Strip steel parameter setting method under hot rolling fast-paced production

Abstract

The invention relates to a strip steel parameter setting method under hot rolling fast-paced production, and belongs to the technical field of hot continuous rolling plate production methods. According to the technical scheme, the length of the tail of the strip steel out of an F7 finishing mill is collected, the accurate position of the tail of the strip steel on a laminar flow roller way is accurately judged, and accurate setting of model control completion points of the strip steel at different rolling speeds is achieved by applying the position of the strip steel on the laminar flow roller way; and the length is flexibly set on an operation interface according to the thickness of the strip steel and the rolling speed, so that the model can timely collect a strip steel control completion point under different rolling rhythms of a hot rolling production line. The method has the beneficial effects that the model is ensured to accurately set and control each coil of strip steel, the quality problem that the performance of a product is not consistent due to the fact that the strip steel cannot receive set data under fast-paced production can be effectively eradicated, and on-site implementation is facilitated.

Description

Technical Field

[0001] The present invention relates to a method for setting strip parameters under high-speed hot rolling production, belonging to the technical field of hot continuous rolling sheet production methods. Background Art

[0002] The 2250mm hot continuous rolling production line has a high degree of automation and a fast production rhythm, requiring high measurement accuracy of on-site instruments and high accuracy of the automatic control system in the production field. In the past two years, the hot rolling production rhythm has been further improved. The rolling rhythm of the strip has been shortened from 115.4 seconds / coil to 106.72 seconds / coil, and the rolling rhythm has been increased from 30 coils / hour to 41 coils / hour. The automatic control system, especially the CTC mathematical model, cannot well adapt to the fast-paced production of the hot rolling production line. The CTC mathematical model detects the strip based on two on-site hot detection measurement signals and the steel biting signal of the F1 finishing mill. When the load signal of the F1 finishing mill is 1, the CTC mathematical model sets the course f for the strip. At this time, the self-learning value of the previous coil of strip is applied and sent to the first-level PLC to execute this setting; when the detection signal of the hot detection HMD changes from 1 to 0, the mathematical model considers that the strip rolling is completed. At this time, the mathematical model calculates and updates the self-learning value according to the control deviation between the actual coiling temperature and the target coiling temperature of the current strip. Therefore, for the CTC mathematical model setting, the required fastest rolling rhythm is that when the F1 finishing mill is loaded, the tail of the previous coil of strip has passed through the roller table area where the hot detection is located and the mathematical model has completed the self-learning of the strip coiling temperature. In actual production, the fastest rolling rhythm of the strip has reached 41 coils / hour. As Figure 1 shown, under normal circumstances, the mathematical model applies the self-learning value of the No. 2 strip to set the No. 3 strip. However, in actual production, when the No. 2 strip has not completed rolling in the hot detection area, the No. 3 strip has entered the F1 finishing mill. At this time, the CTC model applies the learning value of the second previous No. 1 strip for parameter setting. When the No. 2 strip passes through the roller table of the CT hot detection area, the model performs self-learning. At this time, the two self-learning values of the No. 1 and No. 2 strips are superimposed and applied to the setting of the subsequent rolled No. 4 strip. The superimposition of the two self-learning values is higher than the temperature control deviation, and the coiling temperature deviates significantly from the target value during the rolling of the No. 4 strip. During long-term fast-paced production, the setting of each coil of strip uses the cumulative value of the two self-learnings of the model. This problem occurs more frequently in the intermediate specifications of 4.5mm - 13mm, resulting in high and low fluctuations in the coiling temperature of the strip, unqualified properties in the front section of the strip, and seriously affecting the quality control of hot-rolled products. Summary of the Invention

[0003] The object of the present invention is to provide a strip parameter setting method under hot rolling fast production rhythm. By collecting the length of the strip tail exiting the F7 finishing mill, accurately judging the precise position of the strip tail on the laminar roller table, and applying the position of the strip on the laminar cooling roller table to achieve the precise setting of the model control completion point of the strip under different rolling speeds; flexibly setting this length according to the strip thickness and rolling speed on the operation interface, realizing the timely acquisition of the strip control completion point by the model under different rolling rhythms in the hot rolling production line, ensuring the precise setting and control of the model for each coil of strip; effectively preventing the occurrence of quality problems such as unqualified product performance caused by the strip not receiving the set data under fast production rhythm, with flexible design, simple operation, relatively high automatic control accuracy, convenient for on-site implementation, and effectively solving the above problems existing in the background technology.

