A gradient temperature control method, system and computer equipment for a stainless steel cold rolling and annealing process

Abstract

The present application relates to the technical field of metal material heat treatment, and particularly relates to a gradient temperature control method for a stainless steel cold rolling annealing process. The method comprises the following steps: dividing an annealing furnace into independent temperature control sections along the transmission direction of the steel strip, configuring each temperature control section with an electric heating wire group, a forced convection cooling air duct and a high-precision infrared temperature measurement array, collecting temperature data in real time and generating a target gradient temperature curve; constructing a phase change power-temperature correlation model to output a theoretical gradient temperature curve; using three-level MPC collaborative control to output control signals; adjusting the heating power and the cooling intensity in real time according to the control signals, and if the temperature fluctuation is out of limit, compensating the power and adjusting the speed of the steel strip through a PID algorithm to form a closed-loop feedback. Through multi-section independent temperature control, thickness-temperature dynamic modeling and three-level collaborative control architecture, the present application realizes accurate tracking of the gradient temperature curve and improves the quality and efficiency of the annealing process.

Description

Technical Field

[0001] This invention relates to the field of heat treatment technology for metallic materials, and specifically to a gradient temperature control method for the cold rolling annealing process of stainless steel. Background Technology

[0002] In the field of stainless steel cold rolling, annealing is one of the core processes that determines the mechanical properties, microstructure, and surface quality of the strip. Its core objective is to eliminate work hardening generated during cold rolling by precisely controlling the temperature field distribution, while simultaneously promoting uniform grain growth to form austenitic or ferrite structures that meet the requirements.

[0003] Traditional annealing furnaces typically employ a zoned isothermal control mode, dividing the furnace body into fixed functional zones such as heating, holding, and cooling sections, with each zone set to a constant temperature. This control method struggles to adapt to the dynamic temperature gradient requirements of stainless steel during phase transformation. For instance, in the annealing of martensitic stainless steel, the critical phase transformation range (e.g., 780-850℃) requires a precise temperature gradient to control the precipitation rate and distribution of carbides. Traditional isothermal modes can lead to uncontrolled phase transformation kinetics, resulting in defects such as coarse grains and residual stress concentration. Furthermore, for strips with significant thickness variations (e.g., 0.1mm ultrathin strip versus 5mm thick plate), the heat transfer efficiency within the same isothermal zone differs considerably, potentially causing overheating in thin strips or insufficient annealing in thick strips.

[0004] Patent CN111354839B describes a heating control method and annealing furnace for an annealing furnace. While it employs a staged heating system using primary and auxiliary heat sources, it lacks a dynamic temperature model based on material properties such as steel grade and thickness. This results in fixed process parameters, failing to adapt to the phase transformation requirements of different materials. Relying on preset heating steps without real-time temperature distribution monitoring and feedback adjustment mechanisms makes it difficult to eliminate localized temperature anomalies. Patent CN119800056A describes a temperature control method and annealing furnace temperature control system. Although it incorporates strip speed feedback to adjust furnace temperature, it relies solely on a PID controller for global power regulation, lacking a high-frequency correction mechanism for localized temperature anomalies, leading to insufficient response speed. The control model does not incorporate material phase transformation kinetics, causing a disconnect between theoretical temperature curves and actual process requirements, making deviations in the phase transformation process more likely. While it divides the process into multiple segments, it fails to propose a compensation method for heat conduction interference between adjacent temperature zones, making it difficult to guarantee temperature field uniformity. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a gradient temperature control method, system, and computer equipment for the cold rolling annealing process of stainless steel. This invention achieves precise tracking of the gradient temperature curve through multi-segment independent temperature control, thickness-temperature dynamic modeling, and a three-level collaborative control architecture, thereby improving the quality and efficiency of the annealing process.

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

[0007] One objective of this invention is to provide a gradient temperature control method for the cold rolling annealing process of stainless steel, comprising:

[0008] The annealing furnace is divided into several independent temperature control zones along the strip transport direction. Each temperature zone is independently equipped with an electric heating wire group and a forced convection cooling air duct. A high-precision infrared temperature measurement array is arranged above each temperature zone. By collecting the surface temperature distribution data of the strip in real time, the target gradient temperature curve is plotted.

[0009] Based on the theoretical temperature distribution data of the strip surface in the cold rolling and annealing process of stainless steel, a material phase transformation dynamics-temperature correlation model is established by combining the material phase transformation dynamics principle. The model outputs temperature data that meets the requirements of the heat treatment process of the material and plots the theoretical gradient temperature curve.

[0010] Based on a comparison between the target gradient temperature curve and the theoretical gradient temperature curve, a three-level collaborative control architecture consisting of a main MPC loop, a secondary MPC loop, and a fine-tuning MPC loop is adopted for temperature regulation, and a control signal is output; that is:

[0011] The main MPC loop takes the deviation between the target gradient temperature curve and the theoretical gradient temperature curve as input and outputs the basic adjustment signal.

[0012] The secondary MPC ring handles heat conduction interference and outputs compensation adjustment amount based on the temperature field coupling relationship between adjacent temperature ranges;

[0013] The fine-tuning MPC loop performs high-frequency PWM modulation on local anomalies detected in the temperature field, and the control parameters of each MPC loop are dynamically adjusted according to the strip running speed to achieve accurate tracking of the gradient temperature curve.

[0014] Based on the control signal output by MPC, the heating temperature of the electric heating wire group and the cold zone temperature of the forced convection cooling duct are dynamically adjusted to change the target gradient temperature according to the theoretical gradient temperature curve.

