Tunnel kiln fuzzy PID temperature control technology with strong anti-interference adaptive capability

Abstract

The invention relates to the technical field of industrial furnace temperature control, and discloses a tunnel furnace fuzzy PID temperature control process with strong anti-interference adaptive ability, which comprises the following steps: temperature signal acquisition: acquiring actual temperature values of each area in real time through temperature sensors arranged in different heating areas of a tunnel furnace, and calculating temperature signals of the actual temperature values; the collected temperature signal is converted into a digital signal to be transmitted to the temperature controller; and interference signal identification and classification: calling a preset interference identification model by the temperature controller, and carrying out fluctuation characteristic analysis on the acquired temperature signal. By adopting a dual-redundancy temperature acquisition mode combining distributed optical fibers and K-type thermocouples and combining an interference identification model based on an LSTM neural network, accurate identification and classification of interferences such as feed quantity fluctuation, power supply fluctuation and airflow disturbance in a kiln are realized, and through dynamic correction of fuzzy PID (Proportion Integration Differentiation) parameters and an interference compensation mechanism, the disturbance of the kiln can be accurately identified and classified. And the anti-interference capability of the tunnel kiln temperature control system is obviously improved.

Description

Technical Field

[0001] This invention relates to the field of industrial furnace temperature control technology, specifically a fuzzy PID temperature control process for tunnel kilns with strong anti-disturbance and adaptive capabilities. Background Technology

[0002] Tunnel kilns, as key equipment for continuous heat treatment of materials in industries such as ceramics, metallurgy, and building materials, directly affect the physical properties, chemical composition, and finished product qualification rate of products due to the accuracy of their temperature control. Therefore, stringent requirements are placed on the stability, anti-interference capability, and adaptive adjustment performance of the temperature control system. Currently, the temperature control process for tunnel kilns mainly employs traditional PID control and conventional fuzzy PID control technologies. Traditional PID control is widely used due to its simple structure and ease of implementation. However, relying on fixed proportional, integral, and derivative parameters, it struggles to quickly respond to and correct temperature deviations when faced with unavoidable fluctuations in feed rate, power supply pressure, and airflow disturbances during kiln operation. This often results in problems such as large overshoot, long settling time, and significant temperature fluctuations, especially in high-temperature critical areas. The temperature fluctuation can even reach ±10℃ or more, seriously affecting the uniformity of material heat treatment. To improve the shortcomings of traditional PID, conventional fuzzy PID control introduces fuzzy inference rules to dynamically adjust PID parameters, which improves the system's adaptability to a certain extent. However, the rule base of existing fuzzy PID control is mostly based on experience and is fixed in the long term. It cannot be updated in real time according to the changes in the type and intensity of interference and the kiln process stage (heating, holding, cooling) during actual operation, resulting in limited targeted adjustment capability for specific interferences. At the same time, the temperature acquisition of existing temperature control systems mostly uses a single type of sensor (such as thermocouples only), which is susceptible to electromagnetic interference, high temperature aging, etc., resulting in measurement errors. Moreover, it lacks a precise identification and classification mechanism for interference signals, making it difficult to distinguish the characteristics of different interferences and adopt differentiated compensation strategies.

[0003] Furthermore, most systems lack effective feedback optimization and self-learning mechanisms, requiring frequent manual parameter adjustments during long-term operation. This not only increases operating costs but also makes it difficult to continuously improve temperature control accuracy and energy-saving effects. In summary, existing tunnel kiln temperature control processes still have significant shortcomings in terms of resistance to complex interference, parameter adaptive adjustment accuracy, multi-scenario adaptability, and intelligence level. There is an urgent need for a temperature control process that can accurately identify interference, dynamically optimize control parameters, adapt to different process stages, and possess self-learning capabilities to meet the high precision and high stability requirements of industrial production for tunnel kiln temperature control. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a fuzzy PID temperature control process for tunnel kilns with strong anti-disturbance and adaptive capabilities, thus solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a fuzzy PID temperature control process for tunnel kilns with strong anti-disturbance and adaptive capabilities, comprising the following steps:

[0006] Temperature signal acquisition: By arranging multiple temperature sensors in different heating zones of the tunnel kiln, the actual temperature values ​​of each zone are collected in real time, and the collected temperature signals are converted into digital signals and transmitted to the temperature controller.

