JPH1133616A - Controller for winding temperature of steel strip - Google Patents

Abstract

PROBLEM TO BE SOLVED: To perform proper learning according to the change of an operation condition, and to properly cool the head part of the steel strip by obtaining a deviation from the true value of a thermal conductivity according to a difference between the predicted value of the winding temperature of the steel strip and an actual value by the sequential type least square, method setting it to a learning value, and correcting the thermal conductivity of a cooling control part. SOLUTION: The forecast cooling speed of the steel strip 1 is calculated by a cooling controller 20 in consideration of a cooling speed from the signals of finish outlet side thermometer 8, finish speed detector 13, winding thermometer 9 and winding speed detector 12. By using the thermal conductivity, water injection is controlled so that this predicted value and a target value coincide. The information of a cooling control part 20 and the information of the operation condition are inputted in a learning calculation part 30, the learning value of the thermal conductivity of the head part of the steel strip 1, is calculated previously for each group of various steel strips 1, and they are stored. According to the operation condition, the thermal conductivity of the cooling control part 20 is corrected by the stored learning value by the learning calculation part 30. For the thermal conductivity to be used for the calculation of a predicted cooling temperature in the divided area of a water injection bank 4, the sequential type least square method is applied.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus for controlling a temperature of winding a steel strip, and more particularly to an apparatus for optimizing learning of a heat transfer coefficient between steel strips.

[0002]

2. Description of the Related Art In the above-mentioned steel strip winding temperature control apparatus, it is important to control the winding temperature (CT) in order to ensure the quality of a hot-rolled steel strip (steel sheet). This is because the quality such as the workability of the steel strip is determined by this. One of the difficult points in controlling the winding temperature is that it is not possible to obtain a large number of measured temperatures. The actual measurement of the temperature is generally performed only on the finishing side and the take-in side, or at most, only two actual measurement points are provided in the middle. The reason is that water is usually used to cool the steel strip and a radiation thermometer is used to measure the temperature.However, steam and water on the board generated by pouring cooling water into the steel strip are used for temperature measurement. In addition, equipment environmental measures such as a blower to be removed and a drainer spray are required, that is, a space restriction and a cost increase occur.

Therefore, generally, feedforward (FF) control for injecting water is performed such that the temperature of the steel strip is predicted and calculated from the temperature on the finishing side and the predicted value matches the target value. In the above water injection control, feedback (F
B) Water injection correction is performed by control. In this feedback control, the actual value of the winding temperature (actual CT) of the steel strip is compared with the target value. If the cooling is insufficient, the water injection amount is increased, and if the cooling is excessive, the water injection amount is decreased.

Further, in the water injection control, the accuracy of temperature prediction is improved by learning control in the steel strip for correcting a difference between the predicted value and the actual value of the temperature of the steel strip. However, despite the feedback control, the feedforward control, and the learning control in the steel strip, the temperature control accuracy of the steel strip head region is poor. The reason is that, in the steel strip head region, the feedback control using the actual value of the winding temperature and the learning control in the steel strip are not in time, so the feedforward control must be performed by accurately predicting the temperature of the steel strip. Nevertheless, the steel strip head region often has a different cooling characteristic (heat transfer coefficient) than the central portion and thereafter, and the temperature prediction calculation is likely to deviate. For example, heating unevenness such as overheating or insufficient heating in the heating stage before hot rolling induces a change in the surface state due to a change in the scale generation state, and finally causes a change in the heat transfer coefficient in the cooling stage. Also, during the cooling of the head area,
Since the tip is not fixed, for example, there is a large change in shape such as bending or warping, water is generated, and there is a case where the cooling becomes slightly supercooled.

As a prior art for improving the temperature accuracy of the steel strip head region, there is one disclosed in Japanese Patent Application Laid-Open No. 3-198905. It discloses that a correction amount for the target value is obtained from the average of the difference between the target value and the actual value of the winding temperature for the head region, and this is exponentially smoothed and reflected in control. Japanese Patent Application Laid-Open No. 64-62206 discloses a method of correcting the cooling capacity by water-cooling and air-cooling for each area divided into upper and lower areas by a bank using a sequential least squares method. A method is proposed in which the values are learned in the steel strip and applied to the next steel strip.

