A metal strip heat treatment flow regulation control system

Abstract

The present application belongs to the field of metal plate strip heat treatment, and proposes a kind of metal plate strip heat treatment flow regulation control system, including cooling module, water supply module, data management system, prediction module, self-learning module.Prediction module reads information before plate strip cooling, calculates adjustment parameter and sends to data management system, and data management system controls cooling module and water supply module.Self-learning module automatically modifies adjustment parameter by analyzing actual change of automatic equipment and instrument data of plate strip in cooling process, and sends to data management system after cooling ends.The present application meets the accurate control demand of water flow, water pressure and cooling path of water spraying system for multiple varieties, variable specifications, complex heat treatment process and production rhythm, has fast adjustment speed, good stability, water and energy saving, high automation degree, effectively improves the distortion of flow curve caused by flow header blockage, and realizes high flatness, high uniformity and high efficiency heat treatment of metal plate strip.

Description

Technical Field

[0001] This invention relates to the field of heat treatment of metal sheet and strip, and more particularly to a flow rate regulation and control system for heat treatment of metal sheet and strip. Background Technology

[0002] Heat treatment is a key process for improving the overall performance of sheet and strip metals, especially in enhancing their strength, toughness, corrosion resistance, high-temperature performance, fatigue performance, and machinability. A wide variety of sheet and strip metals are subjected to heat treatment, including plain carbon steel, stainless steel, aluminum alloys, titanium alloys, and high-temperature alloys. These materials vary greatly in size, ranging from 2 to 300 mm in thickness and 800 to 5000 mm in width. The heat treatment processes are complex, including normalizing, tempering, solution treatment, quenching, controlled cooling, and weak water cooling.

[0003] Cooling is a crucial heat treatment process for sheet and strip metals. By designing and controlling different cooling paths, the microstructure of sheet and strip metals can be significantly improved, resulting in superior performance. Typically, water or aqueous solutions are used as the cooling medium in sheet and strip heat treatment. Water's inexpensive availability, non-toxicity, non-flammability, stable physicochemical properties, and strong cooling capacity allow for different cooling regimes to meet the requirements of various heat treatment processes. Currently, sheet and strip heat treatment is developing towards refinement, complexity, high efficiency, and low energy consumption, placing higher demands on the valve configuration of water spray systems, precise control and rapid response of spray parameters, scientifically sound spray control, and accurate and reliable automatic spray systems. Existing sheet and strip heat treatment cooling water spray systems suffer from low precision, long settling times, large leakage, and low automation levels, which urgently need to be addressed.

[0004] Among existing patents, Chinese patent application publication number CN201447494U discloses a water spray system for a high-temperature rapid quenching device for seamless steel pipes, comprising three sets of water spray devices, each set of which is equipped with a control valve. This control valve only provides opening and closing functions and cannot precisely regulate flow rate or pressure.

[0005] Chinese patent application publication number CN202030785U discloses a cooling device suitable for a dual-frequency surface hardening apparatus, which is connected in sequence to a water tank, a water supply pump, a control valve, and a water sprayer via pipelines. The cooling device's water spray control system can only control the on / off state of the cooling water, and does not provide cooling water flow and pressure control functions. Furthermore, it only provides one set of control valves, and cannot realize complex functions such as multi-nozzle opening and closing and flow regulation. Summary of the Invention

[0006] To address the problems of low control accuracy, long adjustment time, and low automation in existing water spray systems for cooling and heat treatment of metal sheets and strips, the purpose of this invention is to provide a flow rate regulation and control system for heat treatment of metal sheets and strips. This system and method can effectively improve the control accuracy and adjustment speed of water spray process parameters in the cooling and heat treatment process of metal sheets and strips, save water and energy, and meet the goal of automated production of multi-variety, variable-specification, and complex heat treatment processes.

[0007] The technical solution of the present invention is as follows: A flow rate regulation and control system for heat treatment of metal sheet and strip includes a cooling module, a water supply module, a data management system, a prediction module, and a self-learning module; the data management system is connected to the cooling module, the water supply module, the prediction module, and the self-learning module respectively; the cooling module and the water supply module send and read data from each other with the data management system respectively; the self-learning module is connected to and sends data to the prediction module;

[0008] The prediction module reads information from the data management system and accesses the corresponding parameter data in the prediction module's basic database for the n most recent steel grades and thicknesses that are identical. When the prediction module does not read parameter data for the same steel grade and thickness, it filters similar processes in the priority order of total process flow, nozzle type and number of open groups, steel grade, thickness, and process water pressure, with parameter data filtering weights of 50%, 30%, 10%, 5%, and 5%, respectively, to obtain filtered process data. The steel grade and thickness in the filtered process data are compared with the actual steel grade and thickness of the metal sheet and strip. Based on different comparison results, the prediction module adopts different processing methods for the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters, and finally generates a set of preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters for the current production of metal sheet and strip, which is stored in the data cache area of ​​the data management system for later use.

[0009] The self-learning module reads real-time cooling data from the data management system. This data includes actual valve opening degree, number of valve groups open, actual flow rate, actual water pressure, and the variable frequency drive (VFD) operating status information of the water supply module. During the cooling process, the self-learning module analyzes and compares the real-time cooling data from the data management system with the set cooling data. It monitors the trends in the stabilization time of the actual water pressure and flow rate, as well as the deviations between the actual and set flow rates. The self-learning module optimizes the self-learning adjustment parameters in three stages. These parameters include the preset valve opening degree, closed-loop fine-tuning base value, and the VFD control parameters of the water supply module. The first stage focuses on the fluctuation difference P between the set and actual water pressure during the cooling process. d The self-learning adjustment parameters are used as a reference factor; the second stage uses the flow stabilization time T. d The self-learning adjustment parameters are used as a reference factor; the third stage uses the deviation value F between the actual flow rate and the set flow rate. dThe self-learning adjustment parameters are adjusted as reference factors; the optimized self-learning adjustment parameters are stored in the basic database of the prediction system, covering the last set of parameters generated by the prediction module. The original sets of parameters in the basic database of the prediction system are moved to the previous set in turn to cover the previous set until the original first set of parameters is covered.

