Method and device for determining continuous casting process parameters based on ti n precipitation prediction

Abstract

The application discloses a continuous casting process parameter determination method and device based on TiN precipitation prediction, relates to the technical field of steel metallurgy continuous casting process control, and mainly aims to solve the problem of low TiN precipitation size control precision in the continuous casting control process. Mainly includes obtaining the steel grade composition data and temperature field data of the target continuous casting steel material, and determining the billet solidification period cooling rate curve according to the initial pulling speed and initial specific water quantity in the initial control parameter; the expected TiN precipitation time and temperature window are predicted according to the temperature field data, the steel grade composition data and the billet solidification period cooling rate curve, and the expected TiN precipitation size is determined; in the case that the expected TiN precipitation size is greater than the upper limit of the TiN precipitation size, the target cooling rate curve meeting the upper limit of the TiN precipitation size is inversed through the TiN precipitation size and the cooling rate relationship model; the target specific water quantity parameter is calculated through the quantitative relationship model of the cooling rate and the specific water quantity. Mainly used for determining the continuous casting process parameter.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel metallurgy continuous casting process control, in particular to a continuous casting process parameter determination method and device based on TiN precipitation prediction. BACKGROUND

[0002] Continuous casting (referred to as "continuous casting") is the core process in modern steel industry, which continuously pours high-temperature molten steel and directly solidifies into the required cross-sectional shape of the casting blank. In the continuous casting process, in order to improve the mechanical properties of steel, micro-alloying elements such as titanium (Ti) are often added. Titanium combined with nitrogen (N) in steel generates titanium nitride (TiN) particles, which can effectively inhibit the growth of austenite grains after precipitation in the high-temperature austenite region, thereby refining the microstructure of the final product. However, TiN particles are a "double-edged sword". If the size, quantity or distribution of TiN particles precipitated during solidification and cooling is too large, too many or uneven, these hard and brittle inclusions will become crack sources, seriously deteriorating the plasticity, toughness and fatigue properties of the steel, therefore, the precipitation of TiN needs to be controlled.

[0003] The existing continuous casting process control technology mainly relies on conventional adjustment of macroscopic parameters such as pouring temperature, blank drawing speed, and secondary cooling water quantity, and lacks accurate description of the quantitative relationship between the precipitation behavior of TiN and process parameters. Because the precipitation kinetics process leading to TiN is extremely complex and difficult to capture. The current operation mode cannot achieve accurate prediction and active control of the TiN precipitation process (especially the precipitation size), resulting in low control precision and large fluctuations of the TiN particle precipitation size in the final casting blank, which has become a key technical bottleneck restricting the quality stability and consistency of high-end products. SUMMARY

[0004] Therefore, the present application provides a continuous casting process parameter determination method and device based on TiN precipitation prediction, which mainly aims to solve the problem of low control precision of TiN particle precipitation size in the existing thick plate continuous casting process.

[0005] According to one aspect of the present application, a continuous casting process parameter determination method based on TiN precipitation prediction is provided, comprising:

[0006] Obtaining the steel grade composition data of the target continuous casting steel and the temperature field data under the initial control parameters, and determining the casting blank solidification period cooling rate curve, wherein the temperature field data includes temperature data corresponding to each casting stream position;

[0007] According to the temperature field data, the steel grade composition data and the casting blank solidification period cooling rate curve, the expected precipitation time and temperature window of TiN are predicted, and the expected TiN precipitation size is determined according to the expected precipitation time and temperature window and the casting blank solidification period cooling rate curve;

[0008] in the case of expecting that the TiN precipitation size is greater than the upper limit of TiN precipitation size, a target cooling rate curve satisfying the upper limit of TiN precipitation size is obtained by inversion of a TiN precipitation size and cooling rate relationship model;

[0009] According to the target cooling rate curve, a target specific water quantity parameter is calculated by a quantitative relationship model of cooling rate and specific water quantity.

[0010] Further, the expected precipitation time and temperature window of TiN are predicted according to the temperature field data, the steel composition data and the slab solidification period cooling rate curve, comprising:

[0011] According to the empirical regression of the steel composition data, the liquidus temperature and the solidus temperature of the target continuous casting steel material are calculated, and the secondary dendrite arm spacing is calculated according to the slab solidification period cooling rate curve;

[0012] According to the comparison results of the temperatures at different positions in the temperature field data with the liquidus temperature and the solidus temperature, the solid phase fractions at different positions are calculated;

[0013] According to the initial liquid phase concentration, the secondary dendrite arm spacing and the solid phase fractions at different positions, the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration at different positions are calculated by Ohnaka model, and the TiN supersaturation is calculated according to the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration;

[0014] The position where both the TiN supersaturation and the solid phase fraction meet the corresponding threshold value is determined as the precipitation starting position, and the expected precipitation time and temperature window are calculated according to the precipitation starting position, the initial withdrawal rate and the solid phase fraction.

[0015] Further, the TiN supersaturation is calculated according to the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration, comprising:

[0016] Based on the interaction coefficients of different elements with Ti, the Ti activity coefficient is calculated, and based on the interaction coefficients of different elements with N, the N activity coefficient is calculated;

[0017] According to the withdrawal rate and the slab solidification period cooling rate curve, the temperature corresponding to different positions is calculated, and the equilibrium solubility product at different positions is calculated according to the temperature at different positions;

[0018] For each position, the TiN supersaturation at different positions is calculated according to the equilibrium solubility product, the Ti theoretical liquid phase concentration, the N theoretical liquid phase concentration, the Ti activity coefficient and the N activity coefficient.

[0019] Further, the target cooling rate curve satisfying the upper limit of TiN precipitation size is obtained by inversion of a TiN precipitation size and cooling rate relationship model, comprising:

[0020] The upper limit of the TiN precipitation size is taken as a target output, and the solidification end cooling rate is inversely calculated by substituting the TiN precipitation size and cooling rate relationship model, and the target cooling rate curve of different control sections is calculated according to the solidification end cooling rate;

[0021] The TiN precipitation size and cooling rate relationship model is expressed as: ; wherein d represents the precipitation size, represents the TiN initial size, K represents the diffusion coefficient of nitrogen in the liquid phase, represents the TiN precipitation time and temperature window, and CR is the solidification end cooling rate.

[0022] The target cooling rate curve is used to calculate the target specific water amount parameter through the quantitative relationship model of cooling rate and specific water amount, including:

[0023] For each control section of the target cooling rate curve, the specific water amount of the control section is calculated by inputting the cooling rate of the control section into the quantitative relationship model of cooling rate and specific water amount according to the cooling rate, and the target specific water amount parameter containing the specific water amount of multiple control sections is obtained;

[0024] The quantitative relationship model of cooling rate and specific water amount is expressed as: ; wherein CR represents the cooling rate, W represents the specific water amount, a, b, c are fitting coefficients, and a, b are greater than 0, and c is less than 0.

