Gradient temperature control method and system for stainless steel cold rolling annealing process and computer equipment

Abstract

The invention relates to the technical field of metal material heat treatment, in particular to a gradient temperature control method for a stainless steel cold rolling annealing process. Comprising the steps that an annealing furnace is divided into independent temperature control sections in the strip steel conveying direction, each temperature section is provided with an electric heating wire set, a forced convection cooling air duct and a high-precision infrared temperature measurement array, temperature data are collected in real time, and a target gradient temperature curve is generated; constructing a phase change power-temperature correlation model, and outputting a theoretical gradient temperature curve; a three-stage MPC is adopted to cooperatively control and output a control signal; and the heating power and the cooling strength are adjusted in real time according to the control signal, if the temperature fluctuation exceeds the limit, the power is compensated through a PID algorithm, the strip steel speed is adjusted, and closed-loop feedback is formed. Through multi-section independent temperature control, thickness-temperature dynamic modeling and a three-level cooperative control framework, accurate tracking of a gradient temperature curve is achieved, and the quality and efficiency of the annealing process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heat treatment of metal materials, and in particular to a gradient temperature control method for a stainless steel cold rolling annealing process. Background Art

[0002] In the cold-rolling of stainless steel, annealing is a key process that determines the strip's mechanical properties, microstructure, and surface quality. Its core objective is to precisely control the temperature distribution to eliminate the work hardening caused during cold rolling, while also promoting uniform grain growth and forming the desired austenite or ferrite structure.

[0003] Traditional annealing furnaces usually adopt a zoned constant temperature control mode, that is, the furnace body is divided into fixed functional areas such as heating section, insulation section and cooling section, and the temperature of each area is set to a constant value. This control method is difficult to adapt to the dynamic demand of stainless steel materials for gradient temperature during the phase change process. For example, in the annealing process of martensitic stainless steel, the critical phase change range (such as 780-850℃) requires a precise temperature gradient to control the precipitation rate and distribution of carbides, while the traditional constant temperature mode will cause the phase change kinetic process to be out of control, which is prone to defects such as coarse grains and residual stress concentration. In addition, for strips with large thickness differences (such as 0.1mm ultra-thin strips and 5mm thick plates), the heat conduction efficiency in the same constant temperature section is significantly different, which may lead to problems such as overburning of thin strips or insufficient annealing of thick strips.

[0004] Patent CN111354839B discloses a heating control method and an annealing furnace, which heat the furnace in stages using the original heat source and the auxiliary heat source. However, a dynamic temperature model is not established based on the material properties such as steel type and thickness, resulting in fixed process parameters and an inability to adapt to the phase change requirements of different materials. Relying on preset heating steps, it does not adopt a real-time temperature distribution monitoring and feedback adjustment mechanism, making it difficult to eliminate local temperature anomalies. Patent CN119800056A discloses a temperature control method and an annealing furnace temperature control system, which introduces strip speed feedback to adjust the furnace temperature. However, it only relies on a PID controller for global power regulation, and does not design a high-frequency correction mechanism for local temperature anomalies, resulting in insufficient response speed. The control model does not incorporate the principles of material phase change dynamics, resulting in a disconnect between the theoretical temperature curve and the actual process requirements, and the phase change process is prone to deviations. Although the process is divided into multiple sections, no compensation method for heat conduction interference in adjacent temperature zones is proposed, making it difficult to ensure the uniformity of the temperature field. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this paper proposes a gradient temperature control method, system, and computer device for the stainless steel cold-rolled annealing process. Through multi-zone independent temperature control, thickness-temperature dynamic modeling, and a three-level collaborative control architecture, this method achieves precise tracking of the gradient temperature curve, improving the quality and efficiency of the annealing process.