[0010] The prediction module reads information from the data management system when the remaining time for the strip to exit the furnace is 3 to 5 minutes, and finally sends the data to the data management system when the remaining time for the cooled strip to exit the furnace reaches 1 minute. If the prediction module fails to read the data, it repeatedly sends the request command until it succeeds. If the remaining time for the strip to exit the furnace is 3 minutes and the prediction module has not read the required data, the prediction module sends an alarm message to the data management system. The data management system displays the alarm message on the operation terminal, prompting the operator to perform manual intervention. The self-learning module reads information from the data management system during the cooling process of the strip.

[0011] The prediction module adopts different processing methods based on different comparison results, as follows: When the steel type is the same and the thickness deviation is within 2mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are not processed and are directly stored in the data cache area of ​​the data management system for later use; when the selected process parameters are of the same steel type and the thickness difference between the steel type and the actual strip thickness is within +2mm to +5mm or greater than 5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are reduced by different proportions and then stored in the data cache area of ​​the data management system for later use; when the selected process parameters are of the same steel type and the thickness difference between the steel type and the actual strip thickness is within -2mm to -5mm or less than -5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are increased by different proportions and then stored in the data cache area of ​​the data management system for later use.

[0012] When the selected process parameters are of the same steel grade and the difference between the thickness and the actual strip thickness is within +2mm to +5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are reduced proportionally to A1, A2, and A3, respectively. When the selected process parameters are of the same steel grade and the difference between the thickness and the actual strip thickness is greater than 5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are reduced proportionally to C1, C2, and C3, respectively. When the selected process parameters are of similar steel grade and the difference between the thickness and the actual strip thickness is within -2mm to -5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are increased proportionally to B1, B2, and B3, respectively. When the selected process parameters are of the same steel grade and the difference between the thickness and the actual strip thickness is less than -5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are increased proportionally to D1, D2, and D3, respectively.

[0013] The proportions A1, A2, and A3 are 10-30% smaller than proportions C1, C2, and C3, respectively; the proportions B1, B2, and B3 are 10-30% smaller than proportions D1, D2, and D3, respectively.

[0014] The self-learning module optimizes the self-learning adjustment parameters in three stages; the first stage specifically involves: the fluctuation difference |P| during the cooling process. d The duration of pressure ≥0.05MPa shall not exceed the total cooling time C. t 10%, or fluctuation difference |P d | < 0.05 MPa, do not adjust the closed-loop fine-tuning base value or the frequency conversion control parameters of the water supply module; when the fluctuation difference is | P d The duration of pressure ≥0.05 MPa exceeds C. t When the flow rate reaches 10%, the self-learning module will adjust the closed-loop fine-tuning base and the frequency conversion control parameters of the water supply module to varying degrees; the second stage specifically involves: the flow stabilization time T d If the time is ≤60s, then the closed-loop fine-tuning base value and the frequency conversion control parameters of the water supply module will not be adjusted; if T d At >60s, adjust the closed-loop fine-tuning base value and the frequency conversion control parameters of the water supply module; the third stage is specifically: when -5m 3 / h≤F d ≤5m 3 / h and the duration is greater than the flow stabilization time T. d When |F is 70%, do not adjust the preset valve opening degree or the closed-loop fine-tuning base; when |F d |>5m 3 / h and the duration is greater than the flow stabilization time T. d When the value is 30%, adjust the preset valve opening degree and the closed-loop fine-tuning base value.

[0015] The fluctuation difference |P d |≥0.05MPa is divided into three cases, namely, the duration of which accounts for C t The higher the percentage of 10%–30%, 30–50%, and 50–100%, the greater the reduction in the closed-loop fine-tuning base and the frequency conversion control parameters of the water supply module; the T d >60s can be divided into three cases: when 60s≤T d <100s, 100s≤T d ≤130s or T d When the flow rate stabilizes for more than 130 seconds, reduce the closed-loop fine-tuning base value and the P value in the frequency conversion control parameters of the water supply module. d The longer the length, the greater the reduction ratio; P value refers to the P parameter in frequency conversion control pressure PID control; when |F d |>5m 3 / h and the duration is greater than the flow stabilization time T. d When it reaches 30%, there are four cases, namely 5m 3 / h <F d <10m 3 / h、F d >10m 3 / h、-10m 3 / h <F d <-5m 3 / h and F d <-10m 3 / h; when 5m 3 / h <F d <10m 3 / h、F d >10m 3 When / h, reduce the preset valve opening and reduce the closed-loop fine-tuning base value; the larger the deviation, the larger the adjustment ratio. When 10m 3 / h <F d <-5m 3 / h、F d <-10m 3 When the value is / h, increase the preset valve opening and decrease the closed-loop fine-tuning base value. The larger the absolute value of the deviation, the larger the adjustment ratio.

