A method and system for controlling temperature drop in finish rolling

Abstract

The application provides a kind of finish rolling temperature drop control method and system, comprising: obtaining the first detection temperature of current intermediate blank at the exit of rough rolling last pass, current gauge roller bed temperature drop self-learning coefficient, the second detection temperature at the entrance of finish rolling;According to the first detection temperature, determine the first temperature calculation value at the entrance of finish rolling, adjust the roller bed temperature drop self-learning coefficient according to second detection temperature;According to the second detection temperature, determine the second temperature calculation value at the exit of finish rolling, adjust the interstand temperature drop self-learning coefficient according to the measured temperature at the exit of finish rolling;According to the new roller bed and interstand temperature drop self-learning coefficient, adjust the first temperature calculation value and the second temperature calculation value of the next intermediate blank of the same gauge, to improve the accuracy of the first temperature calculation value and the second temperature calculation value.The application can effectively improve the temperature calculation accuracy of intermediate blank finish rolling entrance and exit, improve the intermediate blank head finish rolling stability and the quality control accuracy of finished strip head thickness.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of steel production and application, and particularly relates to a finishing rolling temperature drop control method and system. BACKGROUND

[0002] In the existing hot continuous rolling finishing rolling temperature drop control technology, due to the high-temperature detector detection deviation, the working condition change between finishing rolling racks and other reasons, the rough rolling machine after the heat output roll way and the temperature drop calculation deviation between the finishing rolling racks are often large, the finishing rolling first and second presetting strip head temperature calculation deviation is large, and then the finishing rolling strip head rolling force, roll gap, thickness, shape and other product quality control deviation is large, which affects the rolling stability and product quality precision control. SUMMARY

[0003] In view of the problems existing in the prior art, the present application provides a finishing rolling temperature drop control method and system, which mainly solves the problem that the existing finishing rolling presetting temperature drop calculation deviation is large, affecting the strip rolling stability and product quality precision control.

[0004] In order to achieve the above-mentioned purpose and other purposes, the technical scheme adopted by the present application is as follows.

[0005] The present application provides a finishing rolling temperature drop control method, comprising: obtaining a first detection temperature of a current intermediate blank at the outlet of the last pass of rough rolling, a current gauge roll way temperature drop self-learning coefficient, and a second detection temperature at the entrance of finishing rolling; determining a first temperature calculation value at the entrance of finishing rolling according to the first detection temperature; determining a first temperature drop self-learning coefficient of a next intermediate blank of the same gauge according to the first detection temperature, the roll way temperature drop self-learning coefficient, the first temperature calculation value and the second detection temperature, so as to correct the temperature calculation value at the entrance of finishing rolling of the next intermediate blank of the same gauge based on the first temperature drop self-learning coefficient.

[0006] In an embodiment of the present application, after obtaining the first detection temperature of the current intermediate blank at the outlet of the last pass of rough rolling, the current gauge roll way temperature drop self-learning coefficient and the second detection temperature at the entrance of finishing rolling, it further comprises: obtaining a first temperature error self-learning update speed in the hot coil box empty state and a second temperature error self-learning update speed in the hot coil box winding state; determining a first temperature error self-learning coefficient of the next intermediate blank of the same gauge according to the first temperature error self-learning update speed and the current temperature error self-learning coefficient in the hot coil box empty state; determining a second temperature error self-learning coefficient of the next intermediate blank of the same gauge according to the second temperature error self-learning update speed and the current temperature error self-learning coefficient in the hot coil box winding state; correcting the detection temperature at the outlet of the last pass of rough rolling according to the first temperature error self-learning coefficient or the second temperature error self-learning coefficient.

[0007] In one embodiment of this application, after obtaining the first detection temperature at the exit of the last pass of the roughing mill, the temperature drop self-learning coefficient of the current specification roller table, and the second detection temperature at the entrance of the finishing mill, the method further includes: obtaining the temperature drop self-learning coefficient between the finishing mill stands for the current specification; determining the second temperature drop self-learning coefficient of the next intermediate billet of the same specification in the finishing mill rolling process based on the first temperature calculation value, the second detection temperature, and the temperature drop self-learning coefficient between the stands, so as to correct the total temperature drop of the next intermediate billet of the same specification in the finishing mill rolling process based on the second temperature drop self-learning coefficient.

