A dynamically adjustable winding temperature control system

Abstract

This invention belongs to the field of automatic control technology for hot strip rolling, specifically relating to a coiling temperature control system based on dynamic adjustment. The system includes a rolling strategy module, a coiling temperature preset module, a micro-tracking module, a coiling temperature dynamic setting module, and a self-learning module. The control strategy module provides target and process requirements; the preset module is configured to perform preset calculations based on the control target and process requirements; and the dynamic setting module performs dynamic adjustment calculations based on measured data of the strip. This invention enables precise control of the coiling temperature after hot-rolled strip rolling, and dynamic adjustment of changes in equipment and upstream / downstream influencing parameters in the delamination cooling zone. It is suitable for scenarios where the temperature drop of the rolling line equipment cannot be accurately measured after a long period of shutdown followed by restarting rolling.

Description

Technical Field

[0001] This invention belongs to the field of automatic control technology for hot continuous rolling, and specifically relates to a coiling temperature control system based on dynamic adjustment. Background Technology

[0002] With the continuous increase in steel production capacity, the post-rolling cooling control of hot-rolled strip steel plays a decisive role in the surface and mechanical properties of the strip steel, especially for silicon steel and high-quality steel, where the requirements for coiling temperature are even more stringent. Faced with the dual requirements of output and quality, traditional control aims for a target deviation of ±20 degrees Celsius for coiling temperature, while silicon steel and high-quality steel require a deviation of approximately ±10 degrees Celsius. In production, efficiency cannot be sacrificed for precision; therefore, the coiling temperature control method must be optimized to meet the requirement of improving quality while maintaining high output. Summary of the Invention

[0003] To address the above technical problems, this invention proposes a coiling temperature control system based on dynamic adjustment, including a rolling strategy module, a preset module, a micro-tracking module, a dynamic setting module, and a self-learning module.

[0004] The rolling strategy module is configured to provide control objectives and process requirements;

[0005] The preset module is configured to perform preset calculations based on the control objectives and the process requirements;

[0006] The dynamic setting module performs dynamic adjustment calculations based on the measured data of the strip steel. The dynamic adjustment calculations include the following steps: calculating whether the time interval between two adjacent strip steel pieces is greater than a set value to determine whether it is a long-term rolling stop; and retrieving the stored historical data to calculate the optimal rolling stop time under the same working conditions.

[0007] This invention enables precise control of the coiling temperature after hot-rolled strip steel is rolled, as well as dynamic adjustment of changes in equipment and upstream and downstream influencing parameters in the layer cooling zone. It is suitable for solving the scenario where the temperature drop of the rolling line equipment cannot be accurately measured after a long period of shutdown and restart. Attached Figure Description

[0008] Figure 1 : Schematic diagrams of control system modules and their control principles in some embodiments. Detailed Implementation

[0009] The primary objective of the coiling temperature control system is to ensure that the strip reaches and maintains a target temperature at the coiling inlet. Secondly, it sets an initial cooling temperature to be reached or maintained at a certain level during strip operation. As the strip is cooled by water spraying through the output rail, the system compensates for variations in the strip's flow rate by adjusting the water flow rate. The strip temperature is monitored at the inlet and several designated locations. Terms used in this specification are explained as follows:

[0010] Feedforward: Calculate the target value factor variables based on the mathematical model.

[0011] Feedback: Deviations between actual and target values ​​are processed in real time.

[0012] Actuators: including water valves controlled by a programmable logic controller.

[0013] Manufacturing Execution System (MES): A control system used to help manufacturing companies perform a series of tasks related to factory operations, production process control and management.

[0014] The following embodiments further illustrate the content of the present invention, but should not be construed as limiting the present invention. Any modifications or substitutions made to the methods, steps, or conditions of the present invention without departing from the spirit and essence of the invention are within the scope of the present invention.

[0015] Some implementations of the system include a rolling strategy module, a preset module, a micro-tracking module, a dynamic setting module, and a self-learning module; the rolling strategy module provides control objectives and process requirements; the dynamic setting module performs dynamic adjustment calculations based on measured data of the strip steel; the dynamic adjustment calculations include: calculating whether the time interval between two adjacent strip steel pieces is greater than a set value to determine whether it is a long-term rolling stop; retrieving stored historical data and calculating the optimal rolling stop time under the same working conditions multiple times.

[0016] In a specific implementation, the tracking module first determines the running position of the strip on the rolling line. When the position is at the high temperature timer at the entrance of the finishing mill, a pre-setting command is sent to the pre-setting module to start the pre-setting; when the position is at the high temperature timer at the exit of the finishing mill, a dynamic setting command is sent to the dynamic setting module to start the dynamic setting; and when the position is at the high temperature timer at the exit of the laminar flow cooler, a self-learning setting command is sent to the self-learning module to start the self-learning setting.