[0007] Interference signal identification and classification: The temperature controller calls the preset interference identification model to analyze the fluctuation characteristics of the collected temperature signal, and performs comprehensive analysis on multiple temperature sensors in the same area to identify the types of interference in the kiln operation. The interference types include feed rate fluctuation interference, power supply pressure fluctuation interference, and kiln airflow disturbance interference.

[0008] Fuzzy PID parameter adaptive adjustment: Based on the identified interference type and the deviation and rate of change between the actual temperature and the set temperature, the temperature controller triggers the fuzzy inference module to dynamically correct the proportional coefficient, integral time constant and derivative time constant of the PID controller; the rule base of the fuzzy inference module is built based on the historical optimization data of temperature control under different interference scenarios of the kiln, and the rule base is updated in real time with the actual operation data.

[0009] Actuator control: The PID controller transmits the corrected control signal to the corresponding heating actuator of the kiln to adjust the oxygen supply rate or fuel supply, set multiple threshold levels, and perform multi-level adjustment to achieve precise temperature control in each zone; at the same time, the temperature controller receives feedback signals from the actuator in real time, judges the control effect, and continuously optimizes the fuzzy PID parameters.

[0010] The above technical solution acquires accurate real-time temperature data for each heating zone through temperature signal acquisition, providing reliable input for subsequent control. Interference signal identification and classification clarifies the specific types of interference during kiln operation (feed fluctuations, power supply pressure fluctuations, and airflow disturbances within the kiln), providing a basis for targeted adjustments. Furthermore, fuzzy PID parameter adaptive adjustment dynamically corrects the core PID parameters based on the interference type and temperature deviation, and relies on a real-time updated rule base to ensure adjustment adaptability. Ultimately, this achieves accurate response to interference and optimization of control parameters, laying a crucial foundation for stable kiln temperature control.

[0011] Preferably, the temperature sensor adopts a dual-redundant acquisition method combining a distributed fiber optic temperature sensor and a K-type thermocouple sensor. The distributed fiber optic temperature sensor has three monitoring points arranged every 0.5 to 1 m along the length of the kiln, evenly distributed around the main structure. The K-type thermocouple sensor is arranged in the high-temperature critical area of ​​the kiln. The data collected by the two are cross-checked, and the valid actual temperature value is output after eliminating abnormal temperature values.

[0012] The above technical solution achieves continuous temperature monitoring by using distributed fiber optic sensors to cover the entire kiln area at intervals, and K-type thermocouples to monitor key high-temperature areas. The data from both are then cross-verified and outliers are removed. This ensures the comprehensiveness of temperature acquisition and the stability of measurement in high-temperature scenarios, while also ensuring the accuracy and reliability of the output actual temperature value. This provides high-quality raw data support for subsequent interference identification and fuzzy PID parameter adjustment, avoiding the impact of single sensor errors on temperature control accuracy.

[0013] Preferably, the interference identification model adopts a classification model based on LSTM neural network. The model takes the temperature fluctuation amplitude, fluctuation frequency and temperature difference between adjacent areas in the past 5 to 10 minutes as input features, and outputs the interference type and interference intensity level through pre-trained model parameters. The interference intensity level is divided into three levels: slight interference, moderate interference and severe interference.

[0014] Through the above technical solution, the interference recognition model based on LSTM neural network plays the following role: taking the temperature fluctuation amplitude, fluctuation frequency and temperature difference between adjacent areas within the past 5 to 10 minutes as input features, and leveraging the analysis capability of LSTM on time series data, it accurately outputs the type of interference (feed fluctuation, power supply pressure fluctuation, airflow disturbance in the kiln) and the intensity of interference in three levels: slight, moderate and severe, through pre-trained parameters. This provides a precise basis for the subsequent differential dynamic correction of fuzzy PID parameters, avoiding the problems of low temperature control efficiency or poor stability caused by indiscriminate adjustment.

[0015] Preferably, the parameter correction logic of the fuzzy inference module is as follows: when a slight interference is identified, only the proportional coefficient is finely adjusted, while the integral time constant and the derivative time constant remain basically unchanged; when a moderate interference is identified, the proportional coefficient and the derivative time constant are adjusted simultaneously to increase the derivative action to suppress temperature fluctuations; when a severe interference is identified, the integral time constant is shortened based on the adjustment of the proportional coefficient and the derivative time constant to accelerate the system's speed in eliminating deviations.