[0008] In one embodiment of this application, after obtaining the self-learning coefficient of the temperature drop between stands of the finishing mill for the current specification, the method further includes: obtaining the third temperature measurement error self-learning update speed in the hot coil box empty state and the fourth temperature measurement error self-learning update speed in the hot coil box winding state; determining the third temperature measurement error self-learning coefficient of the next intermediate billet of the same specification based on the third temperature measurement error self-learning update speed and the current temperature measurement error self-learning coefficient in the hot coil box empty state; determining the fourth temperature measurement error self-learning coefficient of the next intermediate billet of the same specification based on the fourth temperature measurement error self-learning update speed and the current temperature measurement error self-learning coefficient in the hot coil box winding state; and correcting the detected temperature at the finishing mill entrance based on the third temperature measurement error self-learning coefficient or the fourth temperature measurement error self-learning coefficient.

[0009] In one embodiment of this application, the first temperature drop self-learning coefficient is calculated as follows:

[0010] HTC 新 =HTC 旧值 -K HTC *((T1-T2) / (T0-T2) / (1+HTC 旧值 ))

[0011] Among them, HTC 新 The first temperature drop self-learning coefficient, HTC 旧值 The self-learning coefficient for temperature drop of the roller conveyor of the current specification is given, where T1 is the second detection temperature, T2 is the calculated value of the first temperature, T0 is the first detection temperature, and K is the value of the roller conveyor. HTC To reduce the self-learning speed of temperature drop.

[0012] In one embodiment of this application, the calculation method of the first temperature measurement error self-learning coefficient includes:

[0013] DTR 空过 = (1-K) E )*DTR1+K E *DTR 初值

[0014] Among them, DTR 空过K is the self-learning coefficient of the first temperature measurement error under the empty state of the hot coil box, DTR1 is the self-learning coefficient of the current specification temperature measurement error at the exit of the last pass of the roughing mill under the empty state of the hot coil box, and K is the self-learning coefficient of the current specification temperature measurement error. E For hot roll box to have long-term self-learning update speed; DTR 初值 The temperature measurement error self-learning coefficient of the first intermediate billet after specification change at the exit of the last pass of roughing rolling.

[0015] The calculation method for the second temperature measurement error self-learning coefficient includes:

[0016] DTR 卷取 = (1-K) C )*DTR2+K C *DTR 初值

[0017] Among them, DTR 卷取 K is the second temperature measurement error self-learning coefficient under the hot coil box winding state, DTR2 is the current specification temperature measurement error self-learning coefficient at the exit of the last pass of the roughing mill under the hot coil box winding state, and K is the temperature measurement error self-learning coefficient for the current specification. C For long-term self-learning update speed of hot roll box winding; DTR 初值 The temperature measurement error self-learning coefficient of the first intermediate billet after specification change at the exit of the last pass of roughing rolling.

[0018] In one embodiment of this application, the calculation method of the second temperature drop self-learning coefficient includes:

[0019] KTC 新 =KTC 旧值 -K T *((T1-T2) / (T 总 / (1+HTC 旧值 )))

[0020] Wherein, KTC new is the second temperature drop self-learning coefficient, KTC old is the rack-to-rack temperature drop self-learning coefficient of the current specification, T1 is the second detected temperature, T2 is the calculated value of the first temperature, and T... 总 K is the calculated value of the total temperature drop between racks. T The self-learning speed for temperature drop between racks.

[0021] In one embodiment of this application, the calculation method of the third temperature measurement error self-learning coefficient includes:

[0022] DTF 空过 = (1-K) E )*DTF1+K E *DTF 初值

[0023] Among them, DTF 空过K is the self-learning coefficient of the third temperature measurement error under the empty state of the hot coil box, DTF1 is the self-learning coefficient of the current specification temperature measurement error at the entry of the finishing mill under the empty state of the hot coil box, and K E For hot roll box to pass long-term self-learning update speed; DTF 初值 The self-learning coefficient for the temperature measurement error of the first intermediate billet at the finish rolling mill entrance after the specification change;

[0024] The calculation method for the fourth temperature measurement error self-learning coefficient includes:

[0025] DTF 卷取 = (1-K) C )*DTF2+K C *DTF 初值

[0026] Among them, DTF 卷取 K is the self-learning coefficient for the fourth temperature measurement error under the hot coil winding state, DTF2 is the self-learning coefficient for the current specification temperature measurement error at the finishing mill entrance under the hot coil winding state, and K is the self-learning coefficient for the current specification temperature measurement error. C For long-term self-learning update speed of hot roll box winding; DTF 初值 The self-learning coefficient for temperature measurement error at the entry point of the finishing mill for the first intermediate billet after specification change.

