JPH10249426A - Temperature controller of rolling mill - Google Patents

Abstract

PROBLEM TO BE SOLVED: To precisely control the temperature of a material to be rolled at the outlet side of a rolling mill and to improve the rolling precision and the yield by considering the temperature of a rolling roll and making the heat transfer rate between a coolant and the material to be rolled into a regression model in the time of cooling the material to be rolled orderly rolled in plural numbers of rolling stands by using a coolant cooling means. SOLUTION: Inside a temperature controlling part 8, a coolant heat transfer rate part of using an off-line rolling temperature model and an on-line rolling temperature model 12 assembled with a regression equation of the heat transfer rate are installed. Further, the temperature of a material W to be rolled is controlled with a pre-set controlling means 13 of the rolling condition using the on-line rolling model 12 and a feed back controlling means 14. Further, the on-line rolling temperature model 12 is made to include a roll temperature pre-measuring model, based on the temperature of a rolling roll 3 predicted therewith, the regression equation of the heat transfer rate following that is made in a model and the temperature calculation of the material W to be hot-rolled is executed.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a rolling mill provided with a plurality of stands and coolant cooling means, which controls the material temperature of the material to be rolled on the exit side of the rolling mill with high precision to reduce the yield. The present invention relates to a temperature control device for a rolling mill that can be improved.

[0002]

[Related Background Art] In hot rolling, when rolling control or cooling is performed to improve the material such as strength or toughness of the material to be rolled, or when rolling load prediction for controlling the thickness of the material to be rolled is performed. Predicting the temperature accurately is an important issue.
If such a material temperature change can be accurately predicted, for example, the tension of the material to be rolled between a plurality of stands arranged in tandem is stabilized to stabilize the sheet passing characteristics, and the rolling accuracy is increased by increasing the rolling accuracy. The material quality can be made uniform over the entire length of the material. Incidentally, if the prediction accuracy of the material temperature is poor, for example, the rolling accuracy at the tip of the coil of the material to be rolled is deteriorated, the thickness at the tip of the coil becomes uneven, and the yield decreases.

[0003] By the way, the change in the material temperature in hot rolling is mainly caused by cooling by the heat radiation to the atmosphere and convection at the entrance and exit of the rolling mill and cooling by the coolant at the entrance and exit of the rolling mill during the rolling. It is determined by the heat generated during processing and the friction between the roll and the surface of the work (rolled material). Among them, the cooling by the coolant liquid has a large cooling efficiency, and therefore, in accurately predicting the material temperature change and controlling the temperature, it is important to accurately grasp the temperature change of the material to be rolled due to the cooling with the coolant liquid. is there.

[0004] In order to accurately grasp such a cooling effect by the coolant liquid, a heat transfer coefficient αsc between the material to be rolled and the coolant liquid has been obtained in advance by an experiment targeting only the cooling process using the coolant liquid. This heat transfer coefficient α
Sc is modeled by a regression equation such as the material surface temperature. If the material temperature is calculated using the heat transfer coefficient αsc thus regression-modeled, the prediction accuracy can be improved and the rolling accuracy can be improved.

A change in the temperature distribution of the material to be rolled in the rolling process is represented by a rolling temperature model, and preset control is performed using the rolling temperature model so that the material temperature at the exit side of the rolling mill becomes the target temperature. ing. Further, the control condition ai is changed by Δai to calculate the rolling temperature model, and a change ΔTs in the rolling mill exit side temperature Ts at that time is evaluated to determine the influence coefficient (ΔTs / Δ
Control of the material temperature using ai) is also performed.

[0006]

However, although the material temperature is predicted while regressing the heat transfer coefficient αsc using the actual data at each stand of the rolling mill, in actual operation,
For example, as shown in FIG. 1, since the temperature of the rolling roll changes depending not only on the properties of the material to be rolled but also on the rolling conditions, the number of rolls after the roll is replaced, and the like, the prediction accuracy tends to vary. Moreover, as shown in FIG. 2, the heat transfer coefficient αsc identified by the back calculation based on the regression model described above differs depending on the roll temperature actually measured.

Therefore, unless the heat transfer coefficient αsc is identified based on such a change in the roll temperature and the material temperature is not predicted based on the heat transfer coefficient αsc, the preset accuracy for the temperature control becomes low.
In addition, since the accuracy of the influence coefficient is low, there is a problem that overshoot and hunting of control occur in actual operation and rolling characteristics are deteriorated. That is, it is difficult to improve the rolling accuracy and improve the yield unless control is performed in consideration of the change in the roll temperature.