[0016] The above technical solution implements differentiated PID parameter adjustments based on the identified interference intensity (slight, moderate, and severe). For slight interference, only the proportional coefficient is finely adjusted to ensure system stability. For moderate interference, both the proportional and derivative coefficients are adjusted simultaneously to suppress temperature fluctuations. For severe interference, the integral time is shortened to accelerate deviation elimination. This achieves precise adaptation of PID parameters to different interference intensities, balancing system stability and deviation response speed, and ensuring temperature control accuracy under various interference scenarios.

[0017] Preferably, the dynamic correction of the PID parameters is also combined with the process stages of the kiln, which include a heating stage, a holding stage, and a cooling stage. In the heating stage, the focus is on increasing the proportional coefficient to accelerate the heating rate; in the holding stage, the focus is on decreasing the proportional coefficient and increasing the integral action to stabilize the temperature; and in the cooling stage, the focus is on adjusting the derivative time constant to avoid a sudden drop in temperature.

[0018] Through the above technical solutions, by increasing the proportional coefficient to accelerate the heating process, decreasing the proportional coefficient and enhancing the integral action to stabilize the temperature during the heat preservation process, and adjusting the derivative time constant to prevent sudden drops during the cooling process, the PID parameters not only adapt to interference scenarios but also fit the core temperature control objectives of each process stage. This improves the accuracy and stability of temperature control throughout the entire kiln process and ensures the quality of materials at different heat treatment stages.

[0019] Preferably, it also includes an interference compensation step: the temperature controller calls a preset interference compensation model according to the interference type and interference intensity level, and outputs a compensation control quantity. The compensation control quantity is superimposed with the control signal after fuzzy PID correction to form a final control signal that is transmitted to the actuator. Among them, the feed quantity fluctuation interference corresponds to the compensation fuel supply quantity, and the gas and power pressure fluctuation interference corresponds to the compensation valve opening degree or power supply.

[0020] The above technical solution calls a preset model to output a targeted compensation control quantity according to the type and intensity level of the interference. This quantity is then superimposed on the control signal corrected by fuzzy PID. Fluctuations in the feed rate correspond to compensation for the fuel supply, while fluctuations in gas and electricity pressure correspond to compensation for the valve opening or power supply. This further offsets the impact of different interferences on the kiln temperature, strengthens the anti-interference capability of the temperature control system, ensures that the final control signal received by the actuator is more accurate, and improves the stability of temperature control.

[0021] Preferably, the heating actuator adopts a zoned independent control method. The tunnel kiln is divided into 3 to 5 independent heating zones along its length. Each heating zone is equipped with an independent fuel valve or electric heating module. The temperature controller outputs an independent control signal for each heating zone based on the type of interference and temperature deviation, thereby achieving precise temperature control for each zone.

[0022] By dividing the tunnel kiln into 3 to 5 independent heating zones and configuring dedicated actuators, the temperature controller can output independent control signals for each zone based on the type of interference and temperature deviation, avoiding mutual interference between different zones. This allows each heating zone to be precisely controlled according to its own operating conditions, ultimately achieving precise zoned temperature control of the entire kiln and ensuring that the temperature in each zone remains stable and meets process requirements.

[0023] Preferably, the temperature control effect feedback optimization step is also included: every 1 to 2 hours, the temperature controller calculates the standard deviation of temperature fluctuation in each area and the maximum deviation of the set value. If the standard deviation of fluctuation exceeds ±2℃ or the maximum deviation of the set value exceeds ±5℃, the iterative optimization of the fuzzy rule base is triggered. By comparing the parameter differences between the current interference scenario and the historical best control scenario, the fuzzy inference rules are updated to improve the anti-interference capability of subsequent temperature control.

[0024] Through the above technical solution, every 1 to 2 hours, the maximum deviation between the standard deviation of temperature fluctuation in each region and the set value is statistically analyzed. When the index exceeds the threshold (standard deviation of fluctuation ±2℃, maximum deviation ±5℃), the fuzzy rule base is triggered for iterative optimization. By comparing the parameter differences between the current and historical best control scenarios, the inference rules are updated to form a closed-loop optimization mechanism for temperature control effect, continuously improving the system's subsequent anti-disturbance capability and ensuring long-term stability of temperature control accuracy.

