Control method for low-cost production of deformed steel bar through pure niobium alloy design

Abstract

The invention discloses a control method for low-cost production of deformed steel bars through pure niobium alloy design. The method specifically comprises the following steps that (1) a continuous casting billet cooling process is conducted, specifically, a first section and a second section of a secondary cooling area of a continuous casting machine are cooled through an aerial fog cooling nozzle in a distinguished mode; (2) based on the material balance and optimization principle, establishing a quadratic function relation model of the alloy element yield and the end point molten steel carbon content and temperature as well as a molten steel alloying final feeding prediction model, and realizing dynamic prediction of the alloy adding amount through computer programming to ensure narrow-range control of molten steel components; and (3) the post-rolling graded controlled cooling process is conducted, specifically, multiple short-distance cooling sections are adopted, and the water pressure and the cooling rate are controlled in a segmented mode. According to the method, steelmaking narrow component control can be achieved, the steelmaking production efficiency and the product quality can be improved, and meanwhile more uniform cooling and more stable structure transformation can be achieved in the cooling process of steel.

Description

Technical Field

[0001] The invention relates to a control method for producing threaded steel with low cost by designing pure niobium alloy. Background Art

[0002] Currently, the vast majority of domestic steel mills use vanadium or niobium-vanadium composite alloys to produce hot-rolled straight HRB400E steel bars. However, due to the high price of vanadium alloys and the relatively low price of niobium alloys, fully leveraging the effects of grain refinement and precipitation strengthening to ensure the qualified performance of rolled steel bars, thereby minimizing the cost of alloy input and reducing the alloy composition in steelmaking, is an important way to reduce costs, increase efficiency, and enhance economic benefits and market competitiveness in the steel industry.

[0003] However, the cracks at the corners and center of the pure Nb-containing steel billet are serious, which leads to cracks at the head and tail of the rolled steel bar, and the steel bar is prone to non-yielding phenomenon, which is prone to non-yielding due to the high bainite content. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a control method for low-cost production of threaded steel using pure niobium alloy in view of the shortcomings of the above-mentioned prior art.

[0005] The technical solution of the present invention to solve the above technical problems is: a control method for designing a pure niobium alloy to produce low-cost threaded steel, the method specifically comprising the following steps:

[0006] (1) Continuous casting slab cooling process: The difference between the first and second cooling zones of the continuous casting machine is cooled by gas mist cooling nozzles;

[0007] (2) Based on the principles of material balance and optimization, a quadratic function relationship model between the alloying element yield and the final molten steel carbon content and temperature was established, as well as a prediction model for the final addition of molten steel alloying. Computer programming was used to achieve dynamic prediction of the alloy addition amount, ensuring a narrow range of control over the molten steel composition.

[0008] (3) Post-rolling graded controlled cooling process: multiple short-distance cooling sections are used, and the water pressure and cooling time are controlled in sections.

[0009] The present invention further defines the scheme:

[0010] The water content of the mist cooling in the aforementioned step (1) is 0.8-1.2 L / kg.

[0011] The aforementioned step (2) is specifically as follows:

[0012] S1: Collect a large amount of steelmaking and smelting related parameter information, including scrap weight, scrap composition, molten iron composition, molten iron weight, final molten steel composition, finished product composition, final temperature, tapping volume, steel grade composition control limit, and the addition amount of various alloys;

[0013] S2: Parameters related to tapping volume are screened from the database. These parameters include scrap weight, scrap composition, molten iron composition, molten iron weight, final molten steel composition, tapping volume, and the addition amount of various alloys. The scrap weight, scrap composition, molten iron composition, molten iron weight, final molten steel composition, and the addition amount of various alloys are used as independent variables, and tapping volume is used as the dependent variable to form the training samples of the BP neural network.

