Rolled piece temperature control system based on steel rolling process

Abstract

The invention discloses a rolled piece temperature control system based on a steel rolling process, which relates to the technical field of steel rolling and comprises a detection module, a control module, an execution module and a cooling module, the detection module comprises infrared thermometer arrays, an ultrasonic thickness gauge, an electromagnetic flowmeter, a pressure transmitter and a thermal resistance thermometer, and the infrared thermometer arrays are used for being arranged in front of and behind a cooling section and respectively collecting initial temperatures before entering a cooling system. Through the self-adaptive PID algorithm, the finite difference heat conduction model and the Smith pre-estimation compensation strategy are combined, the problem of control delay caused by the large lag characteristic in the cooling process is solved, control parameters can be dynamically adjusted according to the rolling speed, the steel grade characteristic and the like, temperature adjustment better fits the actual working condition, and the control precision is improved. The phenomenon of supercooling or insufficient cooling in a traditional control mode is fundamentally avoided, and the uniformity and stability of the performance of rolled pieces are guaranteed.

Description

TECHNICAL FIELD

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

[0002] As a pillar industry of the national economy, the steel industry is directly related to the mechanical properties, surface quality and production efficiency of steel products in the rolling process. In the traditional steel rolling process, the temperature control of the rolled piece has long relied on empirical operation or simple automatic adjustment, making it difficult to cope with dynamic changes under complex working conditions. For example, during high-speed rolling, the surface temperature distribution of the rolled piece is prone to unevenness due to factors such as rolling speed fluctuations, billet composition differences, and environmental temperature changes. Traditional temperature measurement methods are mostly single-point contact measurements, which not only have a lagging response, but also may damage the surface integrity of the rolled piece due to mechanical contact, leading to subsequent processing defects. At the same time, the adjustment of the cooling system is mostly based on fixed parameters, which cannot dynamically adjust the cooling intensity according to the real-time temperature of the rolled piece, often causing overcooling or insufficient cooling, affecting the stability of product performance. This extensive temperature control mode has become one of the bottlenecks restricting the development of steel products towards high-end and fine.

[0003] With the in-depth application of intelligent manufacturing technology in the industrial field, the steel rolling process puts forward higher requirements for the intelligentization and precision of temperature control. However, the existing technical system still has obvious shortcomings: on the one hand, non-contact temperature measurement equipment is easily disturbed by water vapor, iron oxide scale and other factors in a high-temperature environment, leading to distorted measurement data and making it difficult to serve as a basis for precise control; on the other hand, the control strategy of the cooling system lacks adaptability to the dynamic characteristics of the rolling process, especially at the junction of multi-stage cooling, where temperature transitions often fluctuate, affecting the uniformity of the rolled piece structure. In addition, the thermal conductivity characteristics of different steel types (such as low-carbon steel and alloy steel) differ significantly, and the traditional control system lacks a targeted parameter adaptation mechanism, requiring manual re-adjustment during production changeover, which not only prolongs the production preparation time, but also makes it difficult to ensure the consistency of control precision. The existence of these problems makes it difficult for steel enterprises to improve product quality and reduce energy consumption, and there is an urgent need for a temperature control system that can adapt to complex working conditions and has self-adaptability and anti-interference capability.

[0004] To address the problems in the related art, no effective solutions have been proposed so far. SUMMARY

[0005] To address the problems in the related art, the present application proposes a rolled piece temperature control system based on a steel rolling process to overcome the low temperature control precision, slow response speed, high energy consumption and poor adaptability of existing related technology.

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

[0007] A rolling piece temperature control system based on a rolling process, comprising a detection module, a control module, an execution module and a cooling module, wherein

[0008] The detection module comprises an infrared thermometer array, an ultrasonic thickness gauge, an electromagnetic flowmeter, a pressure transmitter and a thermal resistance thermometer, wherein the infrared thermometer array is arranged before and after the cooling section to collect the initial temperature before entering the cooling system as the input parameter of the feedforward control and to measure the actual temperature after the rolling piece is cooled as the basis of the feedback control; the ultrasonic thickness gauge is used to detect the thickness of the iron oxide scale on the surface of the rolling piece in real time for temperature compensation calculation; the electromagnetic flowmeter and the pressure transmitter are respectively used to collect the flow and pressure of the cooling water pipeline; and the thermal resistance thermometer monitors the inlet temperature of the cooling water;

[0009] The control module comprises a basic automation unit and a process automation unit, wherein the basic automation unit performs Kalman filtering processing on the original data of the detection module to remove noise; the process automation unit performs temperature compensation calculation according to the thickness of the iron oxide scale; the process automation unit predicts the temperature of the rolling piece at the outlet of each cooling section based on the finite difference heat conduction model, compares the predicted temperature with the target temperature, calculates the opening of the adjusting valve through the adaptive PID controller, and updates the model parameters according to the real-time feedback of the actual temperature of the detection module;

[0010] The execution module is used to adjust the flow and pressure of the cooling water of the cooling module according to the control signal of the process automation unit,

[0011] The cooling module comprises a pre-precision rolling water cooling section and a post-precision rolling water cooling section.