[0006]

However, in the method described in Japanese Patent Application Laid-Open No. 3-198905, firstly, since learning cannot be performed for each bank, a temperature calculation error occurs when the water injection amount / water injection pattern changes. Secondly, since it is impossible to divide the error of the air-cooling component and the error of the water-cooling component separately, there is a problem that a temperature calculation error occurs when the water injection amount / water injection pattern changes.

In the method described in JP-A-64-62206, the optimum learning value is obtained in the head region and the tail region of the steel strip as described in JP-A-7-32024. Since they are different, there is a problem that an error of the difference occurs. In addition, since the optimal learning value changes with time even between steel strips of the same group grouped for each similar steel type, any of the above methods cannot appropriately reflect this, and the accuracy of the winding temperature can be reduced. There is a problem that improvement cannot be achieved.

FIG. 5 is a diagram for explaining an image of a change in an optimum learning value for each steel strip. This figure shows the optimum learning value for each steel strip of a specific steel type plotted against the hot rolling time of the steel strip. The steel strip is cooled immediately after hot rolling. Rolling and cooling are continuous operations, and the time difference between rolling times between steel strips ≒ the time difference between cooling times. One of a plurality of learning values is plotted for each of air cooling, water cooling, and bank (divided region). The optimum value varies for each steel strip, regardless of the type of steel. As a feature, firstly, those continuously and hot-rolled (within about 60 minutes) are distributed around a certain value, and secondly, the rolling interval is long (for several hours). What cools after a few days) is that the center of the distribution changes.

The cause is that operating conditions such as the above-mentioned heating conditions change according to the situation. The operating conditions such as heating conditions may be changed depending on the situation even with the same steel type, such as when the production amount is emphasized, when the fuel consumption rate is emphasized, or when the control accuracy is emphasized. In addition, it is affected by factors that are not incorporated in the temperature prediction model, such as fluctuations in the temperature of the transport rolls and cooling water temperature, and components that are not completely adjusted and taken in even if incorporated. For example, after the rolling is stopped for several hours or more, the cooling water temperature and the transport roll temperature may decrease.

FIG. 6 is a diagram for explaining a change image of a learning value according to the prior art. As shown in the figure, when continuous rolling is restarted after a long rolling interval, the error of the learned value of the steel strip increases due to the correlation value of the center of the distribution being different from the past value. As described above, since the optimal learning value changes over time between steel strips of the same steel type, the control accuracy of the steel strip head region does not improve unless this is appropriately reflected.

Accordingly, the present invention has been made in view of the above problems, and provides a steel strip winding temperature control apparatus capable of performing appropriate learning according to changes in operating conditions and appropriately cooling a steel strip head. The purpose is to do.

[0012]

SUMMARY OF THE INVENTION The present invention provides a cooling means comprising a plurality of cooling banks each capable of individually controlling the cooling capacity of a steel strip which has been subjected to hot rolling to solve the above-mentioned problems. In a winding temperature control device that cools so that a winding temperature measured after cooling becomes a target value, in order to achieve the target value, a steel strip temperature is predicted using a predetermined heat transfer coefficient. A cooling control unit for controlling the cooling capacity of each cooling bank, and dividing the steel type to which the steel strip belongs into groups for each similar steel type so as to identify in advance the cause of the temperature fluctuation at the head of the steel strip of the same steel type. The deviation from the true value of the heat transfer coefficient used for the prediction based on the difference between the predicted value of the winding temperature and the actual value every time the steel strip advances by a certain length for the head region of the steel strip is calculated by the sequential least squares method. Time difference from the last hot-rolled strip in the same group A learning calculation unit for correcting the heat transfer coefficient of the cooling control unit with the learning value by updating the deviation by exponential smoothing within a certain forgetting time and obtaining the learning value as a learning value. Features.

According to this means, it is possible to appropriately learn the heat transfer coefficient in accordance with the change in the operating conditions, and to appropriately cool the head of the steel strip.

[0014]

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram illustrating an outline of a hot rolling cooling step according to the present invention. As shown in the figure, the steel strip 1 passes from a finishing mill 2 to a coiler 3 and is cooled while passing a water injection bank 4 as a cooling facility. Water injection bank 4
A finishing thermometer 8 for measuring the finishing temperature (FT) is provided at the entrance of the, and a winding thermometer 9 for measuring the winding temperature (CT) is provided at the outlet. The water injection bank 4 includes a control valve 10 and a water injection header 11. The winding speed detector 12 detects the winding speed of the coiler 3. The finishing speed detector 13 detects the speed of the finishing mill 2.

