A method for controlling head and tail warping of a thick plate based on a wide and thick plate production line accelerated cooling process of big data analysis

Abstract

A method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is disclosed. This method establishes quantitative optimization control for each specific process step, forming a comprehensive optimization control across the entire production line. This overall comprehensive optimization control characterizes the control of head and tail warpage. This invention provides a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis. When using this method to produce steel plates with a thickness of 50-100mm using accelerated cooling process, the head and tail shape are differentiated and controlled, significantly improving the current situation of head and tail warpage and reducing the cold straightening flattening rate.

Description

Technical Field

[0001] This invention belongs to the field of rolling and cooling technology for medium and heavy plate production lines, and is directed to the shape control of 50-100mm TMCP process products in heavy plate production lines. Specifically, it involves a method for controlling head and tail warping of thick plates in accelerated cooling process of heavy plate production lines based on big data analysis. Background Technology

[0002] Steel plates with a thickness of 50-100mm are important products of medium and heavy plate production lines. They are generally important raw materials used in major projects and have high performance requirements. They involve controlled rolling and controlled cooling processes. The final rolling temperature in the controlled rolling process is generally low. The ends of the rolled steel plates are prone to warping or buckling in the thickness direction. Even if the warping at the beginning and end is not obvious during the rolling process, the uneven stress caused by the different cooling rate at the beginning and end of the steel plate compared to the middle of the steel plate during the cooling process will result in warping after cooling.

[0003] Currently, the problem of head and tail warping in steel plates with a thickness of 50-100mm is one of the challenges in medium and heavy plate production lines.

[0004] The invention application with application number CN201210286950.6 discloses "a calibration method for a straightening machine", which includes the following steps: Step 1, setting a calibration reference surface for the roll gap before calibrating the roll gap of the straightening rollers; Step 2, making a calibration plate for the roll gap reference surface; Step 3, moving the roll gap calibration plate into the straightening machine, adjusting the position of each straightening roller on both sides of the calibration plate relative to the calibration plate, and adjusting the pressure of each straightening roller; Step 4, parameterizing the calibration effect measurement standard; Step 5, compensating for the deviation in the position of the main hydraulic cylinder.

[0005] The invention application with application number CN 201310169922.0 discloses "a method for controlling the waviness in the middle of a straightening thick plate". First, the steel grade, specifications, initial bending radius of curvature, and initial waviness of the thick plate to be straightened are collected, and the target waviness is set. Then, the residual stress distribution curve of the thick plate is calculated, and the amount of negative bending rollers required to straighten the waviness is calculated using the residual stress distribution curve. Finally, the thick plate is straightened using the amount of negative bending rollers, and the actual flatness of the thick plate after straightening is detected by a straightening machine outlet flatness meter. The straightening operation is repeated until the waviness index meets the finished product requirements, and the straightening work is completed.

[0006] The invention application with application number 201911385397.X discloses "an improved method for addressing warping of thick-gauge wind power steel plates during low-temperature rolling," specifically as follows: S1: Upper and lower plate temperature control: Arrange 10-16 Q235B low-quality steel plates before and after the thick plate for transition, ensuring uniform slab temperature; improve the cooling water valve, replace the descaling spray valve of the finishing mill, and reduce the pre-filling water; restore the side spray water of the finishing mill to reduce residual water on the steel plate surface; the roller cooling water is opened to maximum before rolling, and the mill cooling water is opened to maximum. The roll system is set at 40%-60% opening, and the lower roll system is set at 100%-120% opening; S2: Intermediate billet length setting: The intermediate billet length should be greater than 4.2m. The thickness to be heated can be modified within the allowable range of the rolling process. If it cannot meet the requirements, it should be returned to the furnace; S3: The rolling speed is controlled. After strictly implementing the improvement measures on site, the phenomenon of scrap due to head lifting no longer occurred when rolling thick-gauge wind power steel. The rolling process is stable, the plate shape is straight, the effect is obvious, and the phenomenon of head lifting scrap is greatly reduced. Summary of the Invention

[0007] To address the above problems, this invention provides a method for controlling head and tail warpage in the accelerated cooling process of thick plates in a thick plate production line based on big data analysis. The specific technical solution is as follows:

[0008] A method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis, characterized by:

[0009] The method establishes quantitative optimization controls for each specific process, thereby forming a comprehensive optimization control across the entire production line.

