A multi-stage laminar flow cooling feedback control method with furnace self-learning

Abstract

The present application belongs to the technical field of hot continuous rolling production, and particularly relates to a method for multi-stage laminar flow cooling feedback control with furnace self-learning, which is used to distinguish the calculation values of heat transfer coefficients of different slabs at the cooling setting according to the identification code; the target value of coiling temperature CT of the slabs in different heating furnaces is independently calculated; in the laminar flow cooling stage, a multi-stage feedback control strategy is adopted to adjust the water quantity of the fine adjustment header; when the heat exchange coefficient learning point of the head of the strip reaches the coiling temperature CT detection position, the actual coiling temperature CT data of the first group of samples is collected, the average coiling temperature CT is compared with the target value of the coiling temperature CT, and the first water quantity adjustment of the fine adjustment header is made according to the difference between the two; the deviation of the sample coiling temperature CT data of the present group after feedback adjustment from the target value of the coiling temperature CT is used to feedback adjust the next group of samples until the tail of the strip. The advantage is that the influence of the heating furnace state on the self-learning parameter copying is solved.

Description

Technical Field

[0001] This invention belongs to the field of hot continuous rolling production technology, and particularly relates to a method for feedback control of furnace self-learning and multi-stage laminar flow cooling. Background Technology

[0002] In the production of high-grade pipeline steel, low-temperature coiling is often used to reduce alloy content and control production costs. However, this process results in a large amount of iron oxide scale discontinuously distributed on the strip surface, and even a significant amount of water on the surface. This leads to large fluctuations in the strip surface temperature detected by the pyrometer, causing the feedback control system to adjust incorrectly and exacerbating the coiling temperature (CT) fluctuations. Furthermore, the steel-burning conditions of different heating furnaces vary considerably. Existing coiling temperature control models (CTC) use only a single heat transfer coefficient for pre-setting, failing to differentiate between the operating conditions of different heating furnaces. During concentrated production scheduling, the cooling settings for continuous steel tapping exhibit a "fluctuating" phenomenon, indicating poor continuity in the self-learning model. In addition, temperature deviations between the front and rear coils lead to significant differences in the waiting time, and laminar flow cooling water application can cause large deviations in the overall coil temperature.

[0003] In the prior art, patent application number CN202410422849.1 discloses a control method for improving the hit rate of hot-rolled pipeline steel coiling temperature. This method employs a front-end centralized cooling process without feedback control, optimizing the speed lead and lag rates in the laminar cooling zone to achieve a match between the strip's running speed in the laminar cooling zone and the speed set by the mathematical model. This avoids deviations in the pipeline steel coiling temperature from the target value due to differences in slab heating temperatures, improves the prediction accuracy of the mathematical model, enhances the stability of pipeline steel coiling temperature control, reduces the adverse effects of feedback cooling water on coiling temperature control, and allows for rapid adjustment during strip rolling without affecting other steel grades. However, the feedback control is relatively coarse, and while the front-end centralized cooling without feedback reduces interference, it sacrifices dynamic adjustment capabilities. Xi Xiaoling, Zhou Xinliang, Wu Shengtian. "Calculation and Optimization of Self-Learning Parameters for Hot Continuous Rolling Mill Multi-Heating Furnace Setting Model," *Metallurgical Automation*, 2014, No. 2. This paper proposes calculating the self-learning parameters of the hot continuous rolling mill setting model for each furnace, using exponential smoothing to smooth oscillating parameters. Based on this, different self-learning parameters are optimized using long-genetic and short-genetic methods respectively, enabling the process control system to adapt well to the heating furnace. However, the cooling heat transfer coefficient is not correlated. Summary of the Invention

[0004] To overcome the shortcomings of the existing technology, the purpose of this invention is to provide a method with furnace-specific self-learning and multi-stage laminar cooling feedback control. The method adopts a furnace-specific self-learning mode for the heat transfer coefficient of strip steel, and uses multi-stage feedback control for adjustment during the laminar cooling process to improve the CT hit rate of thick steel plates under low-temperature coiling process conditions.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for feedback control of furnace-specific self-learning and multi-stage laminar flow cooling includes the following steps:

[0007] (1) Identify the furnace number and slab according to the identification code, and distinguish the calculated values ​​of the heat transfer coefficient of different slabs when the cooling setting is set; so that the CTC self-learning model can distinguish the slabs according to the identification code;

[0008] (2) Calculate the target value of coiling temperature CT independently for slabs in different heating furnaces and store it in the CTC self-learning model;

[0009] (3) During the laminar flow cooling stage, a multi-stage feedback control strategy is adopted to adjust the fine-tuning manifold water flow, specifically including:

[0010] When the heat transfer coefficient learning point of the strip head reaches the coiling temperature CT detection position, the actual coiling temperature CT data of the first group of samples is collected. The average coiling temperature CT after mean filtering is compared with the target value of coiling temperature CT. The water volume of the fine adjustment manifold is adjusted for the first time based on the difference between the two.