[0025] Further, the initial control parameter further includes an initial reduction parameter, and the expected precipitation time and temperature window include a precipitation start time.

[0026] The method further includes:

[0027] The expected cumulative reduction amount before precipitation is calculated according to the precipitation start time and the initial reduction parameter;

[0028] In the case that the expected cumulative reduction amount before precipitation does not meet the cumulative reduction amount before precipitation condition, the reduction roller number range and single roller reduction amount range of different position intervals are calculated according to the cumulative reduction amount before precipitation target interval.

[0029] Further, after the target specific water amount parameter is calculated through the quantitative relationship model of cooling rate and specific water amount according to the target cooling rate curve, the method further includes:

[0030] According to the target cooling rate curve and the target specific water amount parameter, the drawing speed is optimized in the reduction roller number range and the single roller reduction amount range to obtain the target drawing speed, the target reduction roller number and the target single roller reduction amount;

[0031] The target continuous casting machine executing mechanism is controlled according to the target pulling speed, the target number of pressure down rolls and the target single roll pressure down amount, and the target specific water amount parameter is controlled according to the target specific water amount parameter to perform the continuous casting production.

[0032] In the continuous casting production process, real-time cooling rates and TiN precipitation size data are collected, and the real-time cooling rates and the TiN precipitation size data are visually displayed.

[0033] According to another aspect of the present application, a TiN precipitation prediction-based continuous casting process parameter determination device is provided, comprising:

[0034] An acquisition module is configured to acquire steel grade composition data of a target continuous casting steel material and temperature field data under initial control parameters, and determine a solidification period cooling rate curve of a casting blank, wherein the temperature field data comprises temperature data corresponding to different casting flow positions respectively.

[0035] A prediction module is configured to predict an expected TiN precipitation time and temperature window according to the temperature field data, the steel grade composition data and the solidification period cooling rate curve of the casting blank, and determine an expected TiN precipitation size according to the expected TiN precipitation time and temperature window and the solidification period cooling rate curve of the casting blank.

[0036] An inversion module is configured to, in a case where the expected TiN precipitation size is greater than an upper limit of TiN precipitation size, inversely calculate a target cooling rate curve meeting the upper limit of TiN precipitation size through a TiN precipitation size and cooling rate relationship model.

[0037] A calculation module is configured to calculate a target specific water amount parameter through a quantitative relationship model between cooling rate and specific water amount according to the target cooling rate curve.

[0038] According to still another aspect of the present application, a storage medium is provided, wherein at least one executable instruction is stored in the storage medium, and the executable instruction causes a processor to perform operations corresponding to the TiN precipitation prediction-based continuous casting process parameter determination method.

[0039] According to still another aspect of the present application, a terminal is provided, comprising a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus.

[0040] The memory is configured to store at least one executable instruction, and the executable instruction causes the processor to perform operations corresponding to the TiN precipitation prediction-based continuous casting process parameter determination method.

[0041] Through the above technical solution, the technical solution provided by the embodiments of the present application has at least the following advantages:

[0042] The application provides a continuous casting process parameter determination method and device based on TiN precipitation prediction, and the embodiment of the application obtains the steel grade composition data of target continuous casting steel material and the temperature field data under initial control parameters, and determines the billet solidification period cooling rate curve according to the initial withdrawal speed and the initial specific water quantity in the initial control parameters; the expected precipitation time and temperature window of TiN are predicted according to the temperature field data, the steel grade composition data and the billet solidification period cooling rate curve, and the expected TiN precipitation size is determined according to the expected precipitation time and temperature window and the billet solidification period cooling rate curve; in the case that the expected TiN precipitation size is greater than the upper limit of the TiN precipitation size, the target cooling rate curve meeting the upper limit of the TiN precipitation size is inversely calculated through the TiN precipitation size and the cooling rate relationship model; and the target specific water quantity parameter is calculated through the quantitative relationship model of the cooling rate and the specific water quantity according to the target cooling rate curve. The size of the harmful TiN precipitate is greatly reduced, the risk of performance deterioration of the steel material caused by the large-size TiN inclusion is reduced, and meanwhile, the continuous casting process parameter adjustment is changed from experience dependence to model-based precise quantitative calculation, thereby greatly improving the consistency and stability of the quality of high-end steel products.

[0043] The above description is only a summary of the technical scheme of the application, in order to more clearly understand the technical means of the application, the application can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the application more obvious and easy to understand, the specific embodiments of the application are described below. BRIEF DESCRIPTION OF DRAWINGS

[0044] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are intended to further assist in understanding the preferred embodiments, and are not considered limiting of the present application. Moreover, like reference numerals denote like parts throughout the several views in the drawings. In the drawings:

[0045] Figure 1 A flow chart of a continuous casting process parameter determination method based on TiN precipitation prediction provided by the embodiment of the application is shown;

[0046] Figure 2 A flow chart of another continuous casting process parameter determination method based on TiN precipitation prediction provided by the embodiment of the application is shown;

[0047] Figure 3 A flow chart of determination of continuous casting pressing-down parameters and cooling rate provided by the embodiment of the application is shown;

[0048] Figure 4 A block diagram of a continuous casting process parameter determination device based on TiN precipitation prediction provided by the embodiment of the application is shown;

[0049] Figure 5 Fig. 1 shows a schematic diagram of a terminal according to an embodiment of the application. DETAILED DESCRIPTION

[0050] Exemplary embodiments of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings; however, they are not limited to the embodiments set forth herein but can be implemented in various forms. The present disclosure will be described herein with reference to exemplary embodiments. It is to be understood that the present disclosure is not limited to a particular embodiment, and as such can vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present disclosure. It must be noted that, as used herein, the singular forms "a", "an" and "the" include plural referents unless the context clearly dictates otherwise. The term "comprises" means "includes, but is not limited to". The term "comprising" also means "including, but not limited to".

[0051] In view of the low control precision of TiN particle precipitation size in the existing thick plate continuous casting process, the present application provides a continuous casting process parameter determination method based on TiN precipitation prediction, as shown in Figure 1 The method comprises the following steps.

[0052] 101. Obtain the steel grade composition data of the target continuous casting steel material and the temperature field data under the initial control parameters, and determine the casting blank solidification period cooling rate curve according to the initial casting speed and the initial specific water volume in the initial control parameters.