[0010] Based on this holistic and comprehensive optimization control characterization, the head and tail warping is controlled.

[0011] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0012] The aforementioned process includes roughing, finishing, and cooling processes.

[0013] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0014] Based on the quantitative optimization control of the roughing process, the correlation analysis between the head and tail shape of the steel plate and the flattening rate based on historical data is established, and the shape control of the target slab is determined accordingly.

[0015] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0016] Quantitative optimization control based on the finishing rolling process is achieved by establishing a logic library for controlling five dimensions of factors: rolling direction, rolling speed, pass reduction parameters, elevation, and head-off coefficient.

[0017] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0018] Based on the quantitative optimization control of the cooling process, the quantitative optimization of the cooling water volume for each section of the steel plate is achieved by analyzing the temperature distribution of the final pass.

[0019] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0020] The setting process also includes: a pre-straightening process and a hot straightening process.

[0021] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0022] Quantitative optimization control based on the pre-straightening process is achieved by establishing quantitative tilting of the tail end.

[0023] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0024] Quantitative optimization control based on the hot straightening process is achieved by establishing quantitative optimization of the tail tilt and straightening speed.

[0025] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0026] The aforementioned "establishing a correlation analysis between the head and tail shape of steel plates and the flattening rate based on historical data" specifically refers to:

[0027] First, for each historical steel plate, the head and tail shape feature values ​​are calculated based on the steel plate outline, and different head and tail shapes are distinguished based on the feature values;

[0028] Secondly, a correlation analysis of the head and tail shape and the flattening rate was conducted based on historical data, and the target shape was determined based on the analysis results.

[0029] Finally, the corresponding plate shape control of the target slab is performed according to the target shape.

[0030] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0031] The shape control of the target slab is performed on steel billets with widening passes.

[0032] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0033] The optimization of the rolling steel discharge direction is achieved by setting the last rolling pass as a no-pressure pass.

[0034] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0035] The optimization of rolling speed is specifically established as follows:

[0036] Starting from the set pass, the rolling is performed at a constant speed according to the set rolling speed, while the bite speed, rolling speed and steel ejection speed are set to be equal.

[0037] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0038] The optimization of the logic library for controlling the pass-by-pass pressure parameters specifically includes:

[0039] First, a summary of steel plates for which no additional flattening process was performed was compiled based on historical data;

[0040] Secondly, the maximum rolling force corresponding to different reduction amounts is determined by iterating through the historical dataset, and a maximum rolling force dataset is formed accordingly.

[0041] Then, the rolling data of the steel plate corresponding to each maximum rolling force in the maximum rolling force dataset is used as historical data, and a sample space is established accordingly. Based on this sample space, a binary linear regression analysis based on the steel plate pass entry thickness and rolling width is established to obtain the binary linear regression equation.

[0042] Finally, based on this binary linear regression equation, the reduction amount for each pass is calculated, and a reduction parameter control logic library is established accordingly.

[0043] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0044] The establishment of elevation optimization is accomplished by setting a correction setting for the elevation of a given track based on two factors: the amount of reduction and the elevation adjustment coefficient.

[0045] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0046] The optimization of the buckle head coefficient is achieved by setting constraints on the buckle head coefficient.

[0047] The constraints include a first constraint and a second constraint;

[0048] The first constraint is: the speed of the lower roller is set to be greater than the speed of the upper roller;

[0049] The second constraint is: set the speed difference ratio according to the speed of the lower roller and the speed of the upper roller.

[0050] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0051] Based on the temperature distribution of the last pass, the cooling water volume within the set range for the beginning and end is adjusted as follows:

[0052] First, determine the head shading ratio and the tail shading ratio based on the temperature distribution.

[0053] Then, the amount of water reduced by each shading is determined based on their respective shading water volume ratios.

[0054] Finally, based on the determined reduction in water volume due to shielding, the corresponding adjustment amount is determined, and the cooling water supply within the set range of the head and tail is determined according to this adjustment amount.

[0055] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0056] The aforementioned quantitative tilting of the tail section is used to increase the curvature between the straightening rollers to complete the setting.

[0057] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0058] The establishment of quantitative optimization for straightening speed specifically involves:

[0059] Set the speed range for the head and tail of the steel plate to the slow speed zone.

[0060] The straightening speed in the non-slow speed zone is the straightening speed set by the process.

[0061] The straightening speed in the slow zone is adjusted by decreasing the process-set straightening speed by a set percentage.