[0027] This application provides a finishing mill temperature drop control system, comprising: a data acquisition module for acquiring a first detected temperature at the exit of the last pass of the roughing mill for the current intermediate billet, a temperature drop self-learning coefficient of the current specification roller table, and a second detected temperature at the entry point of the finishing mill; a finishing mill first setting module for determining a first calculated temperature value at the entry point of the finishing mill based on the first detected temperature; and a finishing mill second setting module for determining a first temperature drop self-learning coefficient for the next intermediate billet of the same specification based on the first detected temperature, the roller table temperature drop self-learning coefficient, the first calculated temperature value, and the second detected temperature, so as to correct the calculated temperature value of the next intermediate billet of the same specification at the entry point of the finishing mill based on the first temperature drop self-learning coefficient.

[0028] As described above, the finishing mill temperature drop control method and system proposed in this application have the following beneficial effects.

[0029] This application corrects the self-learning coefficient of the temperature drop of the next slab of the same specification on the roller table based on the first detection temperature of the last pass of roughing mill, the self-learning coefficient of the temperature drop of the current specification roller table, and the second detection temperature of the finishing mill inlet. Based on the corrected self-learning coefficient of the temperature drop of the roller table, the total temperature drop of the intermediate slab from the exit of the last pass of roughing mill to the inlet of finishing mill is accurately calculated, thereby improving the calculation accuracy of the pre-set temperature of the first finishing mill. At the same time, based on the first temperature calculation value, the second detection temperature, and the self-learning coefficient of the temperature drop between stands, the self-learning coefficient of the temperature drop between stands is corrected for the self-learning coefficient of the temperature drop between stands of the next slab of the same specification during the finishing mill rolling process. Based on the corrected self-learning coefficient of the temperature drop between stands, the total temperature drop of the intermediate slab during the finishing mill rolling process is accurately calculated, thereby improving the calculation accuracy of the pre-set temperature of the second finishing mill, and thus improving the product quality control accuracy such as the stability of the intermediate slab head threading and the thickness of the strip head. Attached Figure Description

[0030] Figure 1 This is a flowchart illustrating the finishing rolling temperature drop control method in one embodiment of this application.

[0031] Figure 2 This is a block diagram of the finishing mill temperature drop control system in one embodiment of this application. Detailed Implementation

[0032] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0033] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0034] The inventor discovered through research that:

[0035] The hot-rolled coil production line in the steel rolling mill is equipped with a pyrometer (RDT) at the roughing mill exit to detect the temperature of the intermediate slab in the final roughing pass, a pyrometer (FET) at the finishing mill inlet to detect the temperature of the intermediate slab at the finishing mill inlet, and a pyrometer (FDT) at the finishing mill exit to detect the temperature of the strip at the finishing mill exit. During the intermediate slab's journey from the roughing mill exit to the finishing mill F1 inlet, the primary heat dissipation method is thermal radiation. Compared to thermal radiation, heat conduction between the intermediate slab and the conveyor rollers is negligible. During the intermediate slab's entry into the finishing mill for rolling, the main heat dissipation methods include: strip descaling water temperature drop, strip cooling water spray temperature drop between stands, thermal radiation temperature drop between stands, work roll contact temperature drop, and rolling heat rise. In the hot continuous rolling process, the initial pre-setting of the finishing mill uses the measured temperature of the intermediate billet in the roughing mill to calculate the temperature at the FET (Frost Temperature Gauge) at the finishing mill inlet. Specifically, it calculates the temperature drop of the intermediate billet from the roughing mill exit to the FET at the finishing mill inlet. The temperature at the finishing mill inlet FET is calculated by subtracting the temperature drop during transport from the intermediate billet in the last pass of the roughing mill from the temperature measured at the intermediate billet. This calculated temperature is used for the initial finishing mill pre-setting calculation. The secondary pre-setting of the finishing mill uses the actual sampled value of the FET at the finishing mill inlet temperature. Due to factors such as pyrometer detection deviations, the FET temperature calculated in the initial finishing mill pre-setting is often inaccurate, resulting in low accuracy. Furthermore, pyrometer detection deviations and changes in the operating conditions of the finishing mill stands (such as variations in the maximum flow rate of descaling water and strip cooling water) often lead to large deviations in the calculated temperature drop between finishing mill stands, resulting in large deviations in the calculated temperature drop at the inlet and outlet of each finishing mill stand, and consequently, a large discrepancy between the calculated and actual temperatures at the finishing mill outlet. The large deviations in the calculation of temperature drop between the finishing mill stands and at the exit lead to significant deviations in the quality control of products such as finishing mill rolling force, roll gap, thickness, and plate shape, affecting rolling stability and product quality control.

