Continuous casting process parameter determination method and device based on TiN precipitation prediction

Abstract

The invention discloses a continuous casting process parameter determination method and device based on TiN precipitation prediction, relates to the technical field of ferrous 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. The method mainly comprises the steps that steel grade component data and temperature field data of target continuous casting steel are obtained, and a casting blank solidification period cooling rate curve is determined according to the initial pulling speed and the initial specific water flow in initial control parameters; according to the temperature field data, the steel grade component data and the casting blank solidification period cooling rate curve, the expected TiN precipitation time and temperature window are predicted, and the expected TiN precipitation size is determined; under the condition that the expected TiN precipitation size is larger than the upper limit of the TiN precipitation size, a target cooling rate curve meeting the upper limit of the TiN precipitation size is inversed through the relation model of the TiN precipitation size and the cooling rate; and a target specific water flow parameter is calculated through the quantitative relation model of the cooling rate and the specific water flow. The method is mainly used for determining continuous casting process parameters.

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 their size is too large, the number is too large, or the distribution is uneven during solidification and cooling, 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 the process parameters. Because the TiN precipitation kinetics process 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 TiN particle precipitation size control precision 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: 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; predicting the expected precipitation time and temperature window of TiN according to the temperature field data, the steel grade composition data and the casting blank solidification period cooling rate curve, and determining the expected TiN precipitation size according to the expected precipitation time and temperature window and the casting blank solidification period cooling rate curve; In the case where the expected 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; 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.

[0006] Further, the expected TiN precipitation time and temperature window are predicted according to the temperature field data, the steel grade composition data and the casting blank solidification period cooling rate curve, comprising: According to the steel grade 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; 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; 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; The position where both the TiN supersaturation and the solid phase fraction satisfy 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.

[0007] Further, the TiN supersaturation is calculated according to the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration, comprising: 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; According to the casting speed and the casting blank solidification period cooling rate curve, the temperatures corresponding to different positions are calculated, and the equilibrium solubility products at different positions are calculated according to the temperatures at different positions; 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.

[0008] 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: The upper limit of TiN precipitation size is taken as a target output and substituted into the TiN precipitation size and cooling rate relationship model, the solidification end cooling rate is calculated inversely, and the target cooling rate curve of different control sections is calculated according to the solidification end cooling rate; 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 cooling rate at the end of solidification.

[0009] The target specific water quantity parameter is calculated by the cooling rate and specific water quantity quantitative relationship model according to the target cooling rate curve, and includes: For each control section of the target cooling rate curve, the specific water quantity of the control section is calculated by inputting the cooling rate into the cooling rate and specific water quantity quantitative relationship model according to the cooling rate corresponding to the control section, to obtain the target specific water quantity parameter containing the specific water quantities of multiple control sections; The cooling rate and specific water quantity quantitative relationship model is expressed as: ; wherein CR represents the cooling rate, W represents the specific water quantity, a, b, and c are fitting coefficients, a and b are greater than 0, and c is less than 0.

[0010] Further, the initial control parameter further includes an initial reduction parameter, and the expected precipitation time and temperature window includes a precipitation start time. The method further includes: The expected cumulative reduction amount before precipitation is calculated according to the precipitation start time and the initial reduction parameter; In the case that the expected cumulative reduction amount before precipitation does not meet the cumulative reduction amount before precipitation condition, the reduction roller quantity range and single roller reduction amount range of different position intervals are calculated according to the cumulative reduction amount before precipitation target interval.

[0011] Further, after the target specific water quantity parameter is calculated by the cooling rate and specific water quantity quantitative relationship model according to the target cooling rate curve, the method further includes: The drawing speed is optimized in the reduction roller quantity range and single roller reduction amount range according to the target cooling rate curve and the target specific water quantity parameter, to obtain the target drawing speed, the target reduction roller quantity, and the target single roller reduction amount; The continuous casting machine actuator is controlled according to the target drawing speed, the target reduction roller quantity, and the target single roller reduction amount, and the secondary cooling water spraying system is controlled according to the target specific water quantity parameter to perform continuous casting production; During the continuous casting production process, real-time cooling rate and TiN precipitation size data are collected, and the real-time cooling rate and the TiN precipitation size data are visualized and displayed.