[0025] Preferably, the interference identification also includes sudden interference handling: when the temperature signal fluctuates within 10 seconds by more than ±10℃, it is determined to be a sudden interference. The temperature controller directly calls the preset emergency control strategy, instantly increases the differential action and cuts off the heating power of some non-critical areas. After the temperature fluctuation drops to within ±5℃, it switches back to the conventional fuzzy PID adjustment mode.

[0026] Through the above technical solution, when the temperature fluctuates by more than ±10℃ within 10 seconds, it is quickly identified as a sudden disturbance. The emergency strategy is directly invoked to instantly increase the differential action and cut off the heating power of some non-critical areas to quickly suppress the drastic temperature fluctuation. Once the fluctuation drops to within ±5℃, the system switches back to the normal mode, thereby achieving a rapid response and effective handling of sudden disturbances, avoiding significant temperature deviations from the set value that could affect the heat treatment quality of materials, and ensuring the stability of the temperature control system.

[0027] This invention provides a fuzzy PID temperature control process for tunnel kilns with strong anti-disturbance and adaptive capabilities. It offers the following advantages:

[0028] 1. This invention employs a dual-redundant temperature acquisition method combining distributed optical fiber and K-type thermocouples, along with an interference identification model based on LSTM neural network, to accurately identify and classify interferences such as feed fluctuations, power supply fluctuations, and airflow disturbances within the kiln. Furthermore, through dynamic correction of fuzzy PID parameters and an interference compensation mechanism, it significantly improves the anti-interference capability of the tunnel kiln temperature control system, controlling the standard deviation of temperature fluctuations in each area within ±2℃. This effectively solves the problem of poor temperature stability in traditional temperature control processes under complex interference scenarios, ensuring uniform heating of materials within the kiln and improving product qualification rates.

[0029] 2. This invention combines a fuzzy reasoning rule base with the kiln process stages and introduces a temperature control effect feedback optimization and self-learning mechanism, enabling the system to continuously iterate and optimize the PID parameter adjustment logic based on actual operating data. This achieves adaptive matching of temperature control requirements for different process stages, while reducing energy consumption losses during long-term operation, reducing the workload of manual parameter tuning, and improving the intelligence level and operational economy of the tunnel kiln temperature control system. Attached Figure Description

[0030] Figure 1 This is a flowchart of the interference identification and classification process of the present invention;

[0031] Figure 2 This is a flowchart of the fuzzy PID parameter adaptive adjustment process of the present invention. Detailed Implementation

[0032] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Please see the appendix Figure 1 - Appendix Figure 2 This invention provides a fuzzy PID temperature control process for tunnel kilns with strong anti-disturbance and adaptive capabilities, comprising the following steps:

[0034] Temperature signal acquisition: Temperature sensors are deployed in different heating zones of the tunnel kiln to collect the actual temperature values ​​of each zone in real time, and the collected temperature signals are converted into digital signals and transmitted to the temperature controller. The temperature sensors adopt a dual-redundancy acquisition method combining distributed fiber optic temperature sensors and K-type thermocouple sensors. The distributed fiber optic temperature sensors are arranged at three monitoring points every 0.5 to 1m along the length of the kiln, evenly distributed around the main structure. The K-type thermocouple sensors are arranged in the high-temperature key areas of the kiln. The data collected by the two are cross-checked, and the valid actual temperature value is output after eliminating abnormal temperature values.

[0035] Specifically, the working principle of temperature signal acquisition is to construct a dual-redundant acquisition system of distributed fiber optic temperature sensors and K-type thermocouple sensors to achieve accurate and reliable monitoring of the temperature of the entire tunnel kiln and key areas. The distributed fiber optic temperature sensors are deployed along the length of the kiln at intervals of 0.5 to 1 meter, forming a continuous temperature sensing along the entire length of the kiln to cover the temperature distribution of each heating zone. The K-type thermocouple sensors are specifically deployed in the high-temperature key areas of the kiln, utilizing their measurement stability under high-temperature conditions to achieve focused monitoring of the temperature in these key areas. Both sensors synchronously and in real-time acquire the actual temperature values ​​of the corresponding areas and convert them into digital signals, which are then transmitted to the temperature controller. The controller cross-compares the temperature data acquired by the two types of sensors using a preset data verification algorithm, identifying and eliminating abnormal temperature values ​​caused by sensor momentary failures, electromagnetic interference, or environmental noise. Finally, it outputs the verified and confirmed valid actual temperature values, providing highly reliable raw temperature data support for subsequent interference identification, parameter adjustment, and precise temperature control.