[0016] The data management system establishes a connection with the cooling module and water supply module via Ethernet, and uses communication messages to read the archived data of the cooling module and water supply module, providing the necessary data foundation for the normal operation of the prediction module and the self-learning module. Before the cooling module and water supply module are ready to work, the system receives control parameters sent by the prediction module and controls the cooling module and water supply module according to these control parameters to improve the stability of the water supply module and the speed and stability of the water volume regulation of the cooling module. During the cooling process of the strip, the system sends the valve group status, actual flow rate, actual water pressure, and variable frequency power system operation status signals on the cooling pipeline to the self-learning module to improve the self-learning adjustment parameters.

[0017] The cooling module combines the dispersed digital automatic devices inside the cooling module through two fieldbuses, PROFINET and PROFIBUS-DP, and establishes information exchange between the cooling devices. According to the working requirements of the data management system, the detection data of the cooling pipeline opening and closing valve group, cooling pipeline regulating valve group, cooling pipeline flow detection device, and cooling pipeline water pressure detection device are sent to the data management system according to different sampling cycles. At the same time, the module receives control commands from the data management system in a specified mode to adjust the water volume.

[0018] The water supply module combines the dispersed digital automatic devices within the water supply module through two fieldbuses, PROFINET and PROFIBUS-DP, and establishes information exchange between the water supply devices. According to the working requirements of the data management system, the detection data of the water supply pipeline valve group, water supply pipeline frequency converter, water supply pipeline inspection instrument group and water supply pipeline equipment alarm instrument group, water pump motor operating status information, and frequency converter power system operating information are sent to the data management system at different sampling cycles. At the same time, it receives control commands from the data management system in a specified mode to control the water supply to the cooling module.

[0019] The sampling periods for the cooling module and the water supply module are 100ms, 500ms, and 1000ms, respectively.

[0020] The beneficial effects of this invention are as follows: This invention meets the precise control requirements of water flow, water pressure, and cooling path in a spray system for multi-variety, variable-specification, complex heat treatment processes and production rhythms. It features fast adjustment speed, good stability, water and energy saving, high automation, and effectively improves the flow curve distortion caused by flow manifold blockage, achieving high flatness, high uniformity, and high-efficiency heat treatment of metal sheets and strips. The prediction module reads the sheet and strip information before cooling, calculates the adjustment parameters, and sends them to the data management system. The data management system then controls the cooling and water supply modules based on these parameters, achieving rapid and stable flow regulation and constant pressure water supply. The self-learning module automatically modifies the adjustment parameters by analyzing the actual changes in data from automatic equipment and instruments during the cooling process, and sends the data back to the data management system after cooling, thus gradually improving the database. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the flow rate regulation and control system for heat treatment of metal sheet and strip in this invention.

[0022] Figure 2 This is a schematic diagram of data transmission between the data management system, self-learning module, and prediction module in the flow regulation and control system for heat treatment of metal sheet and strip in this invention.

[0023] Figure 3 This is a control flowchart of the prediction module of the flow regulation and control system for heat treatment of metal sheet and strip in this invention.

[0024] Figure 4 This is a flowchart of the self-learning module of the flow regulation and control system for heat treatment of metal sheet and strip in this invention.

[0025] Figure 5 This is a flowchart of the flow regulation and control system for heat treatment of metal sheet and strip in this invention.

[0026] In the diagram: S100 - Data Management System; S101 - Prediction Module; S102 - Self-Learning Module; S103 - Water Supply Module; S104 - Cooling Module; S105 - Water Supply Pipeline Valve Assembly; S106 - Water Supply Pipeline Variable Frequency Unit; S107 - Water Supply Pipeline Inspection Instrument Assembly; S108 - Water Supply Pipeline Equipment Alarm Instrument Assembly; S109 - Cooling Pipeline On / Off Valve Assembly; S110 - Cooling Pipeline Regulating Valve Assembly; S111 - Cooling Pipeline Flow Check Device; S112 - Cooling Pipeline Pressure Check Device; S201 - Data A; S202 - Data B; S203 - Data C; S204 - Data D. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the appendices in the embodiments of the present invention will be described below. Figure 1-3The technical solutions in the embodiments of the present invention will be clearly and completely described.

[0028] Example 1

[0029] Figure 1 This is a schematic diagram showing the connection between the data management system S100, the water supply module S103, and the cooling module S104 in the flow regulation and control system for heat treatment of metal sheet and strip in this invention.

[0030] The water supply module S103 includes a water supply pipeline valve group S105, a water supply pipeline frequency converter unit S106, a water supply pipeline inspection instrument group S107, and a water supply pipeline equipment alarm instrument group S108; the water supply pipeline equipment alarm instrument group S108 consists of distributed digital automatic devices and instruments within the water supply module S103. The cooling module S104 includes a cooling pipeline on / off valve group S109, a cooling pipeline regulating valve group S110, a cooling pipeline flow detection device S111, and a cooling pipeline pressure detection device S112; the cooling pipeline pressure detection device S112 consists of distributed digital automatic devices and instruments within the cooling module S104. These automated devices and instruments are all equipped with network communication interfaces and are connected to the cooling module S104 via bus communication. The cooling module S104 is then connected to the data management system S100. The data management system S100 can indirectly collect and archive data from various digital automatic devices and instruments according to different collection cycles through the water supply module PLCS103 and the cooling module PLCS104. At the same time, it can send control commands to the water supply module PLCS103 and the cooling module PLCS104 at specific times to indirectly control external automatic devices and instruments.

[0031] Figure 2 This is a schematic diagram of data transmission between the data management system, self-learning module, and prediction module in the flow regulation and control system for heat treatment of metal sheet and strip in the invention.