[0062] Based on this, the straightening speed in the slow zone and the straightening speed in the non-slow zone form the straightening speed along the length of the steel plate.

[0063] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0064] The specific binary linear regression equation is as follows:

[0065] Frate=1.24+1.48*EH+0.08*W,

[0066] in,

[0067] Frate: Rolling force correction factor;

[0068] EH: Steel plate entry thickness, unit: m;

[0069] W: Rolling width, unit: m;

[0070] Based on this equation, the correction for the unit rolling force is completed.

[0071] According to the present invention, a method for controlling head and tail warpage in a thick plate production line using accelerated cooling process based on big data analysis is characterized by:

[0072] The aforementioned set number of rounds specifically refers to the last four rounds.

[0073] This invention discloses a method for controlling head and tail warpage in a thick plate production line using accelerated cooling technology based on big data analysis. By analyzing historical big data, it establishes quantitative optimization controls for each set process, forming a comprehensive optimization control across the entire production line. This overall comprehensive optimization control characterizes the control of head and tail warpage. The set processes mainly include roughing, finishing, and cooling; auxiliary processes include pre-straightening and hot straightening. When the main and auxiliary processes are optimized simultaneously, the resulting solution constitutes the optimal solution of this technical solution. This invention's method for controlling head and tail warpage in a thick plate production line using accelerated cooling technology based on big data analysis is used to produce steel plates with a thickness of 50-100mm using accelerated cooling technology. It differentiates and controls the head and tail shape, significantly improving the current situation of head and tail warpage and reducing the cold straightening flattening rate. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of the steel plate head shape in an embodiment of the present invention;

[0075] Figure 2 This is a schematic diagram of the shape feature values ​​of the optimized part of the roughing process in the working process and principle of the present invention.

[0076] Figure 3 This is a fishtail-shaped schematic diagram of the optimized roughing process in the working process and principle of the present invention.

[0077] Figure 4 This is a tongue-shaped schematic diagram of the optimized roughing process in the working process and principle of the present invention;

[0078] Figure 5 This is a schematic diagram of the adjustment of the MAS midpoint in the optimization part of the roughing process in the working process and principle of the present invention.

[0079] Figure 6 This is a schematic diagram of the rolling line height and roll speed difference in the optimized part of the finishing rolling process in the working process and principle of this invention;

[0080] Figure 7 This is a schematic diagram of the temperature distribution along the length of the steel plate after rolling, which is part of the optimized cooling process in the working process and principle of this invention. Detailed Implementation

[0081] The following is a detailed description of a method for controlling head and tail warpage in a thick plate production line based on big data analysis, using the accompanying drawings and specific embodiments of the present invention.

[0082] A method for controlling head and tail warping in a thick plate production line using accelerated cooling process based on big data analysis.

[0083] The method establishes quantitative optimization controls for each specific process, thereby forming a comprehensive optimization control across the entire production line.

[0084] Based on this holistic and comprehensive optimization control characterization, the head and tail warping is controlled.

[0085] in,

[0086] The aforementioned process includes roughing, finishing, and cooling processes.

[0087] in,

[0088] Based on the quantitative optimization control of the roughing process, the correlation analysis between the head and tail shape of the steel plate and the flattening rate based on historical data is established, and the shape control of the target slab is determined accordingly.

[0089] in,

[0090] Quantitative optimization control based on the finishing rolling process is achieved by establishing a logic library for controlling five dimensions of factors: rolling direction, rolling speed, pass reduction parameters, elevation, and head-off coefficient.

[0091] in,

[0092] Based on the quantitative optimization control of the cooling process, the quantitative optimization of the cooling water volume for each section of the steel plate is achieved by analyzing the temperature distribution of the final pass.

[0093] in,

[0094] The setting process also includes: a pre-straightening process and a hot straightening process.

[0095] in,

[0096] Quantitative optimization control based on the pre-straightening process is achieved by establishing quantitative tilting of the tail end.

[0097] in,

[0098] Quantitative optimization control based on the hot straightening process is achieved by establishing quantitative optimization of the tail tilt and straightening speed.

[0099] in,

[0100] The aforementioned "establishing a correlation analysis between the head and tail shape of steel plates and the flattening rate based on historical data" specifically refers to:

[0101] First, for each historical steel plate, the head and tail shape feature values ​​are calculated based on the steel plate outline, and different head and tail shapes are distinguished based on the feature values;

[0102] Secondly, a correlation analysis of the head and tail shape and the flattening rate was conducted based on historical data, and the target shape was determined based on the analysis results.

