Method for controlling or regulating the temperature of a steel strip when hot forming in a hot strip mill train

Abstract

The invention relates to a method for controlling or regulating the temperature of a steel strip during hot forming in a hot strip mill train. The superior control or regulating unit has a process model which determines a temperature development of the hot strip in advance. The target values of the individual units are adjusted on the basis of the predetermined temperature development.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for controlling or regulating the temperature of a steel strip during hot forming in a hot strip mill train. BACKGROUND

[0002] Hot forming of a steel strip is usually carried out in a hot strip mill train. The hot strip mill train comprises different individual units, for example, a furnace, a rolling stand, a drive device, an unwinding and winding device of the steel strip or a cooling section. A large number of different devices or methods are known for the control or regulation of these units. The control or regulation is mainly based on a comparison of a target value and an actual value, and a corresponding corrective measure is derived to follow the target value. Here, the target value to be followed is defined on the basis of empirical knowledge and / or previous process analyses. Furthermore, a relationship is usually formed in advance between the product properties of the steel strip and the target values to be set for the units. Usually, there is a complex relationship between the various target values and the product properties striven for in the production of the steel strip.

[0003] Due to the increasing digitalization of the plant technology, process models related to the units are used to produce suitable target values, which lead to the desired product properties. For this purpose, statistical models, analytical models or neural networks can be used for the process models related to the units, depending on the data situation, the complexity of the relationships and / or the effort, for example.

[0004] A disadvantage of such a regulation scheme for a hot strip mill train with a plurality of units is that the mutual influences when the target value pre-sets (Sollwert-Vorgaben) or the actual values on the different units change are not reflected by the process models related to the units and / or the control or regulation of the units. Especially in steel strip production with high requirements on the material quality, the complex mutual influences of time, temperature, microstructure development are difficult to optimize and are hardly optimized by the quasi-static individual unit regulation.

[0005] Furthermore, the control or regulation of the individual units of the hot strip mill train is disadvantageous in that the optimization of the process control of the individual units does not always necessarily lead to an optimization of the overall production process. Especially in plants combined with, for example, a continuous casting plant, energy and production costs can be saved by a more dynamic process control. SUMMARY

[0006] It is an object of the present application to improve the known control or regulation of a hot strip mill train, so that the target value pre-sets of the individual units are optimized in terms of, for example, the product properties of the steel strip in the overall plant.

[0007] The object of the present application is achieved by the method according to the present application. In a data processing system assigned to a hot strip mill train, an upper process model stores and / or exchanges setpoint values and / or actual values with at least two control or regulation units of a cell online, the setpoint values and / or actual values having time, speed, temperature, cooling rate and / or heating rate. The upper process model determines the temperature of the steel strip online at at least one point before the hot strip is coiled based on the exchanged setpoint values and / or actual values and / or stored setpoint values and / or actual values and by means of lower process models, for example a temperature model of a furnace, a temperature model of a cooling section or a model of the shaping in the hot strip mill train. If the predetermined temperature at the point deviates from the setpoint, the upper process model determines a new setpoint preset of the cell, which is transmitted to the control or regulation unit of the cell in order to adhere to the setpoint preset for the temperature of the steel strip. The new setpoint preset is determined by means of an optimization algorithm which comprises at least one lower process model.

[0008] The upper process model depicts the actual production state of the steel strip based on the setpoint values and / or actual values of the cell. By means of suitable process models, for example an energy and material balance for a homogenization furnace or a statistical model for the microstructure development of the steel strip, the upper process model determines the development of the temperature profile, for example, until the future coiling. Differences between the setpoint presets and possible deviations at the individual cells can thus be recognized early. The optimization algorithm running in the upper optimization model can optimize the setpoint presets such that the setpoint presets of the hot strip before the coiling are reached taking into account a prior determined optimization target. The prior determined optimization target may, for example, be a production target, in particular an energy, yield or quality target.

[0009] A preferred form of the method is given below. According to the present application, it is preferred that the primary product from the casting machine has a thickness d B ≥ 1 mm to d B ≤ 300 mm, preferably d B ≥ 50 mm to d B ≤ 160 mm, and the upper process model takes into account the casting speed, which is preferably between VG ≥ 4 m / min and VG ≤ 6 m / min, more preferably between VG ≥ 5 m / min and VG ≤ 6 m / min, and the casting machine exit temperature of the slab, which is preferably T GE ≥ 800 °C, when determining the setpoint preset.

[0010] Preferably, according to the application, the optimization targets comprise energy consumption, yield, process reliability, product properties, production costs and / or equipment wear, which are preferred control parameters in the field of steel production.

