Dedusting control method and dedusting system based on three-layer air valve

Abstract

The invention provides a dust removal control method and system based on a three-layer air valve, and the method comprises the steps: S1, collecting dust concentration data in an industrial environment through a sensor, and generating a purified concentration value sequence through a filtering algorithm; s2, calculating a concentration change rate according to the concentration value sequence, and determining a grading response level according to the change rate; s3, angle position information of an air valve is obtained, and the angle is adjusted to a target value matched with the grading response level through a control algorithm; s4, extracting a stability index from the adjusted angle value, and monitoring and locking a stable state through a feedback loop; s5, optimizing airflow distribution according to the angle position in the stable state, and generating an enhanced dust removal performance index; and S6, updating control algorithm parameters according to the enhanced dust removal performance indexes to form a closed-loop control loop. The self-adaptive control of the dust concentration is realized, the dust removal efficiency is remarkably improved, the energy consumption is reduced, and the comprehensive technical effects of intelligent management, energy conservation and environmental protection are shown.

Description

Technical Field

[0001] This invention relates to the field of energy conservation and environmental protection technology, and in particular to a dust removal control method and dust removal system based on a three-layer air valve. Background Technology

[0002] Dust control in industrial environments is a crucial area for ensuring production safety and environmental protection, especially in high-dust industries such as steel smelting and cement production. Efficient dust collection systems directly impact worker health, equipment lifespan, and energy consumption. Dust control not only requires reducing the concentration of particulate matter in the air but also balancing system operating efficiency with energy consumption. However, many current dust collection systems face significant challenges in complex industrial settings and struggle to meet the actual needs of dynamic environments.

[0003] Existing dust removal methods typically rely on fixed operating modes or simple sensor feedback, making it difficult to cope with rapid changes in dust concentration. For example, in steel smelting workshops, furnace operation or material handling can cause a sudden surge in dust concentration within a short period. Existing systems often fail to adjust in time due to slow response, leading to dust escape or system overload. Furthermore, environmental noise interference, such as temperature, humidity, or equipment vibration, can render sensor data inaccurate, thus affecting the reliability of control strategies. These limitations result in low dust removal efficiency and increased unnecessary energy consumption. The core technical challenge lies in achieving dynamic response and precise control of dust concentration. In industrial environments, dust concentration changes often exhibit sudden and non-linear characteristics. For instance, in cement production, high concentrations of dust may suddenly be generated during raw material crushing or transportation, and existing systems struggle to quickly identify and adjust to such changes. Insufficient dynamic response not only leads to poor dust control but can also result in energy waste due to excessive operation of fans or valves. More importantly, noise interference reduces the reliability of concentration data. If these unstable data cannot be effectively processed, the system will be unable to accurately determine the trend of concentration changes, and thus will be unable to achieve precise valve adjustment.

[0004] Therefore, the key issue of this study is how to achieve rapid and accurate adjustment of the damper angle based on reliable dust concentration data in complex industrial environments to adapt to sudden changes in concentration and optimize energy consumption. Summary of the Invention

[0005] To address the technical problems mentioned in the background section, the first aspect of this invention provides a dust removal control method based on a three-layer damper, comprising: S1, collecting dust concentration data in an industrial environment using sensors and generating a purified concentration value sequence using a filtering algorithm; S2, calculating the concentration change rate based on the concentration value sequence and determining a graded response level based on the change rate; S3, acquiring the angular position information of the damper and adjusting the angle to a target value matching the graded response level using a control algorithm; S4, extracting a stability index from the adjusted angle value and monitoring and locking the stable state through a feedback loop; S5, optimizing the airflow distribution based on the angular position in the stable state to generate an enhanced dust removal performance index; and S6, updating the control algorithm parameters based on the enhanced dust removal performance index to form a closed-loop control loop.

[0006] Optionally, step S1 further includes: step S11, the sensor includes a dust concentration sensor, which collects dust concentration data in the industrial environment in real time; step S12, the filtering algorithm uses Kalman filtering or mean filtering to remove noise interference in the dust concentration data; step S13, the purified concentration value sequence is generated from the noise-removed data.

[0007] Optionally, step S12 further includes: simultaneously using Kalman filtering and mean filtering for data processing.

[0008] Optionally, step S2 further includes: step S21, obtaining the concentration change rate by performing time differentiation calculation on the purified concentration value sequence; step S22, if the concentration change rate exceeds a preset change rate threshold, it is determined to be a high-concentration sudden scenario; step S23, determining the graded response level based on the high-concentration sudden scenario.

