Bar temperature filtering control method based on steel rolling process

Abstract

The invention discloses a steel rolling process-based bar temperature filtering control method, which relates to the technical field of steel rolling production and comprises the following steps of: acquiring bar temperature original data, corresponding steel rolling process parameters and steel grade component data in advance; and a material temperature feature library is constructed, steel grade components are identified in real time according to an online spectrum analysis technology, and a matched filtering parameter reference template is called. By constructing the material temperature feature library and combining the online spectrum analysis technology, accurate recognition of different steel types and intelligent calling of the matched filtering parameter template are achieved, the problem that a traditional filtering method cannot adapt to the characteristic difference of the steel types is effectively solved, the universality and pertinence of filtering control are remarkably improved, and the method is suitable for large-scale popularization and application. Effective smoothness of high-fluctuation temperature data is guaranteed, the problem of lag or excessive filtering caused by a fixed window is avoided, and the dynamic adaptability of the system to a complex steel rolling scene is greatly enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel rolling, in particular to a bar temperature filtering control method based on a steel rolling process. BACKGROUND

[0002] In the steel rolling production, the temperature has the most direct influence on the performance of the billet, and the process requires that the pre-precision rolling outlet temperature and the cold bed tempering temperature on the production line must be continuously controllable and have a certain precision to ensure the performance dispersion. However, the main factors affecting the measurement value fluctuation of the temperature measuring instrument in the production process are the residual water on the surface of the bar and the temperature inhomogeneity generated in the rolling process. When the measurement point of the temperature measuring instrument scans different positions of the bar, the measurement value will fluctuate greatly, which affects the model self-learning effect, causes the adjusting valve to frequently act in the temperature control, and reduces the control precision and the equipment life.

[0003] The phase transition temperature and the heat conduction characteristics of different steel grades (such as carbon steel and alloy steel) are significantly different, resulting in different temperature fluctuation characteristics. For example, high-carbon steel has poor heat conductivity, and the temperature distribution inhomogeneity is more obvious, and the temperature fluctuation amplitude is larger; and the alloy steel has more complex temperature change law in the phase transition process due to the alloying elements. In the prior art, the mean filtering method with a fixed window is usually used to process the temperature data, or the filtering parameters are adjusted only according to a single process parameter, which cannot adapt to the differences in temperature characteristics of different steel grades, and it is also difficult to cope with the complex and changeable steel rolling process conditions, and the filtering effect is not ideal. Therefore, there is an urgent need for a temperature filtering control method which can dynamically adjust the filtering parameters in combination with the steel grade characteristics and the steel rolling process characteristics.

[0004] At present, no effective solution has been proposed for the problems in the related art. SUMMARY

[0005] In view of the problems in the related art, the present application proposes a bar temperature filtering control method based on a steel rolling process, which constructs a material temperature characteristic library, identifies the steel grade composition in real time by combining an online spectrum analysis technology, calls a matched filtering parameter template, and adjusts the filtering window size in real time according to the rolling speed, the cooling water pressure and other process parameters, effectively eliminates the interference noise in the temperature measurement data, improves the temperature control precision, and overcomes the defects in the prior art that cannot adapt to the differences in temperature characteristics of different steel grades.

[0006] The technical scheme of the present application is implemented as follows:

[0007] A bar temperature filtering control method based on a steel rolling process, comprising the following steps:

[0008] Pre-acquire bar temperature original data, corresponding steel rolling process parameters and steel grade composition data;

[0009] A material temperature feature library is constructed, and the steel composition is identified in real time based on online spectral analysis technology, and the matching filter parameter reference template is called.

[0010] A dynamic window adjustment model is constructed based on the benchmark template of rolling process parameters and filtering parameters to determine the initial window size and dynamic adjustment coefficient of temperature filtering.

[0011] The raw temperature data is filtered using a dynamic window mean filtering algorithm to obtain filtered temperature data.

[0012] The filtered temperature data is used as a feedback signal and compared with the temperature feedforward signal before pre-finishing to obtain the temperature deviation. The opening of the electric regulating valve of the cooler is adjusted by the PID controller according to the temperature deviation to achieve closed-loop control of the bar cooling temperature.

[0013] Furthermore, the rolling process parameters include at least: rolling speed, cooling water pressure, and water tank number.