[0036] Based on the problems existing in the prior art, this application proposes a method and system for controlling the temperature drop in finishing rolling. The solution of this application will be described in detail below with reference to specific embodiments.

[0037] Please see Figure 1 , Figure 1 This is a schematic flowchart of a finishing mill temperature drop control method according to an embodiment of this application. The method includes the following steps:

[0038] Step S100: Obtain the first detection temperature of the current intermediate billet at the exit of the last pass of the roughing mill, the self-learning coefficient of the temperature drop of the current specification roller table, and the second detection temperature at the entrance of the finishing mill.

[0039] In one embodiment, the temperature value of the head of the intermediate billet at the last pass of the roughing mill can be collected in real time by a temperature sensor set at the exit of the roughing mill as the first detection temperature. At the same time, the self-learning coefficient of the current roller table temperature drop recorded by the control terminal or the background server can be read. The temperature of the head of the intermediate billet entering the finishing mill can also be collected by a temperature detection device such as a high temperature gauge set in front of the finishing mill flying shear as the second detection temperature.

[0040] Step S110: Determine the first temperature calculation value at the entry point of the finishing mill based on the first detected temperature.

[0041] In one embodiment, after obtaining the first detection temperature of the current intermediate billet, a calculated temperature value is obtained based on this first detection temperature at the entry point of the finishing mill. This calculated temperature value is then used to perform a pre-set calculation for the first stage of finishing milling. Since the temperature drop during the transportation of the intermediate billet from the exit of the last pass of the roughing mill to the entry point of the finishing mill mainly considers the temperature drop caused by thermal radiation, i.e., heat loss due to heat exchange with the air, the temperature drop over this distance can be calculated based on influencing parameters such as the first detection temperature of the intermediate billet, the contact area with air, and its volume. The specific temperature drop calculation process is existing technology and will not be elaborated here. By calculating the temperature drop of this control section, the first calculated temperature value is equal to the difference between the first detection temperature and the temperature drop of this section.

[0042] Step S120: Determine the first temperature drop self-learning coefficient of the next intermediate billet of the same specification based on the first detected temperature, the temperature drop self-learning coefficient of the current specification roller table, the first temperature calculation value, and the second detected temperature, so as to correct the temperature calculation value of the next intermediate billet of the same specification at the finishing mill entrance based on the first temperature drop self-learning coefficient.

[0043] In one embodiment, the first temperature drop self-learning coefficient is calculated as follows:

[0044] HTC 新 =HTC 旧值 -K HTC *((T1-T2) / (T0-T2) / (1+HTC 旧值 ))

[0045] Among them, HTC 新 The first temperature drop self-learning coefficient, HTC 旧值 The self-learning coefficient for the temperature drop of the roller conveyor of the current specification is given, where T1 is the second detection temperature, T2 is the calculated value of the first temperature, T0 is the first detection temperature, and K is the value of the roller conveyor. HTC To improve the self-learning speed, the temperature reduction is typically achieved by sending a signal to K. HTC The default value is 0.4, but this value can be set and adjusted according to actual application needs, and there are no restrictions here.

[0046] In one embodiment, after calculating the first temperature drop self-learning coefficient, this coefficient is incorporated into the temperature drop calculation of the heat output roller table's thermal radiation, thereby correcting the calculated temperature value of the next intermediate billet at the finishing mill entrance. Specifically, the heat output roller table's thermal radiation temperature drop = calculated heat output roller table's thermal radiation temperature drop * (1 + HTC) 新 Based on this correction method, the calculated temperature value at the entry point of the finishing mill is used for the first-stage pre-setting calculation of the finishing mill, thereby improving the control accuracy of the first-stage pre-setting calculation of the finishing mill.

[0047] In one embodiment, after obtaining the first detection temperature of the current intermediate billet at the exit of the last pass of the roughing mill, the self-learning coefficient of the roller table temperature drop, and the second detection temperature at the entrance of the finishing mill, the method further includes: obtaining the first temperature measurement error self-learning update speed under the hot coil box empty state and the second temperature measurement error self-learning update speed under the hot coil box winding state; determining the first temperature measurement error self-learning coefficient of the next intermediate billet of the same specification based on the first temperature measurement error self-learning update speed and the current temperature measurement error self-learning coefficient under the hot coil box empty state; determining the second temperature measurement error self-learning coefficient of the next intermediate billet of the same specification based on the second temperature measurement error self-learning update speed and the current temperature measurement error self-learning coefficient under the hot coil box winding state; and correcting the detection temperature at the exit of the last pass of the roughing mill based on the first temperature measurement error self-learning coefficient or the second temperature measurement error self-learning coefficient.