[0032] The prediction module transmits data AS201 to the data management system. Data AS201 includes: preset valve opening degree, closed-loop fine-tuning base value, and frequency conversion control parameters of the water supply module.

[0033] The data management system sends data B S202 to the prediction module. Data B S202 contains information on the strip material, the type and number of cooling nozzles in operation, and the quenching process, including the total process flow rate, the type and number of nozzles in operation, the steel grade, the thickness, and the process water pressure.

[0034] The data management system transmits data CS203 to the self-learning module. Data CS203 includes: actual valve opening degree, number of valves open, actual flow rate, and frequency conversion operation parameters of the water supply module.

[0035] The self-learning module transmits data DS204 to the prediction module. Data DS204 includes: preset valve opening degree × adjustment coefficient A, closed-loop fine-tuning base × adjustment coefficient B, and water supply module frequency conversion control parameters × adjustment coefficient C.

[0036] like Figure 2 As shown, before the metal strip is ready for quenching, the prediction module S101 reads data B S202 from the data management system S100. After successful reading, the prediction module S101 calculates the final control parameters using partial information from data B S202 as index conditions. After the calculation, the prediction module S101 sends data A S201 to the data management system S100 before the metal strip is ready for quenching. When the strip is quenched inside the cooling module S104, the data management system S100 sends data C S203 to the self-learning module S102. After quenching is completed, the self-learning module S102 sends the fine-tuned data DS204 to the prediction module S101 based on the content of data C S203.

[0037] The prediction module reads information from the data management system and accesses the parameter data of the most recent n steel grades and thicknesses in the prediction module's basic database based on the read information. When no parameter data of the same specifications is read, the process is screened according to the priority order of total process flow, nozzle type and number of opening groups, steel grade, thickness, and process water pressure, with a screening weight of 50%, 30%, 10%, 5%, and 5%. The steel grade and thickness in the screened process data are compared with the actual steel grade and thickness of the metal sheet and strip. Based on different comparison results, the prediction module adopts different processing methods for the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters, and finally generates a set of preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters for the current production of metal sheet and strip, which is stored in the data cache area of ​​the data management system for later use.

[0038] The self-learning module reads real-time cooling data from the data management system. This data includes actual valve opening degree, number of valve groups open, actual flow rate, actual water pressure, and the variable frequency drive (VFD) operating status information of the water supply module. During the cooling process, the self-learning module analyzes and compares the real-time cooling data from the data management system with the set cooling data. It monitors the trends in the stabilization time of the actual water pressure and flow rate, as well as the deviations between the actual and set flow rates. The self-learning module optimizes the self-learning adjustment parameters in three stages. These parameters include the preset valve opening degree, closed-loop fine-tuning base value, and the VFD control parameters of the water supply module. The first stage focuses on the fluctuation difference P between the set and actual water pressure during the cooling process. d The self-learning adjustment parameters are used as a reference factor; the second stage uses the flow stabilization time T. dThe self-learning adjustment parameters are used as a reference factor; the third stage uses the deviation value F between the actual flow rate and the set flow rate. d The self-learning adjustment parameters are adjusted as reference factors; the optimized self-learning adjustment parameters are stored in the basic database of the prediction system, overwriting the third set of parameters generated by the prediction module. The original third set of parameters and the original second set of parameters in the basic database of the prediction system are overwritten to the positions of the original second set of parameters and the original first set of parameters. Figure 3 This is a flowchart of the prediction module of the flow regulation and control system for heat treatment of metal sheet and strip in this invention.

[0039] Selected process thickness: Th1

[0040] Thickness of the strip to be produced: Th2

[0041] The prediction module S101 reads the production information from the data management system 3-5 minutes before the metal strip is prepared for quenching. If the process parameters of the same specification strip are read, they are sent directly to the data buffer. If the process parameters of the same specification strip are not read, a preliminary screening is performed according to the priority order of total process flow rate, nozzle type and number of opening groups, steel grade, thickness, and process water pressure, with screening weights of 50%, 30%, 10%, 5%, and 5%, respectively. Th1 and Th2 are compared, and process adjustment parameters are screened and calculated under five different conditions: |Th1-Th2|≤2mm, 2mm≤|Th1-Th2|≤5mm, -5mm≤|Th1-Th2|≤-2mm, |Th1-Th2|≥5mm, and |Th1-Th2|≤-5mm. The calculation results are sent as predicted values ​​to the data buffer and then to the data management system S100 at a specific time.

[0042] Figure 4 This is a flowchart of the self-learning module of the flow regulation and control system for heat treatment of metal sheet and strip in this invention.

[0043] Closed-loop fine-tuning base: X1

[0044] Water supply module frequency converter control parameters: X2

[0045] Preset valve opening degree: X3

[0046] The difference between the actual flow rate and the set flow rate is within 5m. 3 / h <F d <10m 3 The duration within the range of / h: TH1;

[0047] The difference between actual and set traffic fluctuations is within F d ≥10m 3 The duration within the range of / h: TH2;

[0048] The difference between the actual flow rate and the set flow rate is within -10m. 3 / h <F d <-5m 3 The duration within the range of / h: TL1;

[0049] The difference between actual and set traffic fluctuations is within F d ≤-10m 3 The duration within the / h range: TL2; During the strip cooling process, the self-learning module reads information such as actual flow rate, actual pressure, and the variable frequency operation status of the water supply module from the data management system. It also monitors the changing trends of actual pressure, flow rate stabilization time, and the deviation between actual flow rate and set flow rate during the cooling process. This process involves three stages of self-learning for the flow rate adjustment parameters. The first stage involves self-learning and adjusting X1 and X2 by analyzing the frequency of changes in the fluctuation difference between the set and actual water pressure during the cooling process. The second stage involves further adjusting X1 and X2 by monitoring the flow rate stabilization time. The third stage involves self-learning and adjusting X1 and X3 by analyzing the magnitude of the fluctuation in the deviation between the actual and set flow rates during the cooling process.