[0014] Furthermore, the material temperature feature library includes: phase transformation temperature range, thermal conductivity, temperature fluctuation feature parameters and corresponding filter parameter benchmark templates for different steel grades. The filter parameter benchmark templates include window size benchmark coefficients, outlier removal threshold coefficients and initial values ​​of weight coefficients.

[0015] Furthermore, the online spectral analysis technology includes real-time acquisition of spectral data on the surface of the bar stock by a laser-induced breakdown spectrometer installed at the mill inlet, and identification of steel composition and alloy element content by characteristic wavelength matching.

[0016] Furthermore, the construction of the dynamic window adjustment model includes the following steps:

[0017] Based on the correlation between rolling speed and the time it takes for the bar to pass through the temperature measurement zone, combined with the window size reference coefficient k in the filter parameter reference template... m Set the initial window size baseline value W0, which is represented as:

[0018] W0 = k m ×k×v -1 ;

[0019] Where k is the process coefficient and v is the rolling speed;

[0020] The calculation of the dynamic adjustment coefficient includes: weighting the influence of the thermal conductivity λ of the steel grade and the cooling water pressure p, combined with the weighting coefficient a in the filter parameter benchmark template. m b m Calculate the dynamic adjustment coefficient α, which is expressed as:

[0021] α=a m ×λ+bm ×p -1 ;

[0022] wherein, a m , b m are weight coefficients.

[0023] Further, the obtaining filtered temperature data comprises the following steps:

[0024] The window size is adjusted in real time, including: based on the initial window size reference value W0, the actual window size W = W0 × (1 + a) is calculated according to the dynamic adjustment coefficient a, and it is ensured that W is within the preset effective range;

[0025] The abnormal value detection and elimination is performed, and the temperature data in the window is statistically analyzed to calculate the mean value μ and the standard deviation σ, and based on the abnormal value elimination threshold coefficient t m in the filter parameter reference template, the actual threshold range is determined as [μ-t m ×3σ, μ+t m ×3σ], and the abnormal data exceeding the range is eliminated;

[0026] The mean value is calculated, including: the mean value of the remaining data in the window is calculated as the filtered temperature output value, which represents the filtered real-time feedback temperature T fb .

[0027] Further, the temperature feedforward signal is obtained, represented as:

[0028] T ff = T L + ΔT B - ΔT S ;

[0029] Wherein, T L is the billet tapping temperature, ΔT B is the deformation heat increment, and the calculation formula is:

[0030]

[0031] Wherein, η is the heat conversion efficiency, which is 0.3-0.5, P is the rolling power, t is the rolling time, m is the bar mass, c p is the specific heat capacity of steel grade;

[0032] Wherein, ΔT S is the heat loss, represented as:

[0033] ΔT S = Σ (h i · A· Δt i · (T-T0) ) / (m· c p );

[0034] wherein h i is the heat dissipation coefficient of different stages, A is the surface area of the bar, Δt i is the duration of each stage, T is the real-time temperature of the bar, and T0 is the ambient temperature.

[0035] Further, the adjusting the opening degree of the electric regulating valve of the cooler according to the temperature deviation by the PID controller comprises the following steps:

[0036] According to the filtered real-time feedback temperature T fb and the feedforward signal T ff , the temperature deviation is calculated and expressed as:

[0037] e(t)=w1·(T set -T fb )+w2·(T set -T ff );

[0038] wherein w1 and w2 are weight coefficients, and take values of 0.7 and 0.3, respectively; T set is the target temperature of the bar after cooling, and is a process set value;

[0039] The adjusting amount, i.e., the opening degree correction value of the electric regulating valve, is output by the PID controller and expressed as:

[0040]

[0041] wherein u0 is an initial opening degree reference value;

[0042] The opening degree adjustment is performed by driving the electric regulating valve.

[0043] The present application has the following beneficial effects:

[0044] The present application realizes the accurate identification of different steel types and the intelligent calling of the matching filter parameter template by constructing the material temperature feature library and combining the online spectrum analysis technology, effectively solves the problem that the traditional filtering method cannot adapt to the differences in steel characteristics, and significantly improves the universality and pertinence of the filtering control. The dynamic window adjustment model integrates the rolling speed, cooling water pressure and other process parameters and the steel heat conduction characteristics, so that the filtering window can change adaptively according to the real-time working condition, which not only ensures the effective smoothing of high fluctuation temperature data, but also avoids the lag or excessive filtering problem caused by the fixed window, greatly enhancing the dynamic adaptability of the system to complex rolling scenarios. Based on the abnormal value detection mechanism of the steel characteristics, the interference noise caused by factors such as surface water stains and uneven temperature is accurately excluded through differentiated threshold setting, so that the temperature data is more in line with the actual working condition, providing a reliable basis for subsequent control.