[0011] Furthermore, according to the application, it is preferred that the lower process model determines the microstructure development of the steel strip in the hot strip mill train for at least one point, preferably before the coiling of the hot rolled strip. The resulting microstructure development, in addition to the optimized temperature control, is of decisive significance for further material properties and / or handling of the steel strip. A more precise control or adjustment of the microstructure development during the process enables an early reaction to deviations and reduces the amount of waste and / or post-processing.

[0012] According to the application, during hot forming, it is desirable to use a roughing stand and a finishing stand. By dividing the hot forming section into a roughing stand and a finishing stand, an advantageous temperature profile and sequence can be adjusted, and they can also be better represented by a greater number of measurement and adjustment points. Thus, the upper process model can react better to deviations. Furthermore, there are also more options to intervene in the target value specification of the hot rolling.

[0013] According to the application, it is preferred that, for the target value of the entry temperature into the finishing stand, the temperature target value specified by the upper process model is T FS ≥ 850 °C to T FS ≤ 1050 °C, preferably T FS ≥ 900 °C to T FS ≤ 1000 °C, even more preferably T FS ≥ 900 °C to T FS ≤ 950 °C. Furthermore, it is preferred that, for the target value of the exit temperature from the finishing stand, the temperature target value specified by the upper process model is within T FE ≥ 750 °C to T FE ≤ 950 °C, preferably within T FE ≥ 750 °C to T FE ≤ 900 °C, even more preferably within T FE ≥ 800 °C to T FE ≤ 850 °C.

[0014] According to the application, for the target value of the entry speed into the finishing stand, the speed target value specified by the upper process model is preferably v F ≥ 0.4 m / s to v F ≤ 1 m / s.

[0015] According to the application, for the target value of the entry temperature into the roughing stand, the temperature target value specified by the upper process model is T VS≥ 1000 °C to T VS ≤ 1150 °C. The target value for the exit temperature leaving the roughing stand is preset by the superordinate process model to be in the range of T VE ≥ 950 °C to T VE ≤ 1100 °C.

[0016] Ideally, according to the application, the target value preset by the superordinate process model for the target value for the entry thickness into the finishing stand is d FS ≥ 20 mm to d FS ≤ 70 mm. The target value for the target coiling temperature is preferably preset by the process model to be in the range of T H ≥ 30 °C to T VE ≤ 750 °C, more preferably to be in the range of T H ≥ 450 °C to T H ≤ 550 °C.

[0017] According to the application, it is preferred that the content of the alloying element C in the steel strip is limited to 0.03 wt% to 0.15 wt%, and / or the content of the alloying element manganese in the steel strip is limited to 0.50 wt% to 2.00 wt%.

[0018] According to the application, it is preferred that the optimized target value is preset for the production of a subsequent hot-rolled strip having the same production targets, in particular mechanical properties. Thus, the already existing optimized process described by the respective target value preset can be related to the continued production of the same material or steel strip type. This saves optimization time and enables a reaction to slow equipment changes in advance.

[0019] According to the application, it is preferred that in a data processing system assigned to the hot-rolling strip plant, the superordinate process model can exchange and / or store target values and / or actual values with at least two control or regulating units of the units online, the target values and / or actual values having a time, a speed, a temperature, a cooling rate and / or a heating rate. The superordinate process model determines the temperature of the steel strip online in advance for at least one point before the hot-rolled strip is coiled on the basis of the exchanged target values and / or actual values and / or the stored target values and / or actual values and by means of the subordinate process models, and determines a new target value preset of the respective unit when the determined temperature at the point deviates from the target value preset. The new target value preset is transmitted by the superordinate process model to the control or regulating unit of the respective unit in order to adhere to the target value preset for the temperature of the steel strip. Here, the new target value preset is determined by means of an optimization algorithm comprising at least one subordinate process model.

[0020] The method according to the application is explained in detail below with reference to the figures mentioned in the form of examples. Identical technical elements are denoted by identical reference numerals in all figures. BRIEF DESCRIPTION OF DRAWINGS

[0021] The description is accompanied by three figures, in which,

[0022] Figure 1 A schematic diagram of the equipment of a hot strip mill train is shown;

[0023] Figure 2 A regulation diagram with an upper process model is shown;

[0024] Figure 3 A comparison of the target value and the actual value of the temperature profile is shown. DETAILED DESCRIPTION

[0025] Figure 1 A possible schematic diagram of the equipment of a hot strip mill train for the production of hot strip is shown, in which the method according to the application is used. The hot strip mill train comprises a casting installation 1, two shears 2, 10, two furnaces 3, 6, two roughing stands 4, a transferable cooling section 5, an induction heating section 7, three finishing stands 8, a cooling section 9 and a coiler 11 for coiling the hot strip. An upper data processing system 12 has an integrated temperature and microstructure model. The target values and the actual values are exchanged by means of different equipment or associated regulation sections, control sections and / or measuring devices and are stored, for example, in the form of a database.

