JP2023030272A - Temperature prediction device of steel material, cooling control device, method and program - Google Patents

Abstract

A steel material temperature can be predicted with high accuracy in consideration of transformation heat generation.
SOLUTION: In a hot rolling facility including a rolling mill 2, an ROT cooling device 3, and a coiling device 4, a cooling performance data extracting unit 102 extracts a target data from a database 107 based on steel type information of a target steel plate 1. The cooling performance data of the steel plate of the same steel type as the steel plate 1 is extracted. The TTT curve generation unit 103 uses the cooling performance data extracted by the cooling performance data extraction unit 102 and a temperature drop amount prediction model that predicts the winding temperature by reflecting the prediction result of the transformation heat value based on the TTT curve. , to generate the TTT curve for the target steel plate 1 . The TTT curve generation unit 103 newly generates a curve indicating the start of transformation and a curve indicating the end of transformation, which constitute the TTT curve. It is generated by interpolating using a curve represented by a polynomial.
[Selection drawing] Fig. 3

Description

TECHNICAL FIELD The present invention relates to a steel material temperature prediction device, a cooling control device, a method, and a program.

In the hot rolling equipment, the steel plate after finish rolling is cooled by a cooling device and wound into a coil by a winding device.
In order to maintain high productivity and obtain good product quality, it is important to control the coiling temperature, which is the temperature of the steel sheet before the coiling device. For example, in the case of medium-carbon steel and high-carbon steel, excessive cooling in a cooling device and a low coiling temperature result in the formation of a hard layer, making cracks more likely to occur. Therefore, quality and yield are lowered. On the other hand, if the cooling in the cooling device is insufficient and the coiling temperature rises, the structural change (transformation) from austenite to ferrite progresses in the state of the coil after winding, and winding loosening occurs and the coil collapses (elliptical It may be deformed into a shape and need to be rewound) may occur. Therefore, a correction process such as rewinding is required, which lowers productivity.
In order to prevent such deterioration in quality, yield, and productivity, the target temperature of the winding temperature is set in advance, and the cooling device is operated to control the winding temperature to the target temperature. There is a need.

However, when the steel sheet is cooled to progress the transformation, transformation heat is generated. In particular, medium-carbon steel and high-carbon steel, which contain a large amount of carbon, have a large amount of heat generated during transformation, and the amount of heat that raises the temperature of the steel sheet by 100° C. or more may be generated from the start to the end of the transformation. Since transformation heat is generated in this way, it is difficult to predict the coiling temperature with high accuracy, and the accuracy of control for setting the coiling temperature to the target temperature may deteriorate.

Patent Document 1 discloses a method for manufacturing a steel product by cooling a steel plate processed by a finishing rolling mill with a cooling device, in which a TTT curve (isothermal transformation curve, called an isothermal transformation curve) is used. It is disclosed that the transformation calorific value of the steel sheet is calculated using the method, and the coiling temperature of the steel sheet is predicted in consideration of the transformation calorific value.

JP 2008-161924 A JP 2011-212743 A Japanese Patent Application Laid-Open No. 2006-281306

However, if the composition of additional elements such as carbon in the steel differs, the transformation behavior of the steel will also differ. Can not do it. Therefore, when manufacturing multiple steel grades, it is necessary to newly prepare a large number of different TTT curves for each steel grade, which takes time and costs.
Therefore, in Patent Document 1, in order to save time and reduce costs, the existing TTT curve diagram is modified according to the composition of the steel plate to be cooled by the cooling device (specifically, the existing TTT curve The figure is used by simply translating it in the temperature axis direction and the time axis direction on the temperature-time plane. Therefore, the prediction accuracy can be improved if the target is a steel type with a small difference in the composition of the additive elements with respect to the steel type of the existing TTT curve diagram. However, in the case of targeting a steel type whose additive element composition is significantly different from the steel type of the existing TTT curve diagram, the effect of improving the prediction accuracy becomes small.
In general, the TTT curve is generated using the measurement results of the transformation behavior when the test material is heated and cooled in a test apparatus. It is difficult and sometimes the coiling temperature cannot be predicted with high accuracy.

The present invention has been made in view of the above-mentioned points, and the temperature of the steel material considering the transformation heat generation can be determined without preparing in advance a large number of TTT curves that differ for each steel type using a test device or the like. The purpose is to enable highly accurate prediction regardless of the steel type.