[0045] In addition, by using the PID control strategy combining feedforward and feedback, the feedforward signal is used for advance adjustment, the feedback signal after filtering is used for real-time correction, and the parameter self-adaptive adjustment rule is used, so that the invalid action of the regulating valve is reduced, the equipment loss is reduced, the temperature control precision is greatly improved, and the stability of the mechanical properties of the rolled steel product is ensured. In addition, the whole method can complete the whole process closed-loop control from steel grade identification, data filtering to temperature adjustment without manual intervention, can be widely applied to multi-steel rolling and high-speed low-temperature rolling and other high-end scenes, provides a strong support for the intelligentization and high efficiency of the rolled steel production, and has a positive significance for promoting the green and low-carbon development of the steel industry. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0047] Figure 1 is a flowchart of a rod temperature filtering control method based on a rolling process according to an embodiment of the present application. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the present application.

[0049] According to an embodiment of the present application, a rod temperature filtering control method based on a rolling process is provided.

[0050] As shown in Figure 1 , the rod temperature filtering control method based on a rolling process according to an embodiment of the present application includes the following steps:

[0051] Step S1, data acquisition is performed in advance, including: rod temperature original data, corresponding rolling process parameters and steel composition data;

[0052] Among them, the temperature original data is obtained by a temperature measuring instrument installed before and after the cooler; the rolling process parameters include rolling speed, which can be provided by a rolling mill control system in real time, cooling water pressure, which can be collected by a pressure sensor, and water tank number, which can identify the position of the cooling water tank corresponding to the current temperature measuring point; the steel composition data is collected in real time by a laser-induced breakdown spectroscopy instrument installed at the entrance of the rolling mill.

[0053] Step S2, a material temperature characteristic library is constructed, and online spectrum analysis technology is combined to identify the steel grade composition in real time, and a matched filter parameter benchmark template is called;

[0054] The material temperature characteristic library is constructed, including pre-acquisition of thermodynamic characteristic parameters of multiple typical steel grades, such as HRB400E, Q235, 45# steel, 20CrMnTi, etc., including phase transition temperature range, thermal conductivity λ, temperature fluctuation characteristic parameters such as fluctuation frequency and amplitude range, and corresponding filter parameter benchmark templates are determined by experiment optimization for each steel grade, including window size benchmark coefficient k m , abnormal value elimination threshold coefficient t m and weight coefficient initial value a m , b m , and the material temperature characteristic library is established and stored in an industrial control computer.

[0055] At the same time, the steel grade composition is identified in real time, including real-time acquisition of spectral data on the surface of the bar by a laser-induced breakdown spectrometer, extraction of characteristic wavelength information, matching with standard spectral data in the material temperature characteristic library, identification of the steel grade composition and main alloy element content of the current bar, and calling of the filter parameter benchmark template corresponding to the steel grade.

[0056] Step S3, a dynamic window adjustment model is constructed based on rolling process parameters and filter parameter benchmark templates to determine the initial window size and dynamic adjustment coefficient of temperature filtering;

[0057] The dynamic window adjustment model is constructed, including: according to the correlation between rolling speed and the time of the bar passing through the temperature measurement area, combining the window size benchmark coefficient k m in the filter parameter benchmark template, setting the initial window size benchmark value W0, which is represented as:

[0058] W0=k m ×k×v -1 ;

[0059] Wherein, k is a process coefficient, which can be determined according to on-site debugging, and the value range is 5-10, and v is the rolling speed. For high-carbon steel and other poor thermal conductivity steel grades, the k m value is larger, and the initial window benchmark value is correspondingly increased.

[0060] The dynamic adjustment coefficient is calculated, including: according to the influence weight of the thermal conductivity λ of the steel grade and the cooling water pressure p, combining the weight coefficients a m , b m in the filter parameter benchmark template, calculating the dynamic adjustment coefficient α, which is represented as:

[0061] α=a m ×λ+b m ×p-1 ;

[0062] wherein, a m , b m are weight coefficients, a m +b m =1. The greater the thermal conductivity, the faster the temperature conduction, the greater the fluctuation range, and the window needs to be increased; the higher the cooling water pressure, the more significant the cooling effect, the more intense the temperature fluctuation, and the window needs to be reduced.