[0012] Further, the basic automation unit performs Kalman filtering processing on the original data of the detection module, which is represented as:

[0013]

[0014] Wherein, is the temperature estimation value at time k, z k is the measurement value, K k is the Kalman gain, which is dynamically adjusted by the system noise and measurement noise covariance.

[0015] Further, the process automation unit performs temperature compensation calculation according to the thickness of the iron oxide scale, which is represented as:

[0016] T real = T measured + ΔT compensation ;

[0017] Wherein, ΔT compensation=k*delta, k is a compensation coefficient, and delta is the oxide scale thickness.

[0018] Further, the predicted temperature of the rolling piece at the outlet of each cooling section is represented as:

[0019]

[0020] wherein rho is the density, c p is the specific heat capacity, k is the thermal conductivity, q v is the internal heat source term.

[0021] Further, the adaptive PID controller calculates the opening degree of the regulating valve, including setting PID parameters according to the rolling speed v and the target temperature T, and is represented as:

[0022]

[0023] wherein K p0 , K i0 and K d0 are reference parameters, and a, b, c and d are adjustment coefficients.

[0024] Further, the method further comprises: for the water cooling section after the finishing mill, a Smith prediction compensation algorithm is adopted, and is represented as:

[0025]

[0026] wherein G p (s) is a transfer function of the controlled object, and L is a pure lag time.

[0027] Further, the model parameters are updated according to the real-time feedback of the actual temperature by the detection module, including: based on the least square method, the convection heat transfer coefficient alpha is identified online, and is represented as:

[0028]

[0029] wherein q is the heat exchange amount, T s is the surface temperature, T ∞ is the cooling water temperature, is the water flow, and A is the heat exchange area.

[0030] The present application has the following beneficial effects:

[0031] 1、The present application adopts infrared thermometer array combined with multi-sensor fusion technology, cooperates Kalman filter algorithm and iron oxide scale thickness compensation mechanism, effectively eliminates the influence of interference factors such as water vapor and surface oxide layer on temperature measurement accuracy in high temperature environment, realizes real-time and stable monitoring of the surface temperature of the rolled piece, and provides reliable data support for subsequent control decision. In addition, through the adaptive PID algorithm, combined with the finite difference heat conduction model and the Smith prediction compensation strategy, not only the control delay problem caused by the large lag characteristic of the cooling process is solved, but also the control parameters can be dynamically adjusted according to the rolling speed, steel characteristics and other dynamic conditions, so that the temperature regulation is more in line with the actual working condition, and the phenomenon of undercooling or insufficient cooling in the traditional control mode is fundamentally avoided, and the uniformity and stability of the rolled piece performance are ensured.

[0032] 2、The present application further expands the adaptability and economy of temperature control, adopts pre-precise rolling water cooling section and post-precise rolling water cooling section, can flexibly adapt to the cooling demand of rolled pieces of different specifications, cooperates with the quick size changing mechanism, greatly shortens the changeover debugging time, and improves the flexibility level of the production line. In addition, based on the heat exchange coefficient self-learning module, the system can dynamically optimize the cooling water flow and pressure parameters under the premise of ensuring the cooling effect, reduce the energy and water resource consumption, and meet the green and low-carbon development trend of the steel industry. This technical scheme integrating precise control, adaptive adjustment and energy saving and efficiency not only provides a more reliable temperature control means for the rolling process, but also provides a strong support for steel enterprises to improve product quality and reduce production cost. BRIEF DESCRIPTION OF DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used 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.

[0034] Figure 1 It is a principle block diagram of a rolled piece temperature control system based on a rolling process according to an embodiment of the present application. DETAILED DESCRIPTION

[0035] 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. All other embodiments obtained by those skilled in the art based on the embodiments in the present application belong to the scope of protection of the present application.

[0036] According to an embodiment of the present application, a rolled piece temperature control system based on a rolling process is provided.