[0006] The technical solutions of the present invention are as follows:

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

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

[0009] Based on the theoretical temperature distribution data of the strip surface during the cold-rolled annealing process of stainless steel, a material phase change dynamics-temperature correlation model is established in combination with the material phase change dynamics principle. The temperature data that meets the requirements of the material heat treatment process is output, and a theoretical gradient temperature curve is drawn.

[0010] According to the comparison between the target gradient temperature curve and the theoretical gradient temperature curve, a three-level collaborative control architecture consisting of the main MPC loop, the secondary MPC loop and the fine-tuning MPC loop is used to adjust the temperature and output the control signal; namely:

[0011] Among them, 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 loop processes heat conduction interference and outputs compensation adjustment based on the temperature field coupling relationship between adjacent temperature intervals;

[0013] Fine-tuning the MPC loop performs high-frequency PWM modulation on local abnormal points detected in the temperature field. 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] According to 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, and the target gradient temperature is changed according to the theoretical gradient temperature curve;

[0015] The execution effect is monitored in real time. When it is detected that the temperature fluctuation exceeds the allowable range, power compensation and speed adjustment are performed in sequence to form a closed-loop control.

[0016] Based on the further improvement of this method, the independent temperature control section is divided into 8-12 sections, including:

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

[0018] The initial heating section is equipped with a high-power-density nickel-chromium alloy resistance wire (Cr20Ni80) electric heating wire group, which is arranged in a staggered or spirally wound manner and equipped with an overload protection device.

[0019] The electric heating wire group in the critical phase change section works in conjunction with the forced convection cooling air duct to maintain temperature fluctuations ≤±2°C;

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

[0021] Based on the further improvement of this method, the parameters of the material phase transformation dynamics include austenitizing temperature, critical cooling rate, and phase transformation activation energy;

[0022] Among them, 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 by the following formula:

[0023] Q = R·T′·ln(k0 / k);

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

[0025] Based on the further improvement of this method, the material phase change dynamics-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 in strip surface temperature over time, T0 represents the initial temperature, which is usually room temperature, ΔT represents the difference between the target temperature and the initial temperature, k(T) represents the phase change rate constant, which is related to the temperature T, t represents time, k0 represents the pre-exponential factor, Q represents the phase change activation energy, R represents the gas constant, and T′ represents the absolute temperature.

[0029] Based on a further improvement of this method, the step of the main MPC loop outputting the basic adjustment signal 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 calculation formula of the deviation input is as follows:

[0031] ΔT i =T 理论i -T 实际i ;

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

[0033] The main MPC loop predicts the temperature change trend 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 basic regulation signals.

[0035] Based on further improvements of this method, the secondary MPC loop processes heat conduction interference including:

[0036] The secondary MPC loop establishes a heat conduction model that describes the heat conduction behavior. The heat conduction model is expressed as:

[0037]

[0038] Among them, q ij is the heat transfer rate, k′ is the thermal conductivity, A is the contact area, ΔT′ is the temperature difference between the two temperature intervals, and Δx is the distance;

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

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

[0041] Dynamically adjust the PWM signal duty cycle according to the temperature deviation degree of the local abnormal point;

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

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

[0044] Based on the further improvement of this method, the power compensation dynamically adjusts the power of the electric heating wire group and the wind speed of the cooling air duct through the PID control algorithm;

[0045] The speed regulation is specifically as follows: when the temperature is higher than the theoretical value, the running speed of the strip steel is reduced; when the temperature is lower than the theoretical value, the running speed of the strip steel is increased, and the adjustment range is positively correlated with the temperature deviation;

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

[0047] Collect temperature fluctuation data, power compensation and speed adjustment in real time, and optimize MPC loop control parameters through machine learning algorithms.