[0017] In some implementations, the rolling strategy module is configured to: acquire control progress in real time; generate strip running diagram; read process parameters from human-machine interface or manufacturing execution system; write instructions to human-machine interface or manufacturing execution system; generate segment targets for control of different sections; and send the segment targets to a preset module.

[0018] The rolling strategy module mainly obtains raw material information, target information, and process requirements from the MES system and HMI, thereby providing the target and process requirements for the entire control system.

[0019] In some implementations, the micro-tracking module is configured to: collect process data for different stages, including but not limited to final rolling temperature, steel plate speed, and steel plate thickness, through on-site testing institutions; track coiling temperature fluctuations caused by changes in the process data; and track the position of the strip passing through the laminar cooling zone in real time based on the process data.

[0020] The micro-tracking module uses a detection instrument to determine the position of the strip steel as it passes through the cooling zone, thereby ensuring that the calculated amount of water is poured onto the corresponding strip steel surface.

[0021] In some implementations, the preset module is configured to: read the process parameters of each section; calculate the preset values ​​of the segmented coiling temperature for different sections; send the preset values ​​of the segmented coiling temperature to the actuators located in each section in real time; and output the preset results of each section to the rolling strategy module.

[0022] In some more specific implementations, the preset module mainly provides preset information to the PLC to avoid insufficient processing of the head temperature during dynamic adjustment. Specifically, two TCPIP connections are established between the control backend server and the PLC, and the use of preset and dynamic settings is distinguished by ID changes at specific locations. This is mainly used to solve the problems of head speed and data discrimination when two pieces of steel appear in the layer cooling area.

[0023] Some implementations of the dynamic adjustment calculation include a feedforward control loop and a feedback control loop. The feedforward control loop calculates the water allocation through a mathematical model, and the feedback control loop corrects the control parameters of the feedforward control loop in real time when the feedforward control loop deviates from the target value.

[0024] The feedforward control loop calculates the predicted spray volume using a mathematical model represented by the following formula:

[0025]

[0026] Where, N FF To predict the amount of water sprayed; P i Preset water spray volume; R i Steel plate velocity influence coefficient; v is the steel plate velocity in m / s; v s a1 is the reference rolling speed for steel plates (m / s); a2 is the coefficient of influence of final rolling temperature on decanting temperature; a2 is the water temperature compensation coefficient; T FA The target temperature for finishing rolling is ℃; T FS The standard temperature value for the exit of the steel plate finishing mill (determined by interpolation based on the steel plate thickness); T CA The target temperature for cooling the steel plate layer is °C; T CAS The standard value of the target cooling temperature for the steel plate layer is ℃; the correction value of the target cooling temperature for ΔT layer is ℃; Q is the comprehensive heat transfer coefficient; h is the thickness of the steel plate in mm.

[0027] Some specific implementation methods, such as the standard values ​​of the steel plate rolling reference speed, the standard temperature value of the steel plate finishing mill exit, and the standard value of the steel plate layer cooling target temperature in the mathematical model represented by Equation (1), are obtained by interpolation based on the steel plate thickness.

[0028] Some specific implementations use the optimal data from five consecutive historical shutdowns as the control parameter input for the latest long-term shutdown. When the shutdown time is long, the equipment and water temperature gradually decrease. While water temperature can be detected with a thermometer, the temperature drop of the rolling mill equipment cannot be accurately measured. This invention combines the cumulative shutdown time with the optimal data from multiple historical shutdowns for comprehensive processing. The optimal data includes the optimal shutdown time. Specific implementation steps:

[0029] (1) The gap interval time is calculated by using the dynamic setting module based on the steel present and steel ejection signals of the pyrometer.

[0030] (2) When the time is greater than 5 minutes, it is judged as a long-term rolling stop.

[0031] (3) When the long-term rolling stop conditions arrive, take the self-learning coefficient of the third block after the five historical long-term rolling stop conditions and weight it according to the time distance, and use it as the coefficient for this long-term rolling stop.

[0032] (4) Store the new coefficients of the third self-learned block after the long-term shutdown of rolling in the stack to ensure the timeliness of the coefficients.

[0033] In some implementations, the self-learning module calculates the deviation information of each strip steel using a smoothing exponential algorithm and outputs it to the feedforward control loop of the dynamic setting module.

[0034] Some implementation methods include Figure 1 The functional modules in the system are based on a server, a programmable logic controller (PLC), field detection instruments, and upstream and downstream IO communication interfaces. Through the mathematical model of Equation (1), combined with the feedback from the field detection instruments, the calculated value of the predicted water spray volume is sent to the PLC in real time to control the water valve in the field to complete the calculation of the feedforward control loop for dynamic adjustment.

[0035] Rolling strategy: Mainly obtain raw material information, target information, and process requirements from the MES system and HMI system, thereby providing the target and process requirements for the entire control system.