[0037] As Figure 1 shown in the figure, the rolling piece temperature control system based on the rolling process according to the embodiment of the application comprises a detection module, a control module, an execution module and a cooling module, wherein;

[0038] The detection module comprises an infrared thermometer array, an ultrasonic thickness gauge, an electromagnetic flowmeter, a pressure transmitter and a thermal resistance thermometer, wherein the infrared thermometer array is arranged before and after the cooling section to respectively collect the initial temperature before entering the cooling system as the input parameter of the feedforward control and measure the actual temperature after the rolling piece is cooled as the basis of the feedback control; the ultrasonic thickness gauge is used to detect the thickness of the iron oxide scale on the surface of the rolling piece in real time for temperature compensation calculation; the electromagnetic flowmeter and the pressure transmitter are respectively used to collect the flow and pressure of the cooling water pipeline; and the thermal resistance thermometer is used to monitor the inlet temperature of the cooling water.

[0039] In the application, the infrared thermometer array can adopt the model Raytek Marathon MR1S, the response time is ≤10 ms, the measurement accuracy is ±1℃, is arranged before and after the cooling section, each thermometer is provided with an independent blowing device, the compressed air pressure is 0.4-0.6 MPa, and the water vapor interference is eliminated; the ultrasonic thickness gauge adopts the model Panametrics 35DLPLUS, detects the thickness of the iron oxide scale on the surface of the rolling piece in real time, the accuracy is ±0.05 mm, and is used for temperature compensation calculation; the electromagnetic flowmeter adopts the model Rosemount 8700W, and the pressure transmitter adopts the model Rosemount 3051, the flow of the cooling water pipeline is collected, the accuracy is ±0.5%, the pressure is collected, and the accuracy is ±0.075%; and the thermal resistance thermometer adopts PT100, the accuracy is ±0.1℃, and the inlet temperature of the cooling water is monitored.

[0040] The control module comprises a basic automation unit and a process automation unit, wherein the basic automation unit performs Kalman filtering processing on the original data of the detection module to remove noise, and is expressed as:

[0041]

[0042] Wherein, T is the temperature estimation value at the k moment, z k is the measurement value, K k is the Kalman gain, which is dynamically adjusted through the system noise and the measurement noise covariance;

[0043] The process automation unit performs temperature compensation calculation according to the thickness of the iron oxide scale, and is expressed as:

[0044] T real = T measured + ΔT compensation ;

[0045] Wherein, ΔTcompensation =k·δ, k is a compensation coefficient, taking a value of 0.8-1.2, and δ is the thickness of the oxide scale;

[0046] The process automation unit predicts the temperature of the rolled piece at the outlet of each cooling section based on a finite difference heat conduction model, and is expressed as:

[0047]

[0048] wherein ρ is the density, c p is the specific heat capacity, k is the thermal conductivity, and q v is the internal heat source term;

[0049] And by comparing the predicted temperature with the target temperature, the opening of the regulating valve is calculated by an adaptive PID controller, including setting the PID parameters according to the rolling speed v and the target temperature T, and is expressed as:

[0050]

[0051] wherein K p0 , K i0 and K d0 are the reference parameters, and a, b, c, d are the adjustment coefficients.

[0052] wherein for the water cooling section after the finishing mill with large hysteresis, a Smith prediction compensation algorithm is adopted, and is expressed as:

[0053]

[0054] wherein G p (s) is the transfer function of the controlled object, and L is the pure lag time;

[0055] And according to the real-time feedback of the actual temperature by the detection module, and the process automation unit compares the actual value with the predicted value, if the deviation exceeds the threshold, the heat transfer coefficient learning module is triggered to update the model parameters, including: online identification of the convective heat transfer coefficient α based on the least square method, and is expressed as:

[0056]

[0057] wherein q is the heat transfer amount, T s is the surface temperature, T ∞ is the cooling water temperature, is the water flow, and A is the heat transfer area.

[0058] The execution module is configured to adjust the cooling water flow and pressure of the cooling module according to the control signal of the basic automation unit.

[0059] Specifically, the execution module can adopt hierarchical regulation, including: the main loop adopts a DN150 pneumatic diaphragm regulating valve, and the flow regulation range is 5-150m3 / h, precision ±0.5%; branch adopts DN80 electric switch valve, response time ≤1 second, realizes quick start and stop.

[0060] The cooling module comprises a pre-roughing water cooling section and a roughing mill post water cooling section, each section being equipped with an independent water tank and a guide groove system.