[0015] The system monitors the execution effect in real time. When a temperature fluctuation is detected to exceed the allowable range, power compensation and speed adjustment are executed sequentially to form a closed-loop control.

[0016] Based on further improvements to this method, the independent temperature control zone is divided into 8-12 zones, including:

[0017] The initial heating section, the critical phase change section, and the slow cooling section each account for 30%-40%, 40%-50%, and 20%-30% of the total length, respectively.

[0018] The initial heating zone is equipped with a high-power-density nickel-chromium alloy resistance wire (Cr20Ni80) electric heating wire assembly, which is arranged in an alternating or spiral winding pattern and is equipped with an overload protection device.

[0019] The electric heating wire assembly in the critical phase change zone works in conjunction with the forced convection cooling duct to maintain temperature fluctuations ≤ ±2℃.

[0020] The slow cooling section achieves a linear cooling mode through a forced convection cooling duct, and the cooling rate is dynamically adjusted by the strip thickness and the material phase change activation energy.

[0021] Based on further improvements to this method, the parameters of the material phase transformation kinetics include austenitizing temperature, critical cooling rate, and phase transformation activation energy.

[0022] The austenitizing temperature is usually determined by differential scanning calorimetry, the critical cooling rate is determined by quenching experiments, and the phase transformation activation energy is calculated using the following formula:

[0023] Q = R·T′·ln(k0 / k);

[0024] Where Q represents the phase transition activation energy, R represents the gas constant, T′ represents the absolute temperature, k0 represents the pre-exponential factor, and k represents the phase transition rate constant.

[0025] Based on further improvements to this method, the material phase transition kinetic-temperature correlation model is expressed as:

[0026] T(t) = T0 + ΔT·[1-e -k(T)·t ];

[0027] k(T) = k0·e -Q / (RT′) ;

[0028] Where T(t) represents the change of strip surface temperature with time, T0 represents the initial temperature, usually room temperature, ΔT represents the difference between the target temperature and the initial temperature, k(T) represents the phase transition rate constant, which is related to temperature T, t represents time, k0 represents the pre-exponential factor, Q represents the phase transition activation energy, R represents the gas constant, and T′ represents the absolute temperature.

[0029] Based on a further improvement to this method, the step of outputting the basic adjustment signal from the main MPC ring includes:

[0030] The main MPC loop uses the deviation between the target gradient temperature curve and the theoretical gradient temperature curve as the deviation input. The formula for calculating the deviation input is as follows:

[0031] ΔT i =T 理论i -T 实际i ;

[0032] Among them, T 理论i T represents the theoretical temperature value of the theoretical gradient temperature curve in the i-th temperature zone. 实际i ΔT represents the actual temperature value of the target gradient temperature curve in the i-th temperature zone. i This represents the deviation between the theoretical gradient temperature curve and the target gradient temperature curve in the i-th temperature region;

[0033] The main MPC loop will predict the trend of temperature change in the future based on the current deviation input and historical data;

[0034] The main MPC loop combines the prediction results and optimization objectives to generate a basic adjustment signal.

[0035] Based on further improvements to this method, the secondary MPC ring handles heat conduction interference by including:

[0036] A heat conduction model describing the heat conduction behavior is established in the secondary MPC ring. The heat conduction model is expressed as follows:

[0037]

[0038] Where, q ij Δ is the heat transfer rate, k′ is the thermal conductivity, A is the contact area, ΔT′ is the temperature difference between the two temperature ranges, and Δx is the distance.

[0039] The secondary MPC ring generates compensation adjustment quantities based on the heat conduction model.

[0040] Based on further improvements to this method, the fine-tuning of the MPC ring high-frequency PWM modulation includes:

[0041] The duty cycle of the PWM signal is dynamically adjusted based on the degree of temperature deviation at local anomalies.

[0042] When the temperature deviates beyond the threshold, the duty cycle is increased to enhance the correction capability;

[0043] When the temperature returns to the allowable range, reduce the duty cycle to avoid overshoot.

[0044] Based on a further improvement of this method, the power compensation dynamically adjusts the power of the electric heating wire assembly and the airflow speed of the cooling duct through a PID control algorithm.

[0045] The speed adjustment specifically involves reducing the strip running speed when the temperature is higher than the theoretical value and increasing the strip running speed when the temperature is lower than the theoretical value. The adjustment range is positively correlated with the temperature deviation.

[0046] The closed-loop control also includes a parameter self-optimization step:

[0047] Real-time acquisition of temperature fluctuation data, power compensation, and speed adjustment data; optimization of MPC loop control parameters using machine learning algorithms.

[0048] The second objective of this invention is to provide a gradient temperature control system for the cold rolling annealing process of stainless steel, comprising:

[0049] The zone temperature control module divides the annealing furnace into several independent temperature control sections along the strip steel conveying direction. Each temperature zone is independently equipped with an electric heating wire group and a forced convection cooling air duct. A high-precision infrared temperature measurement array is arranged above each temperature zone. By collecting real-time strip steel surface temperature distribution data, the target gradient temperature curve is plotted.

[0050] The model generation module is based on the theoretical temperature distribution data of the strip surface of stainless steel cold rolling annealing process. It combines the material phase transformation dynamics principle to establish a material phase transformation dynamics-temperature correlation model, outputs temperature data that meets the requirements of the material heat treatment process, and plots the theoretical gradient temperature curve.

