A control method for adjusting a target temperature of a hot-rolled strip online

Abstract

The present application relates to a kind of control methods of adjusting hot-rolled strip target temperature online, specifically comprising the following steps: according to the chemical composition of target billet of tapping mark, the chemical composition of target billet is recorded;According to the chemical composition content of each alloy element in the chemical composition of target billet, alloy content range performance coefficient table is prepared;The chemical composition content of each alloy element in the chemical composition of target billet is compared with alloy content range performance coefficient table, and comprehensive performance prediction guide coefficient is obtained;According to the size of comprehensive performance prediction guide coefficient, temperature adjustment value is selected;Increase or reduce temperature adjustment value based on original target temperature, obtain new target temperature.The present application has the advantages that: according to the corresponding comprehensive performance prediction guide coefficient obtained by simulating and calculating the slab chemical composition, for the not ideal composition interval slab, corresponding finish rolling temperature adjustment and coiling temperature adjustment are taken in hot continuous rolling mill set, and online automatic identification and temperature control are realized.

Description

Technical Field

[0001] This invention relates to the field of steel rolling technology, and in particular to a control method for online adjustment of the target temperature of hot-rolled strip steel. Background Technology

[0002] A hot-rolling mill produces strip and sheet steel with thicknesses ranging from 1.8 to 19 mm and widths from 900 to 1630 mm. The main product types include cold-rolled raw material coils, structural steel, high-strength steel, hot-rolled low-carbon structural steel, automotive structural steel, marine steel, container steel, and pipeline steel. In the past two years, customer feedback regarding shape and transverse fold marks has primarily concerned ordinary carbon structural steel Q235B, with significant quantities and thicknesses concentrated between 2.5 and 5.0 mm.

[0003] Investigations revealed significant fluctuations in the chemical composition control of Q235B steel during the upstream steelmaking process of hot rolling. To reduce costs, upstream processes lowered carbon and manganese content to the lower limit of technical standards, resulting in a softer steel. This often leads to transverse crease defects after uncoiling, severely impacting the processing and use for surface coating applications. In the hot rolling process, to improve the mechanical strength of the strip, the temperature drop between the final rolling temperature and the coiling temperature is increased—that is, the final rolling temperature is increased while the coiling temperature is decreased. However, this decrease in coiling temperature increases the difficulty of control, becoming a persistent technical challenge for hot rolling mills. Summary of the Invention

[0004] The purpose of this invention is to provide a control method for adjusting the target temperature of hot-rolled strip steel online. The method sets a new target temperature according to the chemical composition of the incoming slab, thereby improving the frequent changes in the mechanical properties of hot-rolled strip steel caused by fluctuations in the chemical composition of the slab, reducing the probability of transverse folding defects, improving the strip shape quality, and reducing the difficulty of control.

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

[0006] A method for controlling the online adjustment of the target temperature of hot-rolled strip steel, comprising an original target temperature and a new target temperature, specifically including the following steps:

[0007] S1. Before or after the slab enters the furnace, the chemical composition of the target slab is entered into the MES system and the chemical composition of the target slab is matched with the tapping mark.

[0008] S2. Record the chemical composition of the target steel billet according to the tapping mark of the target steel billet;

[0009] S3. Based on the chemical composition content of each alloying element in the target steel billet, compile an alloy content range efficiency coefficient table through simulation calculation;

[0010] S4. By comparing the chemical composition content of each alloying element in the target steel billet with the efficiency coefficient table of alloy content range, the comprehensive performance prediction guidance coefficient is obtained through simulation calculation.

[0011] S5. Select the temperature adjustment value based on the magnitude of the comprehensive performance prediction guidance coefficient;

[0012] S6. Increase or decrease the temperature adjustment value based on the original target temperature to obtain a new target temperature.

[0013] In step S3, the alloy content range efficiency coefficient table includes the performance impact ratio, chemical composition range, comprehensive performance prediction guidance coefficient, impact value data range, and temperature adjustment value. The compilation of the alloy content range efficiency coefficient table specifically includes the following:

[0014] 1) The chemical composition range matches the chemical composition of the slab. The chemical composition range includes the composition range and the corresponding value. The information of the composition range matches the corresponding value.