The steel temperature prediction device of the present invention is a steel temperature prediction device for predicting the temperature of a steel to be cooled, and is based on the actual cooling data for each steel type and the prediction result of the transformation heat value based on the TTT curve. TTT curve generation means for generating a TTT curve for the target steel material using a prediction model for predicting the temperature of and a temperature prediction means for predicting the temperature of the target steel material by reflecting the temperature in the target steel material.
The cooling control device of the present invention is a cooling control device for controlling the temperature of steel to be cooled by a cooling device, wherein the temperature of the target steel predicted by the steel temperature prediction device of the present invention is set to a preset target temperature. It is characterized by comprising control means for operating the cooling device so as to
The steel temperature prediction method of the present invention is a steel temperature prediction method for predicting the temperature of a steel to be cooled. A TTT curve generation step for generating a TTT curve for the target steel using a prediction model for predicting the temperature of the and a temperature prediction step of predicting the temperature of the target steel material.
The cooling control method of the present invention is a cooling control method for controlling the temperature of a steel material to be cooled by a cooling device. A TTT curve generation step for generating a TTT curve for the target steel using a prediction model for predicting temperature, and a transformation heat generation prediction result based on the TTT curve generated in the TTT curve generation step is applied to the prediction model. and a step of operating the cooling device so that the temperature of the target steel predicted in the temperature prediction step becomes a preset target temperature. characterized by having
The program of the present invention is a program for predicting the temperature of the steel material to be cooled, and predicts the temperature of the steel material by reflecting the actual cooling data for each steel type and the prediction result of the transformation heat value based on the TTT curve. A TTT curve generating means for generating a TTT curve for a target steel using a model, and a prediction result of transformation heat generation based on the TTT curve generated by the TTT curve generating means are reflected in the prediction model, The computer is made to function as temperature prediction means for predicting the temperature of the target steel.
The program of the present invention is a program for controlling the temperature of the steel material cooled by the cooling device, and the temperature of the steel material is controlled by reflecting the actual cooling data for each steel type and the prediction result of the transformation heat value based on the TTT curve. TTT curve generation means for generating a TTT curve for the target steel using a prediction model for prediction; a temperature predicting means for predicting the temperature of the target steel material; and a computer as control means for operating the cooling device so that the temperature of the target steel material predicted by the temperature predicting means reaches a preset target temperature. make it work.

According to the present invention, since the TTT curve for the target steel material optimized for the steel type is generated using the cooling performance data for the steel type, a large number of TTT curves different for each steel type are generated using a testing device or the like. Without preparing in advance, it is possible to predict the temperature of the steel material considering the heat of transformation with high accuracy regardless of the steel type.

It is a figure which shows schematic structure of a hot-rolling facility. It is a figure which shows the example of a TTT curve. It is a figure which shows the functional structure of the cooling control apparatus which concerns on embodiment. FIG. 4 is a diagram for explaining generation of a TTT curve; FIG. 4 is a diagram for explaining generation of a TTT curve; 4 is a flowchart showing processing executed by the cooling control device according to the embodiment; It is a figure which shows the TTT curve in an Example. It is a figure which shows the TTT curve in a comparative example.

Preferred embodiments of the present invention will now be described with reference to the accompanying drawings.
First, referring to FIG. 1, a schematic configuration of a hot rolling facility will be described. FIG. 1 is a diagram showing a schematic configuration of a hot rolling facility. The hot rolling equipment includes a rolling mill 2 , a ROT (Run out table) cooling device 3 and a winding device 4 . The rolling mill 2 finish-rolls the steel plate 1 . The steel sheet 1 after finish rolling by the rolling mill 2 is conveyed on a conveying table and passes through the ROT cooling device 3 . The ROT cooling device 3 cools the steel plate 1 by spraying cooling water onto the upper and lower surfaces of the steel plate 1 from a plurality of cooling headers (not shown) having nozzle groups. The winding device 4 winds the steel plate 1 cooled by the ROT cooling device 3 into a coil.

At the delivery side of the rolling mill 2 and the entry side of the ROT cooling device 3, there are a plate thickness measuring instrument 5 for measuring the thickness of the steel plate 1 and a temperature measuring device for measuring the temperature of the steel plate 1 (referred to as finishing delivery side temperature). A total of 6 are installed. A thermometer 7 for measuring the coiling temperature, which is the temperature of the steel sheet 1 in front of the coiling device 4, is installed on the exit side of the ROT cooling device 3 and the entry side of the coiling device 4. As shown in FIG. Also, a conveying speed meter 8 for measuring the conveying speed of the steel plate 1 is installed.