[0051] A three-level collaborative control module, based on a comparison between the target gradient temperature curve and the theoretical gradient temperature curve, employs a three-level collaborative control architecture consisting of a main MPC loop, a secondary MPC loop, and a fine-tuning MPC loop to regulate temperature and output control signals; that is:

[0052] The main MPC loop takes the deviation between the target gradient temperature curve and the theoretical gradient temperature curve as input and outputs the basic adjustment signal.

[0053] The secondary MPC ring handles heat conduction interference and outputs compensation adjustment amount based on the temperature field coupling relationship between adjacent temperature ranges;

[0054] The fine-tuning MPC loop performs high-frequency PWM modulation on local anomalies detected in the temperature field, and the control parameters of each MPC loop are dynamically adjusted according to the strip running speed to achieve accurate tracking of the gradient temperature curve.

[0055] The execution module dynamically adjusts the heating temperature of the electric heating wire group and the cold zone temperature of the forced convection cooling duct according to the control signal output by the MPC, and changes the target gradient temperature according to the theoretical gradient temperature curve.

[0056] The closed-loop feedback module monitors the execution effect in real time. When it detects that the temperature fluctuation exceeds the allowable range, it sequentially performs power compensation and speed adjustment to form a closed-loop control.

[0057] A third objective of this invention is to provide a computer device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to achieve the gradient temperature control method for the cold rolling annealing process of stainless steel as described in one objective.

[0058] The beneficial effects of the technical solutions provided in this application include at least the following:

[0059] This invention proposes a gradient temperature control method, system, and computer equipment for the cold rolling annealing process of stainless steel. By dividing the annealing furnace into multiple independent temperature control zones and configuring them with electric heating wire groups, forced convection cooling ducts, and high-precision infrared temperature measurement arrays, a temperature correlation model is established based on the principle of material phase transformation dynamics. A three-level MPC collaborative control architecture is employed to achieve precise tracking of the target gradient temperature curve. This scheme solves the problem that traditional isothermal control modes cannot adapt to the dynamic requirements of phase transformation, improves the uniformity of the temperature field and the stability of the annealing process, significantly improves the mechanical properties, microstructure, and surface quality of the strip steel, and simultaneously reduces energy consumption and production costs, providing an efficient and intelligent solution for the cold rolling annealing process of stainless steel. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the overall process of gradient temperature control method for stainless steel cold rolling annealing;

[0061] Figure 2 S100 flowchart of a gradient temperature control method for a stainless steel cold rolling annealing process;

[0062] Figure 3 S200 flowchart of a gradient temperature control method for a cold rolling annealing process of stainless steel;

[0063] Figure 4 This is a flowchart of step S300 for a gradient temperature control method in a stainless steel cold rolling annealing process. Detailed Implementation

[0064] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0065] Cold rolling annealing of stainless steel is a crucial process for improving the material's mechanical properties and surface quality. Traditional annealing processes often employ single-temperature range control, which struggles to adapt to the dynamic requirements of different steel grades and thicknesses, leading to uneven temperature distribution, incomplete phase transformation, or excessive energy consumption. While segmented temperature control methods exist in current technologies, the strong coupling of heat conduction within the annealing furnace and frequent localized temperature anomalies make precise tracking of the temperature gradient difficult. Furthermore, existing control models lack dynamic adaptation to material phase transformation kinetics and process parameters, resulting in insufficient process stability. Please refer to [link to relevant documentation]. Figure 1 To address the above problems, this invention provides a gradient temperature control method for a stainless steel cold rolling annealing process, comprising:

[0066] S100: The annealing furnace is divided into several independent temperature control zones along the strip transport direction. Each temperature zone is independently equipped with an electric heating wire group and a forced convection cooling air duct. A high-precision infrared temperature measurement array is arranged above each temperature zone. By collecting the surface temperature distribution data of the strip in real time, the target gradient temperature curve is plotted.

[0067] S200: Based on the theoretical temperature distribution data of the strip surface of stainless steel cold rolling annealing process, a material phase transformation dynamics-temperature correlation model is established by combining the material phase transformation dynamics principle, outputting temperature data that meets the requirements of the heat treatment process of the material, and plotting the theoretical gradient temperature curve.

[0068] S300: Based on the comparison between the target gradient temperature curve and the theoretical gradient temperature curve, a three-level collaborative control architecture consisting of a main MPC loop, a secondary MPC loop, and a fine-tuning MPC loop is used for temperature regulation, and a control signal is output; that is:

[0069] The main MPC loop takes the deviation between the target gradient temperature curve and the theoretical gradient temperature curve as input and outputs the basic adjustment signal.

[0070] The secondary MPC ring handles heat conduction interference and outputs compensation adjustment amount based on the temperature field coupling relationship between adjacent temperature ranges;

[0071] The fine-tuning MPC loop performs high-frequency PWM modulation on local anomalies detected in the temperature field. The control parameters of each MPC loop are dynamically adjusted according to the running speed of the strip steel to achieve accurate tracking of the gradient temperature curve.

[0072] S400: The system dynamically adjusts the heating temperature of the electric heating wire group and the cold zone temperature of the forced convection cooling duct according to the control signal output by the MPC, and changes the target gradient temperature according to the theoretical gradient temperature curve.

[0073] S500: Monitors the execution effect of steps S300 and S400 in real time. When the temperature fluctuation is detected to exceed the allowable range, power compensation and speed adjustment are executed in sequence to form closed-loop control.

[0074] To improve the stability and material properties of the stainless steel cold rolling annealing process, the specific technical solution adopted is as follows:

[0075] In the gradient temperature control method of stainless steel cold rolling annealing process, S100 is the first major step of the whole gradient temperature control method. It involves dividing the annealing furnace into multiple independent temperature control zones and configuring corresponding heating and cooling equipment. At the same time, a high-precision temperature measuring device is arranged to collect the surface temperature distribution data of the strip steel in real time.