[0012] According to another aspect of the present application, a continuous casting process parameter determination device based on TiN precipitation prediction is provided, which includes: The 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 the casting blank, wherein the temperature field data comprises temperature data corresponding to different casting flow positions respectively; The 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. The inversion module is configured to, in a case where the expected TiN precipitation size is greater than an upper limit of the TiN precipitation size, inversely determine a target cooling rate curve meeting the upper limit of the TiN precipitation size through a TiN precipitation size and cooling rate relationship model. The calculation module is configured to calculate a target specific water quantity parameter through a quantitative relationship model between the cooling rate and the specific water quantity according to the target cooling rate curve.

[0013] According to another aspect of the present application, a storage medium is provided, and the storage medium stores at least one executable instruction, and the executable instruction causes a processor to perform operations corresponding to the TiN precipitation prediction-based continuous casting process parameter determination method.

[0014] According to still another aspect of the present application, a terminal is provided, and the terminal comprises a processor, a memory, a communication interface and a communication bus, and the processor, the memory and the communication interface complete communication with each other through the communication bus. 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.

[0015] Through the above technical solutions, the technical solutions provided by the embodiments of the present application have at least the following advantages: 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 inversed 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.

[0016] 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

[0017] 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 intended to limit the application thereto. Moreover, like reference numerals designate like parts throughout the several views in the drawings. In the drawings: 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; 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; 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; 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; Figure 5 A structure schematic diagram of a terminal provided by the embodiment of the application is shown. DETAILED DESCRIPTION

[0018] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood, and so that the scope of the present disclosure can be completely conveyed to those skilled in the art.

[0019] In view of the low control accuracy of TiN particle precipitation size in the existing thick slab continuous casting process, an embodiment of the present application provides a continuous casting process parameter determination method based on TiN precipitation prediction, as shown in the figure, the method comprises: Figure 1 101. 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 quantity in the initial control parameters.

[0020] In the embodiment of 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 continuously cast. 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 of continuous casting (such as the crystallizer outlet and the fan-shaped section), which can be obtained by numerical simulation or based on sensor measurement under the initial control parameters such as casting speed and specific water quantity. According to the initial casting speed and the initial specific water quantity, 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.

[0021] 102. 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 cooling rate curve of the precipitation period is determined according to the expected precipitation time and temperature window, and then the expected TiN precipitation size is determined.

[0022] ​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.

[0023] 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 before the actual production of the casting blank, thereby realizing the pre-control of quality problems.

[0024] 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.

[0025] 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 the 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 control during the predicted precipitation temperature window is obtained.

[0026] 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 required cooling rate target value for controlling the TiN size within the qualified range is calculated in reverse according to the established theoretical model, thereby avoiding blind trial and error.

[0027] 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.

[0028] 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.

[0029] 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 inclusion size of the produced casting blank stably meets the process requirements.

[0030] 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: 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.

[0031] 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.

[0032] 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.

[0033] 204、The positions where both the TiN supersaturation and the solid phase fraction meet the corresponding threshold values are determined as the precipitation starting positions, and the expected precipitation time and temperature window are calculated according to the precipitation starting positions, the initial casting speed and the solid phase fraction.

[0034] In the embodiments of the present application, the liquidus temperature and the solidus temperature are used to divide the solidification interval to determine the temperature range in which TiN can be precipitated. The liquidus temperature and the solidus temperature are calculated based on the empirical regression of the steel composition. The formula of the liquidus temperature is expressed as: ; wherein C, Si, Mn represent the mass fraction (%) of the respective corresponding symbol chemical element.

[0035] The formula of the solidus temperature is expressed as: ; wherein C, Si, Mn, P, Al, Cr, Ni, S represent the mass fraction (%) of the respective corresponding symbol chemical element.

[0036] The secondary dendrite arm spacing λ2 is a key parameter of the dendrite structure in the solidification process, which directly affects the diffusion distance of elements between dendrites. The greater the cooling rate, the faster the dendrite growth, the smaller the secondary dendrite arm spacing, the shorter the element diffusion path, and the inhibition of TiN particle growth. The relationship between the secondary dendrite arm spacing and the cooling rate is obtained by fitting the empirical formula, which is expressed as: ; wherein CR represents the cooling rate, and C represents the mass fraction (%) of carbon.