In such a hot rolling facility, the cooling control device 100 uses a prediction model to predict the coiling temperature, which is the temperature of the target steel plate 1 after cooling by the ROT cooling device 3, and the predicted coiling temperature The ROT cooling device 3 is operated for control to reach the target temperature.

The cooling control device 100 will be described in detail below, but before that, a prediction model for predicting the winding temperature will be described.
As a prediction model for predicting the coiling temperature, a temperature drop prediction model for predicting the temperature drop of the steel sheet is used. Equations (1) to (3) show a temperature drop amount prediction model for calculating the amount of temperature drop in the steel sheet from the delivery side of the rolling mill 2 to the front of the winding device 4. The thermometers 6 to 7 are divided into a plurality of zones, and the heat transfer coefficients of water cooling, radiation, and convection are given to each zone from the operating state of the ROT cooling device 3, and the temperature drop amount Δθ (i ). In equation (1), the first term on the right side represents the temperature change of the steel sheet due to water cooling, and the second term on the right side represents the temperature change of the steel sheet due to heat radiation, the temperature change of the steel sheet due to contact with air, and the temperature of the steel sheet due to transformation heat generation. represents change. By subtracting the amount of temperature drop Δθ(i) of the steel sheet in each zone from the finish delivery side temperature measured by the thermometer 6, the temperature of the steel sheet in each zone can be calculated.
i: subscript indicating target zone Δθ(i): temperature drop of steel sheet (°C)
θ(i): Steel plate temperature at the zone entry side (referred to as zone entry temperature) (°C)
θ _W : Cooling water temperature (°C)
θ _A : Air temperature (°C)
Δt(i): Zone transit time (hr)
α _U (i), α _L (i): upper and lower heat transfer coefficients (kcal/m ² /hr/°C)
C _P (i): zone specific heat (kcal/kg/°C)
ρ: Density of steel plate (kg/m ³ )
h: plate thickness of steel plate (m)
ε: Emissivity (-)
σ: Stefan Boltzmann constant θ _K : 273.15°C
α _A : Convective heat transfer coefficient (kcal/m ² /hr/°C)
q _H (i): zone transformation heating value (kcal/m ³ /hr)
W(i): zone water volume density (m ³ /m ² /min)
_a1-4 : constant

As shown in equations (1) to (3), the temperature drop prediction model includes zone transformation heat generation q _H (i). Therefore, the coiling temperature is predicted by reflecting the prediction result of the transformation heat generation amount based on the TTT curve in the temperature drop amount prediction model of formulas (1) to (3).
FIG. 2 is a diagram showing an example of a TTT curve. As shown in FIG. 2, the TTT curve shows changes in the structure of steel with temperature and time, with the vertical axis representing temperature and the horizontal axis representing time. The TTT curve consists of a curve indicating the start of transformation (curve indicating the relationship between time and temperature at which isothermal transformation starts) and a curve indicating the end of transformation (curve indicating the relationship between time and temperature at which isothermal transformation ends). represents the transformation start time and the transformation end time when the steel is held at a certain temperature. At the zone entrance temperature θ(i) of zone i, the transformation starts at time t _s (i) and ends at time t _f (i). Using the transformation start time t _s (i) and the transformation end time t _f (i) derived from the TTT curve in this way, the zone transformation heat generation amount q _H ( Calculate i).
i: subscript indicating the target zone θ (i): temperature of the steel sheet on the entry side of the zone (°C)
t _s (i): transformation start time (s) at θ(i) (derived from TTT curve)
t _f (i): transformation finish time (s) at θ(i) (derived from TTT curve)
ξ _S : Transformation rate at the start of transformation (=0.01)
ξ _F : Transformation rate at the end of transformation (=0.99)
ξ(i), ξ(i+1): Transformation rates on the entry side and exit side of the zone t(i): Elapsed time (s) of the transformation rate equation required to match ξ(i)
Δt(i): Zone transit time (s)
q _H (i): zone transformation heating value (kcal/m ³ /hr)
α _H : transformation latent heat (kcal/kg)

As described above, the transformation start time t _s (i) and the transformation end time t _f (i) at the zone entrance temperature θ(i) of each zone are derived from the TTT curve, and the zone transformation heat generation amount q _H (i ) can be calculated. Then, if the temperature drop amount Δθ(i) of the steel sheet and the zone transformation heat generation amount _qH (i) are calculated for each zone from the finishing delivery side temperature, it is possible to predict the coiling temperature in consideration of the transformation heat generation. be possible.