The present invention has been made in view of such circumstances, and its object is to improve the set-up accuracy and improve the set-up accuracy by regression modeling the heat transfer coefficient αsc in consideration of the temperature of the rolling roll. It is an object of the present invention to provide a temperature control device of a rolling mill which can improve controllability under an accurate influence coefficient, control the material temperature with high accuracy, and increase the yield.

[0009]

In order to achieve the above-mentioned object, the present invention provides a rolling mill having a plurality of stands for sequentially rolling a material to be rolled and a coolant cooling means for cooling the material to be rolled. Regarding the built-in temperature control device, the heat transfer coefficient αsc obtained based on the actual data at each stand is converted into a regression model in consideration of the effect of the roll temperature,
The rolling speed is preset using an online rolling temperature model and a roll temperature prediction model incorporating the regression model of this heat transfer coefficient αsc, and the influence coefficient is obtained from the change of the heat transfer coefficient αsc accompanying the change of the rolling speed etc. It is characterized in that the control gain is set.

That is, the temperature control device for a rolling mill according to the present invention uses an off-line rolling temperature model for calculating the temperature change of the material to be rolled in accordance with the rolling performance of each stand, and calculates the calculated temperature on the exit side of the rolling mill. A first means for back-calculating the heat transfer coefficient αsc between the material to be rolled and the coolant liquid to be equal to the temperature, and online rolling incorporating a regression equation of the heat transfer coefficient αsc identified by the first means. Using a temperature model, according to the property data of the coolant liquid and the material temperature of the material to be rolled, the rolling conditions are preset and controlled so that the material temperature of the material to be rolled at the rolling mill exit side matches the target value, and the online rolling temperature is controlled. A second means for performing feedback control of the rolling conditions using an influence coefficient for the temperature at the exit of the rolling mill calculated using the model, In the second means, it is characterized in that to model the regression equation of the heat transfer coefficient αsc rolling temperature calculated based on the temperature of the rolling rolls to be predicted according to the roll temperature prediction model.

[0011]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A temperature control device for a rolling mill according to one embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a conceptual diagram showing the overall configuration of a rolling mill to which the present invention is applied. In this example, a first tandem three-stage configuration is used.
To a third rolling mill including third to third stands 1, 2, and 3. Each of the stands 1, 2, 3 includes a pair of rolling rolls (work rolls) 5 and a backup roll 6, and is controlled under the control of a rolling control unit 7 to reduce the load (load) between the rolling rolls 5. Rolled material (work) W to be guided
And the workpiece W is moved from the first stand 1 to the third
The steel sheet is continuously rolled on the stand 3 so that the thickness of the product on the exit side of the third stand 4 is adjusted to a desired product specification.

In the figure, reference numeral 8 denotes a system control unit mainly composed of a computer such as a microprocessor. Under the control of the system control unit 8, each of the stands 1, 2, 3
, The operation of the rolling control unit 7 is controlled, and the rolling load and rolling speed by the rolling roll 5 are adjusted. 9a and 9b are thickness gauges and sheet thermometers provided on the upstream side of the first stand 1 (the entrance side of the rolling mill) and on the downstream side of the third stand 3 (the exit side of the rolling mill). Sensor. By means of these sensors 9a and 9b, the thickness H0 of the material W to be rolled before rolling and the temperature thereof, and the thickness hi and the temperature of the material W after rolling through the continuous rolling mill, ie, after rolling, are respectively obtained. Measured.

Now, the state of heat flow in and out of the material W to be rolled by the rolling mill constructed as described above is as follows.
It can be modeled and shown as shown in FIG.
In FIG. 4, T (0),... T (20) represent the temperature of the material W to be rolled (the temperature of the material on the incoming side), the air cooling (air cooling) temperature in each of the stands 1, 2, and 3, and the cooling. (Radiant cooling) temperature, coolant cooling (cooling with coolant liquid) temperature, processing temperature by rolling rolls (heating temperature by rolling), friction temperature (friction heating temperature between rolling rolls and material to be rolled), roll heat transfer temperature (rolling) (The temperature at which the material escapes along the roll).