[0076] Please refer to Figure 2 It illustrates a flowchart of an exemplary gradient temperature control method S100 for a stainless steel cold rolling annealing process, the contents of which include:

[0077] S110: Based on the thickness, steel type, and annealing process requirements of the stainless steel strip, the annealing furnace is divided into 8-12 independent temperature control zones along the strip transport direction.

[0078] Based on actual production needs and equipment conditions, the annealing furnace needs to be divided into 8-12 independent temperature-controlled sections along the strip transport direction. The length of each section should be determined by comprehensively considering the strip running speed, annealing process requirements, and equipment capacity.

[0079] The specific principles for dividing independent temperature control zones are as follows:

[0080] Initial heating section (first 1 / 3 section): accounting for 30%-40% of the length, used for rapid heating to the phase change initiation temperature;

[0081] Critical phase transformation section (middle 1 / 3 section): accounting for 40%-50% of the length, maintaining temperature stability to complete the austenite-ferrite phase transformation;

[0082] Slow cooling section (last 1 / 3 section): accounting for 20%-30% of the length, residual stress is avoided through gradient cooling.

[0083] S120: Each temperature control zone is independently equipped with an electric heating wire group and a forced convection cooling air duct is arranged. A non-contact high-precision infrared temperature measurement array is arranged above each temperature zone.

[0084] Each temperature zone is equipped with an independent electric heating wire assembly, using nickel-chromium alloy resistance wire (Cr20Ni80) as the heating element.

[0085] In one possible implementation, the electric heating wire assembly meets the following requirements:

[0086] The power of the electric heating wire assembly should be based on the material, thickness, and temperature requirements of the strip steel to meet the heating power required for each section.

[0087] To ensure the uniformity of the temperature field, the electric heating wire assembly should be arranged in an alternating or spiral winding pattern.

[0088] Each electric heating wire assembly should be equipped with an overload protection device to prevent equipment damage or safety accidents caused by unexpected situations.

[0089] In addition to the electric heating wire assembly, forced convection cooling ducts are arranged in each temperature control zone. The main function of the ducts is to quickly reduce the surface temperature of the strip steel when necessary, preventing excessively high temperatures from affecting material properties.

[0090] Arranging a high-precision infrared temperature measurement array above each temperature control zone is an important means of achieving real-time temperature monitoring. A non-contact infrared temperature measurement array with a wavelength range of 8-14μm is arranged above each temperature zone, and a linear scanning mode is used to cover the entire width of the strip steel.

[0091] S130: Based on the collected strip surface temperature distribution data, plot the target gradient temperature curve.

[0092] Based on real-time temperature data, target curves are generated in three intervals: initial heating, critical phase transition, and slow cooling.

[0093] Specifically:

[0094] Initial heating range: The goal of this stage is to rapidly raise the strip temperature from room temperature to a predetermined temperature.

[0095] Critical phase transition range: This stage is under isothermal control, with temperature fluctuations ≤ ±2℃, and the duration is determined by the phase transition characteristics of the material.

[0096] Slow cooling zone: The purpose of this stage is to allow the strip steel to cool slowly to a safe temperature, avoiding problems such as internal stress or cracks. The cooling rate adopts a linear cooling mode.

[0097] In the gradient temperature control method of stainless steel cold rolling annealing process, S200 establishes a thickness-temperature correlation model based on the theoretical temperature distribution data of the strip surface of stainless steel cold rolling annealing process and the principle of material phase transformation kinetics. The input end of the model is the steel grade and strip thickness parameters, and the output end calculates and outputs temperature data that meets the requirements of the heat treatment process of the material, and plots the theoretical gradient temperature curve, which includes three characteristic segments: the initial heating range, the critical phase transformation range, and the slow cooling range.

[0098] Please refer to Figure 3 It illustrates a flowchart of an exemplary gradient temperature control method S200 for a stainless steel cold rolling annealing process, the contents of which include:

[0099] S210: Determine the theoretical temperature distribution data and material phase transformation kinetic parameters during the cold rolling and annealing process of stainless steel.

[0100] Theoretical temperature distribution data refers to the data on the temperature change patterns of strip steel at different stages during the annealing process.

[0101] Material phase transformation kinetic parameters include austenitizing temperature, critical cooling rate, and phase transformation activation energy. These parameters directly affect the kinetic behavior of the phase transformation process.

[0102] In one possible implementation, the austenitizing temperature is typically determined by differential scanning calorimetry (DSC). In DSC determination, the austenitizing temperature corresponds to the onset point of the exothermic peak.

[0103] In one possible implementation, the critical cooling rate is determined by a quenching experiment.

[0104] In one possible implementation, the phase transition activation energy can be calculated using the Arrhenius equation, as shown in the following formula:

[0105] Q = R·T′·ln(k0 / k);

[0106] Where Q represents the phase transition activation energy, R represents the gas constant, T′ represents the absolute temperature, k0 represents the pre-exponential factor, and k represents the phase transition rate constant.

[0107] S220: Construct a material phase transformation dynamic-temperature correlation model based on theoretical temperature distribution data and material phase transformation dynamic parameters.

[0108] The core of the phase transformation kinetic-temperature correlation model is to determine the effect of temperature on the phase transformation process. The model input consists of material phase transformation kinetic parameters, and the output is temperature data that meets the requirements of the heat treatment process. The principle of the material phase transformation kinetic-temperature correlation model is: the higher the temperature, the faster the phase transformation rate. The degree of phase transformation gradually increases over time until an equilibrium state is reached. Temperature differences may exist at different locations on the strip surface, so the uniformity of the spatial temperature field must be considered.