Next, the cooling control device 100 will be described in detail with reference to FIG. FIG. 3 is a diagram showing the functional configuration of the cooling control device 100. As shown in FIG.
The cooling control device 100 includes an input unit 101 , a cooling performance data extraction unit 102 , a TTT curve generation unit 103 , a storage unit 104 , a temperature prediction unit 105 and a control unit 106 . The cooling control device 100 also connects to a database 107 that stores cooling performance data for each steel type. The cooling control device 100 configured as described above can be realized by a computer device including, for example, a CPU, a ROM, and a RAM. Functions 101-106 are realized. In this embodiment, it is assumed that a process computer that controls the production process at the steelworks functions as the cooling control device 100 .

The input unit 101 inputs steel type information and a target coiling temperature for a target steel plate 1 to be rolled, cooled, and coiled in a hot rolling facility. In addition, the input unit 101 inputs the operating state of the ROT cooling device 3 (including the amount of water in the cooling header in each zone), the thickness of the target steel plate 1 measured by the thickness measuring meter 5, the target steel plate measured by the thermometer 6 1, the winding temperature of the target steel sheet 1 measured by the thermometer 7, the conveying speed measured by the conveying speed meter 8, the water temperature of the cooling water, and the air temperature. In addition, the input unit 101 inputs information such as various constants necessary for the formulas (1) to (10). good. The input unit 101 may input the information directly from an external device or via a network, or input manually by the user, and the input method is not limited. Note that the temperature of the cooling water and the air temperature may be treated as fixed values if the fluctuation range is limited.

Based on the steel type information of the target steel plate 1 input by the input unit 101 , the cooling performance data extraction unit 102 extracts the cooling performance data regarding the steel plate of the same or similar steel grade as the target steel plate 1 from the database 107 . Here, “similar” means a range in which a predetermined percentage of variation (which varies depending on the steel type) is allowed for the additive element composition. The cooling performance data for each steel type stored in the database 107 includes the measured values (actual values) of the finishing delivery side temperature, the measured values (actual values) of the coiling temperature, and each Includes actual values of zone passing speed, actual values of water volume in cooling headers in each zone, and actual values of cooling water temperature and air temperature. Note that the temperature of the cooling water and the air temperature may be treated as fixed values if the fluctuation range is limited.

The TTT curve generation unit 103 generates a TTT curve for the target steel plate 1 using the cooling performance data extracted by the cooling performance data extraction unit 102 and the temperature drop amount prediction model of formulas (1) to (3). do.
The TTT curve generation unit 103 newly generates two curves (a curve indicating the start of transformation and a curve indicating the end of transformation) that constitute the TTT curve as the TTT curve for the target steel plate 1 . Each curve is generated by giving a plurality of nodes on a two-dimensional plane of temperature and time and interpolating between the nodes using a curve represented by a polynomial. At that time, using the measured value of the coiling temperature included in the cooling performance data of the steel plate of the same or similar steel grade as the target steel plate 1 extracted by the cooling performance data extraction unit 102 and the cooling performance data, the formula (1 ) to Equation (3), the TTT curve is optimized based on the evaluation function including the difference from the predicted value of the winding temperature predicted by the temperature drop amount prediction model.

The method for generating the TTT curve will be described in detail below with reference to FIGS. 4 and 5. FIG.
4 and 5 are diagrams for explaining the generation of the TTT curve. As shown in FIG. 4, on a two-dimensional plane of temperature and time, a plurality of nodes F ₁ to F ₆ of a curve t=f(θ) indicating the start of transformation and a curve t=g(θ) indicating the end of transformation are shown. , and perform _time and temperature _optimization . In the example of FIG. 4, six nodes F ₁ to F ₆ and G ₁ to G ₆ are set on each curve, but the number of nodes may be other than six.

For _{nodes F 1 ( θ 1 , t 1 ) , .} It is necessary to determine the temperature .theta. Since it is desirable that the number of parameters to be optimized is small, the temperature θ ₁ and the time t ₁ of the node F ₁ that is the end point of the lowest temperature on the curve t=f(θ), and the curve t=g(θ ), the temperature θ ₇ and time t ₇ of the node G ₁ , which is the end point on the lowest temperature side, are treated as fixed values. Also, the time t ₆ of the node F ₆ that is the end point on the slow side of the curve t=f(θ) and the time t 6 of the node G ₆ that is the end point on the slow side of the curve t=g(θ) ₁₂ is treated as a fixed value. As a result, the number of parameters to be optimized can be reduced to 18, and the optimization calculation load can be reduced. For example, when the target steel plate 1 is a medium carbon steel or a high carbon steel, _it is unlikely that the ROT cooling device ₃ will cool the steel plate to 500°C or less. Even if the respective temperatures θ ₁ , θ ₇ and times t ₁ , t ₇ are fixed, there is no practical problem in predicting the transformation heat generation amount.