[0004] The technical solution of the present invention is: a strip parameter setting method under hot rolling fast production rhythm, comprising the following steps: (1) Collect the rolling information of strips with different thickness specifications in the strip control parameter database, and generate data including the rolling completion time and rolling speed of the strip in each process with the coil number as the index; (2) Classify the strip rolling information according to the set interval of thickness according to the mathematical model; (3) Statistically analyze the rolling interval time of strips with different thickness specifications, and calculate the interval time between the completion of the rolling of the previous coil and the model setting for the next strip under the fastest rolling rhythm; (4) Analyze the model setting delay time for the rolling speed of strips with different thickness specifications for the steel coils with model learning values superimposed; (5) Set the model control completion point by applying the length of the strip tail exiting the F7 finishing mill; (6) Create a data window, and correspondingly modify the length corresponding to the model setting of the strip tail exiting the F7 finishing mill according to the different model delay times of strips with different thickness specifications.

[0005] In the step (1), collect the rolling data of the hot rolling production coils, and generate a strip control parameter database for each steel type on site, including the steel type, width, thickness of the rolled strip, and rolling information of the mathematical model self-learning completion time.

[0006] In the step (2), the hot rolled strip is divided into 26 thickness setting intervals in the mathematical model, and different rolling target thickness ranges correspond to different thickness codes; during production, the model sets parameters for the corresponding strip according to the steel type thickness code. The smaller the strip target thickness, the greater the rolling speed, and there are differences in the corresponding model control completion times for different rolling speeds of the same steel type; apply the strip control parameter database to generate different data tables according to the steel type and thickness code, and statistically analyze the model control completion time.

[0007] In step (3), the CTC mathematical model setting time for all strip steels of different thickness specifications of each steel type is statistically analyzed using the strip steel control parameter database, and the time when the model completes the control of the strip steel is also statistically analyzed. The time when the model sets the strip steel earlier than the control completion time of the previous strip steel during continuous production is calculated.

[0008] In step (4), the model setting delay time calculated in step (3) is different under different rolling rhythms. At the same time, the rolling speeds of strip steels of different steel types and different thickness specifications are different. Therefore, the distance that the CTC mathematical model needs to move the strip steel control completion point forward is different. The optimal CTC model control completion point is obtained by analyzing statistical data.

[0009] In step (5), the length of the strip steel tail from the F7 finishing mill is collected and tracked. The CTC mathematical model adjusts the number of opened headers in the laminar cooling area to regulate the coiling temperature of the strip steel, tracks the position of the strip steel tail in the laminar roller table area, and the system sends a model control completion signal after the strip steel tail passes through a set of headers in the last laminar cooling area.

[0010] In step (6), a window is created to quickly adjust the model control completion point, and the length of the strip steel tail leaving the F7 finishing mill is input according to the actual control situation of the steel type to set the model control completion point for the strip steel.

[0011] The beneficial effects of the present invention are as follows: By collecting the length of the strip steel tail leaving the F7 finishing mill, the accurate position of the strip steel tail in the laminar roller table is accurately judged, and the accurate setting of the model control completion point for the strip steel at different rolling speeds is realized by using the position of the strip steel in the laminar roller table; this length is flexibly set on the operation interface according to the strip steel thickness and rolling speed, realizing the timely acquisition of the model control completion point for the strip steel under different rolling rhythms in the hot rolling production line, ensuring the accurate setting and control of the model for each coil of strip steel; it can effectively prevent quality problems such as the strip steel not receiving the set data under high-speed production, resulting in unqualified product performance. The design is flexible, the operation is simple, the automatic control accuracy is relatively high, and it is convenient for on-site implementation. Description of the Drawings

[0012] Figure 1 is a schematic structural diagram of the model setting for the strip steel of the present invention; Figure 2 is a working flow chart of the present invention. Detailed Embodiments