[0026] Figure 2 A flow chart is shown, in which two units, more precisely the regulation sections of the two units, are exemplarily networked with the respective process models. An upper data processing system I transmits target values to an upper process model II of the hot strip mill train. On the basis of the target values, for example the strength, the upper process model II determines a plurality of target values or target value ranges, for example a temperature profile with the respective minimum and maximum temperatures, which are transmitted to the lower process models Ilia, IIIb. The lower process models Ilia, IIIb derive therefrom specific target values for the respective unit. For example, target settings for the combustion control in the furnace 3 or target settings for the water quantity control in the cooling section 9 are derived from a preset temperature curve with associated time points. These are passed on to the corresponding regulation sections of the respective unit.

[0027] If the value is not achieved within the unit, the lower process models Ilia, IIIb can adjust the target settings. Likewise, the process models Ilia, IIIb can also be optimized automatically here by a self-learning algorithm. If the target actual parameters deviate from the target value preset V of the upper process models IIa, IIb, the target values are recalculated in the upper layer II and, if necessary, adjusted.

[0028] Figure 3 A diagram with the target temperature profile B, the measured and the precalculated temperature profile A is shown. The target temperature profile B starts at the end of the casting installation 1 and illustrates the profile up to the coiler 11. The actual values from the end of the casting installation 1 up to the roughing stand 4 are plotted. Here, the measured temperature is higher than the target temperature. From this point, the superior process model II precalculates the temperatures at different locations in the hot strip mill train. Based on the temperature profile different target values can be re-set at different locations in order to correct the temperature deviation. Here, different process models, material or microstructure models and / or optimization algorithms can be used to determine the optimal adjustment strategy.

[0029] Table 1 : Reference signs

[0030]

Claims

1. A method for controlling or regulating the temperature of a steel strip when hot-forming a primary product into a hot-rolled strip in a hot-rolling strip mill, the steel strip having the following alloying elements: in, The thickness of the primary product is in d V ≥ 1 mm and d V ≤ 300 mm Hot-rolled strip has a thickness of d. WB ≤ 25 mm, and the width of the hot-rolled strip is within b WB ≥ 900 mm and b WB ≤ 2100 mm Target winding temperature T H ≥ 30℃ to T H ≤ 750℃, The hot-rolled strip mill unit comprises: at least one furnace for heating and / or temperature homogenizing the primary product; at least one mill stand for hot-rolling the primary product; a cooling section for selectively cooling the hot-rolled strip after forming; and a coiler for coiling the hot-rolled strip into coils, wherein each unit has its own control or adjustment unit for preset target values. Its features are, - In the data processing system allocated to the hot-rolled strip mill, the upper-level process model exchanges and / or stores expected and / or actual values ​​online with at least two control or regulation units of the unit, the expected and / or actual values ​​having time, speed, temperature, cooling rate and / or heating rate; - The higher-level process model, based on exchanged expected and / or actual values ​​and / or stored expected and / or actual values ​​and with the aid of the lower-level process model, pre-determines the temperature of the steel strip online for at least one point before coiling the hot-rolled strip; and - When the predetermined temperature at a given point deviates from the expected value preset, the higher-level process model determines a new expected value preset for the unit and transmits this new expected value preset to the unit's control or adjustment unit to ensure compliance with the expected temperature preset for the steel strip; and - The new target value preset is determined by using an optimization algorithm that includes at least one lower-level process model.

2. The method according to claim 1, characterized in that, The target winding temperature is T. H ≥ 400℃ to T H ≤750℃.

3. The method according to claim 1, characterized in that, - The primary product is a billet from a casting machine, having a thickness of d. B ≥ 50 mm to d B ≤ 160 mm; and - When determining the preset target value, the higher-level process model considers the casting speed and the casting machine exit temperature of the billet, wherein the casting speed is... VG ≥ 4 m / min and VG ≤ 6 m / min, and the casting machine exit temperature is T GE ≥ 800℃.

4. The method according to claim 3, characterized in that, The casting speed is at v G ≥ 5 m / min and VG ≤ 6m / min.

5. The method according to any one of claims 1 to 4, characterized in that, Optimization targets include energy consumption, output, process reliability, product characteristics, production costs, and / or equipment wear and tear.