[0053] In the present application, the temperature field data includes temperature data corresponding to each casting stream position. The target continuous casting steel material is the steel material that needs to be produced by continuous casting. The steel grade composition data is the chemical composition of the target continuous casting steel material, for example, the content of each element in C, Ti, N, P, S, Si, and Mn. The temperature field data is the temperature time sequence of each position in continuous casting (such as the crystallizer outlet and the fan-shaped section), which can be obtained by numerical simulation or sensor measurement under the initial control parameters such as casting speed and specific water volume. According to the initial casting speed and the initial specific water volume, and combined with the existing heat transfer model, the temperature of a certain point (usually the solid-liquid front or the center) in the casting blank during the solidification process can be calculated, that is, the cooling rate curve of the casting blank during the solidification period, which is used to describe the cooling speed of the molten steel under the current process.

[0054] 102. Predict the expected precipitation time and temperature window of TiN according to the temperature field data, the steel grade composition data, and the cooling rate curve of the casting blank during the solidification period, and determine the cooling rate curve of the precipitation period and the expected TiN precipitation size according to the expected precipitation time and temperature window.

[0055] In the embodiment of the present application, the temperature field data (knowing the temperature of each position of the casting blank at what time), the steel composition data (calculating the solubility product of TiN through a thermodynamic model) and the casting blank solidification period cooling rate curve are combined to simulate and predict that the temperature and composition conditions at a certain position or time of the casting meet the conditions for TiN to begin to precipitate in large quantities and continue until the end of precipitation. This time period is the expected precipitation time and temperature window. Since the cooling rate is the core kinetic parameter affecting the size of TiN precipitation, it determines the growth degree of TiN particles by changing the element diffusion rate at the end of solidification, the dendrite arm spacing and the precipitation time. Therefore, based on the expected precipitation time and temperature window and the precipitation period cooling rate curve, the average size of TiN particles under the process conditions of the initial control parameters can be calculated, that is, the expected precipitation size.

[0056] It should be noted that by coupling the thermal history and the steel composition, the precipitation time and the expected size of TiN inclusions can be scientifically predicted. This enables the initial control parameters to be set in advance to meet the requirements of TiN size, and realizes the pre-control of quality problems.

[0057] 103、In the case where the expected TiN precipitation size is greater than the upper limit of the TiN precipitation size, the target cooling rate curve that meets the upper limit of the TiN precipitation size is obtained by inversely calculating the TiN precipitation size and the cooling rate relationship model.

[0058] In the embodiment of the present application, the calculated expected TiN precipitation size is compared with the TiN precipitation size control target, that is, the upper limit of the TiN precipitation size, which needs to be reached by the process control. If the expected TiN precipitation size is less than or equal to the upper limit of the TiN precipitation size, it indicates that the current initial control parameters are available, and the production of target continuous casting steel can be carried out according to the initial control parameters. If the expected TiN precipitation size is greater than the upper limit of the TiN precipitation size, it indicates that the current initial control parameters need to be adjusted to meet the process control requirements. In the case where the initial control parameters need to be adjusted, the TiN precipitation size and cooling rate relationship model established in advance is called, which describes the functional relationship between the cooling rate as the independent variable and the TiN precipitation size as the dependent variable. By substituting the upper limit of the TiN precipitation size into this model as the known cooling rate, the target cooling rate curve that needs to have a cooling rate as large as the upper limit of the TiN size during the predicted precipitation temperature window is obtained.

[0059] It should be noted that the quality target is converted into an executable process index through inverse calculation. When the predicted TiN size exceeds the standard, the target cooling rate value necessary to control the TiN size within the qualified range is calculated in reverse according to the established theoretical model, avoiding blind trial and error.

[0060] 104、According to the target cooling rate curve, the target specific water quantity parameter is calculated through a quantitative relationship model of cooling rate and specific water quantity.

[0061] In the embodiment of the present application, considering that the on-site equipment cannot directly set the cooling rate, it is necessary to convert it into an executable equipment parameter, i.e., specific water quantity (liter / ton of steel or liter / minute). A quantitative relationship model of cooling rate and specific water quantity is established in advance. The model is a linear function obtained by deducing a function form through heat transfer mechanism and fitting coefficients based on actual process data. The target cooling rate curve is taken as input, and the new target specific water quantity parameter required by each section of the secondary cooling zone to achieve such a cooling effect is inversely calculated. This enables the operator or the control system to adjust the water spray valve of the secondary cooling zone according to the target specific water quantity parameter, thereby realizing process optimization.

[0062] It should be noted that by pre-establishing the quantitative relationship model of cooling rate and specific water quantity, the inversely obtained cooling rate target that cannot be directly set is accurately converted into the specific water quantity parameter that can be accurately adjusted on site, so as to realize accurate control of the cooling process by adjusting the water quantity of the secondary cooling zone, thereby ensuring that the TiN inclusions of the produced casting blank meet the process requirements stably in size.

[0063] In one embodiment of the present application, in order to further illustrate and limit, as shown in Figure 2 The step of predicting the expected precipitation time and temperature window of TiN according to the temperature field data, the steel composition data and the casting blank solidification period cooling rate curve comprises:

[0064] 201、According to the steel composition data, the liquidus temperature and the solidus temperature of the target continuous casting steel material are calculated by empirical regression, and the secondary dendrite arm spacing is calculated according to the casting blank solidification period cooling rate curve.

[0065] 202、According to the comparison results of the temperatures at different positions in the temperature field data with the liquidus temperature and the solidus temperature, the solid phase fractions at different positions are calculated.

[0066] 203、According to the initial liquid phase concentration, the secondary dendrite arm spacing and the solid phase fractions at different positions, the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration at different positions are calculated through the Ohnaka model, and the TiN supersaturation is calculated according to the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration.

[0067] 204、The position where both the TiN supersaturation and the solid phase fraction meet the corresponding threshold value is determined as the precipitation starting position, and the expected precipitation time and temperature window are calculated according to the precipitation starting position, the initial casting speed and the solid phase fraction.

[0068] In this embodiment of the invention, the liquidus temperature and solidus temperature are used to delineate the solidification range to determine the temperature range in which TiN may precipitate. The liquidus temperature and solidus temperature are calculated based on empirical regression of the steel composition. Liquidus temperature The formula is expressed as:

[0069] ;

[0070] Wherein, C, Si, and Mn represent the mass fraction (%) of their respective symbolic chemical elements.

[0071] solidus temperature The formula is expressed as:

[0072] ;

[0073] Wherein, C, Si, Mn, P, Al, Cr, Ni, and S represent the mass fraction (%) of their respective symbolic chemical elements.