[0013] In order to make the purpose, technical solutions, and advantages of the invention implementation cases clearer, the following will, in combination with the attached drawings in the implementation cases, clearly and completely describe the technical solutions in the implementation cases of the present invention. Obviously, the described implementation cases are a small part of the implementation cases of the present invention, rather than all of them. Based on the implementation cases in the present invention, all other implementation cases obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0014] A method for setting strip parameters under high-speed hot rolling production includes the following steps: (1) Collect the rolling information of strips with different thickness specifications in the strip control parameter database, and generate data including the rolling completion time and rolling speed of the strip in each process, indexed by the coil number. (2) Classify the strip rolling information according to the setting interval of thickness according to the mathematical model. (3) Statistically analyze the rolling interval time of strips with different thickness specifications, and calculate the interval time between the completion of the previous coil rolling and the setting of the next strip by the model under the fastest rolling rhythm. (4) Analyze the model setting delay time for the rolling speed of strips with different thickness specifications for the model learning value superposed coils. (5) Set the model control completion point by applying the length of the strip tail exiting the F7 finishing mill. (6) Create a data window, and correspondingly modify the length corresponding to the model setting of the strip tail exiting the F7 finishing mill according to the different model delay times for strips with different thickness specifications.

[0015] In step (1), collect the rolling data of hot rolling production coils, and generate a strip control parameter database for each steel type on-site, including the steel type brand, width, thickness of the rolled strip, and rolling information of the mathematical model self-learning completion time.

[0016] In step (2), hot-rolled strips are divided into 26 thickness setting intervals in the mathematical model, and different rolling target thickness ranges correspond to different thickness codes; during production, the model sets parameters for the corresponding strips according to the steel type thickness code. The smaller the target thickness of the strip, the greater the rolling speed. For the same steel type, there are differences in the model control completion time corresponding to different rolling speeds; apply the strip control parameter database to generate different data tables according to the steel type brand and thickness code, and statistically analyze the model control completion time.

[0017] In step (3), apply the strip control parameter database to statistically analyze the CTC mathematical model setting time and the model control completion time of all strips with different thickness specifications of each steel type, and calculate the time when the model sets the strip earlier than the control completion of the previous strip during continuous production.

[0018] In step (4), the model setting delay time calculated in step (3) is different under different rolling rhythms. At the same time, the rolling speeds of strip steels with different steel grades and different thickness specifications are different. Therefore, the distances that the CTC mathematical model needs to move the strip steel control completion point forward are different. The optimal CTC model control completion point is obtained by applying statistical data analysis.

[0019] In step (5), the length of the strip steel tail from the F7 finishing mill is collected and tracked. The CTC mathematical model adjusts the number of opened headers in the laminar cooling area to regulate the coiling temperature of the strip steel, tracks the position of the strip steel tail in the laminar roller table area, and the system sends out a model control completion signal after the strip steel tail passes through a set of headers in the last laminar cooling area.

[0020] In step (6), a window is created to quickly adjust the model control completion point, and the length of the strip steel tail leaving the F7 finishing mill is input according to the actual control situation of the steel grade to realize the setting of the model control completion point for the strip steel.

[0021] In practical applications, the present invention improves the setting accuracy of the mathematical model for strip steel by modifying the control program and control method of the programmable logic controller (PLC), tracking the exact position of the strip steel tail in the laminar cooling area, and moving forward the model control completion position, including the following technological processes: 1. Collection of rolling data of produced strip steels The production data of each coil of strip steel, such as set values of grade, thickness, width, threading speed, cooling efficiency, etc., and a large number of model control learning value parameters of the hot rolling CTC mathematical model are scattered and stored in various reports. There is a lack of unified data management for long-term rolling data, making it difficult to conduct data analysis and control process optimization. Therefore, a rolling coil temperature control database is established to collect and centrally store the data related to temperature control during production. This data table can store the strip steel temperature control data produced within one year. The data table includes 205 control parameters such as the model setting time, threading speed, acceleration of each coil of strip steel, the thickness corresponding to each steel grade, the self-learning update times and self-learning values of the target temperature strip steel, etc. More than 160,000 coils of steel are produced throughout the year, and the data volume is large. This data table provides data support for process parameter optimization and accelerating the model setting rhythm.