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

[0049] A zoned temperature control module divides the annealing furnace 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 real-time strip surface temperature distribution data, the target gradient temperature curve is drawn;

[0050] A model generation module, which establishes a material phase change dynamics-temperature correlation model based on theoretical surface temperature distribution data of stainless steel strip during cold rolling and annealing, combined with the material phase change dynamics principle, outputs temperature data that meets the requirements of the material heat treatment process, and draws a theoretical gradient temperature curve;

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

[0052] Among them, 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 loop processes heat conduction interference and outputs compensation adjustment based on the temperature field coupling relationship between adjacent temperature intervals;

[0054] Fine-tuning the MPC loop performs high-frequency PWM modulation on local abnormal points detected in the temperature field. 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] An execution module, which 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 performs power compensation and speed adjustment in sequence to form a closed-loop control.

[0057] A third object of the present 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 implement the gradient temperature control method for a stainless steel cold rolling annealing process as described in one of the objects.

[0058] The beneficial effects brought about by the technical solutions provided in the embodiments of the present application include at least the following beneficial effects:

[0059] This invention proposes a gradient temperature control method, system, and computer equipment for the stainless steel cold-rolled annealing process. By dividing the annealing furnace into multiple independent temperature-controlled sections and configuring them with electric heating wire groups, forced convection cooling ducts, and a high-precision infrared temperature measurement array, a temperature correlation model is established in conjunction with the principles of material phase transition dynamics. A three-level MPC collaborative control architecture is then employed to accurately track the target gradient temperature curve. This solution addresses the difficulty of traditional constant temperature control modes in adapting to the dynamic requirements of phase transitions, improves temperature field uniformity and annealing process stability, significantly improves the mechanical properties, microstructure, and surface quality of the steel strip, and reduces energy consumption and production costs, providing an efficient and intelligent solution for the stainless steel cold-rolled annealing process. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0061] Figure 2 This is a flow chart of step S100 of a gradient temperature control method for a stainless steel cold rolling annealing process;

[0062] Figure 3 This is a flow chart of step S200 of a gradient temperature control method for a stainless steel cold rolling annealing process;

[0063] Figure 4 The figure is a flow chart of the steps S300 of a gradient temperature control method for a stainless steel cold rolling annealing process. DETAILED DESCRIPTION

[0064] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0065] The cold-rolled stainless steel annealing process is a key step in improving the mechanical properties and surface quality of the material. Traditional annealing processes often use a single temperature range control, which is difficult to adapt to the dynamic requirements of different steel grades and thicknesses, resulting in uneven temperature distribution, incomplete phase transformation, or excessive energy consumption. Although there are segmented temperature control methods in existing technologies, due to the strong thermal conductivity coupling within the annealing furnace and frequent local temperature anomalies, it is still difficult to accurately track the gradient temperature. In addition, the existing control model lacks dynamic adaptation to the material phase transformation dynamics and process parameters, resulting in insufficient process stability. Please refer to Figure 1 To solve the above problems, a gradient temperature control method for a stainless steel cold rolling annealing process is provided in one embodiment of the present invention. The method comprises:

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

[0067] S200: Based on the theoretical temperature distribution data of the strip surface during the cold-rolled annealing process of stainless steel, combined with the principles of material phase transition dynamics, a material phase transition dynamics-temperature correlation model is established. The model outputs temperature data that meets the requirements of the material heat treatment process and plots a 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 coordinated control architecture consisting of a main MPC loop, a secondary MPC loop, and a fine-tuning MPC loop is used to adjust the temperature and output a control signal; namely:

[0069] Among them, 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 loop processes heat conduction interference and outputs compensation adjustment based on the temperature field coupling relationship between adjacent temperature intervals;

[0071] The fine-tuning MPC loop performs high-frequency PWM modulation on local abnormal points detected in the temperature field. The control parameters of each MPC loop are dynamically adjusted according to the running speed of the strip 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: Monitor the execution effects of steps S300 and S400 in real time. When it is detected that the temperature fluctuation exceeds the allowable range, perform power compensation and speed adjustment in sequence to form a closed-loop control.