[0103] Finally, the corresponding plate shape control of the target slab is performed according to the target shape.

[0104] in,

[0105] The shape control of the target slab is performed on steel billets with widening passes.

[0106] in,

[0107] The optimization of the rolling steel discharge direction is achieved by setting the last rolling pass as a no-pressure pass.

[0108] in,

[0109] The optimization of rolling speed is specifically established as follows:

[0110] Starting from the set pass, the rolling is carried out at a constant speed according to the set rolling speed. At the same time, the bite speed, rolling speed and steel ejection speed are set to be equal. The set pass specifically refers to the last 4 passes.

[0111] in,

[0112] The optimization of the logic library for controlling the pass-by-pass pressure parameters specifically includes:

[0113] First, a summary of steel plates for which no additional flattening process was performed was compiled based on historical data;

[0114] Secondly, the maximum rolling force corresponding to different reduction amounts is determined by iterating through the historical dataset, and a maximum rolling force dataset is formed accordingly.

[0115] Then, the rolling data of the steel plate corresponding to each maximum rolling force in the maximum rolling force dataset is used as historical data, and a sample space is established accordingly. Based on this sample space, a binary linear regression analysis based on the steel plate pass entry thickness and rolling width is established to obtain the binary linear regression equation.

[0116] Finally, based on this binary linear regression equation, the reduction amount for each pass is calculated, and a reduction parameter control logic library is established accordingly.

[0117] in,

[0118] The establishment of elevation optimization is accomplished by setting the elevation correction for a set number of passes based on two factors: the reduction amount and the elevation adjustment coefficient; specifically, the set number of passes refers to the last four passes.

[0119] in,

[0120] The optimization of the buckle head coefficient is achieved by setting constraints on the buckle head coefficient.

[0121] The constraints include a first constraint and a second constraint;

[0122] The first constraint is: the speed of the lower roller is set to be greater than the speed of the upper roller;

[0123] The second constraint is: set the speed difference ratio according to the speed of the lower roller and the speed of the upper roller.

[0124] in,

[0125] Based on the temperature distribution of the last pass, the cooling water volume within the set range for the beginning and end is adjusted as follows:

[0126] First, determine the head shading ratio and the tail shading ratio based on the temperature distribution.

[0127] Then, the amount of water reduced by each shading is determined based on their respective shading water volume ratios.

[0128] Finally, based on the determined reduction in water volume due to shielding, the corresponding adjustment amount is determined, and the cooling water supply within the set range of the head and tail is determined according to this adjustment amount.

[0129] in,

[0130] The aforementioned quantitative tilting of the tail section is used to increase the curvature between the straightening rollers to complete the setting.

[0131] in,

[0132] The establishment of quantitative optimization for straightening speed specifically involves:

[0133] Set the speed range for the head and tail of the steel plate to the slow speed zone.

[0134] The straightening speed in the non-slow speed zone is the straightening speed set by the process.

[0135] The straightening speed in the slow zone is adjusted by decreasing the process-set straightening speed by a set percentage.

[0136] Based on this, the straightening speed in the slow zone and the straightening speed in the non-slow zone form the straightening speed along the length of the steel plate.

[0137] in,

[0138] The specific binary linear regression equation is as follows:

[0139] Frate=1.24+1.48*EH+0.08*W,

[0140] in,

[0141] Frate: Rolling force correction factor;

[0142] EH: Steel plate entry thickness, unit: m;

[0143] W: Rolling width, unit: m;

[0144] Based on this equation, the correction for the unit rolling force is completed.

[0145] Working process, principle and implementation examples

[0146] Working process and principle

[0147] This solution is derived from an optimization and summary of the causes of warping at the ends of 50-100mm thick steel plates. 50-100mm thick steel plates with accelerated cooling processes are mainly produced through multiple processes, including roughing mill rolling (primarily for controlling planar shape), finishing mill rolling (controlling steel plate thickness and achieving acceptable performance), pre-straightening (controlling plate shape), accelerated cooling device cooling (controlling performance), and hot straightening (controlling plate shape).

[0148] I. Head and tail shape control strategy for steel plates with a thickness of 50-100mm:

[0149] The head and tail shape feature values ​​of the steel plate outline are calculated to distinguish the tongue and the fish tail.