[0015] 2) The performance impact ratio is matched with the chemical composition of the slab;

[0016] 3) Calculate the comprehensive performance prediction guidance coefficient using simulation calculation formulas, as shown in Formula ① and Formula ②:

[0017] Each comprehensive performance prediction guidance coefficient = each performance impact ratio multiplied by its corresponding value ①

[0018] Overall performance forecast guidance coefficient = the sum of each overall performance forecast guidance coefficient ②

[0019] 4) The range of impact value data is divided into several categories, and each comprehensive performance prediction guidance coefficient is matched with each range of impact value data;

[0020] 5) The information for each influencing value range is matched with the temperature adjustment value.

[0021] In step S1, the steel tapping mark is generated based on the steel grade.

[0022] The slab has several chemical components.

[0023] Compared with the prior art, the beneficial effects of the present invention are:

[0024] Based on the chemical composition of the slab, the corresponding comprehensive performance prediction guidance coefficient is obtained through simulation calculation. For slabs with undesirable composition ranges, the final rolling temperature and coiling temperature are adjusted accordingly in the hot continuous rolling mill. This achieves online automatic identification and temperature control, avoiding the instability of mechanical properties caused by fluctuations in chemical composition of some slabs, which in turn affects the end customer's use. It effectively avoids the generation of transverse fold marks and plate shape defects, and improves the company's quality control level. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the control method for adjusting the target temperature of hot-rolled strip steel online. Figure 1 . Detailed Implementation

[0026] 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.

[0027] The following embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operation processes. However, the scope of protection of the present invention is not limited to the following embodiments. Unless otherwise specified, the methods used in the following embodiments are conventional methods.

[0028]

Example 1

[0029] See Figure 1 A method for controlling the online adjustment of the target temperature of hot-rolled strip steel, comprising an original target temperature and a new target temperature, specifically including the following steps:

[0030] S1. After the slab is put into the furnace, the MES system (MES stands for Manufacturing Execution System) updates the chemical composition of the slab entered into the furnace in real time.

[0031] S2. Taking the Q235B steel tapping mark HC3177WW as an example, the MES system identifies the Q235B steel tapping mark HC3177WW.

[0032] S3. The chemical composition of the slab recorded for the steel tapping mark HC3177WW for the target slab is as follows.

[0033] S4. The chemical composition content of each alloy element is collected into a pre-determined performance coefficient table for alloy content range, including important alloy elements C, Si, and Mn. The performance influence ratio of each element is assigned, and comprehensive simulation calculations are performed to compile the performance coefficient table for Q235B alloy content range.

[0034] S5. By comparing the actual chemical composition content of the slab with the performance coefficient table of the alloy content range, the comprehensive performance prediction guidance coefficient of the strip is obtained through simulation calculation formula.

[0035] The compilation of the alloy content range performance coefficient table specifically includes the following:

[0036] The chemical composition range is matched with the chemical composition of the slab. The chemical composition range includes the composition range and the corresponding value. The information of the composition range is matched with the corresponding value.

[0037] The performance impact ratio is matched with the chemical composition of the slab;

[0038] The comprehensive performance prediction guidance coefficient is calculated using simulation calculation formulas, as shown in formulas ① and ②:

[0039] Each comprehensive performance prediction guidance coefficient = each performance impact ratio multiplied by its corresponding value ①

[0040] Overall performance forecast guidance coefficient = the sum of each overall performance forecast guidance coefficient ②

[0041] The impact value data range is divided into six, and each comprehensive performance prediction guidance coefficient is matched with each impact value data range;

[0042] The information for each influencing value range is matched with the temperature adjustment value;

[0043] The performance impact ratio, chemical composition range, comprehensive performance prediction guidance coefficient, impact value data range, and temperature adjustment value in the alloy content range efficiency coefficient table are all empirical values.

[0044] S6. Then, the comprehensive performance prediction guidance coefficient is divided into four intervals. Each interval represents the difference between the performance prediction and the conventional performance test results (yield strength) under different chemical composition combinations.

[0045] S7. For the comprehensive performance prediction guidance coefficient range, specify the corresponding temperature adjustment values, including the final rolling temperature and the coiling temperature.

[0046] S8. The temperature assignment value corresponding to the comprehensive performance prediction guidance coefficient range is related to the temperature target setting. Based on the original target setting, the temperature adjustment value is increased or decreased to obtain the new target temperature.