[0022] 2. Classification of rolling strip steel data In the mathematical model, hot-rolled strip steel is divided into 26 thickness intervals. In actual production, the thickness of the hot-rolled strip steel ranges from 1.8 to 25 mm, and the corresponding thickness codes are from 5 to 25. The mathematical model sets the threading speed and acceleration of the strip steel according to the steel family code and thickness code corresponding to the steel grade. At the same rolling rhythm, the distances of the model control completion points that need to be advanced are different for strip steels with different speeds. Classify the data table by steel type, and establish a rolling information data table for each steel type, including the learning value tmpVrnCt_b for model setting, the self-learning value tmpVrnCt_a updated by the model according to the measured temperature after the current coil is controlled, the number of model self-learning times numUpdCt, the self-learning value of cooling efficiency, and parameters such as model setting for specific cooling processes. This data table realizes the unified management of data for long-term rolled strip steel, facilitating the analysis of various technical problems and the improvement of the model setting rhythm.

[0023] 3. Calculate the model setting delay time for different thickness specifications of each steel type Use the newly established data table to calculate the time interval between the setting and completion of the model for strip steels of each thickness code of each steel type. Calculate the model control completion time of the previous coil of two continuously produced strip steels and the model setting time of the next coil. Statistically calculate the delay time corresponding to the coils that have not received the model setting, and calculate the delay time of the mathematical model control for strip steels of different thickness specifications corresponding to each steel type at the fastest rolling rhythm. Taking the 7.75 mm thickness specification of 700L girder steel as an example, the rolling speed of this specification of strip steel is 4.3 - 5.5 m / s. According to the statistical data, for the three coils of strip steel numbered 1#, 2#, and 3# continuously produced at the fastest rolling rhythm, the model control completion time of the 2# strip steel is 2 s later than the model setting time of the 3# strip steel. In this case, the control of the 2# strip steel is not completed, and the model uses the self-learning parameters of the 1# strip steel for the setting of the 3# strip steel.

[0024] 4. Calculate the model setting advance distance for different thickness specifications of each steel type For strip steels of different steel types and different thickness specifications, the rolling speeds are different, and the model setting delay times calculated above are different. Therefore, calculate using the delay time calculated in step (3) and the strip steel speed collected in the strip steel rolling data table of the present invention to obtain the distance that the mathematical model control completion point should be advanced for strip steels of different thickness specifications corresponding to each steel type at the fastest rolling rhythm. Use statistical data analysis to obtain the optimal CTC model control completion point. Taking the 7.75 mm thickness specification of 700L girder steel as an example, the rolling speed of this specification of strip steel is 4.3 - 5.5 m / s. When producing at the fastest rhythm, the model setting lags behind by 2 s, that is, the model setting completion point corresponding to this steel type and this thickness specification of strip steel should be advanced by 11 meters.

[0025] 5. Strip steel tail signal tracking and setting of the model control completion point Collect the length of the strip tail leaving the F7 finishing mill in the first-level programmable logic controller (PLC). In the original model control, it is judged that the control of the strip in the laminar cooling area is completed when the HMD detection signal changes from 1 to 0. The present invention uses the time when the length judgment model of the strip tail leaving the F7 finishing mill is completed. By tracking the position of the strip tail in the laminar roller table area, the system can send out the model control completion signal after the strip tail passes through a set of headers in the last laminar cooling area, which is 2 - 10 seconds earlier than the original design model control method. Taking the 700L girder steel with a thickness specification of 7.75 mm as an example, the distance between the HMD and the F7 finishing mill is 130 meters. According to the above statistics, the model setting of this specification strip should be advanced by 11 meters under the fastest rolling rhythm. For this, a control program is written in the programmable logic controller (PLC). When the distance of the strip tail leaving the F7 finishing mill is 118 meters, the control signal of the strip in the laminar cooling area is output, and this signal is used to set the model control to be completed. For other steel grades during fast-paced rolling, the parameters can be adjusted accordingly according to the model control completion point calculated from the statistical data.

[0026] 6. Create an operable window to quickly modify the parameters The FSU mathematical model sets different threading speeds and accelerations for strips of different grades and thickness specifications in hot rolling. There are certain differences in the time and distance that need to be advanced for the CTC mathematical model control to be completed under fast-paced production. Create an operation window on the HMI to collect the distance of the strip tail leaving the F7 finishing mill and achieve rapid modification of this value. The minimum value of this distance is 115 meters, which is the distance between the last set of headers in the laminar cooling and the F7 finishing mill, and the maximum value is 130 meters, which is the distance between the HMD and the F7 finishing mill. According to the statistical data, the corresponding parameters can be modified according to production requirements under different rolling rhythms. When the rolling rhythm is slow, this value can be set to 130 meters, and at this time, the model control completion point is consistent with the original method position.