[0150] 1. Determine the maximum distance point and distance L1 from the beginning and end of the width coordinates from 1 / 10 to 1 / 2 of the width to the rounded corner boundary line;

[0151] 2. Determine the minimum distance L3min from the beginning and end points of the width coordinates from 1 / 4 to 3 / 4 of the width to the rounded corner boundary line; the maximum distance point and distance L3max;

[0152] 3. Determine the distance L3 from the point at half the width coordinate to the fillet boundary line;

[0153] 4. Determine the maximum distance L2 between the beginning and end of the width coordinates from 1 / 2 to 9 / 10 of the width to the rounded corner boundary line. For example... Figure 2 As shown.

[0154] like Figure 3 As shown, the criteria for determining the fishtail type are:

[0155] L3min≈L3min (difference not exceeding 50mm),

[0156] L1>L3min, L2>L3min, used as a quantitative measure of the length of irregular shapes.

[0157] When L3min>0, max(L1,L2) is used as the distance for irregular shapes.

[0158] When L3min<0

[0159] max(L1,L2)-L3min is the distance between the head and tail irregular shapes.

[0160] like Figure 4 As shown, the criteria for determining tongue type are:

[0161] L3≈L3max ​​(difference not exceeding 50mm)

[0162] L1 <L3max,

[0163] L2 <L3max,

[0164] L3max ​​is the distance for irregular shapes.

[0165] Big data analysis revealed a correlation between head and tail shape and flattening ratio. The results showed that tongue-shaped heads and tails had a significantly lower flattening ratio than fishtail-shaped heads and tails. Therefore, the head and tail shapes should ideally be tongue-shaped or have rounded edges (with a middle length greater than the edge length). See [link to relevant documentation]. Figure 5 .

[0166] The steel plates without expansion passes are not deliberately controlled;

[0167] For steel plates with widening passes, the MAS roll gap for the final widening pass is set at multiple points, with the midpoint raised by 1-3mm, as shown in the diagram. Figure 5 .

[0168] II. Rolling shape control strategy for steel plates with a thickness of 50-100mm in finishing mills:

[0169] Rolling steel rejection direction: Set the finishing mill to reject steel in the reverse pass, that is, the last rolling pass is a pass without pressure reduction; this measure can ensure that the head of the steel plate (in the direction of hot straightening) is relatively straight after the finishing mill rolls out, and ensure that the pre-straightener bites in and the cooling system cools more evenly.

[0170] Rolling speed setting: The finishing mill starts rolling at a constant speed from the fourth load pass from the end. The bite speed VI, rolling speed VM and steel ejection speed VO are all equal. The rolling speed is set to 1~2m / s. This setting can reduce the warping of the plate shape caused by speed changes.

[0171] Control logic for pass reduction parameters after controlled rolling thickness initial rolling:

[0172] Through big data analysis, an optimized reduction parameter control logic library is established. When the specifications meet the requirements, the reduction pass allocation is set by the logic library. By summarizing steel plates without additional flattening processes, the significant difference between them and those with additional flattening processes lies in the stable relationship between rolling force and rolling width. The maximum rolling force Fmax is set as unit rolling force FW * rolling width W, where the value of FW is affected by the rolling force correction coefficient Frat. The reduction per pass is calculated under this rolling force, and the reduction is calculated for each pass. FW ranges from approximately 1.2 to 1.8 t / mm. Frat is a coefficient related to the entry thickness and width W of the steel plate pass. The larger the entry thickness, the larger the Frat value. The model calculates the reduction based on the maximum rolling force of each pass and checks that the torque load does not exceed the limit. The total number of passes calculated meets the requirements for steel removal direction (as mentioned earlier, if the finishing mill uses reverse pass steel removal, then the last pass calculated has no load, and the steel removal direction is reverse pass steel removal).

[0173] Frate=1.24+1.48*EH+0.08*W (Formula 1)

[0174] Note: EH stands for entrythickness. Both EH and W are in meters (m).

[0175] Elevation and buckle coefficient control:

[0176] The height (also known as the elevation, passline) of the fourth load-bearing pass in the finishing mill is set to 1 / 2 reduction + sf (elevation adjustment coefficient). The cornering coefficient is set so that the lower roll speed Vb is greater than the upper roll speed Vt, and the speed difference ratio is set to sd (calculated as (Vb-Vt) / Vt). This setting can effectively prevent the occurrence of rolling corners and avoid greater bending caused by the impact of the steel plate corners with the roller table.