[0074] In order to improve the stability and material properties of the stainless steel cold rolling annealing process, the specific technical solutions adopted are as follows:

[0075] In the gradient temperature control method for the stainless steel cold rolling annealing process, S100 is the first major step of the entire gradient temperature control method. It involves dividing the annealing furnace into multiple independent temperature control sections, configuring corresponding heating and cooling equipment, and arranging high-precision temperature measurement devices to collect real-time surface temperature distribution data of the strip.

[0076] Please refer to Figure 2 , which shows a flow chart of an exemplary gradient temperature control method S100 of a stainless steel cold rolling annealing process of the present application, and its contents include:

[0077] S110: According to the thickness, steel grade and annealing process requirements of the stainless steel strip, the annealing furnace is divided into 8-12 independent temperature control sections along the strip transmission direction.

[0078] Based on actual production needs and equipment conditions, the annealing furnace should be divided into 8-12 independent temperature-controlled sections along the strip conveying direction. The length of each section should be determined based on a comprehensive consideration of 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): 30%-40% in length, used for rapid heating to the phase transition starting temperature;

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

[0082] Slow cooling section (last 1 / 3 section): Length accounts for 20%-30%, and residual stress is avoided through gradient cooling.

[0083] S120: An electric heating wire group is independently configured in each temperature control section, 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 group, using nickel-chromium alloy resistance wire (Cr20Ni80) as the heating element.

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

[0086] The power of the electric heating wire group should meet the heating power required for each section according to the material, thickness and temperature requirements of the strip.

[0087] In order to ensure the uniformity of the temperature field, the electric heating wire group should be arranged in a staggered or spirally wound layout.

[0088] Each electric heating wire group 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 group, each temperature control section is equipped with a forced convection cooling duct. The main function of the duct is to quickly reduce the surface temperature of the steel strip when necessary to prevent the material properties from being affected by excessive temperature.

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

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

[0092] Based on real-time temperature data, the target curve is generated according to the three intervals of initial heating, critical phase change and slow cooling.

[0093] Specifically:

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

[0095] Critical phase change interval: This stage is under constant temperature control, with temperature fluctuation ≤±2℃, and the duration is determined by the phase change characteristics of the material.

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

[0097] In the gradient temperature control method of the stainless steel cold rolling annealing process, S200 establishes a thickness-temperature correlation model based on the theoretical temperature distribution data of the strip surface in the stainless steel cold rolling annealing process and the principle of material phase transformation dynamics. The model input inputs the steel grade and strip thickness parameters, and the model output calculates and outputs temperature data that meets the requirements of the material heat treatment process, and draws a 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 , which shows a flow chart of an exemplary gradient temperature control method S200 of a stainless steel cold rolling annealing process of the present application, and its contents include:

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

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

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

[0102] In one possible embodiment, the austenitization temperature is generally measured by differential scanning calorimetry (DSC). In the DSC measurement, the austenitization temperature corresponds to the onset of the exothermic peak.

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

[0104] In one possible implementation, the phase change activation energy can be calculated using the Arrhenius equation, as follows:

[0105] Q = R·T′·ln(k0 / k);

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

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

[0108] The core of the phase transformation dynamics-temperature correlation model is to determine how temperature affects phase transformation. The model inputs are the material's phase transformation kinetic parameters, and the output is temperature data that meets the requirements of the heat treatment process. The principle of the phase transformation dynamics-temperature correlation model is that the higher the temperature, the faster the phase transformation rate. The degree of phase transformation gradually increases over time until equilibrium is reached. Temperatures may vary at different locations on the strip surface, so the uniformity of the spatial temperature field must be considered.

[0109] The material phase change dynamics-temperature correlation model is expressed as:

[0110] T(t)=T0+ΔT·[1-e -k(T)·t ];

[0111] k(T)=k0·e -Q / (RT′) ;

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

[0113] S230: Draw a theoretical gradient temperature curve using a material phase change dynamics-temperature correlation model.