[0047]

Example 2

[0048] In this embodiment, the online adjustment of the target temperature of hot-rolled strip steel is the same as in embodiment 1, but an additional process is added to achieve the target values ​​of final rolling temperature and coiling temperature. The sources of the chemical composition of the slab are shown in Table 1.

[0049] Table 1 shows:

[0050]

[0051] The main chemical components adopted include: carbon (C), silicon (Si), and manganese (Mn). Based on empirical values, the performance impact ratios are allocated as 55%, 20%, and 25%, respectively. Each component is divided into four intervals, with corresponding values ​​of 20, 40, 60, and 70. The actual chemical component falls within a given interval, and the corresponding value is used. The performance impact ratio of each component is multiplied by the sum of its corresponding value to obtain a specific comprehensive performance prediction guidance coefficient. Six different data ranges are defined, corresponding to the final rolling and coiling temperature adjustment values. The temperature adjustment value is increased or decreased based on the target value depending on which interval the comprehensive performance prediction guidance coefficient falls within.

[0052] For example, see Table 2;

[0053] Table 2 shows:

[0054] Steel tapping mark C Si Mn HC3176WW 0.16 0.18 0.23

[0055] C = 0.16, corresponding to value 1 = 40; Si = 0.18, corresponding to value 2 = 60; Mn = 0.23, corresponding to value 3 = 40;

[0056] According to formulas ① and ②:

[0057] Each comprehensive performance prediction guidance coefficient = each performance impact ratio multiplied by its corresponding value ①

[0058] Overall performance forecast guidance coefficient = the sum of each overall performance forecast guidance coefficient ②

[0059] The comprehensive performance prediction guidance coefficient is 0.55*40+0.2*60+0.25*40=44. 44>40-51, which falls in the fifth interval, corresponding to a 20℃ increase in coiling temperature and a 20℃ increase in final rolling temperature.

[0060] The work process is as follows:

[0061] The original target temperature for final rolling was 860℃, and the original target temperature for coiling was 600℃. After calculating the comprehensive performance prediction guidance coefficient, the new target temperature for final rolling was changed to 880℃, and the new target temperature for coiling was changed to 620℃. The secondary model sends the new target temperatures for final rolling and coiling to the primary model for execution. On-site, the water spray flow control between stands and the number of opening and closing of laminar cooling water manifolds will be adjusted according to the new target temperatures for final rolling and coiling to ultimately achieve the target value control of final rolling temperature and coiling temperature.

[0062] This invention derives a comprehensive performance prediction guidance coefficient based on the chemical composition of slabs through simulation calculations. For slabs with undesirable composition ranges, corresponding final rolling temperature and coiling temperature adjustments are made in the hot continuous rolling mill. This achieves online automatic identification and temperature control, avoiding the instability of mechanical properties caused by fluctuations in chemical composition in some slabs, which in turn affects the end-customer's use. It effectively avoids the generation of transverse fold marks and plate shape defects, and improves the quality control level of enterprises.

Claims

1. A method for controlling the online adjustment of the target temperature of hot-rolled strip steel, characterized in that, This includes the original target temperature and the new target temperature, specifically involving the following steps: S1. Before or after the slab enters the furnace, the chemical composition of the target slab is entered into the MES system and the chemical composition of the target slab is matched with the tapping mark. S2. Record the chemical composition of the target slab based on the tapping mark of the target slab; S3. Based on the chemical composition content of each alloying element in the target slab, compile an alloy content range efficiency coefficient table through simulation calculation. The table of alloy content range performance coefficients includes the performance impact ratio, chemical composition range, impact value data range, and temperature adjustment value. S4. Compare the chemical composition content of each alloying element in the target slab with the performance coefficient table of alloy content range, and calculate the comprehensive performance prediction guidance coefficient through simulation calculation formulas, as shown in Formula ① and Formula ②: Each comprehensive performance prediction guidance coefficient = each performance impact ratio multiplied by its corresponding value ① Overall performance forecast guidance coefficient = sum of each overall performance forecast guidance coefficient ②; S5. Select the temperature adjustment value based on the magnitude of the comprehensive performance prediction guidance coefficient; S6. Increase or decrease the temperature adjustment value based on the original target temperature to obtain a new target temperature.

2. The control method for online adjustment of target temperature of hot-rolled strip steel according to claim 1, characterized in that, In step S1, the steel tapping mark is generated from the steel grade.

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