[0177] SF and SD are empirical values. SF is related to the rolling force and the thickness of the rolled steel plate. For the last four rolling passes, Fr, SF = 0.5 * Fr / 1000. The actual value of SF will also be slightly adjusted due to the influence of HGC cylinder parameters. SD is the speed difference, with a default value of 0.02. Operators can adjust it according to the actual warpage. The range of SF is approximately 1 to 10 mm, and the range of SD is approximately 0.01 to 0.08. (See diagram below.) Figure 6 .

[0178] III. Straightening Strategies for Steel Plates with a Thickness of 50-100mm Using a Pre-straightening Machine

[0179] During the straightening process, the tilt of the tail section is increased by 0.1–0.5 mm (increasing the curvature between the straightening rollers). This measure can improve or even eliminate the tail section warping of the rolled steel plate, making it straighter before entering the accelerated cooling device.

[0180] IV. Cooling Strategies for 50-100mm Thick Steel Plates to Control the Cooling Process

[0181] Because the temperature distribution of the rolled steel plate shows that the temperature at the beginning and end is lower than that in the middle, when the steel plate enters the accelerated cooling mechanism, the water volume in the first 0.5m range of the beginning and end of the steel plate is reduced by head and tail shielding (head and tail shielding is only adjusted for the first group of water volume). The water volume reduced by shielding is Rflow (the original normal water volume Vflow * the reduction ratio is Drflow). The adjustment is in increments of 50m³ / h, and the result is determined according to the nearest value. For example, if the adjustment is [0, 25], no adjustment is made; if the adjustment is [26, 75], the adjustment is 50m³ / h; if the adjustment is [76, 99], the adjustment is 100m³ / h. Drflow is related to the temperature of the rolled steel plate. Figure 2 The temperature along the length of the middle section of the rolled steel plate. Thead is the average temperature over the first 0.5m, Ttail is the average temperature over the last 0.5m, and Tmid is the average temperature over the middle section of the steel plate. The head shading water ratio Drflow = ha * [(Tmid - Thead) / Tmid], and the tail shading water ratio Drflow = ta * [(Tmid - Ttail) / Tmid], where ha and ta are empirical adjustment coefficients related to the temperature range and the temperature difference between the head / tail and the middle section. Figure 7 The figure shows the temperature curve along the length direction after the steel plate finishing mill is completed.

[0182] V. Straightening Strategies for Steel Plates with a Thickness of 50-100mm Using Hot Straightening Machines

[0183] During the straightening process, the first and last 1m sections require slow straightening (the speed is increased back to normal after passing through the slow zone). The straightening speeds Vhlhead and Vhltail are 90%–95% of the normal set speed, and the tail tilt is increased by 0.1–0.5mm (increasing the curvature between the straightening rollers). This measure can improve or even eliminate the warping of the head and tail sections of the rolled steel plate, making it relatively straight before entering the accelerated cooling device.

[0184] Example

[0185] The following examples illustrate the invention in more detail: The steel used for the engineering structure is Q345E, with a target rolling specification of 70*2600*13800mm (thickness, width, length), and slab number XXX01003212; the slab dimensions are 358*2100*3400mm. The initial thickness during finishing mill rolling is 140mm, and the steel plate width is 2612mm.

[0186] The slab width is significantly smaller than the target steel plate width, necessitating widening rolling. The original MAS (Manual of Standard Rolling) setting for the thickness per pass was 265mm, but this was changed to 267mm based on this technical specification. The head shape after rolling is as follows... Figure 1 The tail is almost the same size as the head, and both the head and tail are rounded.

[0187] The finishing mill, through logical calculations, is designed for reverse pass steel ejection, with the last pass being an unloaded pass. The rolling force is calculated in units of approximately 1.2–1.6 t / mm. The last four loaded passes are uniform speed rolling. The relevant elevation line settings and roll speed differences are detailed in the table below.