[0074] The secondary dendrite arm spacing λ2 is a key parameter of the dendrite structure during solidification, directly affecting the diffusion distance of elements between dendrites. A higher cooling rate leads to faster dendrite growth, while a smaller secondary dendrite arm spacing shortens the element diffusion path and inhibits TiN particle growth. The relationship between the secondary dendrite arm spacing and the cooling rate is obtained through empirical fitting, expressed as:

[0075] ;

[0076] Where CR represents the cooling rate and C represents the mass fraction of carbon (%).

[0077] Since TiN typically precipitates at the end of solidification, the solid fraction is a key parameter for determining the precipitation critical point. Therefore, the solid fraction needs to be determined based on comparisons of the liquidus and solidus temperatures with temperatures at different locations. Methods for determining the solid fraction at any location include: when the steel's temperature is above or equal to the liquidus temperature, it is completely liquid, and the solid fraction is zero; when the steel's temperature is below or equal to the solidus temperature, it is completely solid, and the solid fraction is 1; when the steel's temperature is between the solidus and liquidus, the material is in a solid-liquid two-phase region and is solidifying, and the solid fraction is... Represented as Where T represents the temperature at any location. The solid fraction increases linearly with decreasing temperature, reflecting the degree of liquid-to-solid transition.

[0078] During solidification, uneven distribution of elements in the liquid and solid phases leads to element enrichment (segregation) in the liquid phase. This requires calculating the theoretical liquid phase concentrations of Ti and N using the Ohnaka microsegregation model. The formula for the Ohnaka microsegregation model is as follows:

[0079] ;

[0080] in, This indicates the liquid concentration at the current solid fraction. The initial liquid concentration is represented by ; k represents the partition coefficient (the partition coefficient for Ti is 0.38, and the partition coefficient for N is 0.25). Parameters characterizing interdendritic flow effects. , This represents a dimensionless correction factor related to interdendritic solute diffusion, the value of which is determined by local solidification conditions. D represents the diffusion coefficient. Indicates characteristic time, This represents the spacing between the secondary dendrite arms. This model can take into account both the diffusion and segregation of elements between dendrites, and more accurately reflect the element concentration of the liquid phase during solidification. Then, the TiN supersaturation is calculated based on the theoretical liquid phase concentration of Ti and the theoretical liquid phase concentration of N. After obtaining the TiN supersaturation at different locations, the TiN supersaturation at each location can be compared with the supersaturation threshold (1), and the solid fraction can be compared with the solid fraction threshold (0.9). The locations where the TiN supersaturation is greater than 1 and the solid fraction is greater than 0.9 are determined as the precipitation initiation locations. Once the precipitation initiation location is determined, the time required for molten steel to flow from the meniscus of the crystallizer to that location can be easily calculated based on the initial casting speed of the continuous casting machine. It starts from the initiation time and usually lasts until the micro-element is completely solidified and the solid fraction is equal to 1 or even later in a low-temperature region. Therefore, the expected precipitation time and temperature window refers to the time period from the precipitation initiation time to the precipitation basically end time. The determination of this window also depends on the analysis of the changes in solid fraction and supersaturation with cooling time.

[0081] In one embodiment of the present invention, for further explanation and limitation, the step of calculating the TiN supersaturation based on the theoretical liquid phase concentration of Ti and the theoretical liquid phase concentration of N includes:

[0082] The Ti activity coefficient was calculated based on the interaction coefficients between different elements and Ti, and the N activity coefficient was calculated based on the interaction coefficients between different elements and N.

[0083] The temperature corresponding to different positions is calculated based on the casting speed and the cooling rate curve during the solidification period of the billet, and the equilibrium solubility product at different positions is calculated based on the temperature at different positions.

[0084] For each location, the TiN supersaturation at different locations is calculated based on the equilibrium solubility product, the theoretical liquid phase concentration of Ti, the theoretical liquid phase concentration of N, the activity coefficient of Ti, and the activity coefficient of N.

[0085] In this embodiment of the invention, the effective concentrations (i.e., activities) of titanium (Ti) and nitrogen (N) in molten steel are calculated using a thermodynamic model. Combined with the temperature field determined by the cooling process, the equilibrium solubility product of TiN at each location is calculated. Furthermore, by comparing the actual activity product of titanium and nitrogen with this equilibrium solubility product, the thermodynamic driving force (i.e., supersaturation) for TiN precipitation at each location is accurately quantified. The activity coefficient corrects for the influence of other elements on the chemical potentials of Ti and N, making the activity closer to the true thermodynamic effective concentration. This combines the steel composition, inter-element interactions, and the thermal history of continuous casting, achieving accurate prediction of precipitation conditions.

[0086] Among them, the Ti activity coefficient The calculation formula is expressed as:

[0087] ;in, The commonly used logarithm to represent the activity coefficient of titanium (Ti) at the 1873 Kelvin reference temperature. , This represents the interaction coefficient between element j and Ti. The mass fraction of element j is represented by T; T represents the temperature (Kelvin temperature) at the current location.

[0088] N activity coefficient The calculation formula is expressed as:

[0089] ;in, The commonly used logarithm to represent the activity coefficient of nitrogen (N) at the 1873 Kelvin reference temperature. , This represents the interaction coefficient between element j and N. The mass fraction of element j is represented by T; T represents the temperature (Kelvin temperature) at the current location.

[0090] The supersaturation of TiN is the ratio of the actual activity product to the equilibrium solubility product, expressed as:

[0091] ;in, This indicates the concentration of titanium in the liquid phase. This indicates the concentration of elemental nitrogen in the liquid phase. Represents the equilibrium solubility product of TiN T represents Kelvin temperature.

[0092] In one embodiment of the present invention, for further explanation and limitation, the step of inverting the target cooling rate curve that satisfies the upper limit of TiN precipitation size using a TiN precipitation size versus cooling rate relationship model includes:

[0093] The upper limit of TiN precipitation size is used as the target output and substituted into the model of the relationship between TiN precipitation size and cooling rate to calculate the cooling rate at the end of solidification. Based on the cooling rate at the end of solidification, the target cooling rate curves for different control segments are calculated.

[0094] In this embodiment of the invention, the relationship between TiN precipitation size and cooling rate is expressed as follows: Where d represents the precipitation size, The initial size of TiN is represented by 0.01 micrometers, and K represents the diffusion coefficient of N in the liquid phase. The time window for TiN precipitation (from the start of precipitation to a solid fraction of 0.999) is represented by CR, which is the cooling rate at the end of solidification (solid fraction 0.93-1.0). The upper limit for TiN precipitation size can be 5 micrometers, or it can be customized according to process requirements; this embodiment of the invention does not impose specific limitations. The model relating TiN precipitation size to cooling rate is based on the linear relationship between the cubic particle size and time in LSW theory. This model provides a clear direction for process optimization, enabling process design to move from "trial and error" to "quantitative calculation." It avoids blindly adjusting all parameters, instead focusing on precisely enhancing cooling near the "end of solidification," thus allowing direct calculation of the target cooling rate curve and providing accurate quantitative values ​​for precise automatic control.