[0048] Specifically, the calculation method for the first temperature measurement error self-learning coefficient includes:

[0049] DTR 空过 = (1-K) E )*DTR1+K E *DTR 初值

[0050] Among them, DTR 空过 K is the self-learning coefficient of the first temperature measurement error under the empty state of the hot coil box, DTR1 is the self-learning coefficient of the current specification temperature measurement error at the exit of the last pass of the roughing mill under the empty state of the hot coil box, and K is the self-learning coefficient of the current specification temperature measurement error. E For hot roll box to have long-term self-learning update speed; DTR 初值 The temperature measurement error self-learning coefficient of the first intermediate billet after specification change at the exit of the last pass of roughing rolling.

[0051] The calculation method for the second temperature measurement error self-learning coefficient includes:

[0052] DTR 卷取 = (1-K) C )*DTR2+K C *DTR 初值

[0053] Among them, DTR卷取 K is the second temperature measurement error self-learning coefficient under the hot coil box winding state, DTR2 is the current specification temperature measurement error self-learning coefficient at the exit of the last pass of the roughing mill under the hot coil box winding state, and K is the temperature measurement error self-learning coefficient for the current specification. C For long-term self-learning update speed of hot roll box winding; DTR 初值 The temperature measurement error self-learning coefficient of the first intermediate billet after specification change at the exit of the last pass of roughing rolling.

[0054] In one embodiment, DTR 初值 K E and K C The value can be configured and adjusted according to actual application needs, and there are no restrictions here.

[0055] Since the accuracy of pyrometers is limited, introducing a self-learning coefficient for temperature measurement error to correct the pyrometer readings can effectively improve the accuracy of the pre-calculation of temperature drop in the finishing mill. For example, either a first or second self-learning coefficient for temperature measurement error is introduced into the measured temperature of the final pass in the roughing mill for temperature compensation. The measured temperature of the next slab at the exit of the final pass in the roughing mill is equal to the sum of the pyrometer reading of the final pass and the second (or first) self-learning coefficient for temperature measurement error. The pyrometer reading is corrected using the second self-learning coefficient, and the calculated temperature at the finishing mill inlet is calculated based on the corrected value. This allows for the pre-setting of the finishing mill stage. This temperature measurement error compensation reduces the impact of pyrometer measurement deviation on the accuracy of the finishing mill pre-setting.

[0056] In one embodiment, after obtaining the first detection temperature at the exit of the last pass of the roughing mill, the current roller table temperature drop self-learning coefficient, and the second detection temperature at the entrance of the finishing mill, the method further includes: obtaining the temperature drop self-learning coefficient between the finishing mill stands for the current specification; determining the second temperature drop self-learning coefficient for the next intermediate billet of the same specification during the finishing mill rolling process based on the first temperature calculation value, the second detection temperature, and the temperature drop self-learning coefficient between the stands, so as to correct the total temperature drop of the next intermediate billet of the same specification during the finishing mill rolling process based on the second temperature drop self-learning coefficient.

[0057] Specifically, the pre-set temperature drop calculation for the finishing mill is mainly based on the measured temperature of the intermediate billet head at the FET (Extreme Temperature Gauge) at the finishing mill inlet and the target final rolling temperature. According to various factors affecting the temperature of the finishing mill, the inlet and outlet temperatures of each mill stand are calculated. The main temperature drop calculation factors include: strip descaling water temperature drop, strip cooling water spray temperature drop between stands, thermal radiation temperature drop between stands, work roll contact temperature drop, rolling heat rise, etc. Based on this, the temperature drop setting calculation for the next piece of the same specification during the finishing mill rolling process is automatically adjusted through the finishing mill temperature drop self-learning coefficient and the finishing mill temperature measurement error self-learning coefficient.

[0058] In one embodiment, the calculation method for the second temperature drop self-learning coefficient includes:

[0059] KTC 新 =KTC 旧值 -K T *((T1-T2) / (T 总 / (1+HTC 旧值 )))

[0060] Among them, KTC 新 KTC is the second temperature drop self-learning coefficient. 旧值 The temperature drop self-learning coefficient between racks of the current specification is T1, the second detected temperature is T2, and the calculated value of the first temperature is T. 总 K is the calculated value of the total temperature drop between racks. T K represents the self-learning speed for temperature drop between racks. T The value can be set and adjusted according to actual application needs, and there are no restrictions here.