[0036] Preset: This mainly provides preset information to the PLC to avoid insufficient time to process the head temperature during dynamic adjustment.

[0037] Micro-tracking: By using on-site detection instruments, the position of the strip steel passing through the cooling zone is determined, so that the calculated amount of water can be poured onto the corresponding strip steel surface in a reasonable manner.

[0038] Dynamic setting: The core of the system consists of two main parts: feedforward and feedback. The feedforward accurately predicts the water distribution through a mathematical model, while the feedback makes real-time corrections when the feedforward control deviates from the target value.

[0039] Self-learning: By using a smoothing exponent, deviation information of each strip is provided to the strip to be produced in the future, thereby improving the accuracy of the feedforward.

[0040] Some more specific implementations use a micro-tracking module and a self-learning module to accurately track the tail of the steel plate and separately divide the self-learning coefficients. When a change in specifications occurs, different weights are used to comprehensively process the long-term and short-term coefficients based on the amount of specification change.

[0041] The embodiments of the subject matter described in this specification can be implemented as one or more computer programs, that is, one or more modules of computer program instructions encoded on one or more tangible, non-transitory program carriers, for execution by a data processing device or to control the operation of a data processing device. Alternatively or additionally, the program instructions can be encoded on artificially generated propagation signals, such as machine-generated electrical signals, optical signals, or electromagnetic signals, which are generated as encoded information to be transmitted to an appropriate receiver device executed by the data processing device. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of the above. The processing and logic flows described in this specification can be executed by one or more programmable computers that execute one or more computer programs by processing input data and generating outputs to run functions. The processing and logic flows can also be executed by special-purpose logic circuitry, such as an FPGA (Field-Programmable Gate Array) or an ASIC (Application-Specific Integrated Circuit), and the device can also be implemented as special-purpose logic circuitry.

[0042] Specific implementations of the subject matter have been described. Other implementations are within the scope of the following claims. For example, the activities described in the claims can be performed in a different order and still achieve the desired result. As an example, the processes described in the drawings do not necessarily require a specific order or sequence to be shown in order to achieve the desired result. In certain implementations, multitasking and parallel processing may be advantageous.

Claims

1. A coiling temperature control system based on dynamic adjustment, characterized by, The rolling strategy module, the presetting module, the micro-tracking module, the dynamic setting module, and the self-learning module are included. The rolling strategy module is configured to provide control targets and process requirements. The presetting module is configured to perform presetting calculation according to the control targets and the process requirements. The dynamic setting module performs dynamic adjustment calculation according to measured data of the strip steel. The dynamic adjustment calculation includes the following steps: calculating whether a time interval between two adjacent strip steels is greater than a set value to determine whether it is a long-time stop rolling; and taking stored historical multiple same-condition calculation optimal stop rolling time.

2. The system of claim 1, wherein, The dynamic adjustment calculation includes a feedforward control loop and a feedback control loop. The rolling strategy module is configured to: obtain control processes in real time; generate a strip steel running map; read process parameters of a human-machine interaction interface or a manufacturing execution system; write instructions to the human-machine interaction interface or the manufacturing execution system; generate segmented targets for different segment controls; 3. The system of claim 1, wherein, send the segmented targets to the presetting module. The presetting module is configured to: read process parameters of each segment; calculate segmented coiling temperature preset values of different segments; send the segmented coiling temperature preset values to execution mechanisms located at each segment in real time; 4. The system of claim 1, wherein, output presetting results of each segment to the rolling strategy module. The micro-tracking module is configured to: collect process data including finishing temperature, steel plate speed, and steel plate thickness of different segments through on-site detection mechanisms; track coiling temperature fluctuations caused by changes in the process data; 5. The system of claim 4, wherein, track positions of the strip steel passing through a layer cooling area through the process data.

6. The system of claim 1, wherein, The self-learning module calculates deviation information of each strip steel through a smoothing index algorithm and outputs the deviation information to a feedforward control loop of the dynamic setting module.

7. A method of controlling the coiling temperature based on dynamic adjustment, applied to the system according to any one of claims 1 to 6, characterized in that, The micro-tracking module and the self-learning module are used to accurately track the tail of the steel plate and separately divide self-learning coefficients. The method includes a rolling strategy step, a presetting step, a micro-tracking step, a dynamic setting step, and a self-learning step. The rolling strategy step provides control targets and process requirements. The presetting step performs presetting calculation according to the control targets and the process requirements. The dynamic setting step performs dynamic adjustment calculation according to measured data of the strip steel. The dynamic adjustment calculation includes the following steps: calculating whether a time interval between two adjacent strip steels is greater than a set value to determine whether it is a long-time stop rolling; and taking stored historical multiple same-condition calculation optimal stop rolling time.

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