If the heat transfer coefficients of water cooling, radiation, and convection of the temperature drop prediction model of formulas (1) to (3) are appropriately given, the winding temperature can be treated as a function of the coordinates of the TTT curve. . Therefore, as in formula (11), using the measured value T _act of the coiling temperature included in the actual cooling data of the steel sheet of the same steel type as the target steel sheet 1 and the actual cooling data, the formulas (1) to (3) ), the TTT curve is optimized so that the evaluation function including the difference from the predicted value _Tcal of the winding temperature predicted by the temperature drop prediction model is minimized. Specifically, spline interpolation is performed between coordinates (time and temperature) that are candidates for a plurality of nodal points F ₁ to F ₆ and G ₁ to G ₆ to generate a temporary TTT curve, and the temporary TTT curve and the formula The process of calculating the predicted value T _cal of the winding temperature using (1) to (10) and evaluating it with the evaluation function of formula (11) is repeated until the evaluation function of formula (11) is minimized. Repeat while changing (moving) the coordinates of ₁ to F ₆ and G ₁ to G ₆ . Then, the TTT curve when the evaluation function of formula (11) is minimized is taken as the optimum TTT curve for the target steel plate 1. Such optimization calculation makes it possible to generate an optimum TTT curve that conforms to the actual transformation exothermic phenomenon. As described above, temperature θ ₁ and time t ₁ at node F ₁ , temperature θ ₇ and time t ₇ at node G ₁ , time t ₆ at node F ₆ , and time t ₁₂ at node G ₆ are constants ( fixed value).

As described above, the TTT curve for the target steel plate 1 is generated, and a cubic spline curve is used for spline interpolation between a plurality of nodes. This is an interpolation method that interpolates between nodes using a curve represented by a cubic polynomial so that nodes other than endpoints do not become discontinuous. By interpolating between nodes using a curve represented by a polynomial in this way, a TTT curve represented by a smooth curve can be generated, and it is possible to reproduce the phenomenon close to the actual transformation heat generation phenomenon. become. However, instead of the cubic spline curve, other curves or straight lines may be used for interpolation.

In addition, since the relationship between the amount of movement of the node coordinates and the calculated value T _cal of the winding temperature is highly nonlinear, PSO (particle swarm optimization law) was implemented. In PSO, it is necessary to set _the initial particle generation range for each coordinate _, and as _shown in _FIG . (In FIG. 5, nodes F ₆ and G ₆ are not displayed because they are outside the display range). This initial particle generation range may be set with reference to a known TTT curve described in Patent Document 1, for example. Note that the particle coordinates after optimization are often set outside the initial particle range, and it is not necessary to strictly set the initial particle range.

Returning to FIG. 3 , the storage unit 104 stores and saves the TTT curve for the target steel plate 1 generated by the TTT curve generation unit 103 .

The temperature prediction unit 105 uses the TTT curve for the target steel sheet 1 stored in the storage unit 104 to determine the transformation start time t s (i) and the transformation end time t _s (i) at the zone entrance temperature θ (i) of each zone. The time t _f (i) is derived, and the zonal transformation heat generation amount q _H (i), which is the prediction result of the transformation heat generation amount, is calculated by the equations (4) to (10). Then, the temperature prediction unit 105 reflects the calculated zone transformation heating value q _H (i) in the temperature drop amount prediction model of formulas (1) to (3) to predict the coiling temperature of the target steel sheet 1. do.

The control unit 106 operates the ROT cooling device 3 so that the coiling temperature of the target steel plate 1 predicted by the temperature prediction unit 105 becomes the target temperature. Specifically, the control unit 106 sets an operation amount for adjusting the amount of water in the cooling header of the ROT cooling device 3 so that the coiling temperature of the target steel sheet 1 predicted by the temperature prediction unit 105 approaches the target temperature.