[0014] S3: Establish a three-layer BP neural network, including an input layer, a hidden layer, and an output layer, wherein the independent variables described in S2 constitute the input layer nodes, the number of hidden layer nodes is obtained by training the network, and the steel output is used as the output layer;

[0015] S4: Parameters related to alloying element yield are screened from the database. These parameters include final molten steel composition, finished product composition, final temperature, tapping volume, and the addition amount of various alloys, forming a statistical sample for analyzing alloying element yield;

[0016] The alloying element yield is:

[0017] where ω[i] 规格中限 is the control limit of steel element i; ω[i] 终点残余 End point molten steel element i content; X1, X2, X n are the addition amounts of alloy materials X1, X2, and Xn respectively; ω in is the content of element i in alloy material Xn; Q 出钢量 Steel output

[0018] By using statistical samples to fit the alloying element yield rate with the endpoint molten steel temperature and the endpoint molten steel carbon content, the quadratic function relationship model of the alloying element yield rate with the endpoint molten steel carbon content and temperature is obtained:

[0019]

[0020] Among them, T 终 is the molten steel temperature at the end of smelting, C 终 is the carbon content of molten steel, A1, A2, B1, B2, D are fitting coefficients.

[0021] The aforementioned also include:

[0022] S5: Since the addition of alloy material affects the output of steel, the demand for alloy i needs to be adjusted to:

[0023]

[0024] Where ΔQ is the weight gain of molten steel, the total amount of element i (such as natural decarburization amount)

[0025] S6: Using the optimization principle to determine the optimal cost of each alloy material under various constraints during the steelmaking process, the problem can be described as: Find:

[0026]

[0027] Among them: Z is the total cost of adding alloy materials, C j is the unit price of the jth alloy; ω ij is the content of the i-th element in the j-th alloy; B i is the demand for the i-th element of steel grade; B ui B is the upper limit of the element requirement in the i-th steel grade; li is the lower limit of the element requirement in steel grade i;

[0028] S7: The computer compiled language Visual Basic is used to program the calculation logic and original parameter settings of the optimized alloy feeding model, forming a convenient user interface for application on site.

[0029] The aforementioned step (3) is specifically as follows:

[0030] When the steel bar temperature is ≥1050℃, the water pressure in the first and second sections is controlled at 12-20kg, and the cooling rate is ≥15℃ / s;

[0031] When the steel bar temperature is ≤950℃, the water pressure in the sixth stage is controlled at 5-15kg and the cooling rate is ≤8℃ / s;

[0032] A short temperature recovery section is added between the first and second sections, with a length of ≥0.5m and a temperature recovery time of ≥0.5s.

[0033] The beneficial effects of the present invention are:

[0034] The present invention uses aerosol cooling nozzles to cool the first and second cooling zones of the continuous casting machine. The relatively weak cooling intensity and more uniform secondary cooling gap can effectively reduce cracks and shrinkage defects inside the billet. In addition, the aerosol cooling can still uniformly cool the billet under low water content conditions, effectively solving the problem of existing continuous casting billet water cooling nozzles, which have high intensity but poor cooling uniformity, making defects such as cracks and shrinkage cavities easily generated inside the billet.

[0035] The present invention uses statistical analysis principles to fit the equations of alloying element yield, endpoint carbon content, and endpoint temperature, thereby establishing a prediction model for the final charge of molten steel alloying from a material balance perspective. This model uses precisely collected production data related to the steelmaking process as variables to achieve narrow composition control in steelmaking, thereby improving steelmaking production efficiency and product quality.

[0036] The present invention adopts a graded controlled cooling process, and adopts different water pressure control strategies according to the temperature change of the steel. When the steel temperature is ≥1050℃, in order to cool down as quickly as possible, the water pressure in the first and second sections should be high and controlled between 12-20Kg; when the steel temperature drops to 950℃, in order to avoid abnormal structure, a low-speed cooling strategy is adopted, that is, the water pressure in the sixth section should be low, basically controlled at 5-15Kg. This water pressure control strategy enables the steel to achieve more uniform cooling and more stable structural transformation during the cooling process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of the neural network training and learning process in Example 1;