[0009] Optionally, step S22 further includes setting the change rate threshold to 2 to 3 times the standard deviation of the concentration change rate under normal conditions.

[0010] Optionally, step S23 further includes: the graded response level includes three levels: low, medium, and high.

[0011] Optionally, step S3 further includes: step S31, obtaining the current angular position information of the air valve through an angle sensor; step S32, determining the target angle value according to the graded response level; step S33, adjusting the angle of the air valve to the target angle value using a proportional-integral-derivative control algorithm; and step S34, monitoring the angle change in real time during the adjustment process to ensure matching with the graded response level.

[0012] Optionally, step S32 further includes: setting different target angle values ​​according to different response levels, wherein the target angle value for a low response level is set to increase by 3 to 8 degrees from the current angle, the target angle value for a medium response level is set to increase by 10 to 20 degrees from the current angle, and the target angle value for a high response level is set to the maximum allowable opening of the damper.

[0013] Optionally, step S4 further includes: step S41, calculating a stability index from the adjusted angle value, the stability index including angle offset and response time; step S42, monitoring the angle offset in real time through the feedback loop; step S43, if the angle offset is greater than a preset angle offset threshold, activating a locking mechanism; step S44, the locking mechanism restricts further changes in the damper angle to generate a stable state.

[0014] A second aspect of the present invention provides a dust removal control system based on a three-layer air valve, employing the method described above for dust removal control, the system further comprising:

[0015] The system comprises the following modules: a data acquisition module for collecting dust concentration data from the industrial environment via sensors and generating a purified concentration value sequence using a filtering algorithm; a grading module for calculating the concentration change rate based on the concentration value sequence and determining the graded response level based on the change rate; a matching module for acquiring the angle position information of the air valve and adjusting the angle to a target value matching the graded response level using a control algorithm; a monitoring module for extracting stability indicators from the adjusted angle values ​​and monitoring and locking the stable state through a feedback loop; a generation module for optimizing the airflow distribution based on the angle position in the stable state and generating enhanced dust removal performance indicators; and a control module for updating the control algorithm parameters based on the dust removal performance indicators to form a closed-loop control loop.

[0016] The technical solution provided by this invention has the following beneficial effects:

[0017] This invention discloses a dust removal control method and dust removal system based on a three-layer air valve. By collecting dust concentration data in the industrial environment in real time, and combining filtering algorithms and air valve angle control, it solves the problems of low efficiency and high energy consumption of industrial dust removal systems in complex environments caused by noise interference, sudden concentration changes and insufficient dynamic response.

[0018] This invention accurately collects dust concentration using sensors, processes noise using a combination of Kalman filtering and mean filtering, and generates a stable concentration sequence; dynamically determines graded response levels based on the concentration change rate to quickly identify high-concentration sudden scenarios; precisely adjusts the damper angle using a proportional-integral-derivative control algorithm, and monitors and locks the stable state in real time through a feedback loop.

[0019] This invention achieves adaptive control of dust concentration, significantly improves dust removal efficiency, and reduces energy consumption. It is particularly suitable for high-dust scenarios such as steel smelting and cement production, demonstrating the comprehensive technical effects of intelligent management and energy conservation and environmental protection. Attached Figure Description

[0020] Figure 1 This is a flowchart of a dust removal control method based on a three-layer air valve according to the present invention.

[0021] Figure 2 This is a schematic diagram of a dust removal control system based on a three-layer air valve according to the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0023] like Figure 1 As shown, the first aspect of this invention provides a dust removal control method based on a three-layer damper. This method collects dust concentration data from the industrial environment using sensors, combines this data with filtering algorithms and damper angle control to achieve intelligent management of the industrial dust removal system. This method can adaptively adjust the damper angle according to real-time changes in dust concentration, improving dust removal efficiency and reducing energy consumption.

[0024] Furthermore, the method includes: S1, collecting dust concentration data in the industrial environment through sensors, and generating a purified concentration value sequence using a filtering algorithm. In one embodiment, accurate monitoring of dust concentration is fundamental to effective control in industrial dust removal systems. The raw data collected by sensors often contains various noise interferences, including measurement errors caused by electromagnetic interference, temperature fluctuations, humidity changes, and equipment vibration. These noises can lead to misjudgments in the control system, affecting the dust removal effect. Therefore, it is necessary to process the raw data using a filtering algorithm to extract the true dust concentration information.