[0036] Interference signal identification and classification: The temperature controller calls the preset interference identification model to analyze the fluctuation characteristics of the collected temperature signal and identify the interference types in the kiln operation. The interference types include feed rate fluctuation interference, power supply pressure fluctuation interference, and airflow disturbance interference in the kiln. The interference identification model adopts a classification model based on LSTM neural network. The model takes the temperature fluctuation amplitude, fluctuation frequency and temperature difference between adjacent areas in the past 5 to 10 minutes as input features. It outputs the interference type and interference intensity level through pre-trained model parameters. The interference intensity level is divided into three levels: slight interference, moderate interference and severe interference.

[0037] Specifically, the working principle of interference signal identification and classification is that the temperature controller calls a pre-trained interference identification model based on an LSTM neural network to perform multi-dimensional fluctuation feature analysis on the verified effective temperature signal. The model first extracts the temperature fluctuation amplitude (reflecting the degree of temperature deviation from steady state), fluctuation frequency (reflecting the speed of temperature change), and temperature difference between adjacent areas (reflecting the uniformity of temperature field distribution) within the past 5 to 10 minutes as input features. Utilizing the strong fitting ability of the LSTM neural network to time series data, the model performs deep time series correlation analysis and pattern matching on the above features through preset training parameters. This accurately identifies specific types of interference such as feed rate fluctuation interference, power supply pressure fluctuation interference, or kiln airflow disturbance interference during kiln operation, and simultaneously outputs the corresponding interference intensity level (slight interference, moderate interference, or severe interference). This provides accurate interference feature basis for the subsequent differentiated adjustment of fuzzy PID parameters and the targeted implementation of interference compensation strategies.

[0038] Fuzzy PID parameter adaptive adjustment: Based on the identified interference type and the deviation and rate of change between the actual temperature and the set temperature, the temperature controller triggers the fuzzy inference module to dynamically correct the proportional coefficient, integral time constant, and derivative time constant of the PID controller. The rule base of the fuzzy inference module is built based on historical temperature control optimization data under different interference scenarios in the kiln, and the rule base is updated in real time with actual operating data. The parameter correction logic of the fuzzy inference module is as follows: when a slight interference is identified, only the proportional coefficient is finely adjusted, keeping the integral and derivative time constants basically unchanged; when a moderate interference is identified, the proportional coefficient and derivative time constant are adjusted simultaneously to increase the derivative action to suppress temperature fluctuations; when a severe interference is identified, the integral time constant is shortened in addition to adjusting the proportional coefficient and derivative time constant to accelerate the system's elimination of deviations.

[0039] Specifically, the working principle of fuzzy PID parameter adaptive adjustment is as follows: the temperature controller uses the identified disturbance type (feed fluctuation, power supply pressure fluctuation, kiln airflow disturbance) and the deviation and rate of change between the actual temperature and the set temperature as input conditions to trigger the built-in fuzzy inference module to make parameter adjustment decisions. The rule base of this fuzzy inference module is built based on the historical optimization data of temperature control in the kiln under different disturbance scenarios, and can be iteratively updated in real time with new data generated during actual system operation to ensure rule adaptability. In the specific adjustment process, the module adjusts the parameters according to the preset parameter correction logic, targeting different intensities of disturbance. Differential correction is implemented for disturbances: When a slight disturbance is identified, only the proportional coefficient is finely adjusted, while the integral and derivative time constants remain basically unchanged to maintain system stability; when a moderate disturbance is identified, the proportional coefficient and derivative time constant are adjusted simultaneously to enhance the system's ability to suppress temperature fluctuations by increasing the derivative action; when a severe disturbance is identified, the integral time constant is shortened in addition to adjusting the proportional coefficient and derivative time constant to accelerate the system's elimination of temperature deviations. Ultimately, dynamic and adaptive correction of PID controller parameters is achieved, enabling the temperature control system to accurately match the control requirements under different disturbance scenarios.

[0040] Actuator Control: The PID controller transmits the corrected control signal to the corresponding heating actuator in the kiln, adjusting the heating power or fuel supply to achieve precise temperature control in each zone. Simultaneously, the temperature controller receives feedback signals from the actuator in real time, assesses the control effect, and continuously optimizes the fuzzy PID parameters. Dynamic correction of the PID parameters is also integrated with the kiln's process stages, including heating, holding, and cooling. During heating, the proportional gain is increased to accelerate the heating rate; during holding, the proportional gain is decreased and the integral action is increased to stabilize the temperature; and during cooling, the derivative time constant is adjusted to prevent sudden temperature drops.