[0188] No H(mm) h(mm) B(m) dh(mm) F(t) VM VI / VO passline sd 1 139.7 127.1 2.627 12.58 4033 3 2 14.87 0 2 127.8 117.8 2.628 10.06 4344 -3 -2 14.62 0 3 118.5 109.4 2.628 9.07 4305 3 2 14.58 0 4 109.9 103.4 2.629 6.55 3784 -3 -2 14.63 0 5 104.1 97.2 2.629 6.95 4127 3 2 13.98 0 6 97.2 90.5 2.630 6.68 4051 -3 -2 14.37 0 7 90.7 84.6 2.630 6.14 3907 2 2 5.23 0.02 8 84.7 79.4 2.630 5.38 3627 -2 -2 5.51 0.02 9 79.6 74.7 2.631 4.9 3509 1.8 1.8 5.51 0.02 10 74.8 70.3 2.631 4.12 3152 -1.8 -1.8 4.24 0.01 11 70.3 70.3 2.631 0 0 3 2 15.09 0

[0189] The pre-straightening machine is set with a strategy that increases the tail tilt by 0.2mm to 1.4mm, instead of the original normal straightening tilt of 1.2mm.

[0190] Because the temperature distribution of the rolled steel plate shows a lower temperature at the beginning and end compared to the middle, the water volume in the 0.5m range at the beginning and end of the plate is reduced by shielding when the steel plate enters the accelerated cooling mechanism. The water volume reduced by shielding is Rflow (original normal water volume Vflow * reduction ratio Drflow). The original normal first group water volume Vflow is 700m³ / h at the top. 3 / h, the lower flow rate is 1850m³ 3 / h. The temperature distribution after steel plate finishing mill discharge is Thead = 729℃, Tmid = 739℃, Ttail = 732℃, with relatively small temperature differences between the head and tail and between the middle. The water volume ratio of the head shielding the upper part is Drflow = ha * [(Tmid - Thead) / Tmid] = 0.12 (ha = 9), Rflow = 700 * 0.12 = 84, so the first group of water volume when the head passes 0.5m is 600m. 3 / h, and the lower flow rate is 1650m³ / h, and so on. 3 / h. The tail flow rate is calculated similarly for the upper 600m³. 3 / h, lower part 1650m 3 / h.

[0191] During the straightening process of the hot straightening machine, the head and tail sections need to be straightened slowly. The normal straightening speed is 0.6 m / s. The straightening speed of Vhlhead and Vhltail is 90% of the normal setting speed, which is 0.54 m / s. The original tilt setting is 0.6 mm. After increasing the tilt at the head and tail, the tilt at the head and tail is 0.7 mm.

[0192] As can be seen from the above embodiments, the present invention aims to optimize the head and tail shape of 50-100mm thick cooling process steel plates through targeted measures, thus achieving significant quality improvement.

[0193] This invention discloses a method for controlling head and tail warpage in a thick plate production line using accelerated cooling technology based on big data analysis. By analyzing historical big data, it establishes quantitative optimization controls for each set process, forming a comprehensive optimization control across the entire production line. This overall comprehensive optimization control characterizes the control of head and tail warpage. The set processes mainly include roughing, finishing, and cooling; auxiliary processes include pre-straightening and hot straightening. When the main and auxiliary processes are optimized simultaneously, the resulting solution constitutes the optimal solution of this technical solution. This invention's method for controlling head and tail warpage in a thick plate production line using accelerated cooling technology based on big data analysis is used to produce steel plates with a thickness of 50-100mm using accelerated cooling technology. It differentiates and controls the head and tail shape, significantly improving the current situation of head and tail warpage and reducing the cold straightening flattening rate.