[0114] Using the material phase change dynamics-temperature correlation model established by S220, we calculated temperature data that met the heat treatment process requirements and used this data to draw a theoretical gradient temperature curve. This curve includes three characteristic segments: the initial heating range, the critical phase change range, and the slow cooling range.

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

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

[0117] The model input feeds the material's phase transformation kinetic parameters—namely, austenitization temperature, critical cooling rate, and phase transformation activation energy—into the material's phase transformation dynamics-temperature correlation model. The model output generates temperature data that meets the heat treatment process requirements.

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

[0119] In the gradient temperature control method for the 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 systems, it achieves accurate tracking of the target gradient temperature curve.

[0120] Please refer to Figure 4 , which shows a flow chart of an exemplary gradient temperature control method S300 of a stainless steel cold rolling annealing process of the present application, and its contents include:

[0121] S310: Calculating 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. The current target gradient temperature curve is extracted from the real-time strip surface temperature distribution data and compared with the preset theoretical gradient temperature curve. The deviation between the two is mathematically calculated to generate the deviation input. Specifically, the deviation input is calculated as follows:

[0123] ΔT i =T 理论i -T 实际i ;

[0124] Among them, T 理论i Indicates the theoretical temperature value of the theoretical gradient temperature curve in the i-th temperature zone, T实际i Indicates the actual temperature value of the target gradient temperature curve in the i-th temperature zone, ΔT i 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, perform a preliminary analysis to determine whether the deviation exceeds the allowable range. If the deviation is large, it indicates significant system interference or equipment failure, triggering an alarm and notifying the operator to take corrective action. Furthermore, the deviation input should be broken down into components corresponding to different temperature zones and control targets.

[0126] S320: The main MPC loop outputs a basic adjustment signal.

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

[0128] Specifically include:

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

[0130] Next, the main MPC loop combines the prediction results with optimization objectives to generate basic control signals. These optimization objectives include: reducing the deviation between the target gradient temperature profile and the actual temperature profile; reducing energy consumption while meeting process requirements; and ensuring smooth system operation and avoiding drastic fluctuations.

[0131] To achieve the above objectives, the main MPC loop uses a multi-objective optimization algorithm to adjust the temperature and output a control signal. For example, the optimal control signal is solved through linear programming or nonlinear programming.

[0132] S330: The secondary MPC loop processes heat conduction interference.

[0133] The primary function of the secondary MPC loop is to address the coupled temperature fields between adjacent temperature zones and compensate for interference caused by heat conduction. During the cold-rolling and annealing process of stainless steel, heat transfer between adjacent temperature zones is inevitable, causing the actual temperature profile to deviate from the target curve. Therefore, the secondary MPC loop needs to model and compensate for this coupling effect.

[0134] Specifically include:

[0135] First, the secondary MPC loop establishes a heat conduction model to describe the heat conduction behavior. Heat conduction models are usually based on Fourier's law of heat conduction and consider 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] Among them, q ij is the heat transfer rate, k′ is the thermal conductivity, A is the contact area, ΔT′ is the temperature difference between the two temperature zones, and Δx is the distance. Using the heat conduction model, the secondary MPC loop can accurately estimate the impact of heat transfer on the temperature distribution.

[0139] Secondly, the secondary MPC loop generates a compensation adjustment based on the heat conduction model. The size of the compensation adjustment 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 zone, the secondary MPC loop will appropriately reduce the heating power to that zone to offset the additional heat input. Conversely, if a temperature zone is affected by heat transfer from a downstream zone, the heating power will need to be increased to maintain the target temperature.

[0141] S340: Fine-tune the high-frequency PWM modulation of the MPC loop.

[0142] The fine-tuning MPC loop is responsible for addressing local anomalies detected in the temperature field and ensuring uniformity across the entire temperature distribution. In actual production, although the primary and secondary MPC loops effectively control the overall temperature curve, localized temperature anomalies may still occur due to various reasons, such as equipment aging and environmental changes. Therefore, the fine-tuning MPC loop plays a crucial role.