Next, referring to FIG. 6, processing executed by the cooling control device 100 will be described. FIG. 6 is a flow chart showing processing executed by the cooling control device 100 . The flowchart shown in FIG. 6 is started before the target steel plate 1 enters the ROT cooling device 3 .
In step S<b>1 , the input unit 101 inputs the steel type information and the target temperature of the coiling temperature for the target steel plate 1 .

In step S2, the cooling performance data extracting unit 102 extracts cooling performance data related to steel sheets of the same or similar steel grade as the target steel plate 1 from the database 107 based on the steel type information of the target steel plate 1 input in step S1.

In step S3, the TTT curve generation unit 103 extracts the measured value T _act of the coiling temperature included in the cooling performance data of the steel sheet of the same or similar steel grade as the target steel plate 1 extracted in step S2, and the cooling performance data. Optimization of the TTT curve so that the evaluation function (formula (11)) including the difference from the winding temperature T _cal predicted by the temperature drop prediction model of formulas (1) to (3) using to generate a TTT curve for the target steel plate 1.
In step S4, the TTT curve generation unit 103 stores and saves the TTT curve for the target steel plate 1 generated in step S3 in the storage unit 104. FIG.

When the temperature prediction target portion of the target steel plate 1 reaches the position of the thermometer 6, the processes after step S5 are started.
In step S5, the input unit 101 inputs the operating state of the ROT cooling device 3 (including the amount of water in the cooling header in each zone), the thickness of the target steel plate 1 measured by the thickness measuring meter 5, and the thickness measured by the thermometer 6. Input the finish delivery side temperature of the target steel plate 1, the conveying speed measured by the conveying speed meter 8, the water temperature of the cooling water, and the air temperature. Among the information described here, information that can be input at the stage of step S1 may be input at step S1.

In step S6, the temperature prediction unit 105 uses the TTT curve for the target steel sheet 1 stored in the storage unit 104 to calculate the transformation start time t _s (i ) and the transformation end time t _f (i) are derived, and the zonal transformation heat generation amount q _H (i), which is the prediction result of the transformation heat generation amount, is calculated from equations (4) to (10). Then, the temperature prediction unit 105 reflects the calculated zone transformation heating value q _H (i) in the temperature drop amount prediction model of formulas (1) to (3) to predict the coiling temperature of the target steel sheet 1. do.

In step S7, the control unit 106 operates the ROT cooling device 3 so that the coiling temperature of the target steel sheet 1 predicted in step S6 becomes the target temperature. Specifically, the control unit 106 sets the operation amount for adjusting the amount of water in the cooling header of the ROT cooling device 3 so that the coiling temperature of the target steel sheet 1 predicted in step S7 approaches the target temperature.

At step S8, the control unit 106 determines whether or not the end condition is reached. In this embodiment, the completion condition is that the temperature prediction target portion of the target steel plate 1 reaches the position of the thermometer 7 . If the end condition is not reached, the process returns to step S5. In step S5, if there is changed information, it is input again. For example, since the operating state of the ROT cooling device 3 changes in the process of step S7, it is input again. The processing of steps S5 to S8 is repeated at intervals of several hundred milliseconds. On the other hand, if the termination condition is met, the process proceeds to step S9.

In step S<b>9 , the input unit 101 inputs the coiling temperature of the target steel sheet 1 measured by the thermometer 7 .
In step S10, the input unit 101 stores the cooling performance data of the target steel plate 1 acquired in steps S1, S5, and S9 in the database 107. FIG. The cooling performance data of the target steel plate 1 includes the measured value (actual value) of the finishing delivery side temperature, the measured value (actual value) of the coiling temperature, and the actual value of each zone passage speed sampled in the longitudinal direction of the target steel plate 1. , the actual value of the water volume of the cooling header in each zone, and the actual value of the water temperature and air temperature of the cooling water.

In this embodiment, as shown in the flowchart of FIG. 6, the process from generation of the TTT curve to control of the ROT control device 3 is executed online as a series of processes, but the present invention is not limited to this. . For example, the processing of steps S2 to S4 may be performed off-line to generate a TTT curve for a steel type assumed to be the target steel plate 1 in advance.

As described above, the TTT curve for the target steel plate 1 is generated using the actual cooling data for each steel type and the prediction model that predicts the coiling temperature by reflecting the prediction result of the transformation heat value based on the TTT curve. do. As a result, even when manufacturing multiple steel grades, it is possible to generate a new TTT curve that matches the steel grade, making it possible to accurately predict the coiling temperature that takes transformation heat into account regardless of the steel grade. Become. As a result, it is possible to realize highly accurate control of the winding temperature to the target temperature, maintain high productivity, and obtain good product quality.