[0109] The material phase transition kinetics-temperature correlation model is expressed as follows:

[0110] T(t) = T0 + ΔT·[1-e -k(T)·t ];

[0111] k(T) = k0·e -Q / (RT′) ;

[0112] Where T(t) represents the change of strip surface temperature with time, T0 represents the initial temperature, usually room temperature, ΔT represents the difference between the target temperature and the initial temperature, k(T) represents the phase transition rate constant, which is related to temperature T, t represents time, k0 represents the pre-exponential factor, Q represents the phase transition activation energy, R represents the gas constant, and T′ represents the absolute temperature.

[0113] S230: Plot the theoretical gradient temperature curve using the material phase transformation dynamics-temperature correlation model.

[0114] Using the material phase transformation kinetic-temperature correlation model established by S220, temperature data meeting the requirements of the heat treatment process were calculated, and a theoretical gradient temperature curve was plotted based on these data. This curve includes three characteristic segments: the initial heating range, the critical phase transformation range, and the slow cooling range.

[0115] The steps for plotting a theoretical gradient temperature curve include:

[0116] S221: Calculate temperature data using a material phase transformation kinetic-temperature correlation model.

[0117] The model input terminal takes the material phase transformation kinetic parameters, namely austenitizing temperature, critical cooling rate, and phase transformation activation energy, and inputs them into the material phase transformation kinetic-temperature correlation model. The model output terminal outputs temperature data that meets the requirements of the heat treatment process.

[0118] S222: Based on the temperature data output from the model, plot the theoretical gradient temperature curve. The shape of the curve should match the actual process requirements, including three stages: the initial heating range, the critical phase transition range, and the slow cooling range.

[0119] In the gradient temperature control method of stainless steel cold rolling annealing process, the S300 step adopts a three-level collaborative control architecture consisting of a main MPC loop, a secondary MPC loop and a fine-tuning MPC loop. By dynamically adjusting the heating and cooling system, the target gradient temperature curve can be accurately tracked.

[0120] Please refer to Figure 4 It illustrates a flowchart of an exemplary gradient temperature control method S300 for a stainless steel cold rolling annealing process, the contents of which include:

[0121] S310: Calculate the deviation between the target gradient temperature curve and the theoretical gradient temperature curve.

[0122] The main MPC loop uses the deviation between the target gradient temperature curve and the theoretical gradient temperature curve as input. It extracts the current target gradient temperature curve from the real-time acquired strip surface temperature distribution data and compares it with the preset theoretical gradient temperature curve. The deviation is calculated mathematically to generate the deviation input. Specifically, the formula for calculating the deviation input is as follows:

[0123] ΔT i =T 理论i -T 实际i ;

[0124] Among them, T 理论i T represents the theoretical temperature value of the theoretical gradient temperature curve in the i-th temperature zone.实际i ΔT represents the actual temperature value of the target gradient temperature curve in the i-th temperature zone. i This represents the deviation between the theoretical gradient temperature curve and the target gradient temperature curve in the i-th temperature zone.

[0125] After calculating the deviation input, a preliminary analysis is performed to determine if the deviation exceeds the allowable range. If the deviation is large, it indicates significant interference in the system or equipment malfunction. In this case, an alarm mechanism should be triggered immediately, and operators should be notified to take appropriate measures. Simultaneously, the deviation input should be decomposed into multiple components, each corresponding to a different temperature zone and control target.

[0126] S320: Main MPC ring outputs basic adjustment signal.

[0127] The core task of the main MPC loop is to output a basic adjustment signal based on the deviation input.

[0128] Specifically, it includes:

[0129] First, the main MPC loop predicts the trend of temperature changes over a future period based on the current deviation input and historical data. In one possible implementation, the prediction model typically employs dynamic matrix control (DMC) or a state-space model.

[0130] Secondly, the main MPC loop combines the prediction results and optimization objectives to generate basic adjustment signals. Optimization objectives include: reducing the deviation between the target gradient temperature curve and the actual temperature curve; reducing energy consumption while meeting process requirements; and ensuring stable system operation and avoiding drastic fluctuations.

[0131] To achieve the above objectives, the main MPC loop utilizes a multi-objective optimization algorithm for temperature regulation and outputs a control signal. For example, the optimal control signal is solved using linear programming or nonlinear programming.

[0132] S330: The secondary MPC ring handles thermal conduction interference.

[0133] The primary function of the secondary MPC ring is to handle the temperature field coupling between adjacent temperature zones and compensate for interference caused by heat conduction. During the cold rolling and annealing process of stainless steel, heat transfer inevitably occurs between adjacent temperature zones, causing the actual temperature curve to deviate from the target curve. Therefore, the secondary MPC ring needs to model and compensate for this coupling effect.

[0134] Specifically, it includes:

[0135] First, a heat conduction model describing the heat conduction behavior is established using the secondary MPC ring. Heat conduction models are typically based on Fourier's law of thermal conductivity, considering the heat transfer process between different temperature zones.

[0136] In one possible implementation, for two adjacent temperature zones i and j, the heat conduction model is expressed as:

[0137]

[0138] Where, q ij Let be the heat transfer rate, k′ be the thermal conductivity, A be the contact area, ΔT′ be the temperature difference between the two temperature ranges, and Δx be the distance. Using the heat conduction model, the secondary MPC loop can accurately estimate the impact of heat transfer on temperature distribution.