[0025] Optionally, this step further includes: step S11, whereby the sensor includes a dust concentration sensor that collects dust concentration data in the industrial environment in real time. In one embodiment, the dust concentration sensor typically uses the laser scattering principle or optical counting principle for measurement. The laser scattering principle involves irradiating dust particles suspended in the air with a laser beam; the dust particles scatter the laser light, and the intensity of the scattered light is proportional to the dust concentration. A photodetector inside the sensor receives the scattered light signal, converts the light signal into an electrical signal, and then the signal processing circuit converts the electrical signal into a digital signal for output. In steel smelting workshops, dust concentration sensors need to be installed at representative monitoring points, such as near the furnace outlet, above the raw material conveyor belt, and at the air inlet of dust removal equipment. The sensor's sampling frequency is typically set to 10 to 100 times per second to ensure timely capture of rapid changes in dust concentration. In cement production line applications, the dust concentration sensor needs to consider the effects of high temperature and high humidity environments. The sensor housing is made of high-temperature resistant materials, and the internal optics are specially treated to resist dust contamination. The sensor transmits the collected dust concentration data to the control system in real time via a digital communication interface. The data format includes timestamps, concentration values, sensor status, and other information. To ensure data reliability, the sensor is also equipped with a self-diagnostic function, capable of detecting fault conditions such as light source aging and lens contamination. In step S12, the filtering algorithm uses Kalman filtering or mean filtering to remove noise interference from the dust concentration data. In one embodiment, Kalman filtering is a recursive filtering algorithm, particularly suitable for processing dynamic systems with time correlation. In dust concentration monitoring, Kalman filtering estimates the true dust concentration value by establishing a system state model and an observation model. The system state model describes the change law of dust concentration over time, considering physical processes such as dust generation, diffusion, and deposition. The observation model describes the relationship between the sensor measurement value and the true concentration value, including the statistical characteristics of measurement noise. The Kalman filtering algorithm includes two stages: prediction and update. In the prediction stage, the algorithm predicts the current state based on the state estimate of the previous moment and the system model. In the update stage, the algorithm uses the observation value at the current moment to correct the prediction result and obtain the optimal state estimate. Key parameters in the algorithm include process noise covariance and observation noise covariance, which need to be adjusted according to the actual application environment. In scenarios where dust concentration changes relatively smoothly, a smaller process noise covariance is used, and the algorithm relies more on the prediction results of the system model. In scenarios where dust concentration changes drastically, a larger process noise covariance is used, and the algorithm relies more on observations for correction. Mean filtering is a simple and effective filtering method that suppresses random noise by calculating the average of measurements within a certain time window. Moving average filtering is a common form of mean filtering, using a fixed-length time window. Each time a new measurement arrives, the oldest measurement is discarded and the new value is added to recalculate the average.The length of the time window directly affects the filtering effect. A window that is too short will have limited filtering effect, while a window that is too long will introduce a large time delay. In practical applications, the time window length is usually set to 5 to 20 sampling points, corresponding to a time span of 0.5 to 2 seconds. Optionally, the system uses both Kalman filtering and mean filtering for data processing. This step also includes:

[0026] Step S121: First, use mean filtering to remove high-frequency noise.

[0027] Step S122, then use Kalman filtering to further optimize the results.

[0028] This combined filtering method can effectively suppress various types of noise interference while maintaining response speed. Step S13: Generate the purified concentration value sequence from the noise-removed data. This sequence is used for subsequent rate of change calculation. In one embodiment, the purified concentration value sequence is a numerical sequence arranged in chronological order, where each value represents an estimated dust concentration at a specific moment. The time interval of the sequence is consistent with the sensor sampling frequency to ensure data continuity and integrity. During sequence generation, the system also performs validity checks on the data, removing obviously abnormal values. Outlier detection typically employs statistical methods, such as the 3-standard-deviation criterion or the interquartile range criterion. The sequence data storage format includes two fields: a timestamp and a concentration value. The timestamp uses a high-precision time format to ensure microsecond-level time accuracy. The concentration value is stored in floating-point format, with precision typically maintained to two decimal places. To support subsequent rate of change calculation, the sequence data also includes derived information such as first-order and second-order difference values. In an application example in a coal processing plant, the purified concentration value sequence exhibits obvious periodic variation characteristics. During equipment startup, the dust concentration rises rapidly and reaches its peak within a short time. During normal operation, the dust concentration remains relatively stable with only minor fluctuations. During equipment shutdown, the dust concentration gradually decreases and eventually approaches the ambient background value. This pattern of change provides an important reference for subsequent graded response control. S2, the concentration change rate is calculated based on the concentration value sequence, and the graded response level is determined based on the change rate. In one embodiment, the concentration change rate is an important indicator reflecting the dynamic characteristics of dust concentration. By analyzing the magnitude and direction of the change rate, the intensity and trend of dust generation can be determined. The calculation of the change rate requires consideration of the time scale; a short time scale can capture rapid changes, while a long time scale reflects the overall trend. The setting of the graded response level is based on the actual needs of the industrial site; different levels correspond to different control strategies and response intensities.