[Example]
As an example, a TTT curve was generated by the method described in the embodiment using the actual cooling data of each steel type for structural carbon steel S70C and carbon tool steel SK85. Further, as a comparative example, the TTT curves of structural carbon steel S70C and carbon tool steel SK85 were deformed by the method described in Patent Document 1.
In generating the TTT curve of the example, data for 9 coils in S70C and data for 16 coils in SK85 were used as actual cooling data. The actual cooling data includes the measured values of the finishing delivery side temperature, the measured values of the coiling temperature, the actual values of the passing speed of each zone, and the actual values of the water volume of the cooling header in each zone, which are sampled at intervals of 10 m in the longitudinal direction of the steel plate. , including actual values of cooling water temperature and air temperature. Note that the 50m leading and trailing edge of the steel plate is an unsteady portion, so it is excluded from the actual cooling data.

FIG. 7 is a diagram showing TTT curves of Examples, where (a) shows the TTT curve of steel type S70C, and (b) shows the TTT curve of steel type SK85. FIG. 8 is a diagram showing TTT curves of comparative examples, where (a) shows the TTT curve of steel type S70C, and (b) shows the TTT curve of steel type SK85.
In the comparative example, one existing TTT curve is deformed by sliding it on a two-dimensional plane of temperature and time, and as shown in FIGS. The shape of the curve indicating the end of transformation and the curve indicating the end of transformation are the same, but the positions of temperature and time on the two-dimensional plane are different. On the other hand, in the embodiment, since a new TTT curve is generated using the actual cooling data of each steel type, the shape of the curve indicating the start of transformation and the curve indicating the end of transformation are suitable for the steel type. In the embodiment, in order to reduce the number of nodes to be optimized, the nodes below 500.degree. Since medium-carbon steel and high-carbon steel are rarely cooled to 500° C. or less, there is no practical problem in predicting the transformation heat value.

Using the TTT curve thus obtained, the prediction accuracy of the winding temperature and the control accuracy of the winding temperature were evaluated. Table 1 shows the results.
The root mean squared error (RMSE), which represents the accuracy of the coiling temperature prediction, was evaluated. For this evaluation, coils other than those used to generate the TTT curves were used, 14 coils for S70C and 33 coils for SK85 for both the examples and comparative examples. As a result, it was confirmed that the RMSE was improved by about 5 to 10% in the example as compared with the comparative example. As described above, in the embodiment, it is possible to generate a new TTT curve suitable for the steel type, and to highly accurately predict the coiling temperature in consideration of the transformation heat generation.

In addition, in order to evaluate the control accuracy of the coiling temperature, the target rate of the coiling temperature (the target rate of ±25° C. in the longitudinal direction of the coil) was evaluated. For this evaluation, coils other than those used to generate the TTT curve were used: 28 coils for S70C and 42 coils for SK85 were used in the comparative example, and 7 coils for S70C and 12 coils for SK85 were used in the example. I used a coil. As a result, it was confirmed that the target ±25° C. target rate was about 2 to 4% higher in the example than in the comparative example. As described above, in the embodiment, the winding temperature can be predicted with high accuracy, so that high-precision control for setting the winding temperature to the target temperature can be realized.

The above-described embodiments are merely specific examples for carrying out the present invention, and the technical scope of the present invention should not be construed to be limited by these. That is, the present invention can be embodied in various forms without departing from its technical concept or main features.
In the above-described embodiment, the cooling control device 100 functions as a steel temperature prediction device to which the present invention is applied and a cooling control device to which the present invention is applied. The prediction device and the cooling control device to which the present invention is applied may be configured as separate devices.
Also, in the present embodiment, an example of estimating the coiling temperature in the hot rolling equipment has been described, but the present invention is not limited to this. INDUSTRIAL APPLICABILITY The present invention can be applied to predict the temperature of steel to be cooled, and can be used, for example, to predict the temperature of a steel plate in a thick plate cooling facility or an annealing facility that heats and cools steel. . It may also be used to predict the temperature of the steel sheet at the position of the intermediate thermometer in the hot rolling mill. In this case, for example, in order to improve the accuracy of both the intermediate temperature and the winding temperature, the difference between the predicted value and the measured value at the position of the intermediate thermometer and the predicted value at the position of the winding thermometer The TTT curve can be optimized based on the evaluation function including the difference between the measured values.
The steel material temperature prediction function and cooling control function to which the present invention is applied are provided by supplying software (program) to a system or device via a network or various storage media, and the computer of the system or device executes the program. It can also be implemented by reading and executing. Also, the function of controlling the blast furnace process to which the present invention is applied may be realized by a PLC (Programmable Logic Controller) or may be realized by dedicated hardware such as ASIC.