[0139] Secondly, the secondary MPC loop generates a compensation adjustment value based on the heat conduction model. The magnitude of the compensation adjustment value depends on the intensity of the heat conduction effect and the requirements of the target gradient temperature curve.

[0140] For example, if a temperature zone is affected by heat transfer from an upstream temperature zone, the secondary MPC loop will appropriately reduce the heating power of that temperature zone to offset the additional heat input. Conversely, if a temperature zone is affected by heat transfer from a downstream temperature zone, the heating power needs to be increased to maintain the target temperature.

[0141] S340: Fine-tuning MPC ring high-frequency PWM modulation.

[0142] The fine-tuning MPC loop is responsible for handling local anomalies detected in the temperature field, ensuring the uniformity of the overall temperature distribution. In actual production, although the main and auxiliary MPC loops can effectively control the overall temperature profile, localized temperature anomalies may still occur due to various reasons (such as equipment aging or changes in the external environment). Therefore, the fine-tuning MPC loop plays a crucial role.

[0143] The fine-tuning MPC ring uses high-frequency pulse width modulation (PWM) technology to quickly correct local anomalies. Specifically, the fine-tuning MPC ring monitors the infrared thermometer array data in each temperature zone in real time, identifying areas where the temperature deviates from the normal range. Once an anomaly is detected, the fine-tuning MPC ring quickly adjusts the operating status of the corresponding electric heating wire group or forced convection cooling duct. For example, if a local area is too hot, the fine-tuning MPC ring will increase the cooling airflow to lower the temperature; if a local area is too cold, it will increase the heating power to raise the temperature.

[0144] To achieve precise control, the fine-tuning MPC loop employs high-frequency PWM modulation technology. For example, when the temperature at the abnormal point deviates significantly, the duty cycle of the PWM signal is increased to provide stronger correction capability; conversely, when the temperature at the abnormal point gradually returns to normal, the duty cycle of the PWM signal can be decreased to avoid over-correction.

[0145] In the gradient temperature control method of stainless steel cold rolling annealing process, the goal of S400 is to dynamically adjust the working state of the electric heating wire group and the forced convection cooling duct according to the control signal output by MPC, so as to ensure that the surface temperature of the strip can change according to the theoretical gradient temperature curve.

[0146] The system analyzes the control signals jointly output by the main MPC loop, the secondary MPC loop, and the fine-tuning MPC loop, and rationally distributes the control signals to each independent temperature control zone. The MPC control signals include basic adjustment signals, compensation adjustment amounts, and high-frequency PWM modulation parameters.

[0147] In one possible implementation, the dynamic adjustment steps include:

[0148] First, the system analyzes the basic adjustment signals of the main MPC loop and provides preliminary correction directions.

[0149] For example, if the actual temperature in a certain temperature control zone is lower than the theoretical value, the base adjustment signal may indicate an increase in the power of the electric heating wire assembly; conversely, it may indicate a decrease in power. Simultaneously, the compensation adjustment of the secondary MPC loop is also taken into consideration to counteract heat conduction interference between adjacent temperature zones. Finally, fine-tuning the high-frequency PWM modulation parameters of the MPC loop focuses on the rapid correction of local anomalies.

[0150] Secondly, after completing signal parsing, the system will reorganize the control signals into operation instructions adapted to different temperature control zones based on the physical location and functional characteristics of each zone.

[0151] For example, in the initial heating phase, since the primary task is to rapidly increase the temperature, the control signal typically favors increasing the power of the heating wire assembly while appropriately reducing the effect of the forced convection cooling duct. In the critical phase transition phase, the control signal focuses on temperature stability and uniformity, requiring the heating wire assembly and cooling duct to work together to maintain a constant temperature level. In the slow cooling phase, the control signal prioritizes achieving a linear cooling mode to avoid stress or cracks within the material due to excessively rapid cooling.

[0152] In the gradient temperature control method of stainless steel cold rolling annealing process, S500 monitors the system's performance in real time and, when temperature fluctuations exceed the allowable range, sequentially performs power compensation, speed adjustment, and parameter self-optimization to ultimately form a closed-loop control to ensure the system's stability and accuracy.

[0153] The system utilizes a non-contact, high-precision infrared temperature measurement array positioned above each temperature-controlled section to collect temperature distribution data on the steel strip surface. By comparing the collected data with the requirements of the theoretical gradient temperature curve, the system analyzes the temperature distribution data to determine whether temperature fluctuations exceed the allowable range and evaluates the effectiveness of the current temperature adjustment.

[0154] In cases where temperature fluctuations exceed the allowable range, power compensation is implemented to quickly restore temperature balance. Power compensation involves dynamically adjusting the operating status of the electric heating wire assembly and the forced convection cooling duct to bring the actual temperature back to near the theoretical value as quickly as possible.

[0155] For example, if the actual temperature in a certain section is lower than the theoretical value, the power of the electric heating wire assembly is increased; conversely, the power is decreased. The calculation of the power compensation amount is based on a PID control algorithm.

[0156] For situations where power compensation cannot completely resolve the issue, the strip running speed is adjusted to mitigate temperature fluctuations and improve system stability.

[0157] For example, if the actual temperature of a certain section is higher than the theoretical value, the strip speed is appropriately reduced and its residence time in that section is extended, thereby slowing down the temperature rise. Conversely, if the actual temperature of a certain section is lower than the theoretical value, the strip speed is appropriately increased and its residence time in that section is shortened, thereby accelerating the temperature rise process.