[0037] Since TiN is usually precipitated at the end of solidification, the solid fraction is a key parameter for determining the precipitation critical point. Therefore, it is necessary to determine the solid fraction based on the comparison results of the liquidus temperature and the solidus temperature with the temperature at different positions. The method for determining the solid fraction at any position includes: when the temperature of the steel material is higher than or equal to the liquidus temperature, it is completely in the liquid state, at this time, the solid fraction is zero; when the temperature of the steel material is less than or equal to the solidus temperature, it is completely in the solid state, at this time, the solid fraction is 1; when the temperature of the steel material is between the solidus temperature and the liquidus temperature, the material is in the solid-liquid two-phase region, and is being solidified, the solid fraction is expressed as ; wherein T represents the temperature at any position. The solid fraction increases linearly with the decrease of the temperature, which reflects the degree of transformation from liquid phase to solid phase.

[0038] In the solidification process, the uneven distribution of elements in the liquid and solid phases will lead to the enrichment (segregation) of elements in the liquid phase, which needs to be calculated by Ohnaka micro-segregation model to calculate the theoretical liquid concentration of Ti and the theoretical liquid concentration of N in the liquid phase. The formula of Ohnaka micro-segregation model is expressed as: ; wherein represents the liquid concentration under the current solid fraction, ​​Ci0represents the initial liquid concentration; k represents the partition coefficient (the partition coefficient of Ti is 0.38, and the partition coefficient of N is 0.25); a parameter representing the interdendritic flow effect, , a dimensionless correction coefficient related to interdendritic solute diffusion, the value of which is determined by local solidification conditions, D represents a diffusion coefficient, represents a characteristic time, represents a secondary dendrite arm spacing. Through this model, the diffusion and segregation of interdendritic elements can be considered, and the element concentration in the liquid phase during solidification can be more accurately reflected. Further, the TiN supersaturation is calculated according to the Ti theoretical liquid concentration and the N theoretical liquid concentration. After obtaining the TiN supersaturation at different positions, the TiN supersaturation at each position is compared with the supersaturation threshold (1) and the solid phase fraction is compared with the solid phase fraction threshold (0.9), and the position where the TiN supersaturation is greater than 1 and the solid phase fraction is greater than 0.9 is determined as the precipitation starting position. Once the precipitation starting position is determined, the time required for the molten steel to flow from the meniscus of the mold to the position can be easily calculated according to the initial casting speed of the continuous casting machine. It usually lasts from the starting time to a low-temperature region where the solid phase fraction of the microelement is equal to 1 or even after that. Therefore, the expected precipitation time and temperature window refer to such a time period from the precipitation starting time to the substantially end time of precipitation. The determination of this window also depends on the analysis of the solid phase fraction and the supersaturation with the cooling time.

[0039] In an embodiment of the present application, in order to further illustrate and limit, the step of calculating the TiN supersaturation according to the Ti theoretical liquid concentration and the N theoretical liquid concentration comprises: calculating the Ti activity coefficient based on the interaction coefficient of different elements with Ti, and calculating the N activity coefficient based on the interaction coefficient of different elements with N; calculating the temperature corresponding to different positions according to the casting speed and the cooling rate curve of the solidification period of the casting blank, and calculating the equilibrium solubility product at different positions according to the temperature at different positions; for each position, calculating the TiN supersaturation at different positions according to the equilibrium solubility product, the Ti theoretical liquid concentration, the N theoretical liquid concentration, the Ti activity coefficient, and the N activity coefficient.

[0040] In the embodiment of the present application, the effective concentration (i.e. activity) of titanium (Ti) and nitrogen (N) in the molten steel is calculated through a thermodynamic model, and the equilibrium solubility product of TiN at each position is calculated in combination with the temperature field determined by the cooling process. Further, the thermodynamic driving force (i.e. supersaturation) of TiN precipitation at each position is accurately quantified by comparing the actual activity product of titanium and nitrogen with the equilibrium solubility product. The activity coefficient corrects the influence of other elements on the chemical potential of Ti and N, making the activity closer to the real thermodynamic effective concentration, thereby combining the molten steel composition, element interaction and continuous casting thermal history to realize accurate prediction of the precipitation conditions.

[0041] wherein the Ti activity coefficient is expressed by the following formula: ; wherein, represents the common logarithm of the activity coefficient of titanium (Ti) element at a reference temperature of 1873K, , represents the interaction coefficient of element j to Ti, represents the mass fraction of element j; T represents the temperature (Kelvin temperature) of the current position.

[0042] N activity coefficient is expressed by the following formula: ; wherein, represents the common logarithm of the activity coefficient of nitrogen (N) element at a reference temperature of 1873K, , represents the interaction coefficient of element j to N, represents the mass fraction of element j; T represents the temperature (Kelvin temperature) of the current position.

