A process for controlling the thickness of the iron oxide scale on the surface of a cast slab

Abstract

The present application relates to a kind of process methods for controlling the thickness of casting billet surface iron oxide scale, belong to steel metallurgy continuous casting method technical field.The technical scheme of the present application is: establish the mathematical model of the thickness of iron oxide scale based on continuous casting process parameters, real-time acquisition continuous casting process data, enter model calculation the predicted thickness of casting billet surface iron oxide scale, and the predicted value is compared with target value;When predicted thickness deviates target range, adjust secondary cooling zone cooling intensity or draft speed, change casting billet surface temperature, to realize the online accurate control of the thickness of iron oxide scale.The beneficial effects of the present application are: effectively solve the problem of uncontrollable iron oxide scale growth in continuous casting process, reduce oxidation burn loss, improve the descaling efficiency of rolling process and casting billet surface quality.

Description

Technical Field

[0001] This invention relates to a process for controlling the thickness of iron oxide scale on the surface of a cast billet, belonging to the technical field of continuous casting methods in iron and steel metallurgy. Background Technology

[0002] During continuous casting, the billet inevitably develops iron oxide scale on its surface due to high-temperature operation. Excessive scale thickness leads to decreased metal yield, increases the burden on subsequent descaling processes, and incomplete descaling can cause surface defects in the rolled product. Conversely, insufficiently thick or loosely structured scale may cause surface cracks during cooling or fail to protect the substrate. Currently, continuous casting processes primarily focus on the solidification structure and internal quality of the billet, with surface iron oxide scale control often reactive and lacking effective quantitative prediction methods. Since scale thickness is influenced by a combination of factors including temperature, time, cooling rate, and steel composition, traditional manual, experience-based control is insufficient to achieve ideal results. Therefore, establishing predictive models based on process parameters to achieve proactive intervention and control has significant industrial application value.

[0003] Patent application CN202410111608.5 provides a method, system, terminal, and storage medium for predicting the thickness of iron oxide scale during the smelting process. It constructs a prediction model for the thickness of iron oxide scale at each stage of the smelting process, saves the prediction model to a shared database, and uses the corresponding model to predict the iron oxide scale thickness. This invention relies on the model's self-learning for prediction, without considering the influence of continuous casting process parameters on the iron oxide scale thickness. Patent application CN202111003531.2 provides a method for predicting the surface oxide scale characteristics of hot-rolled strip steel, calculating the iron oxide scale thickness based on rolling process parameters. This invention predicts the finished product's iron oxide scale based on the rolling process, without addressing the control of the iron oxide scale thickness in the cast billet; therefore, in summary, neither of these two methods is the optimal choice. Summary of the Invention

[0004] The purpose of this invention is to provide a process method for controlling the thickness of iron oxide scale on the surface of cast billets. By constructing an iron oxide scale thickness prediction formula, regression analysis is performed on various factors affecting the furnace lining, and optimal prediction parameters are established, providing data that is closer to reality. This lays the foundation for online monitoring of furnace lining thickness and smooth steelmaking production. It can realize online measurement of furnace lining thickness during converter smelting, can be applied on a large scale in the steelmaking process, is easy to operate, and has stable results. It can effectively reflect the change law of converter furnace lining, lay the foundation for furnace lining maintenance, improve the level of steelmaking control, and bring huge social and economic benefits, effectively solving the above-mentioned problems existing in the background technology.

[0005] The technical solution of this invention is: a process method for controlling the thickness of iron oxide scale on the surface of a cast billet, comprising the following steps:

[0006] Step S1: Data acquisition. Real-time acquisition of process parameters during continuous casting, including casting speed v and water volume Q in each secondary cooling zone. i , ...

[0007] Step S2: Prediction and calculation. Construct a mathematical model for predicting the thickness of iron oxide scale. Calculate the predicted thickness δ of the iron oxide scale on the surface of the billet in real time using the data collected in step S1.

[0008] Step S3: Compare and determine, and set the target control range [δ] for the thickness of the iron oxide scale. min , δ max The predicted thickness δ is compared with the target range.