[0061] The technical scheme can adopt three-stage gradient cooling in application, and the total effective cooling length is 42 meters, and the specific configuration is as follows:

[0062] The pre-roughing water cooling section adopts two integral water tanks (1#, 2#), which are shared by A / B lines, and the maximum cooling temperature drop of a single channel is 150 DEG C, and the design parameters are as follows: water tank length 3.5 meters, inner diameter Φ300mm; nozzle type fan-shaped nozzle, spray angle 65 DEG, flow density 80L / (m 2 ·s); the cooling mode is counter-flow cooling, and the cooling water and the rolled piece flow in opposite directions.

[0063] The roughing mill post water cooling section comprises a roughing mill I post water cooling section and a roughing mill II post water cooling section.

[0064] The roughing mill I post water cooling section adopts six split water tanks (3#-5#A / B), the maximum cooling temperature drop of a single channel is 120 DEG C, and the design parameters are as follows: water tank length 3.0 meters, inner diameter Φ280mm; nozzle type split ring nozzle, realizing 360 DEG uniform cooling; temperature control strategy adopts "strong cooling-slow cooling" mode, fast cooling in the first half and slow cooling in the second half, preventing surface quenching.

[0065] The roughing mill II post water cooling section adopts six split water tanks (6#-8#A / B), the maximum cooling temperature drop of a single channel is 120 DEG C, and the design parameters are as follows: water tank length 3.0 meters, inner diameter Φ260mm; nozzle type combined nozzle (atomization+columnar flow), which automatically switches according to temperature gradient; temperature control strategy is based on phase transition kinetics model, and the cooling rate is controlled to be 5-15 DEG C / s in the martensite transformation zone (400-550 DEG C).

[0066] In addition, the guide groove system can adopt an upper and lower split structure, and the material is Cr13Mo. The upper guide groove has an opening rate of 35%, and the lower guide groove has an opening rate of 25%, forming asymmetric cooling; the water beam structure internally circulates cooling water (water temperature ≤35 DEG C), reducing guide groove thermal deformation; the inclined iron is fixed in a manner, and the cross beam can rotate by 90 DEG, realizing quick gauge change (≤5 minutes).

[0067] With the above technical scheme, in the implementation process, taking Q345B steel (Φ20mm) rolling as an example, the specific parameters are as follows:

[0068] Rolling process parameters: rough rolling outlet temperature: 1050±10 DEG C, finish rolling inlet temperature: 950±5 DEG C, final rolling temperature: 850±5 DEG C, cold bed inlet temperature: 650±5 DEG C;

[0069] Cooling system parameters: pre-finish rolling water cooling section: flow 60 m 3 / h, pressure 0.6 MPa, after finish rolling machine I water cooling section: flow 80 m 3 / h, pressure 0.7 MPa, after finish rolling machine II water cooling section: flow 50 m 3 / h, pressure 0.6 MPa;

[0070] Control effect: temperature control precision: ±4.2 DEG C, performance dispersion: sigma b=12 MPa, sigma s=8 MPa, ton steel cooling water consumption: 1.8 m 3 / t (traditional process 2.2 m 3 / t), change specification time: 4 minutes (traditional process 25 minutes).

[0071] In summary, by means of the above technical scheme of the present application, the following effects can be achieved:

[0072] 1、The present application adopts infrared thermometer array combined with multi-sensor fusion technology, cooperates Kalman filter algorithm and iron oxide scale thickness compensation mechanism, effectively eliminates the influence of interference factors such as water vapor and surface oxide layer on temperature measurement accuracy in high temperature environment, realizes real-time and stable monitoring of the surface temperature of the rolled piece, and provides reliable data support for subsequent control decision. In addition, through the adaptive PID algorithm, combined with the finite difference heat conduction model and the Smith prediction compensation strategy, not only the control delay problem caused by the large lag characteristic of the cooling process is solved, but also the control parameters can be dynamically adjusted according to the rolling speed, steel characteristics and other dynamic conditions, so that the temperature regulation is more in line with the actual working condition, and the phenomenon of undercooling or insufficient cooling in the traditional control mode is fundamentally avoided, and the uniformity and stability of the rolled piece performance are ensured.

[0073] 2、The present application further expands the adaptability and economy of temperature control, adopts pre-finish rolling water cooling section and finish rolling machine water cooling section, can flexibly adapt to the cooling demand of rolled pieces of different specifications, cooperates with the quick specification changing mechanism, greatly shortens the changeover debugging time, and improves the flexibility level of the production line. In addition, based on the heat transfer coefficient self-learning module, the system can dynamically optimize the cooling water flow and pressure parameters under the premise of ensuring the cooling effect, reduce the energy and water resource consumption, and meet the green and low-carbon development trend of the steel industry. This technical scheme integrating precise control, adaptive adjustment and energy saving and efficiency provides a more reliable temperature control means for rolling process, and provides a powerful support for steel enterprises to improve product quality and reduce production cost.