[0043] The TiN supersaturation is the ratio of the actual activity product to the equilibrium solubility product, and is expressed as: ; wherein, represents the concentration of titanium element in the liquid phase, represents the concentration of nitrogen element in the liquid phase, represents the equilibrium solubility product of TiN ; T represents the Kelvin temperature.

[0044] In one embodiment of the present application, in order to further illustrate and limit, the step of inverting the target cooling rate curve satisfying the upper limit of TiN precipitation size through the TiN precipitation size and cooling rate relationship model includes: The upper limit of the TiN precipitation size is substituted into the TiN precipitation size and cooling rate relationship model as a target output to back-calculate the solidification end cooling rate, and a target cooling rate curve of different control sections is calculated according to the solidification end cooling rate.

[0045] In the embodiment of the present application, the TiN precipitation size and cooling rate relationship model is expressed as: ; wherein d represents the precipitation size, represents the initial size of TiN (0.01 microns), K represents the diffusion coefficient of N in the liquid phase, represents the TiN precipitation time window (from the start of precipitation to the time when the solid phase fraction is 0.999), and CR is the solidification end (solid phase fraction 0.93-1.0) cooling rate. The upper limit of the TiN precipitation size can be 5 microns, or can be customized according to process requirements, which is not specifically limited in the embodiment of the present application. The TiN precipitation size and cooling rate relationship model is constructed based on the linear relationship between the cubic of the particle size and the time in the LSW theory. The model provides a clear direction for process optimization, which enables the process design to move from "trial and error" to "quantitative calculation". Instead of blindly adjusting all parameters, it should precisely strengthen the cooling near the "solidification end", so that the target cooling rate curve to be achieved can be directly calculated, thereby providing accurate quantitative values for realizing precise automatic control.

[0046] In one embodiment of the present application, in order to further illustrate and limit, the target specific water quantity parameter is calculated by a quantitative relationship model of cooling rate and specific water quantity according to the target cooling rate curve, including: For each control section of the target cooling rate curve, the specific water quantity 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 quantity, and the target specific water quantity parameter containing the specific water quantities of multiple control sections is obtained.

[0047] In the embodiment of the present application, the quantitative relationship model of cooling rate and specific water quantity is constructed based on the heat transfer principle of the continuous casting secondary cooling process, combined with experimental data regression under specific process conditions. The input of the cooling rate into the quantitative relationship model of cooling rate and specific water quantity is expressed as: ; wherein CR represents the cooling rate, W represents the specific water quantity (unit: liter per kilogram), a, b, and c are fitting coefficients, and a and b are greater than 0, and c is less than 0.

[0048] The construction and derivation process of the quantitative relationship model of cooling rate and specific water quantity includes: 1. Relationship between heat exchange and cooling rate The heat dissipation process of the casting blank in the secondary cooling zone follows the law of conservation of energy: the heat released by the casting blank per unit time is equal to the heat absorbed by the cooling water, that is, ; wherein c is the specific heat capacity of steel ≈ 460 J / (kg C), p is the density of the steel ≈ 7850 kg / m3, V is the volume of the strand per unit length (related to the thickness), CR is the cooling rate, is the heat absorbed by the cooling water per unit time (directly related to the specific water quantity W). After simplification, it can be obtained that CR is proportional to the heat exchange per unit mass of the strand, and the heat exchange per unit mass is related to the specific water quantity W (the more water, the stronger the heat absorption capacity). 2. Non-linear relationship between specific water quantity and heat exchange intensity In actual continuous casting, the influence of specific water quantity W on heat exchange is not linear: when W is small, such as between 0.2-0.6 L / kg, the cooling water is fully atomized and well contacts with the surface of the strand, the heat exchange intensity increases significantly with the increase of W, and CR grows faster; when W is large (such as greater than 0.6 L / kg), the excess cooling water forms a steam film on the surface of the strand (which hinders heat transfer), or causes uneven local cooling due to water flow convergence, at this time the heat exchange intensity grows slowly, and the growth rate of CR decreases. The above-mentioned characteristics of "fast growth at first and then slow growth" meet the mathematical characteristics of a quadratic function (opening downward), therefore, the quantitative relationship model of cooling rate and specific water quantity is constructed in the form of a quadratic function. Because the non-linear relationship between heat exchange intensity and specific water quantity is universal (affected by steam film and atomization effect), this model is applicable to all continuous casting secondary cooling processes. Coefficients (a, b, c) can be refitted according to the specific water quantity and cooling rate data obtained according to actual process conditions (such as strand thickness, nozzle type, steel composition). Through the specific water quantity used in actual production and the data obtained by infrared temperature measurement, for the strand with a thickness of 200-250 mm, the typical TiN control steel with a steel composition of C=0.12%-0.18%, Ti=150-250 ppm, and N=30-50 ppm, the optimal values of a, b, and c are 0.85, 0.32, and -0.6, respectively.