6. The method according to any one of claims 1 to 4, characterized in that, The lower-level process model determines the microstructure development of the steel strip in the hot-rolled strip mill at at least one point.

7. The method according to claim 6, characterized in that, The next-level process model determines the microstructure development of the steel strip in the hot-rolled strip mill at at least one point before coiling the hot-rolled strip.

8. The method according to any one of claims 1 to 4, characterized in that, The hot forming process uses a primary rolling mill stand and a finishing rolling mill stand.

9. The method according to claim 8, characterized in that, Regarding the required entry temperature into the finishing mill stand, the preset temperature value T in the upper-level process model is... FS = 850℃ to T FS = 1050℃.

10. The method according to claim 9, characterized in that, Regarding the required entry temperature into the finishing mill stand, the preset temperature value T in the upper-level process model is... FS = 900℃ to T FS = 1000℃.

11. The method according to claim 9, characterized in that, Regarding the required entry temperature into the finishing mill stand, the preset temperature value T in the upper-level process model is... FS = 900℃ to T FS = 950℃.

12. The method according to claim 8, characterized in that, Regarding the expected temperature to be reached when leaving the finishing mill stand, the expected temperature to be reached, preset by the upper-level process model, is within T... FE ≥ 750℃ to T FE ≤ 950℃.

13. The method according to claim 12, characterized in that, Regarding the expected temperature to be reached when leaving the finishing mill stand, the expected temperature to be reached, preset by the upper-level process model, is within T... FE ≥ 750℃ to T FE ≤ 900℃.

14. The method according to claim 12, characterized in that, Regarding the expected temperature to be reached when leaving the finishing mill stand, the expected temperature to be reached, preset by the upper-level process model, is within T... FE ≥ 800℃ to T FE ≤ 850℃.

15. The method according to claim 8, characterized in that, Regarding the required entry speed into the finishing mill stand, the required speed value preset by the upper-level process model is v. F ≥ 0.4 m / s to v F ≤ 1 m / s.

16. The method according to claim 8, characterized in that, Regarding the required entry temperature into the primary rolling mill stand, the preset temperature value T in the upper-level process model is... VS ≥ 1000℃ to T VS ≤ 1150℃.

17. The method according to claim 8, characterized in that, Regarding the expected temperature to be reached upon leaving the primary rolling mill stand, the expected temperature value preset by the upper-level process model is T. VE ≥ 950℃ to T VE ≤ 1100℃.

18. The method according to claim 8, characterized in that, Regarding the required entry thickness into the finishing mill stand, the required value is preset in the upper-level process model at d. FS ≥ 20 mm to d FS Within ≤ 70 mm.

19. The method according to any one of claims 1 to 4, characterized in that, Regarding the target winding temperature, the pre-set temperature value T in the higher-level process model is... H ≥ 30℃ to T VE ≤ 750℃.

20. The method according to claim 19, characterized in that, Regarding the target winding temperature, the pre-set temperature value T in the higher-level process model is... H ≥ 450℃ to T H ≤ 550℃.

21. The method according to any one of claims 1 to 4, characterized in that, The content of the alloying element C in the steel strip is limited to 0.03 wt% to 0.15 wt%, and / or The content of the alloying element Mn in the steel strip is limited to 0.50 wt% to 2.00 wt%.

22. The method according to any one of claims 1 to 4, characterized in that, The optimized expected value is preset for use in the production of subsequent hot-rolled strip with the same production objectives.

23. The method according to claim 22, characterized in that, The optimized expected value is preset for use in the production of subsequent hot-rolled strip with the same mechanical properties.

24. An apparatus for controlling or regulating the temperature of a steel strip according to any one of claims 1 to 23. Its features are, - In the data processing system allocated to the hot-rolled strip mill, through the upper-level process model, it is possible to exchange and / or store expected and / or actual values ​​online with at least two control or adjustment units of the unit, the expected and / or actual values ​​having time, speed, temperature, cooling rate and / or heating rate; - The higher-level process model can determine the temperature of the steel strip online in advance for at least one point before coiling the hot-rolled strip, based on the exchanged expected and / or actual values ​​and / or stored expected and / or actual values ​​and with the help of the lower-level process model. and - When the predetermined temperature at this point deviates from the expected value preset, the higher-level process model determines a new expected value preset for the corresponding unit and transmits this new expected value preset to the control or adjustment unit of the corresponding unit to ensure compliance with the expected value preset for the temperature of the steel strip; and - Determine new target preset values ​​using an optimization algorithm that includes at least one lower-level process model.

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