[0038] Figure 2 This is a low-magnification image of a continuous casting slab using the present invention;

[0039] Figure 3 This is a low-magnification image of a continuous casting slab that does not adopt the present invention;

[0040] Figure 4 This is a diagram of the bainite structure of the pure niobium steel bar without adopting the graded controlled cooling of the present invention;

[0041] Figure 5 This is a diagram of the bainite structure of the pure niobium steel bar produced by graded controlled cooling according to the present invention;

[0042] Figure 6 The graphs are the mechanical property curves before and after adopting the graded controlled cooling of the present invention. DETAILED DESCRIPTION

[0043] Example 1

[0044] This embodiment provides a control method for designing and producing threaded steel bars at low cost using pure niobium alloy, the method specifically comprising the following steps:

[0045] (1) Continuous casting slab cooling process: The difference between the first and second cooling zones of the continuous casting machine is cooled by a mist cooling nozzle, and the water content of the mist cooling is 0.8-1.2L / kg;

[0046] (2) Based on the principles of material balance and optimization, a quadratic function relationship model between the alloying element yield and the final molten steel carbon content and temperature was established, as well as a prediction model for the final addition of molten steel alloying. The dynamic prediction of the alloy addition amount was achieved through computer programming to ensure the narrow range control of the molten steel composition. Specifically:

[0047] S1: Collect a large amount of steelmaking and smelting parameter information, including scrap weight, scrap composition, molten iron composition, molten iron weight, final molten steel composition, finished product composition, final temperature, tapping volume, steel grade composition control limit, and the addition amount of various alloys. All of this information can be directly obtained through the production ERP system, and 300 sets of parameter information are collected as a database;

[0048] S2: Parameters related to tapping volume are screened from the database. These parameters include scrap weight, scrap composition, molten iron composition, molten iron weight, final molten steel composition, tapping volume, and the addition amount of various alloys. The scrap weight, scrap composition, molten iron composition, molten iron weight, final molten steel composition, and the addition amount of various alloys are used as independent variables, and tapping volume is used as the dependent variable to form the training samples of the BP neural network.

[0049] S3: Establish a three-layer BP neural network, including an input layer, a hidden layer, and an output layer, wherein the independent variables in S2 constitute the input layer nodes, the number of hidden layer nodes is obtained by training the network, and the steel output is used as the output layer. By selecting 250 groups of data as training samples for the neural network, and the remaining 50 groups of data as test samples, the effectiveness of the neural network training is verified. At the beginning of training, the number of hidden layer nodes and the weights and thresholds of the connections of each node are selected, and then the 250 groups of training samples are input into the three-layer neural network to train the model, and the number of hidden layer nodes and the weights and thresholds of the connections of each node are obtained. The independent variables of the 50 groups of test samples are input into the neural network to verify the network, and the estimated steel output is compared with the actual value. The number of hidden layer nodes and the weights and thresholds of the connections of each node are readjusted according to the error between the budget value and the actual value. Repeat the above operation until the error between the budget value and the actual value is small enough or zero, determine the number of hidden layer nodes and the weights and thresholds of the connections of each node at this time, and obtain a trained three-layer BP neural network that meets the site requirements. The neural network training and learning process diagram is shown as follows: Figure 1 As shown;

[0050] S4: Parameters related to alloying element yield are screened from the database. These parameters include final molten steel composition, finished product composition, final temperature, tapping volume, and the addition amount of various alloys, forming a statistical sample for analyzing alloying element yield;

[0051] The alloying element yield is:

[0052] where ω[i] 规格中限 is the control limit of steel element i; ω[i] 终点残余 End point molten steel element i content; X1, X2, X n are the addition amounts of alloy materials X1, X2, and Xn respectively; ω in is the content of element i in alloy material Xn; Q出钢量 Steel output

[0053] By using statistical samples to fit the alloying element yield rate with the endpoint molten steel temperature and the endpoint molten steel carbon content, the quadratic function relationship model of the alloying element yield rate with the endpoint molten steel carbon content and temperature is obtained:

[0054]

[0055] Among them, T 终 is the molten steel temperature at the end of smelting, C 终 is the carbon content of molten steel, A1, A2, B1, B2, D are fitting coefficients;

[0056] S5: Since the addition of alloy material affects the output of steel, the demand for alloy i needs to be adjusted to:

[0057]

[0058] Where ΔQ is the weight gain of molten steel, the total amount of element i (such as natural decarburization amount)

[0059] S6: Using the optimization principle to determine the optimal cost of each alloy material under various constraints during the steelmaking process, the problem can be described as: Find:

[0060]

[0061] Among them: Z is the total cost of adding alloy materials, C j is the unit price of the jth alloy; ω ij is the content of the i-th element in the j-th alloy; B i is the demand for the i-th element of steel grade; B ui B is the upper limit of the element requirement in the i-th steel grade; li is the lower limit of the element requirement in steel grade i;

[0062] S7: The computer compilation language Visual Basic is used to program the calculation logic and original parameter settings of the optimized alloy feeding model, forming a convenient user interface for application on site.

[0063] (3) Post-rolling graded controlled cooling process: multiple short-distance cooling sections are used, and the water pressure and cooling rate are controlled in sections, specifically:

[0064] When the steel bar temperature is ≥1050℃, the water pressure in the first and second sections is controlled at 12-20kg, and the cooling rate is ≥15℃ / s;

[0065] When the steel bar temperature is ≤950℃, the water pressure in the sixth stage is controlled at 5-15kg and the cooling rate is ≤8℃ / s;

[0066] A short temperature recovery section is added between the first and second sections, with a length of ≥0.5m and a temperature recovery time of ≥0.5s.

[0067] Using the above method, take a factory as an example:

[0068] Original heat information: furnace number 24504310, molten iron weight 98t, scrap steel weight 16.4t, slag steel 3t, molten iron composition C4.86, Si0.42, Mn0.112.

[0069] Finished product control requirements: C0.22%~0.25%, Nb0.015~0.021%, Mn1.38%~1.48%.

[0070] Process charging information: 720kg of sintered ore, 3000kg of lime, and 3900kg of dolomite.

[0071] The estimated steel output was 104.2t; the actual steel output was 103.2t, with a relative error of 0.97%.

[0072] Alloy element yield estimate: Mn 92.3%, Nb 91.2%, C 82.5%.

[0073] The estimated amount of alloy material added is: 673.4kg of silicon manganese, 40.2kg of ferroniobium, and 133.8kg of carbon powder. The estimated alloy cost is 43.1 yuan per ton of steel.

[0074] Finished product composition: C0.23%, Nb0.019%, Mn1.43%, maximum relative error 4%.

[0075] From the above Figure 2 and Figure 3 By comparison, it can be seen that after adopting the present invention, the center crack is reduced from level 2.5 to level 1.0, and the corner crack is reduced from level 3.0 to level 1.5, so the low magnification of the casting is significantly improved.

[0076] like Figure 4 As shown, without using the method of the present invention, pure niobium HRB400E has more bainite and obvious characteristics, such as Figure 5 As shown in FIG. 1 , after adopting the method of the present invention, the abnormal bainite in the pure niobium HRB400E structure is significantly reduced.

[0077] according to Figure 6 It can be seen that the left side is a comparison between the method of the present invention and the right side, and there is an obvious yield platform after the method of the present invention is adopted (see the red box).

[0078] In addition to the above embodiments, the present invention may also have other implementations. Any technical solution formed by equivalent replacement or equivalent transformation falls within the scope of protection required by the present invention.