[0049] Further, the specific water quantity adjustment value can be calculated according to the required cooling rate: by arranging the relationship into a standard quadratic equation with W as the unknown, applying the quadratic equation root formula to obtain two solutions, and combining the actual constraints that the specific water quantity needs to be in the range of 0.2-1.0 L / kg and CR monotonically increases with W, the "minus" term is selected as the effective solution, and finally the conversion from the target cooling rate to the required specific water quantity adjustment value is realized. Among them, the formula of the required specific water quantity adjustment value is: ; wherein, represents the target cooling rate, represents the required specific water quantity adjustment value.

[0050] In an embodiment of the present application, in order to further illustrate and limit, the method further comprises: calculating an expected cumulative reduction amount before precipitation according to the precipitation start time and the initial reduction parameter; In the case that the accumulated reduction before precipitation does not meet the accumulated reduction before precipitation condition, the number of reduction rolls and the single roll reduction range of different position intervals are calculated according to the accumulated reduction before precipitation target interval.

[0051] In the embodiment of the present application, the initial control parameter further includes an initial reduction parameter, and the expected precipitation time and temperature window include a precipitation start time and temperature. In addition to adjusting the water ratio, the parameters of the pre-solidification reduction section (fs0.4-0.9) can also be adjusted. The adjustment target is to ensure that the reduction reaches more than 8 mm before TiN starts to precipitate, thereby reducing the segregation and aggregation of Ti and N elements in the two-phase region, and further reducing the precipitation size of TiN. Specifically, the temperature-time thermal history represented by the temperature field data and the calculated TiN start precipitation temperature, and the position of the casting blank when TiN precipitates can be determined by the pulling speed. The light reduction roll before the start of TiN precipitation is controlled, and the accumulated reduction is ≥8 mm. The reduction control specifically includes: calculating the number of reduction rolls in the fs0.4-TiN start precipitation interval, and the single roll reduction amount is in the range of 0.2-0.8 mm, and the reduction amount is evenly distributed, and finally the target accumulated reduction reaches 8-15 mm.

[0052] In one embodiment of the present application, in order to further illustrate and limit, after the target water ratio parameter is calculated by the quantitative relationship model of cooling rate and water ratio according to the target cooling rate curve, the method further includes: According to the target cooling rate curve and the target water ratio parameter, the pulling speed is optimized in the range of the number of reduction rolls and the single roll reduction amount, to obtain the target pulling speed, the target number of reduction rolls and the target single roll reduction amount; According to the target pulling speed, the target number of reduction rolls and the target single roll reduction amount, the continuous casting machine actuator is controlled, and the secondary cooling water spraying system is controlled according to the target water ratio parameter to carry out continuous casting production; In the continuous casting production process, real-time cooling rate and TiN precipitation size data are collected, and the real-time cooling rate and the TiN precipitation size data are visualized.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] To verify the adaptability of different Ti, N contents, the Ti content of the steel grade was adjusted from 180 ppm to 220 ppm (N was kept at 35 ppm, and C was kept at 0.15%). The solid phase fraction was 0.6-0.93 in the pressing interval, and the cumulative pressing amount was 8.8 mm. The specific water amount in the secondary cooling zone was adjusted from 0.42 L / kg to 0.63 L / kg. The verification results are as follows: TiN size: 4.0-4.9 μm, and the quality of the casting blank is good.

[0058] The application provides a continuous casting process parameter determination method based on TiN precipitation prediction. In the application, the steel composition data of a target continuous casting steel material and the temperature field data under initial control parameters are obtained, and the solidification period cooling rate curve of a casting blank is determined according to the initial withdrawal speed and the initial specific water amount in the initial control parameters. The expected precipitation time and temperature window of TiN are predicted according to the temperature field data, the steel composition data and the solidification period cooling rate curve of the casting blank, and the expected TiN precipitation size is determined according to the expected precipitation time and temperature window and the solidification period cooling rate curve of the casting blank. 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 meeting the upper limit of the TiN precipitation size is inversed through a TiN precipitation size and cooling rate relationship model. The target specific water amount parameter is calculated through a quantitative relationship model of the cooling rate and the specific water amount 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 large-size TiN inclusions is reduced, and meanwhile, the adjustment of the continuous casting process parameters is changed from experience-based to model-based precise quantitative calculation, thereby greatly improving the consistency and stability of the quality of high-end steel products.