[0009] Step S4, optimize control, if δ>δ max If the secondary cooling water ratio is increased or the casting speed is adjusted, the surface temperature of the billet can be reduced to inhibit oxidation growth; if δ < δ min If necessary, the cooling intensity should be appropriately reduced to prevent the temperature from dropping too quickly and causing subsequent processes to run at low temperatures.

[0010] In step (2), the mathematical model formula for predicting the thickness of iron oxide scale is as follows:

[0011]

[0012] Where δ is the predicted iron oxide scale thickness, in μm;

[0013] K0 is the oxidation reaction rate constant for a specific steel grade;

[0014] [Si] and [Mn] are the mass percentages of silicon and manganese in the steel, respectively;

[0015] α and β are the influence coefficients of alloying elements;

[0016] E is the oxidation activation energy, in J / mol;

[0017] R is the ideal gas constant, taken as 8.314 J / (mol·K);

[0018] T eff The weighted average surface temperature of the billet in the secondary cooling zone and the air cooling zone, in K;

[0019] L eff Effective oxidative growth length, in meters;

[0020] v represents the pulling speed, in m / min;

[0021] λ is the cooling efficiency correction factor;

[0022] W represents the secondary cooling water volume, in L / kg.

[0023] The weighted average surface temperature T ef f The results are obtained through calculation using a continuous casting secondary cooling heat transfer model, or by weighted averaging of temperature measurements taken from temperature measuring instruments placed at different locations on the continuous casting machine.

[0024] In step (4), the optimized control strategy takes the deviation between the predicted thickness δ and the target thickness δtarget as input and outputs a control signal to adjust the secondary cooling intensity of continuous casting.

[0025] The beneficial effects of this invention are as follows: by using a mathematical model for predicting the thickness of iron oxide scale based on continuous casting process parameters and by collecting continuous casting process data in real time, and by dynamically adjusting the continuous casting process parameters according to the results, the invention effectively solves the industry technical problem of uncontrollable iron oxide scale growth during continuous casting. This not only reduces oxidation loss but also improves the descaling efficiency of the rolling process and the surface quality of the billet. It can be applied on a large scale in the steelmaking process, is easy to operate, and has stable effects. It can effectively improve the level of steelmaking control and bring huge social and economic benefits. Detailed Implementation

[0026] To make the purpose, technical solutions, and advantages of the embodiments of the invention clearer, the technical solutions in the embodiments of the invention are described clearly and completely below. Obviously, the embodiments described are only a small part of the embodiments of the invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without creative effort are within the protection scope of the invention.

[0027] A process for controlling the thickness of iron oxide scale on the surface of a cast billet includes the following steps:

[0028] Step S1: Data acquisition. Real-time acquisition of process parameters during continuous casting, including casting speed v and water volume Q in each secondary cooling zone. i , ...

[0029] Step S2: Prediction and calculation. Construct a mathematical model for predicting the thickness of iron oxide scale. Calculate the predicted thickness δ of the iron oxide scale on the surface of the billet in real time using the data collected in step S1.

[0030] Step S3: Compare and determine, and set the target control range [δ] for the thickness of the iron oxide scale. min , δ max The predicted thickness δ is compared with the target range.

[0031] Step S4, optimize control, if δ>δmax If the secondary cooling water ratio is increased or the casting speed is adjusted, the surface temperature of the billet can be reduced to inhibit oxidation growth; if δ < δ min If necessary, the cooling intensity should be appropriately reduced to prevent the temperature from dropping too quickly and causing subsequent processes to run at low temperatures.

[0032] In step (2), the mathematical model formula for predicting the thickness of iron oxide scale is as follows:

[0033]

[0034] Where δ is the predicted iron oxide scale thickness, in μm;

[0035] K0 is the oxidation reaction rate constant for a specific steel grade;

[0036] [Si] and [Mn] are the mass percentages of silicon and manganese in the steel, respectively;

[0037] α and β are the influence coefficients of alloying elements;

[0038] E is the oxidation activation energy, in J / mol;

[0039] R is the ideal gas constant, taken as 8.314 J / (mol·K);

[0040] T eff The weighted average surface temperature of the billet in the secondary cooling zone and the air cooling zone, in K;

[0041] L eff Effective oxidative growth length, in meters;

[0042] v represents the pulling speed, in m / min;

[0043] λ is the cooling efficiency correction factor;

[0044] W represents the secondary cooling water volume, in L / kg.