FIG. 2 is a diagram for explaining a steel strip winding temperature control device. As shown in the figure, the winding temperature control device includes a cooling control unit 20 and a learning calculation unit 30 between steel strips. The cooling control unit 20 receives signals from the finishing output side thermometer 8, the finishing speed detector 13, the winding thermometer 9, and the winding speed detector 12, and estimates the steel strip 1 in consideration of the cooling speed. Cooling temperature (CT)
Is calculated, and water injection control is performed so that the calculated predicted value matches the target value. The heat transfer coefficient is used in this water injection control. Specifically, the calculation is repeatedly performed using the following equations (1) and (2) until the target temperature is reached.

[0016]

(Equation 1)

The calculated CT obtained by such a calculation is corrected as follows based on the actual CT obtained by the actual. The learning calculation unit 30 between the steel strips inputs signals from the finishing output side thermometer 8, the finishing speed detector 13, the winding thermometer 9, and the winding speed detector 12, and further inputs information on operating conditions. The learning value of the heat transfer coefficient of the head of the steel strip 1 is calculated and stored in advance for each group of various steel strips 1. The learning calculation unit 30 corrects the heat transfer coefficient of the cooling control unit 20 with the learning value stored according to the operation condition.

The learning calculator 30 between the steel strips, for simplicity of description, for example, arranges the water injection bank 4 in front, back, up and down 4
The region is divided into two regions, and the heat transfer coefficients used in the calculation of the predicted cooling temperature in each region are set as α ₁ , α ₂ , α ₃ , and α ₄ , and the sequential least squares method is applied as follows. This will be described in detail below. FIG. 3 is a flowchart for explaining the operation of the learning calculator 30 between steel strips.

When the end of the steel strip 1 reaches the finishing thermometer 8, the process starts. As shown in FIG. 3, in step F10, the currently cooled steel strip 1 is classified, and a learning layer (group) is determined. For example, the steel strip 1 is classified for each steel type having similar components, plate thickness, winding temperature, cooling rate, and target temperature pattern. In the group, fluctuations in operating conditions such as a water injection pattern are minimized. Also,
For the head region of the steel strip 1 on which the learning is performed, the target temperature pattern is set in detail and the main cause of the temperature fluctuation of the head, for example, the shape is set for a thin object, and the heating condition is set for a thick object. . In step F11, the cooling start time is stored. In step F12, the previous learning value and the cooling start time are taken. In step F13, it is determined whether or not the last cooling was earlier than the forgetting time. That is, the difference between the cooling start time of the previous steel strip in the same layer and the current time is obtained and compared with the forgetting time. In addition, the forgetting time prevents the cooling waiting time between the steel strips from increasing, and prevents the smoothing of the learning values of the steel strips from being deteriorated due to one of the past values having little correlation in the continuous rolling shown in FIG. It is provided in order to. If it is longer than the forgetting time in step F14, the learning value Δαi
Is reset to the specified value, and the process ends. This value is an average value of learning values for a long period, for example, one year. Step F
At 15, the learned value is transmitted to the cooling control unit, and the heat transfer coefficient is corrected. In step F16, it is determined whether or not the head designation area of the steel strip 1 has completed passing through the winding thermometer 9. If the passage is not completed in step F17, the finishing temperature, the passing speed, and the actual water injection are sampled, and the sequential least square method is applied in consideration of the components, size (thickness, width) of the steel strip 1, and the like. In the same manner as the calculation of the predicted cooling temperature performed by the cooling control unit 20, a calculation error ΔCT of the winding temperature (= actual CT)
Calculation CT) and heat transfer coefficients α ₁ , α ₂ , α
_3. Calculate the influence coefficient (partial derivative) of α ₄ . Step F
18 is a temperature calculation error ΔCT by the recursive least squares method.
The deviations Δβ ₁ , Δβ ₂ , Δβ ₃ , and Δβ ₄ from the true values of the heat transfer coefficients α ₁ , α ₂ , α ₃ , and α ₄ at which is minimized are calculated. That is, Δβ ₁ , Δβ ₂ , Δβ ₃ , and Δβ ₄ are learning values for correcting the heat transfer coefficients α ₁ , α ₂ , α ₃ , and α ₄ . Returning to step F16, the above calculation is repeatedly performed at a constant cycle until the head designation area of the steel strip passes through the winding thermometer 9. In step F19, when the passage is completed in step F14 and the calculation of the head designation area is completed, the learning value is updated by exponential smoothing. In addition, when applying the above-mentioned sequential least squares method and providing a forgetting factor to adjust the forgetting factor, it is necessary to adjust the forgetting factor in accordance with a pattern in which the winding temperature deviates.