[0074] The above descriptions are only the preferred embodiment of the application, not intended to limit the application. Based on the disclosure of the specification and embodiments, other embodiments of the disclosure will be easily conceived by those skilled in the art. The application is intended to cover any variations, uses, or adaptive changes of the disclosure that follow the general principles of the disclosure and include common knowledge or conventional technical means in the technical field not disclosed by the disclosure. The specification and embodiments are only considered as exemplary, and the true scope and spirit of the disclosure are indicated by the claims.

[0075] It should be understood that the present disclosure is not limited to the precise structures as herein described and illustrated in the drawings, and that various modifications and changes can be made without departing from its scope. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A system for controlling the temperature of a rolled piece based on a rolling process, characterized in that, The application relates to a cooling system for a rolling mill, which comprises a detection module, a control module, an execution module and a cooling module. The detection module comprises an infrared temperature detector array, an ultrasonic thickness gauge, an electromagnetic flowmeter, a pressure transmitter and a thermal resistance thermometer, wherein the infrared temperature detector array is arranged before and after a cooling section to collect initial temperatures before entering the cooling system as input parameters of feedforward control and to measure actual temperatures after the rolling piece is cooled as the basis of feedback control; the ultrasonic thickness gauge is used for real-time detection of the thickness of the iron oxide scale on the surface of the rolling piece and is used for temperature compensation calculation; the electromagnetic flowmeter and the pressure transmitter are respectively used for collecting the flow and pressure of the cooling water pipeline; and the thermal resistance thermometer is used for monitoring the inlet temperature of the cooling water. The control module comprises a basic automation unit and a process automation unit, wherein the basic automation unit performs Kalman filtering processing on the original data of the detection module to remove noise; the process automation unit performs temperature compensation calculation according to the thickness of the iron oxide scale; the process automation unit predicts the temperature of the rolling piece at the outlet of each cooling section based on a finite difference heat conduction model, compares the predicted temperature with a target temperature, calculates the opening degree of an adjusting valve through an adaptive PID controller and updates the model parameters according to the real-time feedback of the actual temperature of the detection module. The execution module is used for adjusting the flow and pressure of the cooling water of the cooling module according to the control signal of the process automation unit. The cooling module comprises a pre-rough rolling water cooling section and a post-rough rolling water cooling section. The basic automation unit performs Kalman filtering processing on the original data of the detection module, which is expressed as follows:

2. The strip temperature control system based on a steel rolling process according to claim 1, characterized in that, The process automation unit performs temperature compensation calculation according to the thickness of the iron oxide scale, which is expressed as follows: wherein, is the temperature estimate at time k, z k is the measurement, K k is the Kalman gain, dynamically adjusted by the system noise and measurement noise covariances.

3. A rolled stock temperature control system based on a steel rolling process according to claim 2, characterized in that, The temperature of the rolling piece at the outlet of each cooling section is predicted, which is expressed as follows: T real = T measured + ΔT compensation ; where ΔT compensation = k - δ, k is a compensation factor and δ is the oxide scale thickness.

4. A rolled stock temperature control system based on a steel rolling process according to claim 3, characterized in that, The adaptive PID controller calculates the opening degree of the adjusting valve, which comprises setting PID parameters according to the rolling speed v and the target temperature T, and is expressed as follows: where p is the density, c p is the specific heat capacity, k is the thermal conductivity, q v is the internal heat source term.

5. A rolled stock temperature control system based on a steel rolling process according to claim 4, characterized in that, The application further comprises the following: wherein K p0 , K i0 , and K d0 are reference parameters, and a, b, c, d are adjustment coefficients.

6. A rolled stock temperature control system based on a steel rolling process according to claim 5, characterized in that, For the post-rough rolling water cooling section, a Smith prediction compensation algorithm is adopted, which is expressed as follows: The model parameters are updated according to the real-time feedback of the actual temperature of the detection module, which comprises online identification of the convection heat transfer coefficient alpha based on the least square method, and is expressed as follows: where G p (s) is the plant transfer function and L is the pure time delay.

7. The steel rolling process based workpiece temperature control system of claim 1, wherein, ​ where q is the heat exchange amount, T s is the surface temperature, T ∞ is the cooling water temperature, is the water flow rate, and A is the heat exchange area.

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