[0050] The first stage specifically involves the fluctuation difference |P| during the cooling process. d The duration of pressure ≥0.05MPa shall not exceed the total cooling time C. t 10%, or fluctuation difference |P d | < 0.05 MPa, do not adjust the closed-loop fine-tuning base value or the frequency conversion control parameters of the water supply module; when the fluctuation difference is | P d The duration of pressure ≥0.05 MPa accounts for C t When it exceeds 10%, the duration accounts for C. t The higher the percentage of 10%–30%, 30–50%, and 50–100%, the greater the reduction in the closed-loop fine-tuning base and the frequency conversion control parameters of the water supply module.

[0051] The second phase specifically refers to the traffic stabilization time T. d If the time is ≤60s, then the closed-loop fine-tuning base value and the frequency conversion control parameters of the water supply module will not be adjusted; if T d When >60s, respectively, when 60s≤T d <100s, 100s≤T d ≤130s or T d When the flow rate stabilizes for more than 130 seconds, reduce the closed-loop fine-tuning base value and the P value in the frequency conversion control parameters of the water supply module. d The longer the length, the greater the reduction ratio; P value refers to the P parameter in frequency conversion control pressure PID control.

[0052] The third stage specifically refers to: when -5m3 / h≤F d ≤5m 3 / h and the duration is longer than 70% of the flow stabilization time T d , the preset valve opening and the closed-loop fine-tuning base value are not adjusted; when |F d |>5m 3 / h and the duration is longer than 30% of the flow stabilization time T d , the preset valve opening and the closed-loop fine-tuning base value are adjusted. When |F d |>5m 3 / h and the duration is longer than 30% of the flow stabilization time T d , there are four cases, namely 5m 3 / h<F d <10m 3 / h, F d ≥10m 3 / h, -10m 3 / h<F d <-5m 3 / h and F d ≤-10m 3 / h; when 5m 3 / h<F d <10m 3 / h, F d ≥10m 3 / h, the preset valve opening and the closed-loop fine-tuning base value are decreased, and the larger the deviation value, the larger the adjustment ratio; when -10m 3 / h<F d <-5m 3 / h, F d ≤-10m 3 / h, the preset valve opening is increased and the closed-loop fine-tuning base value is decreased, and the larger the absolute value of the deviation value, the larger the adjustment ratio.

[0053] Specifically, when Td×30%<TH1<Td×75%, Td×75%<TH1<Td×100%, Td×30%<TH2<Td×75%, Td×75%<TH2<Td×100%, the preset valve opening and the closed-loop fine-tuning base value are decreased; when Td×30%<TL1<Td×75%, Td×75%<TL1<Td×100%, Td×30%<TL2<Td×75%, Td×75%<TL2<Td×100%, the preset valve opening is increased, and the closed-loop fine-tuning base value is decreased.

[0054] Figure 5 is the control flowchart of the flow regulation control system for metal strip heat treatment according to the present invention;

[0055] Preparation time T1: Set to 3-5 minutes;

[0056] Preparation time T2: Set to 1 minute;

[0057] When the preparation time for quenching of the metal sheet / strip reaches the preparatory time T1S303, the prediction module S101 sends a data reading request to the data management system S100. The prediction module then determines whether the data reading was successful (S305).

[0058] If reading data BS202 fails or the information is incomplete, the request command is repeatedly sent until successful reading. If the quenching preparation time exceeds the preparation time T1S303 and complete information is not read, the prediction module S101 sends an alarm message to the control terminal HMIS302 through the data management system S100, prompting the operator to manually intervene in this quenching process. After the operator calls the preset cooling module flow adjustment parameters and water supply module adjustment parameters S301 on the control terminal HMIS302, the control terminal HMIS302 sends the control equipment logic and parameters S300 to the water supply module S103 and the cooling module S104.

[0059] If data BS202 is successfully read, the prediction module S101 uses partial information from data BS202 as an index to retrieve the flow control parameters and water supply control parameters of the three most recent identical process parameters from the data management system S100 database. If no identical process parameters are found, the closest process parameter data is retrieved for comparison. After calculating the control parameters, the prediction module S101 sends data AS201 to the data management system S100 when the metal strip preparation quenching time reaches the preparatory time T2. The data management system S100 converts the information from the sent data AS201 into control equipment logic and parameters S300 and sends them to the water supply module S103 and the cooling module S104. The prediction module S101 indirectly guides the water supply module S103 and the cooling module S104 to adjust the process flow rate using this method.

[0060] During the strip cooling process, the self-learning module S102 reads data CS203 from the data management system. During this process, it calculates and adjusts the process data in three stages, using the fluctuation difference between the set and actual water pressure, the flow rate stabilization time, and the deviation between the actual and set flow rates as reference factors. After self-learning adjustments to the process, the data DS204 is sent to the prediction module S101.