FIG. 7 is a diagram for explaining an example in which the head winding temperature of the steel strip rises and goes off. As shown in this figure, when it is known that the region where the temperature deviates from the target by more than the tolerance occupies most of the head region, it is necessary to adjust the forgetting factor so that the convergence of the calculation is accelerated. FIG. 8 is a diagram for explaining an example of a drop in the winding temperature and the local temperature. As shown in this figure, if it is known that the area where the temperature drops and deviates from the target by more than the tolerance is only a small part of the head area, it is necessary to adjust the forgetting coefficient so that the effect of the drop is reduced. .

FIG. 4 is a diagram for explaining a change image of the learning value according to the present invention. Considering the steel type group, the cause of temperature fluctuation of the head of the steel strip, and the cooling time of the steel strip, the learning value of the continuously rolled steel strip can be optimally smoothed as shown in this figure, and the rolling interval Even for a long time, the error of the learning value can be reduced without being influenced by the value of the overestimation of the correlation, that is, the learning value between the steel strips can be set optimally. That is, at the head of the steel strip, the heat transfer coefficients α ₁ and α
Water injection is controlled without time delay by using values obtained by adding the latest updated learning values Δα ₁ , Δα ₂ , Δα ₃ , and Δα ₄ obtained in this manner to ₂ , α ₃ , and α ₄ . It should be noted that the correction accuracy can be further improved by dividing the water injection bank 4 into a plurality of parts before and after and dividing the water injection bank 4 into two parts vertically and two parts vertically. As described above, by performing the correction by dividing the water injection bank 4 into a plurality of parts, it is possible to reduce the calculation amount and the computer load.

Further, as described above, the central part of the steel strip 1 other than the head is not corrected by the learning value, and the heat transfer coefficients α ₁ and α ₂ directly obtained from the equations (4) and (5). , Α ₃ , α ₄ may be used. Alternatively, learning in the steel strip may be performed together.

[0023]

As described above, according to the present invention,
It has become possible to learn an appropriate heat transfer coefficient according to changes in operating conditions.

[Brief description of the drawings]

FIG. 1 is a view schematically illustrating a hot rolling cooling step according to the present invention.

FIG. 2 is a diagram illustrating a steel strip winding temperature control device.

FIG. 3 is a flowchart illustrating an operation of a learning calculation unit 30 between steel strips.

FIG. 4 is a diagram illustrating a change image of a learning value according to the present invention.

FIG. 5 is a diagram illustrating an image of a change in an optimum learning value for each steel strip.

FIG. 6 is a diagram illustrating a change image of a learning value according to the related art.

FIG. 7 is a diagram illustrating an example in which the head temperature of a steel strip rises and goes off.

FIG. 8 is a diagram illustrating an example of a temperature local drop.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 ... Steel plate 4 ... Water injection bank 8 ... Finishing thermometer 9 ... Winding thermometer 20 ... Cooling control part 30 ... Learning calculation part between steel strips

Claims (1)

[Claims] 1. A cooling means comprising a plurality of cooling banks each capable of individually controlling the cooling capacity of a steel strip subjected to hot rolling so that a winding temperature measured after cooling becomes a target value. A cooling control unit for controlling a cooling capacity of each cooling bank by predicting a steel strip temperature using a predetermined heat transfer coefficient in order to achieve the target value; To identify in advance the cause of temperature fluctuations in the head of the steel strip of the steel type, the steel type to which the steel strip belongs is divided into groups for each similar steel type, and each time the steel strip advances by a certain length in the head region of the steel strip. The deviation from the true value of the heat transfer coefficient used for the prediction based on the difference between the predicted value and the actual value of the winding temperature is determined by a sequential least squares method, and the steel strip that has been hot rolled immediately before in the same group is determined. If the time difference is within a certain forgetting time, the deviation is exponentially smoothed. Update calculated as the learned value, coiling temperature control device for a steel strip, characterized in that it comprises a learning calculation section between the steel strip to correct the heat transfer coefficient of the cooling control unit in the learning value.