[0011] When the sample at the entrance of the fine adjustment zone reaches the winding temperature CT detection position during the first adjustment, the actual winding temperature CT data of the second group of samples with the same number of samples as the first group of samples is collected. The average winding temperature CT after mean filtering is compared with the target value of winding temperature CT to determine whether the first feedback adjustment is under-adjusted or over-adjusted, and the water volume of the fine adjustment manifold is adjusted a second time.

[0012] By repeating S1 and S2, or the S2 adjustment process, the deviation between the CT data of the current set of samples after feedback adjustment and the target value of CT is used to adjust the next set of samples, until the tail of the strip.

[0013] Based on the characteristics of the actual winding temperature CT control curve of the steel strip, the number of sample groups is set to 5 to 7.

[0014] The thickness of the steel strip is 15.5~18.5mm.

[0015] The length of each sample group is adaptively adjusted according to the speed of the steel strip.

[0016] The mean filtering method described above uses a sliding window averaging method, with the window width matching the number of sample groups.

[0017] Compared with the prior art, the beneficial effects of the present invention are:

[0018] This invention assigns heat transfer self-learning parameters based on different heating furnace operating conditions, significantly distinguishing the interference of different heating furnace operating conditions on the self-learning trend and solving the influence of heating furnace status on the replication of self-learning parameters. It eliminates the impact of heating furnace capacity differences on CT control, improving model adaptability. Employing a multi-stage feedback control algorithm, through segmented iterative adjustment and mean filtering, it suppresses CT fluctuations, achieving high-precision control. This solves the problem of overall roll temperature being too high or too low due to different waiting times between rolls after the learning function is implemented, balancing control accuracy and production efficiency.

[0019] This invention effectively solves the problems of low CT hit rate and large temperature fluctuation in the low-temperature coiling process of 15.5~18.5mm thick steel by means of the synergistic effect of furnace self-learning and multi-stage feedback, and is suitable for efficient and stable production on hot rolling production lines. Attached Figure Description

[0020] Figure 1 It is a flowchart of a method for furnace self-learning and multi-stage laminar flow cooling feedback control.

[0021] Figure 2 These are the actual CT curves of the No. 1 heating furnace across 5 sample segments.

[0022] Figure 3 These are actual CT curves of the No. 2 heating furnace with 5 sample segments.

[0023] Figure 4 These are actual CT curves of the No. 3 heating furnace with 5 sample segments.

[0024] Figure 5 These are the actual CT curves of the No. 1 heating furnace across 6 sample segments.

[0025] Figure 6 These are the actual CT curves of the No. 2 heating furnace, which consist of 6 sample segments.

[0026] Figure 7 These are the actual CT curves of the No. 3 heating furnace across 6 sample segments.

[0027] Figure 8 These are the actual CT curves of the No. 1 heating furnace across 7 sample segments.

[0028] Figure 9 These are the actual CT curves of the No. 2 heating furnace, which have 7 sample segments.

[0029] Figure 10 These are the actual CT curves of the No. 3 heating furnace across 7 sample segments. Detailed Implementation

[0030] The present invention will now be described in detail with reference to the accompanying drawings, but it should be noted that the implementation of the present invention is not limited to the following embodiments.

[0031] Example:

[0032] See Figure 1 It possesses a furnace self-learning and multi-stage laminar flow cooling feedback control method, including the following steps:

[0033] (1) The three walking beam slab heating furnaces (1#, 2#, 3#) and slabs of a steel plant’s 2150 line are independently identified by the identification code, so that the calculated values ​​of the heat transfer coefficient of different slabs at the cooling setting can be distinguished, and the CTC self-learning model can distinguish the slabs according to the identification code.

[0034] (2) The CTC self-learning model calculates and stores the heat transfer coefficient of the slabs coming out of each heating furnace when the cooling setting is executed, based on the identification code and the target value of the coiling temperature CT. This replaces the traditional single cooling heat transfer coefficient and solves the problem of CT fluctuation caused by the deviation in the steelmaking capacity of the three heating furnaces.

[0035] (3) During the laminar flow cooling stage, a multi-stage feedback control strategy is adopted to adjust the fine-tuning manifold water flow, specifically including:

[0036] Initial adjustment: When the heat transfer coefficient learning point of the strip head reaches the coiling temperature CT detection position, the actual coiling temperature CT data of the first group of samples is collected. The average coiling temperature CT after mean filtering is compared with the target value of coiling temperature CT. The water volume of the fine adjustment manifold is adjusted for the first time based on the difference between the two.

[0037] Iterative adjustment: When the sample at the entrance of the fine-tuning zone reaches the winding temperature CT detection position during the first adjustment, the actual winding temperature CT data of the second group of samples with the same number as the first group of samples are collected. The average winding temperature CT after mean filtering is compared with the target value of winding temperature CT to determine whether the first feedback adjustment is under-adjusted or over-adjusted, and a second water volume adjustment is performed on the fine-tuning manifold. The mean filtering adopts the sliding window averaging method, and the window width is matched with the number of sample groups.

