruction, the processor 2100 executes the program to implement the dual-cooling dynamic water distribution control method in this embodiment of the invention.

[0102] The memory 2200 may include high-speed random access memory (RAM) or non-volatile memory. Optionally, the memory 2200 may be a storage device built into the processor 2100 or a storage device independent of the processor 2100.

[0103] Bus 2300 can be ISA bus 2300, PCI bus 2300 or EISA bus 2300, etc. Figure 13 It is indicated by only one double-headed arrow, but does not mean that there is only one bus 2300 or one type of bus 2300.

[0104] Electronic devices 2000 can be mobile phones, tablets, laptops, desktop computers, and other computer devices.

[0105] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, which, when executed by a processor 2100, implements the aforementioned dynamic water distribution control method for secondary cooling systems. This computer-readable storage medium may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0106] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dynamic water distribution control in a secondary cooling system, characterized in that, The method includes: Obtain the continuous casting parameters of the target billet, including the actual casting speed, casting length, drawing length, water volume of the wide and narrow face of the crystallizer, temperature difference of the wide and narrow face of the crystallizer, secondary cooling water temperature and tundish temperature. The target billet is divided into several slices along the casting direction according to a preset time period; Record the slice data for each slice based on the actual drawing speed, the casting length, and the drawing length. The slice data includes the position of each slice and the growth time at that position. The effective pulling speed of each slice is calculated based on the position of each slice, the growth time at that position, and the actual pulling speed. The target values ​​of the required water volume for each secondary cooling zone are retrieved from the preset database according to the effective pulling speed of each slice; the target values ​​of the required water volume for each secondary cooling zone are adjusted in real time according to the water volume of the wide and narrow face of the crystallizer, the temperature difference of the wide and narrow face of the crystallizer, the temperature of the secondary cooling water, and the temperature of the tundish.

2. The method according to claim 1, characterized in that, The step of recording slice data for each slice based on the actual drawing speed, the casting length, and the drawing length includes: The moving distance of each slice is calculated within the preset time period based on the actual drawing speed and the casting length; The position of each slice is recorded based on the moving distance of each slice and the pulling length; The growth time of each slice is recorded based on the preset time period and the casting length.

3. The method according to claim 1, characterized in that, The calculation of the effective pulling speed of each slice based on its position, growth time at that position, and actual pulling speed includes: The average pulling speed of each slice is calculated based on its position and the growth time at that position; The effective pulling speed of each slice is calculated based on a weighted average of the average pulling speed and the actual pulling speed.

4. The method according to claim 1, characterized in that, The target values ​​for real-time adjustment of the required water volume in each zone of the secondary cooling system based on the water volume across the wide and narrow face of the crystallizer, the temperature difference across the wide and narrow face of the crystallizer, the temperature of the secondary cooling water, and the temperature of the tundish include: The surface temperature and shell thickness distribution of each slice are calculated based on the water volume of the wide and narrow face of the crystallizer, the temperature difference of the wide and narrow face of the crystallizer, the temperature of the secondary cooling water, and the temperature of the tundish. The target value of the water volume required in each secondary cooling zone is adjusted in real time based on the surface temperature of each slice and the thickness distribution of the blank.

5. The method according to claim 4, characterized in that, The calculation of the surface temperature and shell thickness distribution of each slice based on the water volume across the wide and narrow face of the crystallizer, the temperature difference across the wide and narrow face of the crystallizer, the temperature of the secondary cooling water, and the temperature of the tundish includes: The surface temperature of each slice is calculated based on the secondary cooling water temperature and the tundish temperature. The thickness distribution of the blank shell for each slice is calculated based on the water volume on the wide and narrow sides of the crystallizer and the temperature difference on the wide and narrow sides of the crystallizer.

6. The method according to claim 4, characterized in that, The target value for real-time adjustment of the required water volume in each secondary cooling zone based on the surface temperature of each slice and the thickness distribution of the billet shell includes: A real-time temperature curve is generated based on the surface temperature of each slice. The real-time temperature curve is compared with the preset target temperature curve to determine whether there is a deviation between the real-time temperature curve and the preset target temperature curve; If there is a deviation between the real-time temperature curve and the preset target temperature curve, the target value of the water volume required in each of the secondary cooling zones is adjusted according to the real-time temperature curve and the thickness distribution of the billet shell.

7. The method according to claim 6, characterized in that, The required water volume for each secondary cooling zone includes the water volume sprayed by the nozzles in each secondary cooling zone. The target value for adjusting the required water volume for each secondary cooling zone based on the real-time temperature curve and the billet thickness distribution includes: Obtain the operating parameters of the nozzles in each zone of the secondary cooling system; The operating parameters of the nozzles in each of the secondary cooling zones are compared with preset standard parameters to determine whether there are any blocked nozzles in each of the secondary cooling zones. In the event that there are clogged nozzles in the nozzles of the secondary cooling zones, the target value of the amount of water sprayed by the remaining unclogged nozzles in the nozzles of the secondary cooling zones is adjusted according to the real-time temperature curve and the billet thickness distribution.

8. A dynamic water distribution control system for secondary cooling systems, characterized in that, The system includes: The acquisition module is used to acquire the continuous casting parameters of the target billet, including the actual casting speed, casting length, drawing length, water volume of the wide and narrow face of the crystallizer, temperature difference of the wide and narrow face of the crystallizer, secondary cooling water temperature and tundish temperature. The tracking module is used to divide the target billet into several slices along the casting direction according to a preset time period; and to record the slice data of each slice according to the actual casting speed, the casting length and the drawing length, wherein the slice data includes the current position of the slice and the growth time of the current position. The calculation module is used to calculate the effective pulling speed of each slice based on its position, growth time at that position, and actual pulling speed; to query the target value of the water volume required for each secondary cooling zone corresponding to the effective pulling speed of each slice from a preset database; and to adjust the target value of the water volume required for each secondary cooling zone in real time based on the water volume of the wide and narrow face of the crystallizer, the temperature difference between the wide and narrow face of the crystallizer, the temperature of the secondary cooling water, and the temperature of the tundish.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing a computer program that can be executed by the processor, the processor being able to execute the computer program to implement the dual-cooling dynamic water distribution control method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the dynamic water distribution control method for secondary cooling as described in any one of claims 1-7.

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