[0059] Further, as an implementation of the method shown in the above Figure 1 , the application embodiment provides a continuous casting process parameter determination device based on TiN precipitation prediction, as shown in the above Figure 4 , the device comprises: An acquisition module 31 is configured to acquire the steel composition data of a target continuous casting steel material and the temperature field data under initial control parameters, and determine the solidification period cooling rate curve of a casting blank according to the initial withdrawal speed and the initial specific water amount in the initial control parameters. A prediction module 32 is configured to predict the expected precipitation time and temperature window of TiN according to the temperature field data, the steel composition data and the solidification period cooling rate curve of the casting blank, and determine the expected TiN precipitation size according to the expected precipitation time and temperature window and the solidification period cooling rate curve of the casting blank. An inversion module 33 is configured to, in the case where the expected TiN precipitation size is greater than the upper limit of the TiN precipitation size, inversed the target cooling rate curve meeting the upper limit of the TiN precipitation size through a TiN precipitation size and cooling rate relationship model. The computing module 34 is configured to calculate the target specific water quantity parameter according to the target cooling rate curve and a quantitative relationship model of cooling rate and specific water quantity.

[0060] Further, the prediction module 32 comprises: A secondary dendrite arm spacing calculation unit is configured to calculate the liquidus temperature and the solidus temperature of the target continuously cast steel according to empirical regression of the steel grade composition data, and calculate the secondary dendrite arm spacing according to the casting blank solidification period cooling rate curve. A solid phase fraction calculation unit is configured to calculate the solid phase fraction at different positions according to the comparison results of the temperatures at different positions in the temperature field data with the liquidus temperature and the solidus temperature. A supersaturation calculation unit is configured to calculate the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration at different positions by the Ohnaka model according to the initial liquid phase concentration, the secondary dendrite arm spacing and the solid phase fraction at different positions, and calculate the TiN supersaturation according to the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration. An expected precipitation time and temperature window calculation unit is configured to determine the precipitation starting position and the precipitation duration as the positions where both the TiN supersaturation and the solid phase fraction meet the corresponding threshold, and calculate the expected precipitation time and temperature window according to the precipitation starting position, the initial casting speed and the solid phase fraction.

[0061] Further, in a specific application scenario, the supersaturation calculation unit is specifically configured to calculate the Ti activity coefficient based on the interaction coefficient of different elements with Ti, and calculate the N activity coefficient based on the interaction coefficient of different elements with N. The temperature corresponding to different positions is calculated according to the casting speed and the casting blank solidification period cooling rate curve, and the equilibrium solubility product at different positions is calculated according to the temperature at different positions. 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.

[0062] Further, the inversion module 33 is specifically configured to substitute the upper limit of the TiN precipitation size as a target output into the TiN precipitation size and cooling rate relationship model, inversely calculate the solidification end cooling rate, and calculate the target cooling rate curve of different control sections according to the solidification end cooling rate. 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 N in the liquid phase, represents the TiN precipitation time window, and CR is the solidification end cooling rate.

[0063] Further, the computing module 34 is specifically configured to, for each control segment of the target cooling rate curve, calculate the specific water quantity of the control segment according to the specific water quantity quantitative relationship model of the input cooling rate corresponding to the control segment, to obtain the target specific water quantity parameter containing the specific water quantity of multiple control segments. The specific water quantity quantitative relationship model of the input cooling rate is expressed as: wherein CR represents the cooling rate, W represents the specific water quantity, a, b, and c are fitting coefficients, and a and b are greater than 0, and c is less than 0.

[0064] Further, the initial control parameter further includes an initial reduction parameter, the expected precipitation time and temperature window include a precipitation start time; the device further includes: A first reduction amount calculation module is configured to calculate the expected cumulative reduction amount before precipitation according to the precipitation start time and the initial reduction parameter. A second reduction amount calculation module is configured to, in the case that the expected cumulative reduction amount before precipitation does not meet the cumulative reduction amount before precipitation condition, calculate the reduction roller number range and the single roller reduction amount range of different position intervals according to the cumulative reduction amount target interval before precipitation.