[0038] Similarly, during the entire coiling process, after the strip reaches the CT detection position of the coiling temperature, the above iterative adjustment process is repeated repeatedly, using the feedback deviation of the previous set of samples to adjust the next set, until the tail of the strip is cooled.

[0039] Based on the actual winding temperature CT control curve characteristics of high-grade pipeline steel strip, the number of sample groups is set to 5-7. The length of each sample group is adaptively adjusted according to the steel strip speed to ensure a balance between filtering effect and real-time performance. The thickness of the high-grade pipeline steel plate is 15.5-18.5mm.

[0040] By comparing the self-learning effects of 5, 6, and 7 groups of samples (see Tables 1-3 and 1-4), we can better understand the results. Figures 2-10 The effectiveness of the method was verified. Table 1 shows the heat transfer coefficient and CT hit rate of 5 samples in the furnace self-learning mode, Table 2 shows the heat transfer coefficient and CT hit rate of 6 samples in the furnace self-learning mode, and Table 3 shows the heat transfer coefficient and CT hit rate of 6 samples in the furnace self-learning mode. Figures 2-4 The CT curves are for five sample segments from different heating furnaces. Figures 5-7 CT curves for 6 sample segments from different heating furnaces. Figures 8-10 CT curves for 7 sample segments from different heating furnaces.

[0041] Table 1: Furnace self-learning heat transfer coefficient and CT hit rate for 5 sample segments

[0042]

[0043] Table 2: Furnace self-learning heat transfer coefficient and CT hit rate for 6 sample segments

[0044]

[0045] Table 3: Self-learning heat transfer coefficients and CT hit rates for the seven sample segments

[0046]

[0047] The self-learning performance of the furnaces in the above 5, 6, and 7 groups of samples can be seen as follows:

[0048] In the separate furnace mode, the heat transfer coefficient of each heating furnace is calculated independently (e.g., furnace #1: 0.781~0.955, s). 2 (0.00033~0.004268), Furnace #2: 0.80~0.969, s 2 (0.000153~0.002544), Furnace #3: 0.749~0.95, s 2 (0.000146~0.003468)), changes in the heat transfer coefficient of any heating furnace will not affect the heat transfer coefficients of other heating furnaces, significantly reducing the deviation caused by the traditional uniform coefficient. Under multi-stage feedback control, the CT hit rate is stable at 70%~80% (e.g., furnace #1 has the highest hit rate of 80% and furnace #3 has the highest hit rate of 80% in the 7 sample groups), which is 10%~15% higher than the traditional single-stage feedback. Figures 2-10 The CT curves of different heating furnaces showed a decrease in fluctuation amplitude, indicating a significant improvement in the stability of the low-temperature winding process.

Claims

1. A method for feedback control of furnace-specific self-learning and multi-stage laminar flow cooling, characterized in that, Includes the following steps: (1) Identify the heating furnace number and slab according to the identification code, and distinguish the calculated value of the heat transfer coefficient of different slabs when cooling is set; This enables the CTC self-learning model to distinguish slabs based on the identification code; (2) The CTC self-learning model calculates and stores the heat transfer coefficient of the slabs coming out of each heating furnace when the cooling setting is executed, based on the identification code and the target value of the coiling temperature CT. (3) During the laminar flow cooling stage, a multi-stage feedback control strategy is adopted to adjust the fine-tuning manifold water flow, specifically including: Initial adjustment: When the heat transfer coefficient learning point of the strip head reaches the coiling temperature CT detection position, the actual coiling temperature CT data of the first group of samples is collected. The average coiling temperature CT after mean filtering is compared with the target value of coiling temperature CT. The water volume of the fine adjustment manifold is adjusted for the first time based on the difference between the two. Iterative adjustment: When the sample located at the entrance of the fine-tuning zone reaches the winding temperature CT detection position during the first adjustment, the actual winding temperature CT data of the second group of samples with the same number of samples as the first group are collected. The average winding temperature CT after mean filtering is compared with the target value of winding temperature CT to determine whether the first feedback adjustment is under-adjusted or over-adjusted, and a second water volume adjustment is performed on the fine-tuning manifold. The mean filtering adopts the sliding window averaging method, and the window width is matched with the number of sample groups. Similarly, during the entire coiling process, after the strip reaches the CT detection position of the coiling temperature, the above iterative adjustment process is repeated repeatedly, using the feedback deviation of the previous set of samples to adjust the next set, until the tail of the strip is cooled.

2. The method for feedback control of furnace-specific self-learning and multi-stage laminar flow cooling according to claim 1, characterized in that, Based on the characteristics of the actual coiling temperature CT control curve of the strip, the number of sample groups is set to 5 to 7.

3. The method for feedback control of furnace-specific self-learning and multi-stage laminar flow cooling according to claim 2, characterized in that, The thickness of the strip is 15.5~18.5mm.

4. The method for feedback control of furnace-specific self-learning and multi-stage laminar flow cooling according to claim 1, characterized in that, The length of each sample group is adaptively adjusted according to the speed of the steel strip.

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