[0063] Step S4, the temperature original data is filtered by using a dynamic window mean filtering algorithm to obtain filtered temperature data, including the following steps:

[0064] The window size is adjusted in real time, including: based on the initial window size reference value W0, the actual window size W=W0x(1+α) is calculated according to the dynamic adjustment coefficient α, and W is ensured to be within a preset effective range;

[0065] The abnormal value is detected and eliminated, and the temperature data in the window is statistically analyzed to calculate the mean μ and the standard deviation σ, and based on the abnormal value elimination threshold coefficient t m in the filtering parameter reference template, the actual threshold range is determined as [μ-t m ×3σ, μ+t m ×3σ], and the abnormal data exceeding the range is eliminated.

[0066] The mean value is calculated, including: the mean value of the remaining data in the window is calculated as the filtered temperature output value, that is, the filtered real-time feedback temperature T fb .

[0067] Step S5, the filtered temperature data is input into a temperature closed-loop control system, and a feedforward feedback control strategy is combined to realize accurate control of the bar temperature; including: the filtered temperature data is used as a feedback signal, compared with a temperature feedforward signal before pre-rolling, to obtain a temperature deviation, and the opening degree of the electric regulating valve of the cooler is adjusted by a PID controller according to the temperature deviation, to realize closed-loop control of the cooling temperature of the bar, and ensure that the target temperature of the rolled piece after cooling is accurate.

[0068] Specifically, for the above-mentioned temperature feedforward signal, the predicted temperature of the bar when reaching the entrance of the cooling section is calculated in real time by a thermal equilibrium equation, which is used to represent the feedforward signal T ff , and is expressed as:

[0069] T ff =T L +ΔT B -ΔT S ;

[0070] Wherein, T LThe billet discharge temperature is the temperature of the billet before it is heated to the rolling temperature in the heating furnace, and is collected by a temperature measuring instrument installed at the outlet of the heating furnace as the initial reference for the feedforward calculation.

[0071] wherein ΔT B is the deformation heat increment, which is the heat energy converted from the pressure work of the rolling mill on the bar during rolling, and is calculated by the formula:

[0072]

[0073] wherein η is the heat conversion efficiency, taken as 0.3-0.5, P is the rolling power, t is the rolling time, m is the mass of the bar, and c p is the specific heat capacity of the steel grade.

[0074] wherein ΔT S is the heat loss, including the radiation heat loss, the convection heat loss and the heat loss due to the contact with the rolls during rolling, and is calculated by the comprehensive formula:

[0075] ΔT S =∑(h i ·A·Δt i ·(T-T0)) / (m·c p );

[0076] wherein h i is the heat loss coefficient at different stages, A is the surface area of the bar, Δt i is the duration of each stage, T is the real-time temperature of the bar, and T0 is the ambient temperature.

[0077] In addition, for the adjustment of the opening degree of the electric regulating valve of the cooler according to the temperature deviation by the PID controller, the following steps are included:

[0078] According to the filtered real-time feedback temperature T fb and the feedforward signal T ff , the temperature deviation is calculated and expressed as:

[0079] e(t)=w1·(T set -T fb )+w2·(T set -T ff );

[0080] wherein w1 and w2 are weight coefficients, taken as 0.7 and 0.3 respectively; T set is the target temperature of the bar after cooling, which is a process set value.

[0081] The adjustment amount output by the PID controller, i.e. the opening degree correction value of the electric regulating valve, is expressed as:

[0082]

[0083] Wherein, u0 is the initial opening reference value.

[0084] The driving electric regulating valve is driven to perform opening adjustment, and finally temperature stabilization is realized.

[0085] By means of the above technical scheme, taking the water cooling section behind the finishing mill I of a certain high-speed low-temperature rolling production line as an example, the specific implementation steps are as follows:

[0086] The temperature original data of the bar is collected in advance by the temperature measuring instrument installed at the inlet and outlet of the water cooling section behind the finishing mill I, and the sampling frequency is 10 Hz; at the same time, the rolling speed v = 15 m / s, the cooling water pressure p = 0.8 MPa, and the water tank number is 3#; the spectral data of the bar surface is collected by the laser-induced breakdown spectrometer.