[0027] Aiming at problems such as the model setting for some strips being later than the parameter issuance time during fast-paced production, the present invention moves the model control completion position forward to ensure that the self-learning parameters of the same specification strip in the previous coil can be used for the parameter setting of each coil of strip, preventing the learning values of two coils of strip from being superimposed and applied to the setting of one coil of strip, and improving the setting accuracy of the model.

[0028] The above is only an embodiment of the present invention, and it does not impose any form of limitation on the present invention. The present invention can also have other forms of embodiments based on the above structure and function, which will not be listed one by one. Therefore, any person skilled in the art, without departing from the scope of the technical solution of the present invention, any simple modification, equivalent change, and modification made to the above embodiments according to the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for setting strip parameters under high-speed hot rolling production, characterized in that It includes the following steps: (1) Collect the rolling information of strip steel with different thickness specifications in the strip steel control parameter database, and generate data including the rolling completion time and rolling speed of the strip steel in each process with the coil number as the index; (2) Classify the strip steel rolling information according to the set interval of thickness according to the mathematical model; (3) Statistically analyze the rolling interval time of strip steel with different thickness specifications, and calculate the interval time between the completion of the previous coil rolling and the setting of the next strip steel by the model under the fastest rolling rhythm; (4) Analyze the model setting delay time of the rolling speed of strip steel with different thickness specifications for the steel coil with the model learning value superimposed; (5) Set the model control completion point by applying the length of the strip steel tail leaving the F7 finishing mill; (6) Create a data window, and modify the corresponding length of the model setting of the strip steel tail leaving the F7 finishing mill according to the different model delay times of strip steel with different thickness specifications.

2. A strip steel parameter setting method under hot rolling fast-paced production according to claim 1, characterized in that: In the step (1), collect the rolling data of the hot-rolled production coils, and generate a strip steel control parameter database for each steel type on site, including the steel type, width, thickness of the rolled strip steel, and the rolling information of the mathematical model self-learning completion time.

3. A strip steel parameter setting method under hot rolling fast-paced production according to claim 1, characterized in that: In the step (2), the hot-rolled strip steel is divided into 26 thickness setting intervals in the mathematical model, and different rolling target thickness ranges correspond to different thickness codes; during production, the model sets parameters for the corresponding strip steel according to the steel type thickness code. The smaller the target thickness of the strip steel, the greater the rolling speed. There are differences in the model control completion time corresponding to different rolling speeds of the same steel type. Apply the strip steel control parameter database to generate different data tables according to the steel type and thickness code, and statistically analyze the model control completion time.

4. A strip steel parameter setting method under hot rolling fast-paced production according to claim 1, characterized in that: In the step (3), apply the strip steel control parameter database to statistically analyze the CTC mathematical model setting time of all strip steels with different thickness specifications of each steel type and the model control completion time of the strip steel, and calculate the time when the model sets the strip steel earlier than the control completion of the previous strip steel during continuous production.

5. A method for setting strip steel parameters under high-speed hot rolling production according to claim 1, characterized in that: In the step (4), the model setting delay time calculated in the step (3) is different under different rolling rhythms. At the same time, the rolling speeds of strip steels with different steel types and different thickness specifications are different. Therefore, the distance that the CTC mathematical model needs to move forward the strip steel control completion point is different. Apply statistical data analysis to obtain the optimal CTC model control completion point.

6. A method for setting strip steel parameters under high-speed hot rolling production according to claim 1, characterized in that: In the step (5), collect and track the length of the strip steel tail from the F7 finishing mill. The CTC mathematical model adjusts the number of opened headers in the laminar cooling area to regulate the coiling temperature of the strip steel, tracks the position of the strip steel tail in the laminar roller table area, and the system sends a model control completion signal after the strip steel tail passes through a group of headers in the last laminar cooling area.

7. A method for setting strip steel parameters in hot rolling with a fast production rhythm according to claim 1, characterized in that: In the step (6), create a window to quickly adjust the model control completion point, and input the corresponding length of the strip steel tail leaving the F7 finishing mill according to the actual control situation of the steel type to realize the setting of the model control completion point of the strip steel.

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