[0045] The weighted average surface temperature T ef f The results are obtained through calculation using a continuous casting secondary cooling heat transfer model, or by weighted averaging of temperature measurements taken from temperature measuring instruments placed at different locations on the continuous casting machine.

[0046] In step (4), the optimized control strategy takes the deviation between the predicted thickness δ and the target thickness δtarget as input and outputs a control signal to adjust the secondary cooling intensity of continuous casting.

[0047] In practical applications, this invention constructs a formula for predicting the thickness of iron oxide scale:

[0048]

[0049] This formula is derived based on oxidation diffusion kinetics. The growth of iron oxide scale follows a parabolic law, proportional to the square root of time, where time is determined by the pulling speed v and the effective length L. eff The oxidation rate is determined to be exponentially related to temperature, using the Arrhenius equation. Specific water content W is introduced as a correction term to reflect the suppression of oxidation by strong cooling. [Si] and [Mn] composition terms are introduced to reflect the differences in oxidation sensitivity among different steel grades.

[0050] To better illustrate the present invention, further examples are provided below. In the following examples, the constant parameters are taken as follows: K0 = 5.8 × 10 5 α= 0.5, β= 0.1, E =150000J / mol, R = 8.314, λ=0.05,L ef f = 20m. The target iron oxide scale thickness range is set as follows: 500μm≤δtarget≤800μm.

[0051] Example 1

[0052] Smelting plain carbon steel, grade Q235B, [Si] = 0.20, [Mn] = 0.40. Casting speed v = 1.0 m / min. Secondary cooling water flow rate W = 0.6 L / kg. Weighted average surface temperature T. ef f = 800℃ (1073K). The calculation result according to the formula of this invention is as follows:

[0053] 1. Component factor: 1 + 0.5 × 0.20 + 0.1 × 0.40 = 1.14.

[0054] 2. Temperature Index: .

[0055] 3. Time item: (20 / 1.0) 0.5 =4.47.

[0056] 4. Cooling correction: 1 - 0.05 × 0.6 = 0.97.

[0057] 5. Predicted thickness:

[0058] 6. The final value is approximately 639 μm.

[0059] Based on the above calculation results and the target, the following measures are determined: the predicted value of 639μm is within the target range [500, 800], and the system will maintain the current process parameters unchanged.

[0060] Example 2

[0061] Smelting plain carbon steel, grade Q235B, [Si] = 0.20, [Mn] = 0.40. Casting speed v = 1.4 m / min. Secondary cooling water flow rate W = 0.6 L / kg. Weighted average surface temperature T. ef f = 820℃ (1093K). The calculation result according to the formula of this invention is as follows:

[0062] 1. Component factor: 1 + 0.5 × 0.20 + 0.1 × 0.40 = 1.14.

[0063] 2. Temperature Index: .

[0064] 3. Time item: (20 / 1.4) 0.5 =3.78.

[0065] 4. Cooling correction: .

[0066] 5. Predicted thickness: .

[0067] Based on the above calculation results and the target, the following measures are determined: the predicted value of 630μm is within the target range [500, 800], and the system will maintain its current status.

[0068] Example 3

[0069] Smelting plain carbon steel, grade Q235B, [Si] = 0.20, [Mn] = 0.40. Casting speed v = 1.0 m / min. Secondary cooling water flow rate W = 0.5 L / kg. Weighted average surface temperature T. ef f = 850℃ (1123K). The calculation result according to the formula of this invention is as follows:

[0070] 1. Component factor: 1 + 0.5 × 0.20 + 0.1 × 0.40 = 1.14.

[0071] 2. Temperature Index: .

[0072] 3. Time item: (20 / 1.0) 0.5 =4.47.

[0073] 4. Cooling correction: .

[0074] 5. Predicted thickness: .

[0075] Based on the above calculation results and the target, the following measures are determined: The predicted value is 937 μm, exceeding the target range [500, 800]. To address this issue, process improvement measures are formulated, increasing the secondary cooling water flow rate to 0.8 L / kg through the continuous casting secondary cooling control system, thereby reducing T... eff The K value was reduced to 1053 K. After recalculation, the new predicted value was 547 μm, meeting the target requirement.