1: Steel plate, 2: Rolling mill, 3: ROT cooling device, 4: Winding device, 5: Plate thickness measuring instrument, 6, 7: Thermometer, 8: Conveying speed meter, 100: Cooling control device, 101: Input Unit 102: Cooling performance data extraction unit 103: TTT curve generation unit 104: Storage unit 105: Temperature prediction unit 106: Control unit 107: Database

Claims (10)

A steel temperature prediction device for predicting the temperature of steel to be cooled,
a TTT curve generation means for generating a TTT curve for a target steel material using actual cooling data for each steel type and a prediction model for predicting the temperature of the steel material by reflecting the prediction result of the transformation heat value based on the TTT curve;
a temperature prediction means for predicting the temperature of the target steel material by reflecting the prediction result of the transformation heating value based on the TTT curve generated by the TTT curve generation means in the prediction model. Temperature predictor. 2. The TTT curve generation means generates the TTT curve for the target steel material by interpolating between a plurality of nodes represented by time and temperature using a curve represented by a polynomial. The steel material temperature prediction device according to . The TTT curve generation means calculates the difference between the actual temperature value included in the actual cooling data regarding the steel material of the same or similar steel grade as the target steel material and the temperature predicted by the prediction model using the actual cooling data. 3. The steel material temperature predicting device according to claim 2, wherein the time and temperature of the plurality of nodes are optimized based on the evaluation function included. 4. The steel material temperature predicting apparatus according to claim 3, wherein the TTT curve generating means executes a particle group optimization method as an optimization method for optimizing the time and temperature of the node. 5. Any one of claims 2 to 4, wherein said TTT curve generating means treats at least one of time and temperature of some of said plurality of nodes as a fixed value. A temperature prediction device for steel according to the above item. A cooling control device for controlling the temperature of a steel material cooled by a cooling device,
A control means for operating the cooling device so that the temperature of the target steel material predicted by the temperature prediction apparatus for steel materials according to any one of claims 1 to 5 becomes a preset target temperature. A steel material cooling control device characterized by: A steel temperature prediction method for predicting the temperature of steel to be cooled, comprising:
a TTT curve generation step of generating a TTT curve for a target steel material using cooling performance data for each steel type and a prediction model that predicts the temperature of the steel material by reflecting the prediction result of the transformation heat value based on the TTT curve;
a temperature prediction step of predicting the temperature of the target steel material by reflecting the prediction result of the transformation heat value based on the TTT curve generated in the TTT curve generation step in the prediction model. Forecast method. A cooling control method for controlling the temperature of a steel material cooled by a cooling device,
a TTT curve generation step of generating a TTT curve for a target steel material using cooling performance data for each steel type and a prediction model that predicts the temperature of the steel material by reflecting the prediction result of the transformation heat value based on the TTT curve;
a temperature prediction step of predicting the temperature of the target steel material by reflecting in the prediction model the prediction result of the transformation heat generation amount based on the TTT curve generated in the TTT curve generation step;
and a step of operating the cooling device so that the temperature of the target steel material predicted in the temperature prediction step becomes a preset target temperature. A program for predicting the temperature of steel to be cooled, comprising:
a TTT curve generation means for generating a TTT curve for a target steel material using actual cooling data for each steel type and a prediction model for predicting the temperature of the steel material by reflecting the prediction result of the transformation heat value based on the TTT curve;
A program for causing a computer to function as temperature prediction means for predicting the temperature of the target steel material by reflecting the prediction result of the transformation heat generation amount based on the TTT curve generated by the TTT curve generation means in the prediction model. A program for controlling the temperature of a steel material cooled by a cooling device,
a TTT curve generation means for generating a TTT curve for a target steel material using actual cooling data for each steel type and a prediction model for predicting the temperature of the steel material by reflecting the prediction result of the transformation heat value based on the TTT curve;
temperature prediction means for predicting the temperature of the target steel material by reflecting the prediction result of the transformation heat generation amount based on the TTT curve generated by the TTT curve generation means in the prediction model;
A program for causing a computer to function as control means for operating the cooling device so that the temperature of the target steel material predicted by the temperature prediction means becomes a preset target temperature.

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