[0041] Specifically, the working principle of the actuator control is as follows: the PID controller parameters, dynamically corrected by the fuzzy inference module, generate corresponding control signals. These signals are transmitted to the corresponding actuators (such as fuel valves, electric heating modules, etc.) in each heating zone of the kiln. By adjusting the heating power output or fuel supply, the temperature of each zone is precisely controlled according to the set target. At the same time, the temperature controller receives the operating status signals (such as actual power, valve opening, etc.) from the actuators in real time, and evaluates the current control effect based on the deviation between the real-time temperature of each zone and the set value. This evaluation then acts inversely to adjust the fuzzy PID parameters. The logic enables continuous optimization. In this process, the dynamic correction of PID parameters is further deeply coupled with the process stages of the kiln (heating, holding, and cooling). In the heating stage, the proportional coefficient is increased to improve the heating rate and ensure that the process temperature is reached quickly. In the holding stage, the proportional coefficient is decreased and the integral action is enhanced to suppress temperature fluctuations and maintain temperature stability. In the cooling stage, the differential time constant is adjusted to moderate the slope of temperature change and avoid the impact of sudden temperature drops on the materials. Ultimately, the precise adaptation of each process stage and control parameters is achieved, ensuring high precision and high stability of temperature control throughout the tunnel kiln process.

[0042] It also includes an interference compensation step: the temperature controller calls the preset interference compensation model according to the type and intensity level of the interference, outputs the compensation control quantity, and superimposes the compensation control quantity with the control signal after fuzzy PID correction to form the final control signal, which is transmitted to the actuator. Among them, the interference of feed quantity fluctuation corresponds to the compensation of fuel supply quantity, and the interference of gas and power pressure fluctuation corresponds to the compensation of valve opening or power supply.

[0043] Specifically, the working principle of the interference compensation step is as follows: after acquiring the type of interference (feed fluctuation, gas / electricity pressure fluctuation) and the corresponding interference intensity level, the temperature controller calls the preset interference compensation model. This model pre-constructs the compensation amount calculation logic based on the influence law of different interferences on the temperature field, and outputs the compensation control amount in a targeted manner for specific interference types and intensities. The compensation control amount is superimposed with the basic control signal after fuzzy PID correction to form the final control signal transmitted to the actuator. For feed fluctuation interference, the fuel supply is compensated to match the change in material heat demand. For gas and electricity pressure fluctuation interference, the valve opening or power supply is compensated to offset the impact of insufficient energy supply stability. Thus, through the synergistic effect of interference compensation and PID control, the influence of interference on kiln temperature is further weakened, and the anti-interference accuracy and response speed of the temperature control system are improved.

[0044] The heating actuator adopts a zoned independent control method. The tunnel kiln is divided into 3 to 5 independent heating zones along its length. Each heating zone is equipped with an independent fuel valve or electric heating module. The temperature controller outputs an independent control signal for each heating zone based on the type of interference and temperature deviation, so as to achieve precise temperature control in each zone.

[0045] Specifically, the working principle of independent zone control of heating actuators is to divide the tunnel kiln along its length into 3 to 5 independent heating zones. Each heating zone is equipped with a fuel valve or electric heating module as a dedicated actuator, enabling independent heating regulation in each zone. The temperature controller generates and outputs independent control signals based on the type of interference (such as local feed fluctuations, regional airflow disturbances, etc.) and the deviation between the actual temperature and the set value in each heating zone through fuzzy PID control logic. This signal only acts on the actuator of the corresponding heating zone, realizing individual regulation of heating power or fuel supply in each zone. This avoids mutual interference in regulation between different heating zones due to differences in interference and temperature deviation, ensuring that each zone can obtain a precise and suitable control quantity according to its own operating conditions, ultimately achieving precise zone temperature control throughout the entire kiln.

[0046] It also includes a temperature control effect feedback optimization step: every 1 to 2 hours, the temperature controller counts the standard deviation of temperature fluctuation in each area and the maximum deviation of the set value. If the standard deviation of fluctuation exceeds ±2℃ or the maximum deviation of the set value exceeds ±5℃, it triggers the iterative optimization of the fuzzy rule base. By comparing the parameter differences between the current interference scenario and the historical best control scenario, the fuzzy inference rules are updated to improve the anti-interference ability of subsequent temperature control.