[0143] The fine-tuning MPC loop uses high-frequency pulse width modulation (PWM) technology to quickly correct local abnormal points. Specifically, the fine-tuning MPC loop monitors the infrared temperature measurement array data in each temperature zone in real time and identifies areas where the temperature deviates from the normal range. Once an abnormal point is found, the fine-tuning MPC loop will quickly adjust the working status of the corresponding electric heating wire group or forced convection cooling air duct. For example, if the temperature in a local area is too high, the fine-tuning MPC loop will reduce the temperature by increasing the cooling air speed; if the temperature in a local area is too low, the temperature will be increased by increasing the heating power.

[0144] To achieve precise control, the fine-tuning MPC loop uses high-frequency PWM modulation technology. For example, when the temperature at an abnormal point deviates significantly, the PWM signal's duty cycle is increased to provide stronger correction capability. Conversely, as the temperature at the abnormal point gradually returns to normal, the PWM signal's duty cycle is reduced to avoid overcorrection.

[0145] In the gradient temperature control method for the stainless steel cold rolling annealing process, the S400 goal is to dynamically adjust the working status of the electric heating wire group and the forced convection cooling air duct according to the control signal output by the MPC 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 output by the main, secondary, and fine-tuning MPC loops and distributes them to the independent temperature control zones. The MPC control signal includes a base regulation signal, a compensation regulation value, and high-frequency PWM modulation parameters.

[0147] In a possible implementation, the specific steps of dynamic adjustment include:

[0148] First, the system analyzes the basic regulation signal of the main MPC loop and provides preliminary correction direction.

[0149] For example, if the actual temperature in a particular temperature control zone falls below the theoretical value, the base control signal might indicate an increase in the power to the heating element group; otherwise, it might indicate a decrease. Simultaneously, the compensation adjustment of the secondary MPC loop is taken into account to offset thermal conduction interference between adjacent temperature zones. Finally, fine-tuning the high-frequency PWM modulation parameters of the MPC loop focuses on rapidly correcting local anomalies.

[0150] Secondly, after completing the signal analysis, the system will reorganize the control signal into operating instructions suitable for different sections based on the physical location and functional characteristics of each temperature control section.

[0151] For example, in the initial heating section, since the main task is to quickly increase the temperature, the control signal will usually tend to increase the power of the electric heating wire group while appropriately reducing the role of the forced convection cooling duct. In the critical phase change section, the control signal focuses on temperature stability and uniformity, requiring the electric heating wire group and the cooling duct to work together to maintain a constant temperature level. In the slow cooling section, the control signal focuses on achieving a linear cooling mode to avoid stress or cracks in the material due to excessive cooling rate.

[0152] In the gradient temperature control method for the stainless steel cold-rolled annealing process, S500 monitors the system's execution effect in real time. When it detects temperature fluctuations outside the allowable range, it sequentially performs power compensation, speed regulation, and parameter self-optimization, ultimately forming a closed-loop control to ensure system stability and accuracy.

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

[0154] If temperature fluctuations exceed the allowable range, power compensation is performed to quickly restore temperature balance. Power compensation dynamically adjusts the operating status of the electric heating element group and forced convection cooling duct to quickly return the actual temperature to near the theoretical value.

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

[0156] For situations that cannot be fully resolved by power compensation, the strip running speed is adjusted to alleviate temperature fluctuations and improve system stability.

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

[0158] The system aggregates real-time monitoring data, power compensation results, and speed regulation results into a central controller for unified management and scheduling. Based on this information, the central controller dynamically adjusts the operating status of each temperature-controlled zone and generates new control instructions.

[0159] For example, if the temperature fluctuation of a certain section has been effectively controlled, its adjustment range can be appropriately relaxed to reduce unnecessary intervention. On the contrary, if the temperature fluctuation of a certain section is still relatively severe, it is necessary to strengthen the control and improve the adjustment accuracy.