[0095] In one embodiment of the present invention, for further explanation and limitation, the step of calculating the target specific water quantity parameter based on the target cooling rate curve using a quantitative relationship model between cooling rate and specific water quantity includes:

[0096] For each control segment of the target cooling rate curve, the specific water volume of the control segment is calculated based on the quantitative relationship model between the cooling rate and the specific water volume corresponding to the control segment, thus obtaining a target specific water volume parameter that includes the specific water volume of multiple control segments.

[0097] In this embodiment of the invention, the quantitative relationship model between cooling rate and specific water content is constructed based on the heat transfer principle of the continuous casting secondary cooling process, combined with regression analysis of experimental data under specific process conditions. The cooling rate input to the quantitative relationship model between cooling rate and specific water content is expressed as follows: Where CR represents the cooling rate, W represents the specific water volume (unit: liters per kilogram), and a, b, and c are fitting coefficients, with a and b both greater than 0 and c less than 0.

[0098] The construction and derivation of the quantitative relationship model between cooling rate and specific water volume includes: 1. The relationship between heat transfer and cooling rate: The heat dissipation process of the billet in the secondary cooling zone follows the law of energy conservation: the heat released by the billet per unit time is equal to the heat absorbed by the cooling water, i.e. Where c is the specific heat capacity of steel, approximately 460 J / (kg). ℃), ρ is the density of steel ≈ 7850 kg / m³, V is the volume of the billet per unit length (related to thickness), CR is the cooling rate. The heat absorbed by the cooling water per unit time (directly related to the specific water volume W). Simplified, we can see that CR is directly proportional to the heat exchange per unit mass of cast billet, and the heat exchange per unit mass is related to the specific water volume W (the more water, the stronger the heat absorption capacity). 2. Nonlinear relationship between specific water volume and heat transfer intensity. In actual continuous casting, the effect of specific water volume W on heat transfer is not nonlinear: when W is small, such as between 0.2 and 0.6 L / kg, the cooling water is fully atomized and has good contact with the surface of the cast billet, and the heat transfer intensity increases significantly with increasing W, resulting in a rapid increase in CR; when W is large (e.g., greater than 0.6 L / kg), excessive cooling water will form a vapor film on the surface of the cast billet (hindering heat transfer), or localized uneven cooling may occur due to water flow convergence. In this case, the growth of heat transfer intensity slows down, and the rate of increase in CR decreases. The above-mentioned characteristic of "rapid growth followed by a slowdown" conforms to the mathematical characteristics of a quadratic function (opening downwards). Therefore, the quantitative relationship model between cooling rate and specific water volume is constructed as a quadratic function. Because the nonlinear relationship between heat transfer intensity and specific water volume is universal (affected by steam film and atomization effects), this model is applicable to all continuous casting secondary cooling processes. The coefficients (a, b, c) can be refitted based on specific water volume and cooling rate data obtained from actual process conditions (such as billet thickness, nozzle type, and steel composition). Using specific water volume data from actual production and infrared thermography, for typical TiN-controlled steel grades with billet thickness of 200-250 mm and steel composition of C=0.12%–0.18%, Ti=150-250 ppm, and N=30-50 ppm, the optimal values ​​for a, b, and c are 0.85, 0.32, and -0.6, respectively.

[0099] Furthermore, the specific water volume adjustment value can be calculated based on the required cooling rate. This relationship is then transformed into a standard quadratic equation with W as the unknown. Applying the quadratic equation's root-finding formula yields two solutions. Considering the actual constraints in the process where the specific water volume needs to be within the range of 0.2~1.0 L / kg and CR monotonically increases with W, the "minus" term is selected as the effective solution. This ultimately achieves the conversion from the target cooling rate to the required specific water volume adjustment value. The formula for the required specific water volume adjustment value is as follows:

[0100] ;in, Indicates the target cooling rate. This indicates the required water volume adjustment value.

[0101] In one embodiment of the present invention, for further explanation and limitation, the method further includes:

[0102] The expected cumulative reduction before precipitation is calculated based on the precipitation start time and the initial reduction parameters.

[0103] If the expected cumulative reduction before precipitation does not meet the cumulative reduction before precipitation condition, calculate the range of the number of reduction rollers and the range of single roller reduction for different position intervals based on the target range of cumulative reduction before precipitation.

[0104] In this embodiment of the invention, the initial control parameters also include initial reduction parameters, and the expected precipitation time and temperature window includes the precipitation start time and temperature. Besides adjusting the specific water content, the parameters of the pre-solidification reduction section (fs0.4-0.9) can also be adjusted. The adjustment goal is to ensure a reduction of at least 8 mm before TiN precipitation begins, thereby reducing the segregation and aggregation of Ti and N elements in the two-phase region, and thus reducing the size of the TiN precipitation. Specifically, by using the temperature-time thermal history characterized by the temperature field data and the calculated TiN initial precipitation temperature, the position of the billet at the time of TiN precipitation can be determined by the casting speed. The light reduction rolls before TiN precipitation begins are controlled, with a cumulative reduction ≥ 8 mm. The reduction control specifically includes: calculating the number of reduction rolls in the fs0.4~TiN precipitation start range, evenly distributing the reduction within the single-roll reduction range of 0.2~0.8 mm, ultimately achieving a target cumulative reduction of 8~15 mm.

[0105] In one embodiment of the present invention, for further explanation and limitation, after calculating the target specific water quantity parameter based on the target cooling rate curve using a quantitative relationship model between cooling rate and specific water quantity, the method further includes:

[0106] Based on the target cooling rate curve and the target specific water content parameter, the pulling speed is optimized within the range of the number of pressing rollers and the single roller pressing amount to obtain the target pulling speed, the target number of pressing rollers, and the target single roller pressing amount.

[0107] The continuous casting machine actuator is controlled based on the target casting speed, the target number of pressure rolls, and the target single roll reduction, and the secondary cooling water spray system is controlled based on the target specific water parameters to carry out continuous casting production.

[0108] During the continuous casting process, real-time cooling rate and TiN precipitation size data are collected and visualized.