[0061] In one embodiment, after obtaining the second temperature drop self-learning coefficient, this coefficient can be introduced into the calculation process of the total temperature drop between finishing mill stands to compensate for and correct the total temperature drop during the finishing rolling process of the next intermediate billet. Specifically, the total temperature drop between finishing mill stands is equal to the calculated value of the total temperature drop between finishing mill stands * (1 + KTC). 新 The total temperature drop between finishing mill stands includes: temperature drop of descaling water, temperature drop of cooling water for finishing strip, temperature drop of thermal radiation between finishing mill stands, temperature drop of roll contact, and temperature rise during rolling.

[0062] In one embodiment, after obtaining the self-learning coefficient of the temperature drop between stands of the current specification finishing mill, the method further includes: obtaining the third temperature measurement error self-learning update speed in the hot coil box empty state and the fourth temperature measurement error self-learning update speed in the hot coil box winding state; determining the third temperature measurement error self-learning coefficient of the next intermediate billet of the same specification based on the third temperature measurement error self-learning update speed and the current temperature measurement error self-learning coefficient in the hot coil box empty state; determining the fourth temperature measurement error self-learning coefficient of the next intermediate billet of the same specification based on the fourth temperature measurement error self-learning update speed and the current temperature measurement error self-learning coefficient in the hot coil box winding state; and correcting the detection temperature at the finishing mill entrance based on the third temperature measurement error self-learning coefficient or the fourth temperature measurement error self-learning coefficient.

[0063] In one embodiment, the calculation method of the third temperature measurement error self-learning coefficient includes:

[0064] DTF 空过 = (1-K) E )*DTF1+K E *DTF 初值

[0065] Among them, DTF 空过 K is the self-learning coefficient of the third temperature measurement error under the empty state of the hot coil box, DTF1 is the self-learning coefficient of the current specification temperature measurement error at the entry of the finishing mill under the empty state of the hot coil box, and K E For hot roll box to pass long-term self-learning update speed; DTF 初值 The self-learning coefficient for temperature measurement error at the entry point of the finishing mill for the first intermediate billet after specification change.

[0066] In one embodiment, the calculation method of the fourth temperature measurement error self-learning coefficient includes:

[0067] DTF 卷取 = (1-K) C )*DTF2+K C *DTF 初值

[0068] Among them, DTF 卷取 K is the self-learning coefficient for the fourth temperature measurement error under the hot coil winding state, DTF2 is the self-learning coefficient for the current specification temperature measurement error at the finishing mill entrance under the hot coil winding state, and K is the self-learning coefficient for the current specification temperature measurement error. C For long-term self-learning update speed of hot roll box winding; DTF 初值 The self-learning coefficient for temperature measurement error at the entry point of the finishing mill for the first intermediate billet after specification change.

[0069] In one embodiment, K C+ and DTF 初值 The value can be configured and adjusted according to actual application needs, and there are no restrictions here.

[0070] In one embodiment, to reduce the impact of the high-temperature gauge detection error at the finishing mill inlet on the finishing mill threading process, a third or fourth temperature measurement error self-learning coefficient is used to correct the finishing mill inlet temperature sampling value, so that a secondary pre-set calculation for finishing milling is performed based on the corrected temperature sampling value. Specifically, the finishing mill inlet temperature sampling value is equal to the sum of the detection temperature at the head of the intermediate billet finishing mill inlet and the third or fourth temperature measurement error self-learning coefficient.

[0071] Based on the above technical solution of this application, the radiation temperature drop calculation of the intermediate billet from the roughing mill exit to the finishing mill inlet is automatically adjusted by the self-learning coefficient of the heat output roller table temperature drop and the self-learning coefficient of the temperature measurement error. The temperature drop calculation during the finishing mill stand rolling process is automatically adjusted by the self-learning coefficient of the temperature measurement error and the self-learning coefficient of the temperature drop between finishing mill stands. This improves the accuracy of the pre-set temperature drop calculation for the first and second stages of finishing milling, and enhances the quality control accuracy of the finished strip head temperature and thickness. This effectively solves the problem of large deviations in the pre-set heat radiation temperature drop calculation for the first stage of finishing milling, leading to low accuracy in the pre-set calculation of the strip head temperature and thickness; it also effectively solves the problem of inaccurate pre-set temperature drop calculation between the second and third stages of finishing milling, leading to low product quality control accuracy in the finished strip head temperature and thickness; and by automatically adjusting the temperature drop calculation settings for the next piece of the same specification during finishing milling using the temperature drop self-learning coefficient, the stability of the intermediate billet head threading can be effectively improved.