[0065] Further, the device further includes: An optimization module is configured to, according to the target cooling rate curve and the target specific water quantity parameter, optimize the withdrawal speed in the reduction roller number range and the single roller reduction amount range to obtain the target withdrawal speed, the target reduction roller number, and the target single roller reduction amount. A control module is configured to control the continuous casting machine actuator according to the target withdrawal speed, the target reduction roller number, and the target single roller reduction amount, and control the secondary cooling water spraying system according to the target specific water quantity parameter to perform continuous casting production. A display module is configured to, in the continuous casting production process, collect real-time cooling rate and TiN precipitation size data, and visually display the real-time cooling rate and the TiN precipitation size data.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] like Figure 5 As shown, the terminal may include: a processor 402, a communication interface 404, a memory 406, and a communication bus 408.

[0070] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.

[0071] Communication interface 404 is used for network communication with other devices such as clients or other servers.

[0072] 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.

[0073] Specifically, program 410 may include program code that includes computer operation instructions.

[0074] The processor 402 can be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to perform the operations of embodiments of the application. The terminal includes one or more processors, which can be the same type of processor, such as one or more CPUs, or different types of processors, such as one or more CPUs and one or more ASICs.

[0075] The memory 406 is used to store a program 410. The memory 406 can include a high-speed RAM memory, and can also include a non-volatile memory, such as at least one disk memory.

[0076] The program 410 can be specifically used to cause the processor 402 to perform the following operations: 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 billet solidification period cooling rate curve according to the initial withdrawal rate and the initial water ratio in the initial control parameters; Predict the expected precipitation time and temperature window of TiN according to the temperature field data, the steel grade composition data and the billet solidification period cooling rate curve, and determine the expected TiN precipitation size according to the expected precipitation time and temperature window and the cooling rate curve in the expected precipitation time; 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 cooling rate relationship model; According to the target cooling rate curve, the target water ratio parameter is calculated through the quantitative relationship model of cooling rate and water ratio.

[0077] Obviously, those skilled in the art should understand that the above-mentioned modules or steps of the application can be realized by general computing devices, which can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be realized by program codes executable by computing devices, so that they can be stored in storage devices and executed by computing devices, and in some cases, the steps shown or described can be executed in different order, or they can be manufactured into individual integrated circuit modules, or multiple modules or steps can be manufactured into a single integrated circuit module. Thus, the application is not limited to any specific combination of hardware and software.

[0078] The above only describes the preferred embodiments of the application and is not intended to limit the application. For those skilled in the art, the application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for determining a continuous casting process parameter based on TiN precipitation prediction, characterized by, The method comprises the following steps: obtaining the steel grade composition data of the target continuous casting steel material and the temperature field data under the initial control parameters, and determining the solidification period cooling rate curve of the casting blank, wherein the temperature field data comprises temperature data corresponding to different casting position respectively; predicting the expected precipitation time and temperature window of TiN according to the temperature field data, the steel grade composition data and the solidification period cooling rate curve of the casting blank, and determining the expected TiN precipitation size according to the expected precipitation time and temperature window and the solidification period cooling rate curve of the casting blank; 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 obtained through the TiN precipitation size and 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 according to the target cooling rate curve.

2. The TiN precipitation-based continuous casting process parameter determination method according to claim 1, characterized by, The method for predicting the expected precipitation time and temperature window of TiN according to the temperature field data, the steel grade composition data and the solidification period cooling rate curve of the casting blank comprises the following steps: the liquidus temperature and the solidus temperature of the target continuous casting steel material are calculated through empirical regression according to the steel grade composition data, and the secondary dendrite arm spacing is calculated according to the solidification period cooling rate curve of the casting blank; the solid phase fraction of different positions is calculated according to the comparison results of the temperature of different positions in the temperature field data with the liquidus temperature and the solidus temperature respectively; the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration of different positions are calculated through the Ohnaka model according to the initial liquid phase concentration, the secondary dendrite arm spacing and the solid phase fraction of different positions, and the TiN supersaturation is calculated according to the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration; the position meeting the corresponding threshold value in both the TiN supersaturation and the solid phase fraction is determined as the precipitation starting position, and the expected precipitation time window and temperature window are calculated according to the precipitation starting position, the initial casting speed and the solid phase fraction.