The temperature control device according to the present invention executes the temperature control (rolling control) in accordance with a rolling temperature model constructed in consideration of the heat inflow and outflow of the material W to be rolled. In the control unit 8, a coolant heat transfer coefficient reverse calculation unit 11 for back calculating the coolant heat transfer coefficient αsc using an offline rolling temperature model, an online rolling temperature model 12 incorporating a regression equation of the heat transfer coefficient αsc, A preset control means 13 for preset control of rolling conditions using the temperature model 12 and a feedback control means 14 for feedback-controlling the rolling conditions using an influence coefficient calculated using the online rolling temperature model 12 are realized. Is done. Further, the on-line rolling temperature model 12 is constructed so as to include the roll temperature prediction model, and the regression equation of the heat transfer coefficient αsc is modeled based on the rolling roll temperature predicted according to the roll temperature prediction model to calculate the rolling temperature. Is executed.

The coolant heat transfer coefficient back calculation unit 11
Calculates the material temperature at each of the stands 1, 2, and 3 by taking in the rolling results at the stands 1, 2, and 3, and the calculated temperature at the exit of the rolling mill becomes equal to the actual temperature at the exit of the rolling mill. It plays the role of back-calculating such a coolant heat transfer coefficient αsc. At this time, since the coolant heat transfer coefficient αsc changes depending on the roll temperature and the material of the material to be rolled W, the rolling conditions, etc., it is particularly susceptible to the roll temperature. By performing an inverse calculation process, the coolant heat transfer coefficient αsc is converted into a regression model.

FIG. 5 shows a process of calculating the coolant heat transfer coefficient αsc, which is performed by the coolant heat transfer coefficient back calculation unit 11, that is, a process of identifying the heat transfer coefficient αsc. The process of identifying the heat transfer coefficient αsc indicated by this calculation algorithm is performed by first performing the actual rolling value at each stand, specifically, the rolling load Pi by each rolling roll 5, the roll gap Si and the roll peripheral speed Vi of the rolling roll 5, Incoming plate thickness Ho and material temperature T of material W to be rolled in first stand 1
The process starts with collecting the delivery side plate thickness hf and the material temperature Tc of the third stand 3 (final stand) [Step S1]. In this case, the material of the material W to be rolled and the spray state of the coolant liquid, that is, the geometric relationship between the nozzle that discharges the coolant liquid and the material W to be rolled, and the spray flow rate are set in advance. What should I do?

Thereafter, the heat transfer coefficient αsc between the material W to be rolled and the coolant liquid to be cooled using the coolant liquid on the entry side of each stand is assumed (temporarily set) [Step S2]. Next, first, a parameter i for specifying the stand to be calculated is initially set to [1] [Step S
3] Under this parameter i, first, the material temperature after air cooling is calculated [Step S4]. Further, the material temperature after cooling the coolant is calculated based on the heat transfer coefficient αsc [Step S5]. Thereafter, the material temperature after cooling the coolant is set as the input material temperature, and the material temperature after processing heat is calculated based on the above-described actual rolling load [Step S6]. Are sequentially calculated [Steps S7 and S8].

After the calculation of the material temperature at the stand i specified by the above-mentioned parameters is completed, it is determined whether the stand is the last stand [n], in this example, the third stand. [Step S9], and if it is not the last stand, the parameter i is incremented (plus 1) [Step S10], and the above-described calculation processing in Steps S4 to S8 is repeatedly executed.

When the calculation of the material temperature up to the final stand is completed by repeating such calculation processing [Step S9], the material temperature after air cooling on the exit side of the final stand is calculated [Step S11]. The calculated material temperature, that is, the calculated material temperature at the outlet side is compared with the actually measured outlet side material temperature. Then, it is determined whether the temperature difference is within a predetermined error range ε, that is, whether the calculated material temperature is substantially equal to the actual outlet material temperature [Step S12]. The heat transfer coefficient αsc assumed as described above is corrected [Step S13].
Then, the processing procedure shown in steps S3 to S11 described above is repeatedly executed under the corrected heat transfer coefficient αsc,
Again, calculate the exit material temperature at the final stand.

When the outlet material temperature obtained by repeatedly calculating while correcting the heat transfer coefficient αsc as described above becomes substantially equal to the actual outlet material temperature, the above-mentioned value used in the calculation is used. The heat transfer coefficient αsc is identified as the heat transfer coefficient αsc between the material to be rolled W and the coolant liquid, and is output [Step S14]. According to the above-described processing procedure, the heat transfer coefficient αsc between the material to be rolled W and the coolant liquid can be calculated back with high accuracy based on the actual rolling value at each stand and identified. However, the above heat transfer coefficient α
The sc varies depending on the material of the material W to be rolled and the rolling conditions.
The heat transfer coefficient αsc is converted into a regression model while changing the material and rolling conditions. Specifically, while changing the rolling conditions, the heat transfer coefficient αsc is converted into a regression model as αsc = αo + f (a, b, c,...), A, b, c,. To do. Here, αo is a reference heat transfer coefficient, and rolling parameters may be given as a spray amount of a coolant liquid, a particle size of a coolant, a surface temperature of a material W to be rolled, a roll temperature in each stand, and the like.