[0160] This application also provides a gradient temperature control system for stainless steel cold rolling annealing process,

[0161] A zoned temperature control module divides the annealing furnace 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 real-time strip surface temperature distribution data, the target gradient temperature curve is drawn;

[0162] A model generation module, which establishes a material phase change dynamics-temperature correlation model based on theoretical surface temperature distribution data of stainless steel strip during cold rolling and annealing, combined with the material phase change dynamics principle, outputs temperature data that meets the requirements of the material heat treatment process, and draws a theoretical gradient temperature curve;

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

[0164] Among them, 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 loop processes heat conduction interference and outputs compensation adjustment based on the temperature field coupling relationship between adjacent temperature intervals;

[0166] Fine-tuning the MPC loop performs high-frequency PWM modulation on local abnormal points detected in the temperature field. 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] An execution module, which 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 performs power compensation and speed adjustment in sequence to form a closed-loop control.

[0169] The present 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, and the at least one instruction, 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.

[0170] The basic principles of the present application have been described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, and effects mentioned in this application are merely illustrative and not restrictive, and it should not be assumed that these advantages, strengths, and effects are required of each embodiment of this application. In addition, the specific details disclosed above are merely illustrative and facilitating understanding, and are not restrictive. The above details do not limit this application to necessarily being implemented using the above specific details.

[0171] The block diagrams of the devices, devices, equipment, 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 will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, or configured in any manner. Words such as "include," "comprise," "have," and the like are open-ended words, meaning "including but not limited to," and can be used interchangeably therewith. The words "or" and "and" used herein refer to the words "and / or" and can be used interchangeably therewith, unless the context clearly indicates otherwise. The word "such as" used herein refers to the phrase "such as but not limited to," and can be used interchangeably therewith.

[0172] It should also be noted that in the apparatus, device, and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

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

[0174] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present 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-controlled sections along the strip conveying direction. Each temperature zone is independently equipped with an electric heating wire group and a forced convection cooling duct. A high-precision infrared temperature measurement array is placed above each temperature zone. By collecting the strip surface temperature distribution data in real time, the target gradient temperature curve is drawn. Based on the theoretical temperature distribution data of the strip surface during the cold-rolled annealing process of stainless steel, a material phase change dynamics-temperature correlation model is established in combination with the material phase change dynamics principle. The temperature data that meets the requirements of the material heat treatment process is output, and a theoretical gradient temperature curve is drawn. According to 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 to adjust the temperature and output a control signal. Right now: Among them, 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 loop processes heat conduction interference and outputs compensation adjustment based on the temperature field coupling relationship between adjacent temperature intervals; Fine-tuning the MPC loop performs high-frequency PWM modulation on local abnormal points detected in the temperature field. 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. According to 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, and the target gradient temperature is changed according to the theoretical gradient temperature curve; The execution effect is monitored in real time. When it is detected that the temperature fluctuation exceeds the allowable range, power compensation and speed adjustment are performed in sequence 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 section is divided into 8-12 sections, including: The initial heating section, critical phase change section and slow cooling section account for 30%-40%, 40%-50% and 20%-30% of the length respectively; The initial heating section is equipped with a high-power-density nickel-chromium alloy resistance wire (Cr20Ni80) electric heating wire group, which is arranged in a staggered or spirally wound manner and equipped with an overload protection device. The electric heating wire group in the critical phase change section works in conjunction with the forced convection cooling air duct to maintain temperature fluctuations ≤±2°C; The slow cooling section realizes a linear cooling mode through a forced convection cooling air duct, and the cooling rate is dynamically adjusted by the thickness of the strip and the activation energy of the material phase change.