[0047] Specifically, the working principle of the temperature control effect feedback optimization step is as follows: the temperature controller performs statistical analysis on the temperature operation data of each heating zone at a fixed cycle of 1 to 2 hours, specifically calculating the standard deviation of temperature fluctuation and the maximum deviation of temperature from the set value for each zone. The controller compares the statistically obtained standard deviation of fluctuation with the preset ±2℃ threshold and the maximum deviation of set value with the preset ±5℃ threshold. If any indicator exceeds the corresponding threshold, it is determined that the current temperature control effect has not reached the preset standard, and the iterative optimization process of the rule base in the fuzzy inference module is triggered. This optimization process retrieves the historical best control scenario data stored in the system (including the best PID parameters and temperature control effect indicators under the same or similar interference types), compares the current interference scenario and control parameters with multi-dimensional parameter differences, identifies the inference logic in the current rule base that is not adaptable, and then updates the fuzzy inference rules accordingly, so that the updated rule base can more accurately match the actual working conditions, and ultimately achieves continuous improvement in the anti-interference capability of the temperature control system.

[0048] Interference identification also includes handling sudden interference: when the temperature signal fluctuates within 10 seconds by more than ±10℃, it is determined to be sudden interference. The temperature controller directly calls the preset emergency control strategy, instantly increases the differential action and cuts off the heating power of some non-critical areas. After the temperature fluctuation drops to within ±5℃, it switches back to the conventional fuzzy PID adjustment mode.

[0049] Specifically, the working principle of sudden interference handling in interference identification is as follows: the temperature controller monitors the dynamic changes of temperature signals in each area in real time. When the fluctuation of the temperature signal in a certain area exceeds ±10℃ within 10 seconds, it is immediately determined to be a sudden interference event. At this time, the controller does not go through the conventional interference identification and fuzzy inference process, but directly calls the preset emergency control strategy. On the one hand, it instantly enhances the control effect of the differential link to quickly suppress the drastic temperature fluctuation. On the other hand, it cuts off the heating power output of some non-critical heating areas to reduce the impact of additional heat source input on the temperature field. The controller continues to monitor the temperature fluctuation in this area. After the temperature fluctuation amplitude drops back to within ±5℃ and the temperature field tends to stabilize, the emergency control strategy is terminated and switched back to the conventional fuzzy PID adjustment mode. This achieves rapid response and effective handling of sudden interference, avoiding the impact on the heat treatment quality of materials caused by a large deviation of the temperature from the set value due to sudden interference.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A fuzzy PID temperature control process for tunnel kilns with strong anti-disturbance and adaptive capabilities, characterized in that, Includes the following steps: Temperature signal acquisition: By arranging multiple temperature sensors in different heating zones of the tunnel kiln, the actual temperature values ​​of each zone are collected in real time, and the collected temperature signals are converted into digital signals and transmitted to the temperature controller. Interference signal identification and classification: The temperature controller calls the preset interference identification model to analyze the fluctuation characteristics of the collected temperature signal, and performs comprehensive analysis on multiple temperature sensors in the same area to identify the types of interference in the kiln operation. The interference types include feed rate fluctuation interference, power supply pressure fluctuation interference, and kiln airflow disturbance interference. Fuzzy PID parameter adaptive adjustment: Based on the identified interference type and the deviation and rate of change between the actual temperature and the set temperature, the temperature controller triggers the fuzzy inference module to dynamically correct the proportional coefficient, integral time constant and derivative time constant of the PID controller; the rule base of the fuzzy inference module is built based on the historical optimization data of temperature control under different interference scenarios of the kiln, and the rule base is updated in real time with the actual operation data. Actuator control: The PID controller transmits the corrected control signal to the corresponding heating actuator of the kiln to adjust the oxygen supply rate or fuel supply, set multiple threshold levels, and perform multi-level adjustment to achieve precise temperature control in each zone; at the same time, the temperature controller receives feedback signals from the actuator in real time, judges the control effect, and continuously optimizes the fuzzy PID parameters.