3. The method of determining a continuous casting process parameter based on TiN precipitation prediction according to claim 2, characterized by, The method for calculating the TiN supersaturation according to the Ti theoretical liquid phase concentration and the N theoretical liquid phase concentration comprises the following steps: the Ti activity coefficient is calculated based on the interaction coefficient of different elements with Ti, and the N activity coefficient is calculated based on the interaction coefficient of different elements with N; the temperature corresponding to different positions is calculated according to the casting speed and the solidification period cooling rate curve of the casting blank, and the equilibrium solubility product of different positions is calculated according to the temperature of different positions; the TiN supersaturation of 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 for each position.

4. The TiN precipitation-based continuous casting process parameter determination method according to claim 1, characterized by, The method for obtaining the target cooling rate curve meeting the upper limit of the TiN precipitation size through the TiN precipitation size and cooling rate relationship model comprises the following steps: the upper limit of the TiN precipitation size is taken as the target output and substituted into the TiN precipitation size and cooling rate relationship model to inversely calculate the solidification end cooling rate, and the target cooling rate curve of different control sections is calculated according to the solidification end cooling rate; wherein 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 window, and CR is the cooling rate at the end of solidification.

5. The TiN precipitation-based continuous casting process parameter determination method according to claim 1, characterized by, 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. For each control section of the target cooling rate curve, the specific water quantity of the control section is calculated according to the quantitative relationship model of the specific water quantity and the input cooling rate corresponding to the control section, to obtain a target specific water quantity parameter comprising specific water quantities of multiple control sections; Wherein, the quantitative relationship model between the cooling rate input cooling rate and specific water content is expressed as: ; wherein, CR represents the cooling rate, W represents the specific water content, a, b, c are fitting coefficients, and a, b are both greater than 0, and c is less than 0.

6. The TiN precipitation-based continuous casting process parameter determination method according to claim 1, characterized by, The initial control parameter further comprises an initial reduction parameter, and the expected precipitation time and temperature window comprise a precipitation start time; The method further comprises: calculating an expected cumulative reduction amount before precipitation according to the precipitation start time and the initial reduction parameter; in the case that the expected cumulative reduction amount before precipitation does not satisfy the cumulative reduction amount before precipitation condition, calculating the range of the number of reduction rollers and the range of single roller reduction amount of different position intervals according to the target interval of the cumulative reduction amount before precipitation.

7. The TiN precipitation-based continuous casting process parameter determination method according to claim 6, characterized by, After the target specific water quantity parameter is calculated according to the quantitative relationship model of the specific water quantity and the cooling rate based on the target cooling rate curve, the method further comprises: optimizing the pulling speed within the range of the number of reduction rollers and the range of single roller reduction amount according to the target cooling rate curve and the target specific water quantity parameter, to obtain a target pulling speed, a target number of reduction rollers and a target single roller reduction amount; controlling the continuous casting machine actuator according to the target pulling speed, the target number of reduction rollers and the target single roller reduction amount, and controlling the secondary cooling water spraying system according to the target specific water quantity parameter to carry out continuous casting production; During the continuous casting production, real-time cooling rate and TiN precipitation size data are collected and visualized.

8. A continuous casting process parameter determination device based on TiN precipitation prediction, characterized by, Comprise: an acquisition module, configured to acquire steel composition data of a target continuous casting steel material and temperature field data under initial control parameters, and determine a casting blank solidification period cooling rate curve, wherein the temperature field data comprises temperature data corresponding to different casting flow positions respectively; a prediction module, configured to predict an expected TiN precipitation time and temperature window according to the temperature field data, the steel composition data and the casting blank solidification period cooling rate curve, and determine an expected TiN precipitation size according to the expected TiN precipitation time and temperature window and the casting blank solidification period cooling rate curve; an inversion module, configured to, in the case that the expected TiN precipitation size is greater than an upper limit of TiN precipitation size, inversely calculate a target cooling rate curve satisfying the upper limit of TiN precipitation size through a TiN precipitation size and cooling rate relationship model; a calculation module, configured to calculate a target specific water quantity parameter according to the quantitative relationship model of the specific water quantity and the cooling rate based on the target cooling rate curve.

9. A storage medium, characterized by The storage medium stores at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the TiN precipitation prediction based continuous casting process parameter determination method in any one of claims 1-7.

10. A terminal, characterized by comprising: Comprise: a processor, a memory, a communication interface and a communication bus, the processor, the memory and the communication interface complete communication with each other through the communication bus; The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the TiN precipitation prediction based continuous casting process parameter determination method in any one of claims 1-7.

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