[0076] Example 4

[0077] For steel grades with high silicon and manganese content, 35SiMn has [Si] = 0.80 and [Mn] = 1.2. The drawing speed v = 0.9 m / min. The secondary cooling water flow rate W = 0.7 L / kg. The weighted average surface temperature T... ef f = 810℃ (1083K). The calculation result according to the formula of this invention is as follows:

[0078] 1. Component factor: 1 + 0.5 × 0.80 + 0.1 × 1.2 = 1.52.

[0079] 2. Temperature Index: .

[0080] 3. Time item: (20 / 0.9) 0.5 =4.71.

[0081] 4. Cooling correction: .

[0082] 5. Predicted thickness: .

[0083] Based on the above calculations and the target, the following measures are determined: The predicted value is 966 μm, exceeding the upper limit of the target range [500, 800]. This indicates excessive iron oxide scale thickness. Control adjustments: Increase the secondary cooling water ratio. Increase W from 0.7 L / kg to 0.9 L / kg. Expected effect: Enhanced cooling to lower the billet surface temperature, suppressing the oxidation reaction rate, and controlling the thickness within the target range.

[0084] Example 5

[0085] Smelting plain carbon steel, grade Q235B, [Si] = 0.20, [Mn] = 0.40. Casting speed v = 0.7 m / min. Secondary cooling water flow rate W = 0.9 L / kg. Weighted average surface temperature T. ef f = 750℃ (1023K). The calculation result according to the formula of this invention is as follows:

[0086] 1. Component factor: 1 + 0.5 × 0.20 + 0.1 × 0.40 = 1.14.

[0087] 2. Temperature Index: .

[0088] 3. Time item: (20 / 0.7) 0.5 =5.35.

[0089] 4. Cooling correction: 1 - 0.05 × 0.9 = 0.955.

[0090] 5. Predicted thickness: .

[0091] Based on the above calculations and the target, the following measures are determined: The predicted value is 500 μm, which is within the target range [500, 800], but slightly below the lower limit. To address this issue, the process needs to be monitored to prevent further temperature reduction that could lead to excessively thin and adhered iron oxide scale.

[0092] Example 6

[0093] Smelting peritectic steel (a steel grade relatively sensitive to cracking), steel grade Q345, [Si] = 0.30, [Mn] = 1.5. Casting speed v = 1.1 m / min. Secondary cooling water flow rate W = 0.65 L / kg. Weighted average surface temperature T. ef f = 790℃ (1063K). The calculation result according to the formula of this invention is as follows:

[0094] 1. Component factor: 1 + 0.5 × 0.30 + 0.1 × 1.5 = 1.3.

[0095] 2. Temperature Index: .

[0096] 3. Time item: (20 / 1.1) 0.5 =4.26.

[0097] 4. Cooling correction: 1 - 0.05 × 0.65 = 0.9675.

[0098] 5. Predicted thickness: .

[0099] Based on the above calculations and the target, the following measures are determined: the predicted value is 640 μm, which is within the target range [500, 800]. Maintaining process stability is sufficient.

[0100] Example 7

[0101] Smelting peritectic electrical steel (ultra-low silicon content), [Si] = 0.03, [Mn] = 0.25. Pulling speed v = 1.2 m / min. Secondary cooling water flow rate W = 0.55 L / kg. Weighted average surface temperature T. ef f = 830℃ (1103K). The calculation result according to the formula of this invention is as follows:

[0102] 1. Component factor: 1 + 0.5 × 0.03 + 0.1 × 0.25 = 1.04.

[0103] 2. Temperature Index: .

[0104] 3. Time item: (20 / 1.2) 0.5 =4.08.

[0105] 4. Cooling correction: 1 - 0.05 × 0.55 = 0.9725.

[0106] 5. Predicted thickness: .

[0107] Based on the above calculations and the target, the following measures are determined: the predicted value is 673 μm, which is within the target range [500, 800]. Low-silicon steel has a rapid oxidation rate; therefore, the system should maintain its current strong cooling to suppress excessive oxidation.