[0072] Please see Figure 2 This embodiment also provides a finishing mill temperature drop control system for executing the finishing mill temperature drop control method described in the foregoing method embodiments. Since the technical principles of the system embodiment are similar to those of the foregoing method embodiments, the same technical details will not be repeated.

[0073] In one embodiment, a finishing mill temperature drop control system includes: a data acquisition module 10, used to acquire a first detection temperature of the current intermediate billet at the exit of the last pass of the roughing mill, a temperature drop self-learning coefficient of the current specification roller table, and a second detection temperature at the entry point of the finishing mill; a finishing mill first-stage preset module 11, used to determine a first temperature calculation value at the entry point of the finishing mill based on the first detection temperature; and a finishing mill second-stage preset module 12, used to determine a first temperature drop self-learning coefficient of the next intermediate billet of the same specification based on the first detection temperature, the roller table temperature drop self-learning coefficient, the first temperature calculation value, and the second detection temperature, so as to correct the temperature calculation value of the next intermediate billet of the same specification at the entry point of the finishing mill based on the first temperature drop self-learning coefficient.

[0074] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for controlling the temperature drop in finishing rolling, characterized in that, include: Get the first detection temperature of the current intermediate billet at the exit of the last pass of the roughing mill, the self-learning coefficient of the temperature drop of the current specification roller table, and the second detection temperature at the entrance of the finishing mill; The first temperature calculation value at the entry point of the finishing mill is determined based on the first detected temperature. Based on the first detected temperature, the roller table temperature drop self-learning coefficient, the first temperature calculation value, and the second detected temperature, the first temperature drop self-learning coefficient of the next intermediate billet of the same specification is determined, so as to correct the temperature calculation value of the next intermediate billet of the same specification at the fine rolling entrance based on the first temperature drop self-learning coefficient. Obtain the self-learning coefficient of temperature drop between stands in the finishing mill for the current specification; Based on the first calculated temperature value, the second detected temperature, and the inter-stand temperature drop self-learning coefficient, a second temperature drop self-learning coefficient is determined for the next intermediate billet of the same specification during the finishing rolling process, so as to correct the total temperature drop of the next intermediate billet of the same specification during the finishing rolling process based on the second temperature drop self-learning coefficient.

2. The finishing rolling temperature drop control method according to claim 1, characterized in that, After obtaining the first detection temperature of the current intermediate billet at the exit of the last pass of the roughing mill, the self-learning coefficient of the temperature drop of the current specification roller table, and the second detection temperature at the entrance of the finishing mill, the following is also included: The self-learning update speed of the first temperature measurement error under the empty state of the hot roll box and the self-learning update speed of the second temperature measurement error under the winding state of the hot roll box are obtained; The first temperature measurement error self-learning coefficient of the next intermediate billet of the same specification is determined based on the first temperature measurement error self-learning update speed and the current temperature measurement error self-learning coefficient under the empty state of the hot roll box. The second temperature measurement error self-learning coefficient of the next intermediate billet of the same specification is determined based on the second temperature measurement error self-learning update speed and the current temperature measurement error self-learning coefficient under the hot roll box winding state. The detected temperature at the exit of the last pass of the roughing mill is corrected based on either the first temperature measurement error self-learning coefficient or the second temperature measurement error self-learning coefficient.

3. The finishing rolling temperature drop control method according to claim 1, characterized in that, After obtaining the self-learning coefficient for the inter-stand temperature drop of the finishing mill for the current specification, the following is also included: The self-learning update speed of the third temperature measurement error in the empty state of the hot roll box and the self-learning update speed of the fourth temperature measurement error in the winding state of the hot roll box are obtained. The third temperature measurement error self-learning coefficient of the next intermediate billet of the same specification is determined based on the third temperature measurement error self-learning update speed and the current temperature measurement error self-learning coefficient under the empty state of the hot roll box. The fourth temperature measurement error self-learning coefficient of the next intermediate billet of the same specification is determined based on the fourth temperature measurement error self-learning update speed and the current temperature measurement error self-learning coefficient under the hot roll box winding state. The detected temperature at the entry point of the finishing mill is corrected based on the third or fourth temperature measurement error self-learning coefficient.