[0061] Example 2

[0062] A domestic 3800 production line uses 0.8MPa water pressure to mass-produce NM450 steel plates of three specifications with thicknesses of 20mm, 23mm, and 28mm. Under the conventional control mode, since the thickness specifications and quenching processes are very close, the preset valve opening degree of the valve pipeline is between 28.4% and 31.2%, the closed-loop fine-tuning base is 0.005, and the P value of the pressure PID adjustment parameter in the frequency conversion control parameter of the water supply module has always been kept at 0.743. During this process, the water pressure difference fluctuation value is 0.06MPa<Pd<0.1MPa, the flow stabilization time Td>=130s, and the flow deviation value is 8m 3 / h≤Fd≤10m 3 / h. After the prediction module and the self-learning module are put into use, after 10 steel plates are mass-produced, the self-learning system monitors and compares the changes of the actual pressure and actual flow during the cooling process, and finally performs classification processing of the adjusted parameters according to the thickness of the steel plate, and performs self-learning correction on the preset valve opening degree, the closed-loop fine-tuning base, and the frequency conversion control parameters of the water supply module on the basis of the original adjustment parameters. In the subsequent production process, the prediction module analyzes and compares the data in the self-learning module, and sends three different adjustment parameters to the steel plates of three different thickness specifications of 20mm, 23mm and 28mm. The preset valve opening degrees are 27%, 29.5% and 32.0% respectively, the closed-loop fine-tuning bases are 0.0025, 0.0019 and 0.001 respectively, and the P values in the frequency conversion control parameters of the water supply module are 0.732, 0.743 and 0.755 respectively. Under the action of different flow adjustment parameters, the water pressure difference fluctuation value in the subsequent production process is kept at Pd<0.05MPa, the flow stabilization time Td≤95s, and the flow deviation value Fd≤7m 3 / h. It can be seen that after the prediction module and the self-learning module are put into use, the adjustment speed and accuracy are improved, thereby increasing the production rhythm and reducing the energy consumption (power consumption and water consumption per ton of steel) brought about by the process of adjusting the flow rate to a certain extent. The present invention solves the problem of fast and accurate adjustment of water flow and pressure of the water supply pipeline for metal strips of different specifications and different varieties under different heat treatment process systems, and effectively improves the distortion of the conventional flow adjustment curve caused by slight pipeline blockage or valve positioning failure of the water supply pipeline due to long-term use, meets the goals of improving production efficiency, saving water and energy, and significantly improves the control accuracy of the heat treatment process for metal strips.

[0063] Third Embodiment

[0064] A domestic 3800 production line uses 0.8MPa water pressure to mass-produce NM500 steel plates with a thickness of 31.5mm. When the remaining time for the strip to exit the furnace is 5 minutes, the prediction system reads the production information of the strip to be cooled from the data management system: steel grade is NM500, thickness is 31.5mm, cooling nozzle type is 1 set of slits, 3 sets of high-density I, and 1 set of high-density II, process pressure is 0.8MPa, and total process flow rate is 4800m³. 3 / h.

[0065] After acquiring the data, no adjustment parameters with the same steel grade and thickness were found in the internal database. At this point, an adjustment parameter was found based on the screening criteria of total process flow rate (50% specific gravity), nozzle type and number of opening groups (30% specific gravity), steel grade (10%), thickness (5%), and process water pressure (5%). The steel grade was NM450, the thickness was 34mm, the cooling nozzle types were 1 slit group, 3 high-density I groups, and 2 high-density II groups, the process pressure was 0.8MPa, and the total process flow rate was 4850m³ / h. 3 / h, the preset valve opening degrees for the three different nozzle types are 35.2%, 40%, and 26%, respectively, the closed-loop fine-tuning base is 0.0035, and the P value in the water supply frequency conversion control system is 0.750.

[0066] The selected process X has a thickness deviation of 2.5mm from the actual production thickness. The prediction system calls internal parameters A1=1, A2=2.5, and A3=2.2 to calculate the final adjustment parameters as follows: the pre-set valve opening degree for the gap pipe is 34.8%, the pre-set valve opening degree for High-Density I is 39.6%, and the pre-set valve opening degree for High-Density II is 25.7%; the closed-loop fine-tuning base is 0.0034; and the P value in the frequency conversion control parameters of the water supply module is 0.733.

[0067] When the remaining time for the strip to exit the furnace is 1 minute, the data is sent to the data management system. After receiving the adjustment parameters, the data management system adjusts and controls the flow rate through the cooling module and the water supply module.

[0068] The self-learning module calculated the total cooling time Ct of the strip to be 66s in the first stage of self-learning optimization, and monitored |P during the cooling process. d The duration of ≥0.05MPa is 12s. After the first stage of optimization calculation, the closed-loop fine-tuning base value decreases by 0.0001, and the P value in the frequency conversion control parameters of the water supply module decreases by 0.002.

[0069] The self-learning module monitors traffic stability time T during the second phase of self-learning optimization. d After the second stage of optimization calculation is completed (98s), the closed-loop fine-tuning base value decreases by 0.0001, and the P value in the frequency conversion control parameters of the water supply module decreases by 0.001.

[0070] The self-learning module monitors the deviation value F between the actual traffic and the set traffic in the third stage of self-learning optimization. d At 5m 3 / h <F d <10m 3 The duration of fluctuation within the range of / h is TH1, which is 80s. After the third stage optimization calculation, the preset valve opening degree is reduced by 0.05, and the closed-loop fine-tuning base is reduced by 0.0001.

[0071] Example 4

[0072] A domestic 3800 production line uses 0.8MPa water pressure to mass-produce NM400 steel plates with a thickness of 28.5mm. When the remaining time for the strip to exit the furnace is 5 minutes, the prediction system reads the production information of the strip to be cooled from the data management system: steel grade is NM400, thickness is 28.5mm, cooling nozzle type is 1 set of slits, 3 sets of high-density I, and 1 set of high-density II, process pressure is 0.8MPa, and total process flow rate is 4300m³. 3 / h.