If the heat transfer coefficient αsc is regression-modeled in this way, the prediction and calculation of the material temperature in various rolling mills can be performed easily and accurately as described below. In the case of dynamically setting up the rolling conditions of a plurality of stands, for example, the roll gap Si, the set-up conditions can be determined with high accuracy, and the yield and rolling quality can be improved. Moreover, since it is possible to control the outlet plate thickness at each stand with high accuracy,
The prediction accuracy of the advance ratio in each stand is increased, the tension between stands can be stabilized, and the threading can be stabilized, so that the rolling accuracy can be increased.

By identifying the heat transfer coefficient αsc with high accuracy as described above, the material W to be rolled under various rolling conditions can be obtained.
It is possible to predict the material temperature with high accuracy, for example, control rolling and cooling for improving the material such as strength and toughness, and further, rolling load prediction for controlling the sheet thickness, and the temperature of the rolled material for energy saving. This can be effectively used for control and the like.

The heat transfer coefficient αsc identified and regression-modeled as described above is given to the online rolling temperature model 12. The preset control means 13 and the feedback control means 14 execute the rolling control online using the online rolling temperature model 12.
Basically, prior to the start of rolling, conditions such as the current spray flow rate of the coolant liquid, the coolant particle size, the temperature of the material W to be rolled, the pass schedule for each stand, and the like are input to the rolling temperature model and the roll temperature prediction model. A setting is made so that the outlet temperature becomes equal to the target temperature (preset control).

Next, changes in the rolling conditions, that is, the rolling speed, the spray pressure of the coolant, the coolant particle size, the concentration of the coolant, and the temperature thereof are given to the rolling temperature model and the roll temperature prediction model, and the rolling mill is operated. Calculate the coefficient of influence on the outlet temperature. At this time, since the heat transfer coefficient αsc also changes with the change in the rolling condition, the rolling condition is feedback-controlled according to the influence coefficient. FIGS. 6 and 7 show the rolling control executed using the above-described online rolling temperature model. Is shown. This process is started by inputting in advance the current spray pressure, coolant particle size, temperature of the material W to be rolled at each stand [Step S21], and first calculates the temperature of the material W after air cooling [Step S21]. S22].

Next, the coolant heat transfer coefficient αsc identified as described above is given to the online rolling temperature model [Step S23], and the material temperature after cooling the coolant and the material temperature after the heat generation are calculated according to the online rolling temperature model. Then, the calculation of the material temperature after the frictional heating, the calculation of the roll temperature, and the calculation of the material temperature after the roll heat transfer are sequentially executed [Steps S24, S25, S26, S27, S2].
8]. At this time, in the calculation of the roll temperature shown in step S27, the roll temperature is obtained according to a roll temperature prediction model represented by a two-dimensional heat crown model for the roll 5 as shown in FIG. 8, for example. That is, the roll temperature is predicted in accordance with a change in the rolling condition due to a roll change or the like, and the material temperature after the roll heat transfer is accurately predicted based on the predicted roll temperature.

The material temperature calculation based on the online rolling temperature model as described above is executed for each stand up to the final stand [Step S29]. Then, when the material temperature at each stand is obtained in consideration of the change in the roll temperature, the material temperature after air cooling on the exit side of the rolling mill is calculated as shown in FIG. 7 [Step S3].
0], and the calculated material temperature obtained by the calculation is compared with the actually measured outlet material temperature. Then, it is determined whether or not the temperature difference is within a predetermined error range ε, that is, whether or not the calculated material temperature is substantially equal to the actual outlet material temperature [Step S31].

When the calculated material temperature is equal to the actual material temperature, the rolling condition ai at that time and the influence coefficient ΔT / Δai obtained from the online rolling temperature model are used.
Is output, and this is preset [Step S3
2]. If the temperature difference is large, for example, the rolling condition is changed by changing the roll peripheral speed and repeatedly executing the processing from step S22 described above [step S33], and recalculating the exit side material temperature in the final stand. Perform feedback control. That is, by changing the roll peripheral speed, the rolling conditions are feedback-controlled while changing the heat transfer coefficient αsc in accordance with the roll temperature so that the exit temperature of the rolling mill becomes the target temperature.