[0087] The characteristic parameters of HRB400E steel are pre-stored in the material temperature feature library: thermal conductivity λ = 48 W / (m·K), and the filtering parameter reference template is k m = 1.2 (window size reference coefficient), t m = 0.9 (abnormal value elimination threshold coefficient), a m = 0.6, b m = 0.4 (weight coefficient). Through spectral analysis, the current bar is identified as HRB400E steel, and the corresponding filtering parameter reference template is called.

[0088] The dynamic window adjustment model is constructed, including the following steps:

[0089] The initial window size reference value is calculated, taking the process coefficient k = 8, then W0 = 1.2 × 8 × 15 -1 ≈ 0.64, and the integral is 1 data point.

[0090] The dynamic adjustment coefficient is calculated: α = 0.6 × 48 + 0.4 × 0.8 -1 = 28.8 + 0.5 = 29.3.

[0091] In the application, normalization processing is required to make α in the reasonable range of 0.1-0.5, and after normalization, α = 0.2.

[0092] The dynamic window mean filtering processing is performed, the actual window size W = 1 × (1 + 0.2) = 1.2, and the integral is 1 data point; the abnormal value detection is performed on the data in the window, the mean value μ = 850℃, the standard deviation σ = 12℃, the actual threshold range is [850-0.9 × 3 × 12, 850 + 0.9 × 3 × 12] = [850-32.4, 850 + 32.4] = [817.6, 882.4]℃, and there is no data exceeding the range; the mean value is calculated to obtain the filtered temperature data of 850℃.

[0093] The temperature closed-loop control is performed, including that the filtered temperature data 850 DEG C is taken as a feedback signal, a deviation 5 DEG C is obtained by comparing the feedback signal with a feedforward signal 845 DEG C, and the opening degree of the pneumatic diaphragm flow regulating valve of the water tank 3 is adjusted by a PID controller, so that the bar outlet temperature is stabilized in the range of the set value 850 DEG C±5 DEG C.

[0094] In summary, by means of the above technical solutions of the present application, the following effects can be achieved:

[0095] The present application realizes accurate identification of different steel grades and intelligent calling of matching filter parameter templates by constructing a material temperature feature library and combining online spectrum analysis technology, effectively solves the problem that traditional filtering methods cannot adapt to the differences in steel characteristics, and significantly improves the universality and pertinence of filtering control. The dynamic window adjustment model integrates process parameters such as rolling speed and cooling water pressure with the thermal conductivity characteristics of the steel grade, so that the filter window can adaptively change according to the real-time working conditions, ensuring effective smoothing of high fluctuation temperature data, and avoiding the problems of lag or excessive filtering caused by fixed windows, greatly enhancing the dynamic adaptability of the system to complex rolling scenarios. The abnormal value detection mechanism based on the characteristics of the steel grade accurately eliminates interference noise caused by factors such as surface water stains and uneven temperature by setting different thresholds, making the temperature data more consistent with the actual working conditions and providing a reliable basis for subsequent control.

[0096] In addition, by using the PID control strategy combining feedforward and feedback, the feedforward signal is used for advance adjustment, and the filtered feedback signal is used for real-time correction, and the parameter self-adaptive adjustment rule is used, which not only reduces the invalid action of the regulating valve and reduces the equipment loss, but also greatly improves the temperature control precision and ensures the stability of the mechanical properties of the rolled steel product. In addition, the whole method can complete the whole process closed-loop control from steel grade identification, data filtering to temperature regulation without manual intervention, and can be widely applied to multi-steel grade mixed rolling and high-speed low-temperature rolling and other high-end scenes, providing strong support for the intelligentization and high efficiency of rolling production, and having positive significance for promoting the green and low-carbon development of the steel industry.

[0097] The above only describes preferred embodiments of the present application and is not intended to limit the present application. Those skilled in the art will easily think of other embodiments of the present disclosure after considering the disclosure in the specification and examples. The present application is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and examples are only considered as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

[0098] It should be understood that the present disclosure is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the present disclosure. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for filtering and controlling the temperature of bars based on steel rolling processes, characterized in that, Includes the following steps: Pre-collect raw data on bar temperature, corresponding rolling process parameters, and steel composition data; A material temperature feature library is constructed, and the steel composition is identified in real time based on online spectral analysis technology, and the matching filter parameter reference template is called. A dynamic window adjustment model is constructed based on the benchmark template of rolling process parameters and filtering parameters to determine the initial window size and dynamic adjustment coefficient of temperature filtering. The raw temperature data is filtered using a dynamic window mean filtering algorithm to obtain filtered temperature data. The filtered temperature data is used as a feedback signal and compared with the temperature feedforward signal before pre-finishing to obtain the temperature deviation. The opening of the electric regulating valve of the cooler is adjusted by the PID controller according to the temperature deviation to achieve closed-loop control of the bar cooling temperature.