[0109] In this embodiment of the invention, after determining the target specific water content, and under the constraints of the target specific water content and target cooling rate curves, within the feasible range of the pre-calculated number of pressing rolls and single-roll pressing amount, the casting speed is collaboratively optimized. This ultimately outputs an optimal parameter combination (target casting speed, target number of pressing rolls, target single-roll pressing amount) that simultaneously meets the requirements of both cooling and pressing processes. Subsequently, these parameters are sent to the continuous casting machine's actuators and the secondary cooling water spray system to automatically drive production. During production, key data (cooling rate, TiN precipitation size) is collected in real time and visualized to continuously monitor the process. Through the collaborative optimization of multiple process parameters, the optimal balance between product quality (controlling TiN size) and production efficiency (optimizing casting speed) is ensured. Visual monitoring provides an intuitive and reliable basis for evaluating the production status and subsequent optimization, ultimately achieving a stable improvement in the consistency and controllability of high-end steel quality.

[0110] In a specific instance of the above method, such as Figure 3 As shown, the process for determining the continuous casting reduction parameters and cooling rate includes: First, based on the billet size, steel composition, and temperature-time history at different locations, the solid / liquid phase temperature of the steel and the cooling rate (cooling rate) for the solid fraction at different locations in the range of 0.93 to 1 are calculated. Then, by considering a microscopic segregation model with solid-phase diffusion in opposite directions, the activity coefficients of titanium and nitrogen and the solubility product of TiN at different locations are calculated. Next, a simplified kinetic method based on supersaturation is used to calculate the amount of TiN precipitation, and the TiN particle size is calculated based on the LSW ripening theory, thus obtaining the precipitation temperature, time, and size. Based on this, the locations where TiN exceeds 5 μm in size and their cooling rates are determined, and the reduction before precipitation is calculated. Finally, the required cooling rate increment is calculated and converted into a specific water volume increment, while the single-strand reduction increment is also calculated. The above increments are the differences between the target values ​​and initial values ​​of each parameter, i.e., the amount that needs to be adjusted relative to the initial values.

[0111] The above method was applied to a steel grade with C=0.15%, Ti=180ppm, and N=35ppm, with a TiN precipitation size of less than or equal to 5μm as the control target in a practical verification. The verification results were: maximum TiN size 4.9μm, cooling rate 0.15℃ / s. The TiN size histogram showed that the size at various locations in this steel grade was within the range of 4.8-5.2μm; the actual TiN size was 4.8μm. The billet quality was good.

[0112] The above method was applied to a steel grade with C=17.00%, Ti=200.0ppm, and N=40.0ppm, with the TiN size less than or equal to 5μm as the control target in a practical verification. The specific water volume in the secondary cooling zone was adjusted from 0.42 L / kg to 0.63 L / kg. The verification results were: the maximum actual TiN size was 4.8μm, and the billet quality was good.

[0113] To verify the compatibility of different Ti and N contents, the Ti content in the steel grade was increased from 180 ppm to 220 ppm (N remained at 35 ppm, and C remained at 0.15%). The reduction range was 0.6-0.93% solids fraction; the cumulative reduction was 8.8 mm. The specific water content in the secondary cooling zone was adjusted from 0.42 L / kg to 0.63 L / kg. The verification results were: TiN size: 4.0-4.9 μm; billet quality: good.

[0114] This invention provides a method for determining continuous casting process parameters based on TiN precipitation prediction. In this embodiment, the method acquires the steel composition data of the target continuously cast steel and the temperature field data under initial control parameters. Based on the initial casting speed and initial specific water content in the initial control parameters, the method determines the cooling rate curve during the solidification period of the cast billet. Based on the temperature field data, steel composition data, and the cooling rate curve during the solidification period of the cast billet, the method predicts the expected precipitation time and temperature window of TiN. Based on the expected precipitation time and temperature window and the cooling rate curve during the solidification period of the cast billet, the method determines the expected TiN precipitation size. If the expected TiN precipitation size is greater than the upper limit of the TiN precipitation size, the method uses a model of the relationship between TiN precipitation size and cooling rate to invert and obtain a target cooling rate curve that satisfies the upper limit of the TiN precipitation size. Based on the target cooling rate curve, the method uses a quantitative relationship model between cooling rate and specific water content to calculate the target specific water content parameter. This significantly reduces the size of harmful TiN precipitates, lowers the risk of steel performance deterioration caused by large-sized TiN inclusions, and transforms the adjustment of continuous casting process parameters from relying on experience to precise quantitative calculation based on models, thereby greatly improving the consistency and stability of high-end steel product quality.

[0115] Furthermore, as a response to the above Figure 1 The implementation of the method shown in this invention provides a device for determining continuous casting process parameters based on TiN precipitation prediction, such as... Figure 4 As shown, the device includes:

[0116] The acquisition module 31 is used to acquire the steel composition data of the target continuous casting steel and the temperature field data under the initial control parameters, and to determine the cooling rate curve of the billet solidification period based on the initial casting speed and initial specific water content in the initial control parameters.

[0117] The prediction module 32 is used to predict the expected precipitation time and temperature window of TiN based on temperature field data, steel composition data and the cooling rate curve during the solidification period of the billet, and to determine the expected TiN precipitation size based on the expected precipitation time and temperature window and the cooling rate curve during the solidification period of the billet.

[0118] Inversion module 33 is used to invert the target cooling rate curve that satisfies the upper limit of TiN precipitation size by using the relationship model between TiN precipitation size and cooling rate when the expected TiN precipitation size is greater than the upper limit of TiN precipitation size.

[0119] The calculation module 34 is used to calculate the target specific water quantity parameter based on the target cooling rate curve and the quantitative relationship model between cooling rate and specific water quantity.

[0120] Furthermore, the prediction module 32 includes:

[0121] The secondary dendrite arm spacing calculation unit is used to perform empirical regression based on the steel composition data to calculate the liquidus temperature and solidus temperature of the target continuous casting steel, and to calculate the secondary dendrite arm spacing based on the cooling rate curve of the billet solidification period.

[0122] The solid fraction calculation unit is used to calculate the solid fraction at different locations based on the comparison results between the temperature at different locations in the temperature field data and the liquidus temperature and solidus temperature, respectively.

[0123] The supersaturation calculation unit is used to calculate the theoretical liquid phase concentration of Ti and the theoretical liquid phase concentration of N at different positions based on the initial liquid phase concentration, the secondary dendrite arm spacing and the solid phase fraction at different positions using the Ohnaka model, and to calculate the TiN supersaturation based on the theoretical liquid phase concentration of Ti and the theoretical liquid phase concentration of N.

[0124] The expected precipitation time and temperature window calculation unit is used to determine the precipitation start position and precipitation duration at the position where both TiN supersaturation and solid fraction meet the corresponding thresholds, and to calculate the expected precipitation time and temperature window based on the precipitation start position, initial pulling speed and solid fraction.