Claims

1. A method for controlling head and tail warping of a heavy plate based on big data analysis of a heavy plate production line accelerated cooling process, characterized in that: the method forms a comprehensive optimization control on the whole production line by establishing respective quantitative optimization control of the set processes, the comprehensive optimization control of the whole production line represents the control of the head and tail warping, the set processes include rough rolling process, finishing rolling process, cooling process, pre-straightening process and hot straightening process, the quantitative optimization control based on the rough rolling process is achieved by establishing a correlation analysis of the head and tail shape and flattening rate of the steel plate based on historical data, and determining the shape control of the target slab according to the analysis, the quantitative optimization control based on the finishing rolling process is achieved by establishing optimization of five dimensional factors of the rolling and throwing direction, rolling speed, pass reduction parameter control logic library, elevation and warping head coefficient, the quantitative optimization control based on the cooling process is achieved by establishing quantitative optimization of the cooling water quantity of each section of the steel plate based on the temperature distribution of the last pass, the quantitative optimization control based on the pre-straightening process is achieved by establishing quantitative tilting of the tail, and the quantitative optimization control based on the hot straightening process is achieved by establishing quantitative tilting and quantitative optimization of the straightening speed of the tail. 2.The method according to claim 1, characterized in that: the correlation analysis of the head and tail shape and flattening rate based on historical data is specifically: first, for each historical steel plate, the head and tail shape characteristic value is calculated according to the steel plate profile, and different head and tail shape zones are established according to the characteristic value; second, the correlation analysis of the head and tail shape and flattening rate is performed according to the historical data, and the target shape is determined according to the analysis result; finally, the corresponding plate shape control of the target slab is performed according to the target shape. 3.The method according to claim 1 or 2, characterized in that: the shape control of the target slab is performed for the steel slab with spreading passes. 4.The method according to claim 1, characterized in that: the optimization of the rolling and throwing direction is achieved by setting the last rolling pass as a pass without reduction. 5.The method according to claim 1, characterized in that: the optimization of the rolling speed is specifically: uniform speed rolling is performed at the set rolling speed from the set pass, and the bite-in speed, rolling speed and throwing speed are set to be equal. 6.The method according to claim 1, characterized in that: the optimization of the pass reduction parameter control logic library is specifically: first, the steel plates without additional flattening process are summarized based on historical data; ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ Secondly, in the summarized historical data set, the maximum rolling force corresponding to different reductions is determined, and the maximum rolling force data set is formed accordingly; Then, the rolling data of the steel plate corresponding to each maximum rolling force in the maximum rolling force data set is taken as historical data, and a sample space is established accordingly. Based on the sample space, a binary linear regression analysis based on the entry thickness and rolling width of the steel plate pass is established, and a binary linear regression equation is obtained. Finally, according to the binary linear regression equation, the reduction of each pass is calculated, and a reduction parameter control logic library is established.

7. The method according to claim 1, wherein the method for controlling head and tail buckling of a heavy plate by using a wide and heavy plate production line accelerated cooling process based on big data analysis further comprises the following steps: The optimization of the set height is achieved by establishing a correction setting of the set height of the set pass based on the reduction and the height adjustment coefficient.

8. The method according to claim 1, wherein the method for controlling head and tail buckling of a heavy plate by using a wide and heavy plate production line accelerated cooling process based on big data analysis further comprises the following steps: The optimization of the buckling head coefficient is achieved by setting a constraint for the buckling head coefficient; The constraint includes a first constraint and a second constraint; The first constraint is that the lower roller speed is greater than the upper roller speed; The second constraint is that the speed difference ratio of the lower roller speed and the upper roller speed is set.

9. The method according to claim 1, wherein the method for controlling head and tail buckling of a heavy plate by using a wide and heavy plate production line accelerated cooling process based on big data analysis further comprises the following steps: According to the temperature distribution of the last pass, the adjustment of the cooling water quantity of the head and tail setting range is established, which is specifically: First, the head shielding water quantity ratio and the tail shielding water quantity ratio are determined according to the temperature distribution; Then, the shielding reduced water quantity of each is determined according to the respective shielding water quantity ratio; Finally, the corresponding adjustment quantity is determined according to the determined shielding reduced water quantity, and the cooling water quantity of the head and tail setting range is provided according to the adjustment quantity.

10. The method according to claim 1, wherein the method for controlling head and tail buckling of a heavy plate by using a wide and heavy plate production line accelerated cooling process based on big data analysis further comprises the following steps: The quantitative inclination of the tail is established to increase the curvature between the straightening rollers.

11. The method according to claim 1, wherein the method for controlling head and tail buckling of a heavy plate by using a wide and heavy plate production line accelerated cooling process based on big data analysis further comprises the following steps: The quantitative optimization of the straightening speed is specifically: The set range of the head and tail of the steel plate is set as a slow speed zone; The straightening speed of the non-slow speed zone is the process set straightening speed, The straightening speed of the slow speed zone is adjusted by lowering the process set straightening speed by a set percentage point; The straightening speed of the slow speed zone and the straightening speed of the non-slow speed zone form the straightening speed in the length direction of the steel plate.

12. The method according to claim 6, wherein the binary linear regression equation is specifically: Frate = 1.24 + 1.48*EH + 0.08*W, wherein, Frate: rolling force correction coefficient; ​ EH: entry thickness of the steel plate, unit: m; W: rolling width, unit: m; According to the equation, the correction of the unit rolling force is completed.

13. The method according to claim 5 or 7, wherein the method is characterized in that: The set pass is specifically the last 4 passes.

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