[0073] After acquiring the data, no matching steel grade and thickness adjustment parameters were found in the internal database. At this point, based on the screening criteria of total process flow rate (50% specific gravity), nozzle type and number of open groups (30% specific gravity), steel grade (10%), thickness (5%), and process water pressure (5%), an adjustment parameter was found. The steel grade was NM400, the thickness was 25.5 mm, the cooling nozzle types were 1 slit group, 3 high-density I groups, and 2 high-density II groups, the process pressure was 0.8 MPa, and the total process flow rate was 4150 m³ / h. 3 / h, the preset valve opening degrees for the three different nozzle types are 34.1%, 36.5%, and 24%, respectively, the closed-loop fine-tuning base is 0.0031, and the P value in the frequency conversion control parameters of the water supply module is 0.73.

[0074] The selected process X has a thickness deviation of -3mm from the actual production thickness. The prediction system calls internal parameters B1=1, B2=2.5, and B3=2.2 to calculate the final adjustment parameters as follows: the pre-set valve opening degree for the gap pipe is 34.4%, the pre-set valve opening degree for High-Density I is 36.9%, and the pre-set valve opening degree for High-Density II is 24.2%; the closed-loop fine-tuning base is 0.0032; and the P value in the water supply frequency conversion control system is 0.746.

[0075] When the remaining time for the strip to exit the furnace is 1 minute, the data is sent to the data management system. After receiving the adjustment parameters, the data management system adjusts and controls the flow rate through the cooling module and the water supply module.

[0076] The self-learning module calculated the total cooling time Ct of the strip to be 60s in the first stage of self-learning optimization, and monitored |P during the cooling process. d The duration of ≥0.05MPa is 9s. After the first stage of optimization calculation, the closed-loop fine-tuning base value decreases by 0.0001, and the P value in the frequency conversion control parameters of the water supply module decreases by 0.002.

[0077] The self-learning module monitors traffic stability time T during the second phase of self-learning optimization. d After the second stage of optimization calculation is completed, the closed-loop fine-tuning base value decreases by 0.0001, and the P value in the frequency conversion control parameters of the water supply module decreases by 0.001.

[0078] The self-learning module monitors the deviation value F between the actual traffic and the set traffic in the third stage of self-learning optimization. d at -10m 3 / h <F d <-5m 3 The duration of fluctuation within the range of / h is TL1, which is 80s. After the third stage optimization calculation, the preset valve opening degree increases by 0.04, and the closed-loop fine-tuning base decreases by 0.0001.

Claims

1. A flow rate regulation and control system for heat treatment of metal sheet and strip, characterized in that, The heat treatment flow regulation and control system for metal sheet and strip includes a cooling module, a water supply module, a data management system, a prediction module, and a self-learning module. The data management system is connected to the cooling module, the water supply module, the prediction module, and the self-learning module. The cooling module and the water supply module send and read data from each other with the data management system. The self-learning module is connected to and sends data to the prediction module. The prediction module reads information from the data management system and accesses the corresponding parameter data in the prediction module's basic database for the n most recent steel grades and thicknesses that are the same. When the prediction module does not read parameter data for the same steel grade and thickness, it filters similar processes in the priority order of total process flow, nozzle type and number of open groups, steel grade, thickness, and process water pressure, with the parameter data filtering weights being 50%, 30%, 10%, 5%, and 5%, respectively, to obtain the filtered process data. The steel grade and thickness in the screened process data are compared with the actual steel grade and thickness of the metal sheet and strip. Based on different comparison results, the prediction module adopts different processing methods for the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters. Finally, a set of preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters for the current production of metal sheet and strip is generated and stored in the data cache area of ​​the data management system for later use. The self-learning module reads real-time cooling data from the data management system. This data includes actual valve opening degree, number of valve groups open, actual flow rate, actual water pressure, and the variable frequency drive (VFD) operating status information of the water supply module. During the cooling process, the self-learning module analyzes and compares the real-time cooling data from the data management system with the set cooling data. It monitors the trends in the stabilization time of the actual water pressure and flow rate, as well as the deviations between the actual and set flow rates. The self-learning module optimizes the self-learning adjustment parameters in three stages. These parameters include the preset valve opening degree, closed-loop fine-tuning base value, and the VFD control parameters of the water supply module. The first stage focuses on the fluctuation difference P between the set and actual water pressure during the cooling process. d The self-learning adjustment parameters are used as a reference factor; the second stage uses the flow stabilization time T. d The self-learning adjustment parameters are used as a reference factor; the third stage uses the deviation value F between the actual flow rate and the set flow rate. d The self-learning adjustment parameters are adjusted as reference factors; the optimized self-learning adjustment parameters are stored in the basic database of the prediction system, covering the last set of parameters generated by the prediction module. The original sets of parameters in the basic database of the prediction module are moved to the previous set in turn to cover the previous set until the original first set of parameters is covered.

2. The flow rate regulation and control system for heat treatment of metal sheet and strip according to claim 1, characterized in that, The prediction module reads information from the data management system when the remaining time for the strip to exit the furnace is 3 to 5 minutes, and finally sends the data to the data management system when the remaining time for the cooled strip to exit the furnace reaches 1 minute. If the prediction module fails to read the data, it repeatedly sends the request command until it succeeds. If the remaining time for the strip to exit the furnace is 3 minutes and the prediction module has not read the required data, the prediction module sends an alarm message to the data management system. The data management system displays the alarm message on the operation terminal, prompting the operator to perform manual intervention. The self-learning module reads information from the data management system during the cooling process of the strip.