[0158] The system will aggregate real-time monitoring data, power compensation results, and speed adjustment results to the central controller for unified management and scheduling. Based on the information, the central controller will dynamically adjust the operating status of each temperature control zone and generate new control commands.

[0159] For example, if temperature fluctuations in a certain segment have been effectively controlled, the adjustment range can be appropriately widened to reduce unnecessary intervention. Conversely, if temperature fluctuations in a certain segment are still relatively severe, it is necessary to strengthen control and improve adjustment precision.

[0160] This application also provides a gradient temperature control system for the cold rolling and annealing process of stainless steel.

[0161] The zone temperature control module divides the annealing furnace into several independent temperature control sections along the strip steel conveying direction. Each temperature zone is independently equipped with an electric heating wire group and a forced convection cooling air duct. A high-precision infrared temperature measurement array is arranged above each temperature zone. By collecting real-time strip steel surface temperature distribution data, the target gradient temperature curve is plotted.

[0162] The model generation module is based on the theoretical temperature distribution data of the strip surface of stainless steel cold rolling annealing process. It combines the material phase transformation dynamics principle to establish a material phase transformation dynamics-temperature correlation model, outputs temperature data that meets the requirements of the material heat treatment process, and plots the theoretical gradient temperature curve.

[0163] A three-level collaborative control module, based on a comparison between the target gradient temperature curve and the theoretical gradient temperature curve, employs a three-level collaborative control architecture consisting of a main MPC loop, a secondary MPC loop, and a fine-tuning MPC loop to regulate temperature and output control signals; that is:

[0164] The main MPC loop takes the deviation between the target gradient temperature curve and the theoretical gradient temperature curve as input and outputs the basic adjustment signal.

[0165] The secondary MPC ring handles heat conduction interference and outputs compensation adjustment amount based on the temperature field coupling relationship between adjacent temperature ranges;

[0166] The fine-tuning MPC loop performs high-frequency PWM modulation on local anomalies detected in the temperature field, and the control parameters of each MPC loop are dynamically adjusted according to the strip running speed to achieve accurate tracking of the gradient temperature curve.

[0167] The execution module dynamically adjusts the heating temperature of the electric heating wire group and the cold zone temperature of the forced convection cooling duct according to the control signal output by the MPC, and changes the target gradient temperature according to the theoretical gradient temperature curve.

[0168] The closed-loop feedback module monitors the execution effect in real time. When it detects that the temperature fluctuation exceeds the allowable range, it sequentially performs power compensation and speed adjustment to form a closed-loop control.

[0169] This application also provides a computer device comprising: a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to realize a gradient temperature control method for a stainless steel cold rolling annealing process.

[0170] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0171] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0172] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0173] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features of the invention herein.

[0174] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A gradient temperature control method for a stainless steel cold rolling annealing process, characterized in that, include: The annealing furnace is divided into several independent temperature control zones along the strip transport direction. Each temperature zone is independently equipped with an electric heating wire group and a forced convection cooling air duct. A high-precision infrared temperature measurement array is arranged above each temperature zone. By collecting the surface temperature distribution data of the strip in real time, the target gradient temperature curve is plotted. Based on the theoretical temperature distribution data of the strip surface of stainless steel cold rolling annealing process, a material phase transformation dynamics-temperature correlation model is established by combining the material phase transformation dynamics principle, outputting temperature data that meets the requirements of the heat treatment process of the material, and plotting the theoretical gradient temperature curve. Based on the comparison between the target gradient temperature curve and the theoretical gradient temperature curve, a three-level collaborative control architecture consisting of a main MPC loop, a secondary MPC loop, and a fine-tuning MPC loop is adopted to regulate the temperature and output control signals. Right now: The main MPC loop takes the deviation between the target gradient temperature curve and the theoretical gradient temperature curve as input and outputs the basic adjustment signal. The secondary MPC ring handles heat conduction interference and outputs compensation adjustment amount based on the temperature field coupling relationship between adjacent temperature ranges; The fine-tuning MPC loop performs high-frequency PWM modulation on local anomalies detected in the temperature field, and the control parameters of each MPC loop are dynamically adjusted according to the strip running speed to achieve accurate tracking of the gradient temperature curve. Based on the control signal output by MPC, the heating temperature of the electric heating wire group and the cold zone temperature of the forced convection cooling duct are dynamically adjusted to change the target gradient temperature according to the theoretical gradient temperature curve. The system monitors the execution effect in real time. When a temperature fluctuation is detected to exceed the allowable range, power compensation and speed adjustment are executed sequentially to form a closed-loop control.

2. The gradient temperature control method for a stainless steel cold rolling annealing process according to claim 1, characterized in that, The independent temperature control zone is divided into 8-12 zones, including: The initial heating section, the critical phase change section, and the slow cooling section each account for 30%-40%, 40%-50%, and 20%-30% of the total length, respectively. The initial heating zone is equipped with a high-power-density nickel-chromium alloy resistance wire (Cr20Ni80) electric heating wire assembly, which is arranged in an alternating or spiral winding pattern and is equipped with an overload protection device. The electric heating wire assembly in the critical phase change zone works in conjunction with the forced convection cooling duct to maintain temperature fluctuations ≤ ±2℃. The slow cooling section achieves a linear cooling mode through a forced convection cooling duct, and the cooling rate is dynamically adjusted by the strip thickness and the material phase change activation energy.