[0125] Furthermore, in specific application scenarios, the supersaturation calculation unit is specifically used to calculate the Ti activity coefficient based on the interaction coefficients between different elements and Ti, and to calculate the N activity coefficient based on the interaction coefficients between different elements and N.

[0126] The temperature corresponding to different positions is calculated based on the casting speed and the cooling rate curve during the solidification period of the billet, and the equilibrium solubility product at different positions is calculated based on the temperature at different positions.

[0127] For each location, the TiN supersaturation at different locations is calculated based on the equilibrium solubility product, the theoretical liquid phase concentration of Ti, the theoretical liquid phase concentration of N, the activity coefficient of Ti, and the activity coefficient of N.

[0128] Furthermore, the inversion module 33 is specifically used to substitute the upper limit of the TiN precipitation size as the target output into the TiN precipitation size and cooling rate relationship model, calculate the cooling rate at the end of solidification, and calculate the target cooling rate curves for different control segments based on the cooling rate at the end of solidification.

[0129] The relationship between the TiN precipitation size and the cooling rate is expressed as follows: Where d represents the precipitation size, This represents the initial size of TiN, and K represents the diffusion coefficient of N in the liquid phase. This represents the TiN precipitation time window, and CR is the cooling rate at the end of solidification.

[0130] Furthermore, the calculation module 34 is specifically used to calculate the specific water volume of each control segment of the target cooling rate curve based on the quantitative relationship model between the cooling rate and the specific water volume corresponding to the control segment, thereby obtaining a target specific water volume parameter that includes the specific water volume of multiple control segments.

[0131] The quantitative relationship model between the cooling rate input and the specific water volume is expressed as follows: Where CR represents the cooling rate, W represents the specific water volume, and a, b, and c are fitting coefficients, with a and b both greater than 0 and c less than 0.

[0132] Furthermore, the initial control parameters also include initial depressurization parameters, and the expected precipitation time and temperature window includes the precipitation start time; the device further includes:

[0133] The first compression amount calculation module is used to calculate the expected cumulative compression amount before precipitation based on the precipitation start time and the initial compression parameters.

[0134] The second reduction calculation module is used to calculate the range of the number of reduction rollers and the range of single roller reduction for different position intervals based on the target range of cumulative reduction before precipitation, when the expected cumulative reduction before precipitation does not meet the cumulative reduction before precipitation condition.

[0135] Furthermore, the device also includes:

[0136] The optimization module is used to optimize the pulling speed within the range of the number of pressing rollers and the single-roller pressing amount based on the target cooling rate curve and the target specific water quantity parameter, so as to obtain the target pulling speed, the target number of pressing rollers and the target single-roller pressing amount.

[0137] The control module is used to control the continuous casting machine actuator based on the target casting speed, the target number of pressure rolls and the target single roll reduction, and to control the secondary cooling water spray system based on the target specific water parameters for continuous casting production.

[0138] The display module is used to collect real-time cooling rate and TiN precipitation size data during the continuous casting production process, and to visually display the real-time cooling rate and TiN precipitation size data.

[0139] This invention provides a continuous casting process parameter control and determination device based on TiN precipitation prediction. In this embodiment, the device acquires the steel composition data of the target continuously cast steel and the temperature field data under initial control parameters. Based on the initial casting speed and initial specific water content in the initial control parameters, it determines the cooling rate curve during the solidification period of the cast billet. Based on the temperature field data, steel composition data, and the TiN precipitation cooling rate curve, it predicts the expected precipitation time and temperature window of TiN. Based on the expected precipitation time and temperature window and the cast billet solidification cooling rate curve, it determines the expected TiN precipitation size. If the expected TiN precipitation size is greater than the upper limit of the TiN precipitation size, it uses a model of the relationship between TiN precipitation size and cooling rate to invert and obtain a target cooling rate curve that satisfies the upper limit of the TiN precipitation size. Based on the target cooling rate curve, it calculates the target specific water content parameter using a quantitative relationship model between cooling rate and specific water content. This significantly reduces the size of harmful TiN precipitates, lowers the risk of steel performance deterioration caused by large-sized TiN inclusions, and transforms the adjustment of continuous casting process parameters from relying on experience to precise quantitative calculation based on models, thereby greatly improving the consistency and stability of high-end steel product quality.

[0140] According to one embodiment of the present invention, a storage medium is provided, the storage medium storing at least one executable instruction, the computer-executable instruction being able to execute the continuous casting process parameter determination method based on TiN precipitation prediction in any of the above method embodiments.

[0141] Figure 5 The diagram shows a structural schematic of a terminal according to an embodiment of the present invention. The specific implementation of the present invention is not limited to the specific implementation of the terminal.

[0142] like Figure 5 As shown, the terminal may include: a processor 402, a communication interface 404, a memory 406, and a communication bus 408.

[0143] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.

[0144] Communication interface 404 is used for network communication with other devices such as clients or other servers.

[0145] The processor 402 is used to execute program 410, specifically the relevant steps in the above embodiment of the method for determining continuous casting process parameters based on TiN precipitation prediction.

[0146] Specifically, program 410 may include program code that includes computer operation instructions.

[0147] Processor 402 may be a central processing unit (CPU), a specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The terminal includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0148] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0149] Specifically, program 410 can be used to cause processor 402 to perform the following operations:

[0150] Obtain the steel composition data of the target continuously cast steel and the temperature field data under the initial control parameters, and determine the cooling rate curve of the billet solidification period based on the initial casting speed and initial specific water content in the initial control parameters.

[0151] The expected precipitation time and temperature window of TiN are predicted based on temperature field data, steel composition data and cooling rate curve during the solidification period of the billet, and the expected TiN precipitation size is determined based on the expected precipitation time and temperature window and the cooling rate curve during the expected precipitation time.

[0152] When the expected TiN precipitation size is greater than the upper limit of TiN precipitation size, the target cooling rate curve that satisfies the upper limit of TiN precipitation size is obtained by inverting the relationship model between TiN precipitation size and cooling rate;

[0153] Based on the target cooling rate curve, the target specific water quantity parameter is calculated using a quantitative relationship model between cooling rate and specific water quantity.