3. The flow rate regulation and control system for heat treatment of metal sheet and strip according to claim 1 or 2, characterized in that, The prediction module adopts different processing methods based on different comparison results, as follows: When the steel type is the same and the thickness deviation is within 2mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are not processed and are directly stored in the data cache area of ​​the data management system for later use; when the selected process parameters are of the same steel type and the difference between the thickness and the actual strip thickness is within +2mm to +5mm or greater than 5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are reduced by different proportions and then stored in the data cache area of ​​the data management system for later use; when the selected process parameters are of the same steel type and the difference between the thickness and the actual strip thickness is within -2mm to -5mm or less than -5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are increased by different proportions and then stored in the data cache area of ​​the data management system for later use.

4. The flow rate regulation and control system for heat treatment of metal sheet and strip according to claim 3, characterized in that, When the selected process parameters are of the same steel grade and the difference between the thickness and the actual strip thickness is within +2mm to +5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are reduced proportionally to A1, A2, and A3, respectively. When the selected process parameters are of the same steel grade and the difference between the thickness and the actual strip thickness is greater than 5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are reduced proportionally to C1, C2, and C3, respectively. When the selected process parameters are of the same steel grade and the difference between the thickness and the actual strip thickness is within -2mm to -5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are increased proportionally to B1, B2, and B3, respectively. When the selected process parameters are of the same steel grade and the difference between the thickness and the actual strip thickness is less than -5mm, the preset valve opening degree, closed-loop fine-tuning base, and water supply module frequency conversion control parameters are increased proportionally to D1, D2, and D3, respectively.

5. The flow rate regulation and control system for heat treatment of metal sheet and strip according to claim 4, characterized in that, The proportions A1, A2, and A3 are 10-30% smaller than the proportions C1, C2, and C3, respectively; the proportions B1, B2, and B3 are 10-30% smaller than the proportions D1, D2, and D3, respectively.

6. The flow rate regulation and control system for heat treatment of metal sheet and strip according to claim 1 or 2, characterized in that, The self-learning module optimizes the self-learning adjustment parameters in three stages; the first stage specifically involves: the fluctuation difference |P| during the cooling process. d The duration of pressure ≥0.05MPa shall not exceed the total cooling time C. t 10%, or fluctuation difference |P d | < 0.05 MPa, do not adjust the closed-loop fine-tuning base value or the frequency conversion control parameters of the water supply module; when the fluctuation difference is | P d The duration of pressure ≥0.05 MPa exceeds C. t When the flow rate reaches 10%, the self-learning module will adjust the closed-loop fine-tuning base and the frequency conversion control parameters of the water supply module to varying degrees; the second stage specifically involves: the flow stabilization time T d If the time is ≤60s, then the closed-loop fine-tuning base value and the frequency conversion control parameters of the water supply module will not be adjusted; if T d At >60s, adjust the closed-loop fine-tuning base value and the frequency conversion control parameters of the water supply module; the third stage is specifically: when -5m³ / h ≤F d ≤5m³ / h and the duration is greater than the flow stabilization time T d When |F d |> 5 m³ / h and the duration is greater than the flow stabilization time T d When the value is 30%, adjust the preset valve opening degree and closed-loop fine-tuning base value.

7. The flow rate regulation and control system for heat treatment of metal sheet and strip according to claim 6, characterized in that, The fluctuation difference |P d |≥0.05MPa is divided into three cases, namely, the duration of which accounts for C t The higher the percentages of 10%~30%, 30~50%, and 50~100%, the greater the reduction in the closed-loop fine-tuning base and the frequency conversion control parameters of the water supply module; the T d >60s can be divided into three cases: when 60s≤T d <100s, 100s≤T d ≤130s or T d When the flow rate stabilizes for more than 130 seconds, reduce the closed-loop fine-tuning base value and the P value in the frequency conversion control parameters of the water supply module. d The longer the length, the greater the reduction ratio; the P value refers to the P parameter in the PID control of variable frequency control pressure; when |F d |> 5 m³ / h and the duration is greater than the flow stabilization time T d When the flow rate is 30%, there are four scenarios, each with a flow rate of 5 m³ / h. <F d <10m³ / h, F d ≥10m³ / h, -10m³ / h <F d <-5m³ / h and F d ≤-10m³ / h; when 5m³ / h <F d <10m³ / h, F d When the flow rate is ≥10 m³ / h, reduce the preset valve opening and reduce the closed-loop fine-tuning base value; the larger the deviation, the larger the adjustment ratio. When the flow rate is -10 m³ / h... <F d <-5m³ / h, F d When the flow rate is ≤-10m³ / h, increase the preset valve opening and decrease the closed-loop fine-tuning base value. The larger the absolute value of the deviation, the larger the adjustment ratio.

8. The flow rate regulation and control system for heat treatment of metal sheet and strip according to claim 1, 2 or 4, characterized in that, The data management system establishes a connection with the cooling module and water supply module via Ethernet, and uses communication messages to read the archived data of the cooling module and water supply module, providing the necessary data foundation for the normal operation of the prediction module and the self-learning module. Before the cooling module and water supply module are ready to work, the system receives control parameters sent by the prediction module and controls the cooling module and water supply module according to these control parameters to improve the stability of the water supply module and the speed and stability of the water volume regulation of the cooling module. During the cooling process of the strip, the system sends the valve group status, actual flow rate, actual water pressure, and variable frequency power system operation status signals on the cooling pipeline to the self-learning module to improve the self-learning adjustment parameters.

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