4. The finishing rolling temperature drop control method according to claim 1, characterized in that, The first temperature drop self-learning coefficient is calculated as follows: Among them, HTC 新 The first temperature drop self-learning coefficient, HTC 旧值 The self-learning coefficient for temperature drop of the roller conveyor of the current specification is given, where T1 is the second detection temperature, T2 is the calculated value of the first temperature, T0 is the first detection temperature, and K is the value of the roller conveyor. HTC To reduce the self-learning speed of temperature drop.

5. The finishing rolling temperature drop control method according to claim 2, characterized in that, The calculation method for the first temperature measurement error self-learning coefficient includes: Among them, DTR 空过 K is the self-learning coefficient of the first temperature measurement error under the empty state of the hot coil box, DTR1 is the self-learning coefficient of the current specification temperature measurement error at the exit of the last pass of the roughing mill under the empty state of the hot coil box, and K is the self-learning coefficient of the current specification temperature measurement error. E For hot roll box to have long-term self-learning update speed; DTR 初值 The temperature measurement error self-learning coefficient of the first intermediate billet after specification change at the exit of the last pass of roughing rolling. The calculation method for the second temperature measurement error self-learning coefficient includes: Among them, DTR 卷取 K is the second temperature measurement error self-learning coefficient under the hot coil winding state, DTR2 is the current specification temperature measurement error self-learning coefficient at the exit of the last roughing pass under the hot coil winding state, and K is the temperature measurement error self-learning coefficient for the current specification at the exit of the last roughing pass. C For long-term self-learning update speed of hot roll box winding; DTR 初值 The temperature measurement error self-learning coefficient of the first intermediate billet after specification change at the exit of the last pass of roughing rolling.

6. The finishing rolling temperature drop control method according to claim 1, characterized in that, The calculation method for the second temperature drop self-learning coefficient includes: Among them, KTC 新 KTC is the second temperature drop self-learning coefficient. 旧值 The self-learning coefficient for inter-rack temperature drop is given by T1, where T1 is the second detected temperature, T2 is the calculated value of the first temperature, and T is the temperature at the specified current specification. 总 The calculated value for the total temperature drop between racks, K. T The self-learning speed for temperature drop between racks.

7. The finishing rolling temperature drop control method according to claim 3, characterized in that, The calculation method for the third temperature measurement error self-learning coefficient includes: Among them, DTF 空过 K is the self-learning coefficient of the third temperature measurement error under the empty state of the hot coil box, DTF1 is the self-learning coefficient of the current specification temperature measurement error at the entrance of the finishing mill under the empty state of the hot coil box, and K E For hot roll box to have long-term self-learning update speed; DTF 初值 The self-learning coefficient for the temperature measurement error of the first intermediate billet at the entry point of the finishing mill after the specification change; The calculation method for the fourth temperature measurement error self-learning coefficient includes: Among them, DTF 卷取 K is the self-learning coefficient for the fourth temperature measurement error under the hot coil winding state, DTF2 is the self-learning coefficient for the current specification temperature measurement error at the finishing mill entrance under the hot coil winding state, and K is the self-learning coefficient for the current specification temperature measurement error. C For long-term self-learning update speed of hot roll box winding; DTF 初值 The self-learning coefficient for temperature measurement error at the entry point of the finishing mill for the first intermediate billet after specification change.

8. A temperature drop control system for finishing mills, characterized in that, include: The data acquisition module is used to acquire the first detection temperature of the current intermediate billet at the exit of the last pass of the roughing mill, the self-learning coefficient of the temperature drop of the current specification roller table, and the second detection temperature at the entrance of the finishing mill. A pre-setting module for finishing rolling is used to determine the first temperature calculation value at the entry point of the finishing rolling mill based on the first detected temperature. The finishing mill secondary pre-setting module is used to determine the first temperature drop self-learning coefficient of the next intermediate billet of the same specification based on the first detected temperature, the roller table temperature drop self-learning coefficient, the first temperature calculation value, and the second detected temperature, so as to correct the temperature calculation value of the next intermediate billet of the same specification at the finishing mill entrance based on the first temperature drop self-learning coefficient; obtain the temperature drop self-learning coefficient between the finishing mill stands of the current specification; and determine the second temperature drop self-learning coefficient of the next intermediate billet of the same specification during the finishing mill rolling process based on the first temperature calculation value, the second detected temperature, and the temperature drop self-learning coefficient between the stands, so as to correct the total temperature drop of the next intermediate billet of the same specification during the finishing mill rolling process based on the second temperature drop self-learning coefficient.

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