3. The gradient temperature control method for stainless steel cold rolling annealing process according to claim 1, characterized in that, The parameters of the material phase transformation kinetics include austenitizing temperature, critical cooling rate, and phase transformation activation energy; The austenitizing temperature is usually determined by differential scanning calorimetry, the critical cooling rate is determined by quenching experiments, and the phase transformation activation energy is calculated using the following formula: Q = R·T′·ln(k0 / k); Where Q represents the phase transition activation energy, R represents the gas constant, T′ represents the absolute temperature, k0 represents the pre-exponential factor, and k represents the phase transition rate constant.

4. The gradient temperature control method for stainless steel cold rolling annealing process according to claim 3, characterized in that, The material phase transition kinetic-temperature correlation model is expressed as follows: T(t)=T0+ΔT·[1-e -k(T)·t ]; k(T)=k0·e -Q / (RT′) ; Where T(t) represents the change of strip surface temperature with time, T0 represents the initial temperature, usually room temperature, ΔT represents the difference between the target temperature and the initial temperature, k(T) represents the phase transition rate constant, which is related to temperature T, t represents time, k0 represents the pre-exponential factor, Q represents the phase transition activation energy, R represents the gas constant, and T′ represents the absolute temperature.

5. The gradient temperature control method for stainless steel cold rolling annealing process according to claim 1, characterized in that, The steps for the main MPC ring to output the basic adjustment signal include: The main MPC loop uses the deviation between the target gradient temperature curve and the theoretical gradient temperature curve as the deviation input. The formula for calculating the deviation input is as follows: ΔT i =T 理论i -T 实际i ; Among them, T 理论i T represents the theoretical temperature value of the theoretical gradient temperature curve in the i-th temperature zone. 实际i ΔT represents the actual temperature value of the target gradient temperature curve in the i-th temperature zone. i This represents the deviation between the theoretical gradient temperature curve and the target gradient temperature curve in the i-th temperature region; The main MPC loop will predict the trend of temperature change in the future based on the current deviation input and historical data; The main MPC loop combines the prediction results and optimization objectives to generate a basic adjustment signal.

6. The gradient temperature control method for a stainless steel cold rolling annealing process according to claim 1, characterized in that, The secondary MPC ring handles heat conduction interference including: A heat conduction model describing the heat conduction behavior is established in the secondary MPC ring. The heat conduction model is expressed as follows: Where, q ij Δ is the heat transfer rate, k′ is the thermal conductivity, A is the contact area, ΔT′ is the temperature difference between the two temperature ranges, and Δx is the distance. The secondary MPC ring generates compensation adjustment quantities based on the heat conduction model.

7. The gradient temperature control method for stainless steel cold rolling annealing process according to claim 1, characterized in that, The fine-tuning MPC ring high-frequency PWM modulation includes: The duty cycle of the PWM signal is dynamically adjusted based on the degree of temperature deviation at local anomalies. When the temperature deviates beyond the threshold, the duty cycle is increased to enhance the correction capability; When the temperature returns to the allowable range, reduce the duty cycle to avoid overshoot.

8. The gradient temperature control method for a stainless steel cold rolling annealing process according to claim 1, characterized in that, The power compensation dynamically adjusts the power of the electric heating wire assembly and the airflow speed of the cooling duct through a PID control algorithm. The speed adjustment specifically involves reducing the strip running speed when the temperature is higher than the theoretical value and increasing the strip running speed when the temperature is lower than the theoretical value. The adjustment range is positively correlated with the temperature deviation. The closed-loop control also includes a parameter self-optimization step: Real-time acquisition of temperature fluctuation data, power compensation, and speed adjustment data; optimization of MPC loop control parameters using machine learning algorithms.

9. A gradient temperature control system for a stainless steel cold rolling annealing process, characterized in that, include: The zone temperature control module divides the annealing furnace into several independent temperature control sections along the strip steel conveying direction. Each temperature zone is independently equipped with an electric heating wire group and a forced convection cooling air duct. A high-precision infrared temperature measurement array is arranged above each temperature zone. By collecting real-time strip steel surface temperature distribution data, the target gradient temperature curve is plotted. The model generation module is based on the theoretical temperature distribution data of the strip surface of stainless steel cold rolling annealing process. It combines the material phase transformation dynamics principle to establish a material phase transformation dynamics-temperature correlation model, outputs temperature data that meets the requirements of the material heat treatment process, and plots the theoretical gradient temperature curve. The three-level collaborative control module, based on the comparison between the target gradient temperature curve and the theoretical gradient temperature curve, uses a three-level collaborative control architecture consisting of a main MPC loop, a secondary MPC loop, and a fine-tuning MPC loop to regulate the temperature and output control signals. Right now: The main MPC loop takes the deviation between the target gradient temperature curve and the theoretical gradient temperature curve as input and outputs the basic adjustment signal. The secondary MPC ring handles heat conduction interference and outputs compensation adjustment amount based on the temperature field coupling relationship between adjacent temperature ranges; The fine-tuning MPC loop performs high-frequency PWM modulation on local anomalies detected in the temperature field, and the control parameters of each MPC loop are dynamically adjusted according to the strip running speed to achieve accurate tracking of the gradient temperature curve. The execution module dynamically adjusts the heating temperature of the electric heating wire group and the cold zone temperature of the forced convection cooling duct according to the control signal output by the MPC, and changes the target gradient temperature according to the theoretical gradient temperature curve. The closed-loop feedback module monitors the execution effect in real time. When it detects that the temperature fluctuation exceeds the allowable range, it sequentially performs power compensation and speed adjustment to form a closed-loop control.

10. A computer device, characterized in that, The computer device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement a gradient temperature control method for a stainless steel cold rolling annealing process as described in any one of claims 1 to 8.

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