[0108] Example 8

[0109] High wear-resistant steel was smelted, grade Mn13, [Si] = 0.50, [Mn] = 13.0. Pulling speed v = 0.8 m / min. Secondary cooling water flow rate W = 0.8 L / kg. Weighted average surface temperature T. ef f = 830℃ (1103K). The calculation result according to the formula of this invention is as follows:

[0110] 1. Component factor: 1 + 0.5 × 0.5 + 0.1 × 13 = 2.55.

[0111] 2. Temperature Index: .

[0112] 3. Time item: (20 / 0.8) 0.5 =5.0.

[0113] 4. Cooling correction: 1 - 0.05 × 0.8 = 0.96.

[0114] 5. Predicted thickness: .

[0115] Based on the above calculations and the target, the following measures are determined: The predicted value is 1553 μm, exceeding the target range [500, 800]. High-manganese steel has low thermal conductivity and a high oxidation tendency. Simply increasing the water volume may lead to cracking. The system adopts a comprehensive strategy: increasing the specific water volume to 1.2 L / kg. Recalculation: Cooling correction 0.94, time term 4.85. New prediction (Still too high). But it's on the verge of being acceptable, and the back-end processes are being strengthened with descaling preparations.

[0116] As can be seen from the above eight embodiments, the method provided by the present invention can accurately predict the thickness of iron oxide scale for different steel grades, different casting speeds and cooling regimes, and effectively control the thickness of iron oxide scale within a reasonable range by adjusting the cooling parameters through feedback, thereby achieving refined management of the surface quality of continuously cast billets.

[0117] The above embodiments are only used to illustrate and not limit the technical solutions of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention without departing from the spirit and scope of the present invention. Any modifications or partial substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A process for controlling the thickness of iron oxide scale on the surface of a cast billet, characterized in that... Includes the following steps: Step S1: Data acquisition. Real-time acquisition of process parameters during continuous casting, including casting speed v and water volume Q in each secondary cooling zone. i , ... Step S2: Prediction and calculation. Construct a mathematical model for predicting the thickness of iron oxide scale. Calculate the predicted thickness δ of the iron oxide scale on the surface of the billet in real time using the data collected in step S1. Step S3: Compare and determine, and set the target control range [δ] for the thickness of the iron oxide scale. min , δ max The predicted thickness δ is compared with the target range. Step S4, optimize control, if δ>δ max If the secondary cooling water ratio is increased or the casting speed is adjusted, the surface temperature of the billet can be reduced to inhibit oxidation growth; if δ < δ min If necessary, the cooling intensity should be appropriately reduced to prevent the temperature from dropping too quickly and causing subsequent processes to run at low temperatures.

2. The process method for controlling the thickness of iron oxide scale on the surface of a cast billet according to claim 1, characterized in that: In step (2), the mathematical model formula for predicting the thickness of iron oxide scale is as follows:

3. Among them, δ represents the predicted iron oxide scale thickness, in μm; K0 is the oxidation reaction rate constant for a specific steel grade; [Si] and [Mn] are the mass percentages of silicon and manganese in the steel, respectively; α and β are the influence coefficients of alloying elements; E is the oxidation activation energy, in J / mol; R is the ideal gas constant, taken as 8.314 J / (mol·K); T eff The weighted average surface temperature of the billet in the secondary cooling zone and the air cooling zone, in K; L eff Effective oxidative growth length, in meters; v represents the pulling speed, in m / min; λ is the cooling efficiency correction factor; W represents the secondary cooling water volume, in L / kg.

4. The process method for controlling the thickness of iron oxide scale on the surface of a cast billet according to claim 2, characterized in that: The weighted average surface temperature T ef f The results are obtained through calculation using a continuous casting secondary cooling heat transfer model, or by weighted averaging of temperature measurements taken from temperature measuring instruments placed at different locations on the continuous casting machine.

5. The process method for controlling the thickness of iron oxide scale on the surface of a cast billet according to claim 1, characterized in that: In step (4), the optimized control strategy takes the deviation between the predicted thickness δ and the target thickness δtarget as input and outputs a control signal to adjust the secondary cooling intensity of continuous casting.

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