[0154] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0155] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for determining continuous casting process parameters based on TiN precipitation prediction, characterized in that, include: Acquire the steel composition data of the target continuously cast steel and the temperature field data under the initial control parameters to determine the cooling rate curve during the solidification period of the billet. The temperature field data includes the temperature data corresponding to different casting flow positions. The expected precipitation time and temperature window of TiN are predicted based on temperature field data, steel composition data and the cooling rate curve during the solidification period of the billet, and the expected TiN precipitation size is determined based on the expected precipitation time and temperature window and the cooling rate curve during the solidification period of the billet. When the expected TiN precipitation size is greater than the upper limit of TiN precipitation size, the target cooling rate curve that satisfies the upper limit of TiN precipitation size is obtained by inverting the relationship model between TiN precipitation size and cooling rate, including: The upper limit of TiN precipitation size is used as the target output and substituted into the relationship model between TiN precipitation size and cooling rate to back-calculate the cooling rate at the end of solidification. Based on the cooling rate at the end of solidification, the target cooling rate curves for different control segments are calculated. The relationship between the TiN precipitation size and the cooling rate is expressed as follows: Where d represents the precipitation size, This represents the initial size of TiN, and K represents the diffusion coefficient of nitrogen in the liquid phase. This represents the TiN precipitation time window, where CR is the cooling rate at the end of solidification. Based on the target cooling rate curve, the target specific water quantity parameter is calculated using a quantitative relationship model between cooling rate and specific water quantity.

2. The method for determining continuous casting process parameters based on TiN precipitation prediction according to claim 1, characterized in that, Based on temperature field data, steel composition data, and the solidification cooling rate curve of the cast billet, the expected precipitation time and temperature window of TiN are predicted, including: Based on the steel composition data, empirical regression was performed to calculate the liquidus temperature and solidus temperature of the target continuously cast steel, and the secondary dendrite arm spacing was calculated based on the cooling rate curve during the solidification period of the billet. The solid fraction at different locations is calculated based on the comparison results between the temperature at different locations in the temperature field data and the liquidus temperature and solidus temperature, respectively. Based on the initial liquid phase concentration, secondary dendrite arm spacing, and solid fraction at different positions, the theoretical liquid phase concentrations of Ti and N at different positions are calculated using the Ohnaka model, and the TiN supersaturation is calculated based on the theoretical liquid phase concentrations of Ti and N. The position where both TiN supersaturation and solid fraction meet the corresponding thresholds is determined as the precipitation initiation position, and the expected precipitation time window and temperature window are calculated based on the precipitation initiation position, initial pulling speed and solid fraction.

3. The method for determining continuous casting process parameters based on TiN precipitation prediction according to claim 2, characterized in that, The calculation of TiN supersaturation based on the theoretical liquid phase concentrations of Ti and N includes: The Ti activity coefficient was calculated based on the interaction coefficients between different elements and Ti, and the N activity coefficient was calculated based on the interaction coefficients between different elements and N. The temperature corresponding to different positions is calculated based on the casting speed and the cooling rate curve during the solidification period of the billet, and the equilibrium solubility product at different positions is calculated based on the temperature at different positions. For each location, the TiN supersaturation at different locations is calculated based on the equilibrium solubility product, the theoretical liquid phase concentration of Ti, the theoretical liquid phase concentration of N, the activity coefficient of Ti, and the activity coefficient of N.

4. The method for determining continuous casting process parameters based on TiN precipitation prediction according to claim 1, characterized in that, Based on the target cooling rate curve, the target specific water quantity parameter is calculated using a quantitative relationship model between cooling rate and specific water quantity, including: For each control segment of the target cooling rate curve, the specific water volume of the control segment is calculated based on the quantitative relationship model between the cooling rate and the specific water volume corresponding to the control segment, thus obtaining a target specific water volume parameter that includes the specific water volume of multiple control segments. The quantitative relationship model between the cooling rate input and the specific water volume is expressed as follows: Where CR represents the cooling rate, W represents the specific water volume, and a, b, and c are fitting coefficients, with a and b both greater than 0 and c less than 0.

5. The method for determining continuous casting process parameters based on TiN precipitation prediction according to claim 1, characterized in that, The initial control parameters also include initial pressure parameters, and the expected precipitation time and temperature window includes the precipitation start time; The method further includes: The expected cumulative reduction before precipitation is calculated based on the precipitation start time and the initial reduction parameters. If the expected cumulative reduction before precipitation does not meet the cumulative reduction before precipitation condition, calculate the range of the number of pressing rollers and the range of single roller reduction for different position intervals based on the target range of cumulative reduction before precipitation.

6. The method for determining continuous casting process parameters based on TiN precipitation prediction according to claim 5, characterized in that, Based on the target cooling rate curve, after calculating the target specific water quantity parameter using a quantitative relationship model between cooling rate and specific water quantity, the method further includes: Based on the target cooling rate curve and the target specific water content parameter, the pulling speed is optimized within the range of the number of pressing rollers and the single roller pressing amount to obtain the target pulling speed, the target number of pressing rollers, and the target single roller pressing amount. The continuous casting machine actuator is controlled based on the target casting speed, the target number of pressure rolls, and the target single roll reduction, and the secondary cooling water spray system is controlled based on the target specific water parameters to carry out continuous casting production. During the continuous casting process, real-time cooling rate and TiN precipitation size data are collected and visualized.

7. A device for determining continuous casting process parameters based on TiN precipitation prediction, characterized in that, include: The acquisition module is used to acquire the steel composition data of the target continuously cast steel and the temperature field data under the initial control parameters, and to determine the cooling rate curve of the billet solidification period. The temperature field data includes the temperature data corresponding to different casting flow positions. The prediction module is used to predict the expected precipitation time and temperature window of TiN based on temperature field data, steel composition data and the cooling rate curve during the solidification period of the billet, and to determine the expected TiN precipitation size based on the expected precipitation time and temperature window and the cooling rate curve during the solidification period of the billet. The inversion module is used to invert the target cooling rate curve that satisfies the upper limit of TiN precipitation size by using the TiN precipitation size and cooling rate relationship model when the expected TiN precipitation size is greater than the upper limit of TiN precipitation size. Specifically, the inversion module is used to substitute the upper limit of TiN precipitation size as the target output into the TiN precipitation size and cooling rate relationship model to calculate the cooling rate at the end of solidification, and to calculate the target cooling rate curves for different control segments based on the cooling rate at the end of solidification. The relationship between the TiN precipitation size and the cooling rate is expressed as follows: Where d represents the precipitation size, This represents the initial size of TiN, and K represents the diffusion coefficient of N in the liquid phase. This indicates the TiN precipitation time window, where CR is the cooling rate at the end of solidification. The calculation module is used to calculate the target specific water quantity parameter based on the target cooling rate curve and a quantitative relationship model between cooling rate and specific water quantity.

8. A storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the method for determining continuous casting process parameters based on TiN precipitation prediction as described in any one of claims 1-6.

9. A terminal, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the method for determining continuous casting process parameters based on TiN precipitation prediction as described in any one of claims 1-6.

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