2. The bar temperature filtering control method based on rolling mill process according to claim 1, characterized in that, The rolling process parameters include at least: rolling speed, cooling water pressure, and water tank number.

3. The bar temperature filtering control method based on rolling mill process according to claim 1, characterized in that, The material temperature feature library includes: phase transformation temperature range, thermal conductivity, temperature fluctuation feature parameters and corresponding filter parameter benchmark templates for different steel grades. The filter parameter benchmark templates include window size benchmark coefficients, outlier removal threshold coefficients and initial values ​​of weight coefficients.

4. The bar temperature filtering control method based on rolling mill process according to claim 1, characterized in that, The online spectral analysis technology includes real-time acquisition of spectral data on the surface of the bar stock by a laser-induced breakdown spectrometer installed at the mill inlet, and identification of steel composition and alloy element content by matching characteristic wavelengths.

5. The bar temperature filtering control method based on rolling mill process according to claim 1, characterized in that, The construction of the dynamic window adjustment model includes the following steps: Based on the correlation between rolling speed and the time it takes for the bar to pass through the temperature measurement zone, combined with the window size reference coefficient k in the filter parameter reference template... m Set the initial window size baseline value W0, which is represented as: W0=k m ×k×v -1 ; Where k is the process coefficient and v is the rolling speed; The calculation of the dynamic adjustment coefficient includes: weighting the influence of the thermal conductivity λ of the steel grade and the cooling water pressure p, combined with the weighting coefficient a in the filter parameter benchmark template. m b m Calculate the dynamic adjustment coefficient α, which is expressed as: a=a m ×λ+b m ×p -1 ; Among them, a m b m These are the weighting coefficients.

6. The bar temperature filtering control method based on rolling mill process according to claim 1, characterized in that, Obtaining the filtered temperature data includes the following steps: Real-time window size adjustment includes: based on the initial window size baseline value W0, calculating the actual window size W = W0 × (1 + α) according to the dynamic adjustment coefficient α, and ensuring that W is within the preset effective range; Outlier detection and removal are performed by statistically analyzing the temperature data within the window, calculating the mean μ and standard deviation σ, and then applying the outlier removal threshold coefficient t based on the filter parameter benchmark template. m The actual threshold range is determined to be [μ-t]. m ×3σ,μ+t m ×3σ], remove abnormal data that exceeds this range; The process involves calculating the mean of the remaining data within the window, which is then used as the filtered temperature output value, representing the filtered real-time feedback temperature T. fb .

7. The bar temperature filtering control method based on rolling mill process according to claim 1, characterized in that, The temperature feedforward signal is obtained and expressed as follows: T ff =T L +ΔT B -ΔT S ; Among them, T L ΔT represents the billet tapping temperature. B The formula for calculating the deformation heat increment is: Where η is the thermal conversion efficiency, taken as 0.3-0.5, P is the rolling power, t is the rolling time, m is the bar mass, and c is the rolling power. p Specific heat capacity of steel grade; Where, ΔT S Heat loss is represented as: ΔT S =Σ(hi·A·Δt i ·(T-T0)) / (m·c p ); Among them, h i Here, A represents the heat dissipation coefficient at different stages, and Δt represents the surface area of ​​the rod. i The duration of each stage is T, where T is the real-time temperature of the bar and T0 is the ambient temperature.

8. The bar temperature filtering control method based on rolling mill process according to claim 7, characterized in that, The process of adjusting the opening of the electric regulating valve of the cooler according to the temperature deviation using a PID controller includes the following steps: Based on the filtered real-time feedback temperature T fb and feedforward signal T ff The temperature deviation is calculated and expressed as: e(t)=w1·(T set -T fb )+w2·(T set -T ff ); Where w1 and w2 are weighting coefficients, taking values ​​of 0.7 and 0.3 respectively; T set This is the target temperature after the bar is cooled, and it is the process setting value. The adjustment value output by the PID controller, i.e., the correction value for the opening of the electric regulating valve, is expressed as: Where u0 is the initial opening reference value; Drive the electric regulating valve to adjust its opening.

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