3. The gradient temperature control method for a stainless steel cold rolling annealing process according to claim 1, characterized in that: The parameters of the material phase transformation dynamics include austenitizing temperature, critical cooling rate, and phase transformation activation energy; Among them, 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 by the following formula: Q = R·T′·ln(k0 / k); Where Q represents the phase change activation energy, R represents the gas constant, T′ represents the absolute temperature, k0 represents the pre-exponential factor, and k represents the phase change rate constant.

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

5. The gradient temperature control method for a stainless steel cold rolling annealing process according to claim 1, characterized in that: The step of the main MPC loop outputting the basic adjustment signal comprises: The main MPC loop uses the deviation between the target gradient temperature curve and the theoretical gradient temperature curve as the deviation input. The calculation formula of the deviation input is as follows: ΔT i =T 理论i -T 实际i ; Among them, T 理论i Indicates the theoretical temperature value of the theoretical gradient temperature curve in the i-th temperature zone, T 实际i Indicates the actual temperature value of the target gradient temperature curve in the i-th temperature zone, ΔT i Indicates the deviation between the theoretical gradient temperature curve and the target gradient temperature curve in the i-th temperature zone; The main MPC loop predicts the temperature change trend 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 basic regulation signals.

6. The method for controlling the gradient temperature of a stainless steel cold rolling annealing process according to claim 1, wherein: The secondary MPC loop processes heat conduction interference including: The secondary MPC loop establishes a heat conduction model that describes the heat conduction behavior. The heat conduction model is expressed as: Among them, q ij is the heat transfer rate, k′ is the thermal conductivity, A is the contact area, ΔT′ is the temperature difference between the two temperature intervals, and Δx is the distance; The secondary MPC loop generates compensation adjustment variables based on the heat conduction model.

7. The method for controlling the gradient temperature of a stainless steel cold rolling annealing process according to claim 1, wherein: The fine-tuning MPC loop high-frequency PWM modulation includes: Dynamically adjust the PWM signal duty cycle according to the temperature deviation degree of the local abnormal point; When the temperature deviation exceeds the threshold, the duty cycle is increased to enhance the correction capability; When the temperature returns to the allowable range, the duty cycle is reduced to avoid overshoot.

8. The method for controlling the gradient temperature of a stainless steel cold rolling annealing process according to claim 1, wherein: The power compensation dynamically adjusts the power of the electric heating wire group and the wind speed of the cooling air duct through the PID control algorithm; The speed regulation is specifically as follows: when the temperature is higher than the theoretical value, the running speed of the strip steel is reduced; when the temperature is lower than the theoretical value, the running speed of the strip steel is increased, and the adjustment range is positively correlated with the temperature deviation; The closed-loop control also includes a parameter self-optimization step: Collect temperature fluctuation data, power compensation and speed adjustment in real time, and optimize MPC loop control parameters through machine learning algorithms.

9. A gradient temperature control system for a stainless steel cold rolling annealing process, characterized in that: Includes: A zoned temperature control module divides the annealing furnace 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 real-time strip surface temperature distribution data, the target gradient temperature curve is drawn; A model generation module, which establishes a material phase change dynamics-temperature correlation model based on theoretical surface temperature distribution data of stainless steel strip during cold rolling and annealing, combined with the material phase change dynamics principle, outputs temperature data that meets the requirements of the material heat treatment process, and draws a theoretical gradient temperature curve; A three-level collaborative control module, which uses a three-level collaborative control architecture consisting of a main MPC loop, a secondary MPC loop, and a fine-tuning MPC loop to adjust the temperature based on the comparison between the target gradient temperature curve and the theoretical gradient temperature curve, and outputs a control signal; Right now: Among them, 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 loop processes heat conduction interference and outputs compensation adjustment based on the temperature field coupling relationship between adjacent temperature intervals; Fine-tuning the MPC loop performs high-frequency PWM modulation on local abnormal points detected in the temperature field. 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. An execution module, which 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 performs power compensation and speed adjustment in sequence 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, at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the gradient temperature control method for a stainless steel cold rolling annealing process according to any one of claims 1 to 8.

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