Thus, according to the temperature controller functioning as described above, the heat transfer coefficient αsc calculated back using the actual value of each stand is regression-modeled in consideration of the roll temperature at that time. The transmission rate αsc can be given with high accuracy. Then, according to the online rolling temperature model incorporating the regression model of the heat transfer coefficient αsc, the roll temperature is predicted according to the actual data such as the spray pressure and the pass schedule, and the rolling temperature calculation is performed under the predicted roll temperature. Therefore, for example, the accuracy of the pre-setup of the rolling speed can be significantly improved. Therefore, under high-precision temperature control, the rolling accuracy can be increased, and the yield can be improved.

Further, when calculating the influence coefficient based on the online rolling temperature model, since the heat transfer coefficient αsc of the coolant is given with high accuracy as described above, the accuracy of the influence coefficient itself can be improved. . As a result, the responsiveness of the feedback control of the rolling conditions based on the influence coefficient can be improved, and the controllability can be improved. Note that the present invention is not limited to the above embodiment. For example, the number of stands of a rolling mill is not particularly limited. Also, actual data used for control, that is, rolling parameters may be determined according to the specifications.
Further, the heat transfer coefficient αsc back calculation procedure, the preset control procedure based on the online rolling temperature model, and the like can be changed according to the required rolling accuracy and the like. In addition, the present invention can be implemented by variously modifying an arithmetic processing algorithm or the like according to the gist.

[0030]

As described above, according to the present invention, the roll temperature is introduced as a parameter when regression modeling the heat transfer coefficient αsc which is calculated backward according to the rolling performance of each stand. , The heat transfer coefficient αsc can be given with high precision when the rolling control data is preset and the feedback control is performed. Therefore, to improve the preset accuracy,
Further, since feedback control under a high-precision influence coefficient is possible, there is a great effect in practical use, such as the material temperature can be controlled with high accuracy to increase the yield.

[Brief description of the drawings]

FIG. 1 is a diagram showing a change in surface temperature of a roll with the elapse of operating time of a rolling mill.

FIG. 2 shows the heat transfer coefficient α of the coolant with the change of the roll temperature
The figure which shows the change of sc.

FIG. 3 is a diagram showing a schematic configuration of a temperature control device for a rolling mill according to an embodiment of the present invention.

FIG. 4 is a diagram schematically showing how heat flows into and out of a material to be rolled in a rolling mill.

FIG. 5 is a diagram showing an example of a reverse calculation processing procedure of the heat transfer coefficient αsc according to the embodiment of the present invention.

FIG. 6 is a diagram showing an example of a material temperature calculation procedure in each stand which is a part of preset control and feedback control using an online rolling temperature model according to the embodiment of the present invention.

FIG. 7 is a processing procedure following FIG. 6, showing a procedure of calculation of the outlet temperature of the rolling mill and preset control and feedback control based on the calculation result.

FIG. 8 is a view showing the concept of a roll temperature prediction model used in roll temperature calculation according to the online rolling temperature model shown in FIG. 6;

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 1st stand 2 2nd stand 3 3rd stand 5 Rolling roll 7 Rolling control part 8 System control part 11 Coolant heat transfer coefficient back calculation part 12 Online rolling temperature model 13 Preset control means 14 Feedback control means

Claims (1)

[Claims] 1. A temperature control device incorporated in a rolling mill having a plurality of stands for sequentially rolling a material to be rolled and a coolant cooling means for cooling the material to be rolled, wherein the rolling performance of each of the stands is provided. Using an off-line rolling temperature model that calculates the temperature change of the material to be rolled according to the following formula, the heat transfer coefficient α between the material to be rolled and the coolant liquid such that the calculated temperature on the exit side of the rolling mill becomes equal to the actual temperature.
using a first means for back-calculating sc, and an online rolling temperature model incorporating a regression equation for the heat transfer coefficient αsc identified by the first means, using the property data of the coolant liquid and the material of the material to be rolled. According to the temperature, the rolling conditions are preset and controlled so that the material temperature of the material to be rolled matches the target value, and the influence coefficient on the temperature at the exit of the rolling mill calculated using the online rolling temperature model is used. A second means for performing feedback control of rolling conditions, wherein the second means models a regression equation of the heat transfer coefficient αsc based on a temperature of a rolling roll predicted according to a roll temperature prediction model, and performs rolling. A temperature control device for a rolling mill, which performs a temperature calculation.