2. The fuzzy PID temperature control process for a tunnel kiln with strong anti-disturbance and adaptive capabilities according to claim 1, characterized in that, The temperature sensor adopts a dual-redundant acquisition method combining a distributed fiber optic temperature sensor and a K-type thermocouple sensor. The distributed fiber optic temperature sensor has three monitoring points arranged every 0.5 to 1 m along the length of the kiln, evenly distributed around the main structure. The K-type thermocouple sensor is arranged in the high-temperature critical area of ​​the kiln. The data collected by the two are cross-checked, and the valid actual temperature value is output after eliminating abnormal temperature values.

3. The fuzzy PID temperature control process for a tunnel kiln with strong anti-disturbance and adaptive capabilities according to claim 1, characterized in that, The interference identification model adopts a classification model based on LSTM neural network. The model takes the temperature fluctuation amplitude, fluctuation frequency and temperature difference between adjacent areas in the past 5 to 10 minutes as input features, and outputs the interference type and interference intensity level through pre-trained model parameters. The interference intensity level is divided into three levels: slight interference, moderate interference and severe interference.

4. The fuzzy PID temperature control process for a tunnel kiln with strong anti-disturbance and adaptive capabilities according to claim 1, characterized in that, The parameter correction logic of the fuzzy inference module is as follows: when a slight interference is identified, only the proportional coefficient is finely adjusted, while the integral time constant and the derivative time constant remain basically unchanged; when a moderate interference is identified, the proportional coefficient and the derivative time constant are adjusted simultaneously to increase the derivative action to suppress temperature fluctuations; when a severe interference is identified, the integral time constant is shortened based on the adjustment of the proportional coefficient and the derivative time constant to accelerate the system's speed in eliminating deviations.

5. The fuzzy PID temperature control process for a tunnel kiln with strong anti-disturbance and adaptive capabilities according to claim 1, characterized in that, The dynamic correction of the PID parameters is also combined with the process stages of the kiln, which include a heating stage, a holding stage, and a cooling stage. In the heating stage, the focus is on increasing the proportional coefficient to accelerate the heating rate. In the holding stage, the focus is on decreasing the proportional coefficient and increasing the integral action to stabilize the temperature. In the cooling stage, the focus is on adjusting the derivative time constant to avoid a sudden drop in temperature.

6. The fuzzy PID temperature control process for a tunnel kiln with strong anti-disturbance and adaptive capabilities according to claim 1, characterized in that, It also includes an interference compensation step: the temperature controller calls a preset interference compensation model according to the type and intensity level of the interference, and outputs a compensation control quantity. The compensation control quantity is superimposed with the control signal after fuzzy PID correction to form a final control signal that is transmitted to the actuator. Among them, the feed rate fluctuation interference corresponds to the compensation fuel supply, and the gas and power pressure fluctuation interference corresponds to the compensation valve opening or power supply.

7. The fuzzy PID temperature control process for a tunnel kiln with strong anti-disturbance and adaptive capabilities according to claim 1, characterized in that, The heating actuator adopts a zoned independent control method. The tunnel kiln is divided into 3 to 5 independent heating zones along its length. Each heating zone is equipped with an independent fuel valve or electric heating module. The temperature controller outputs an independent control signal for each heating zone based on the type of interference and temperature deviation, so as to achieve precise temperature control in each zone.

8. The fuzzy PID temperature control process for a tunnel kiln with strong anti-disturbance and adaptive capabilities according to claim 1, characterized in that, It also includes a temperature control effect feedback optimization step: every 1 to 2 hours, the temperature controller counts the standard deviation of temperature fluctuation in each area and the maximum deviation of the set value. If the standard deviation of fluctuation exceeds ±2℃ or the maximum deviation of the set value exceeds ±5℃, it triggers the iterative optimization of the fuzzy rule base. By comparing the parameter differences between the current interference scenario and the historical best control scenario, the fuzzy inference rules are updated to improve the anti-interference ability of subsequent temperature control.

9. The fuzzy PID temperature control process for a tunnel kiln with strong anti-disturbance and adaptive capabilities according to claim 1, characterized in that, The interference identification also includes sudden interference handling: when the temperature signal fluctuates within 10 seconds by more than ±10℃, it is determined to be a sudden interference. The temperature controller directly calls the preset emergency control strategy, instantly increases the differential action and cuts off the heating power of some non-critical areas. After the temperature fluctuation drops to within ±5℃, it switches back to the conventional fuzzy PID adjustment mode.

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