Claims

1. A control method for producing threaded steel using pure niobium alloy at low cost, characterized by: The method specifically comprises the following steps: (1) Continuous casting slab cooling process: The difference between the first and second cooling zones of the continuous casting machine is cooled by gas mist cooling nozzles; (2) Based on the principles of material balance and optimization, a quadratic function relationship model between the alloying element yield and the final molten steel carbon content and temperature was established, as well as a prediction model for the final addition of molten steel alloying. Computer programming was used to achieve dynamic prediction of the alloy addition amount, ensuring a narrow range of control over the molten steel composition. (3) Post-rolling graded controlled cooling process: multiple short-distance cooling sections are used, and the water pressure and cooling rate are controlled in sections.

2. The control method for producing threaded steel with low cost using pure niobium alloy according to claim 1, characterized in that: The specific water content of the mist cooling in step (1) is 0.8-1.2 L / kg.

3. The control method for designing and producing threaded steel bars at low cost using pure niobium alloy according to claim 1, characterized in that: The step (2) is specifically as follows: S1: Collect a large amount of steelmaking and smelting related parameter information, including scrap weight, scrap composition, molten iron composition, molten iron weight, final molten steel composition, finished product composition, final temperature, tapping volume, steel grade composition control limit, and the addition amount of various alloys; S2: Parameters related to tapping volume are screened from the database. These parameters include scrap weight, scrap composition, molten iron composition, molten iron weight, final molten steel composition, tapping volume, and the addition amount of various alloys. The scrap weight, scrap composition, molten iron composition, molten iron weight, final molten steel composition, and the addition amount of various alloys are used as independent variables, and tapping volume is used as the dependent variable to form the training samples of the BP neural network. S3: Establish a three-layer BP neural network, including an input layer, a hidden layer, and an output layer, wherein the independent variables described in S2 constitute the input layer nodes, the number of hidden layer nodes is obtained by training the network, and the steel output is used as the output layer; S4: Parameters related to alloying element yield are screened from the database. These parameters include final molten steel composition, finished product composition, final temperature, tapping volume, and the addition amount of various alloys, forming a statistical sample for analyzing alloying element yield; The alloying element yield is: where ω[i] 规格中限 is the control limit of steel element i; ω[i] 终点残余 End point molten steel element i content; X1, X2, X n are the addition amounts of alloy materials X1, X2, and Xn respectively; ω in is the content of element i in alloy material Xn; Q 出钢量 Steel output By using statistical samples to fit the alloying element yield rate with the endpoint molten steel temperature and the endpoint molten steel carbon content, the quadratic function relationship model of the alloying element yield rate with the endpoint molten steel carbon content and temperature is obtained: Among them, T 终 is the molten steel temperature at the end of smelting, C 终 is the carbon content of molten steel, A1, A2, B1, B2, D are fitting coefficients.

4. The control method for producing threaded steel with low cost using pure niobium alloy according to claim 3, characterized in that: Also includes: S5: Since the addition of alloy material affects the output of steel, the demand for alloy i needs to be adjusted to: Alloying element i requirement Where ΔQ is the weight gain of molten steel, the total amount of element i (such as natural decarburization amount) S6: Using the optimization principle to determine the optimal cost of each alloy material under various constraints during the steelmaking process, the problem can be described as: beg: Among them: Z is the total cost of adding alloy materials, C j is the unit price of the jth alloy; ω ij is the content of the i-th element in the j-th alloy; B i is the demand for the i-th element of steel grade; B ui B is the upper limit of the element requirement in the i-th steel grade; li is the lower limit of the element requirement in steel grade i; S7: The computer compiled language Visual Basic is used to program the calculation logic and original parameter settings of the optimized alloy feeding model, forming a convenient user interface for application on site.

5. The control method for producing threaded steel bars using pure niobium alloy at low cost according to claim 1, characterized in that: The specific steps in step (3) are: When the steel bar temperature is ≥1050℃, the water pressure in the first and second sections is controlled at 12-20kg, and the cooling rate is ≥15℃ / s; When the steel bar temperature is ≤950℃, the water pressure in the sixth stage is controlled at 5-15kg and the cooling rate is ≤8℃ / s; A short temperature recovery section is added between the first and second sections, with a length of ≥0.5m and a temperature recovery time of ≥0.5s.

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