[0029] Optionally, this step further includes: step S21, obtaining the concentration change rate by performing time differential calculation on the purified concentration value sequence. Time differential calculation is an approximation of the derivative of a continuous function using numerical methods. In discrete concentration value sequences, differential calculation is typically implemented using finite difference methods. The first-order forward difference method uses the concentration values ​​at the current and next time moments to calculate the change rate, with the formula being: the current change rate equals the concentration value at the next time moment minus the concentration value at the current time moment divided by the time interval. The first-order backward difference method uses the concentration values ​​at the current and previous time moments, with the formula being: the current change rate equals the concentration value at the current time moment minus the concentration value at the previous time moment divided by the time interval. The central difference method uses the concentration values ​​at the previous and next time moments to calculate the change rate at the current time moment, offering higher calculation accuracy. The formula is: the current change rate equals the concentration value at the next time moment minus the concentration value at the previous time moment divided by twice the time interval. This method effectively suppresses the influence of high-frequency noise on the change rate calculation, but introduces a calculation delay of one time step. In practical applications, the system typically employs a multi-point difference method to further improve calculation accuracy. The five-point central difference method uses concentration values ​​from the previous two moments, the previous moment, the current moment, the next moment, and the next two moments for calculation. This method has higher calculation accuracy, but also stricter requirements for data quality. In the application case of dust removal systems in chemical plants, the calculation results of the concentration change rate show obvious stage characteristics. During the reactor feeding stage, the concentration change rate is positive and large, indicating that the dust concentration is rising rapidly. During the reaction stage, the concentration change rate is close to zero, indicating that the dust concentration remains stable. During the cleaning stage, the concentration change rate is negative, indicating that the dust concentration is gradually decreasing. In step S22, if the concentration change rate exceeds the preset change rate threshold, it is judged as a high concentration sudden scenario. In one embodiment, the determination of the change rate threshold needs to comprehensively consider multiple factors such as process characteristics, environmental protection requirements, and equipment capabilities. Setting the change rate threshold too low will cause the system to be too sensitive, frequently triggering response actions, increasing equipment wear and energy consumption. Setting the change rate threshold too high may miss important concentration change events, affecting the dust removal effect. Typically, the preset change rate threshold is set to 2 to 3 times the standard deviation of the concentration change rate under normal equipment operation. A high-concentration emergency scenario refers to an abnormal situation where dust concentration rises sharply within a short period of time. This situation may be caused by factors such as equipment failure, process abnormalities, changes in raw material characteristics, or external interference. In the steel smelting process, high-concentration emergencies are common in processes such as furnace charge dumping, molten steel tapping, and waste slag treatment. In cement production, sudden increases in dust concentration are also prone to occur in stages such as raw material crushing, clinker cooling, and finished product packaging. The system identifies emergencies by comparing the concentration change rate in real time with a preset change rate threshold. When the change rate exceeds the threshold, the system immediately activates the emergency response procedure, including recording the event time, analyzing possible causes, and assessing the scope of impact.Simultaneously, the system checks the sensor status to rule out false alarms caused by equipment malfunctions. In one embodiment, the system employs a dynamic threshold adjustment mechanism. Based on historical data statistical analysis, the system can identify concentration change characteristics under different time periods and operating conditions, and dynamically adjust the change rate threshold. During periods of high dust generation, the change rate threshold is appropriately increased to avoid false alarms. During periods of low dust generation, the change rate threshold is appropriately decreased to improve detection sensitivity. Step S23: Determine the graded response level based on the high concentration surge scenario. The graded response level includes three levels: low, medium, and high. In one embodiment, the design of the graded response level is based on the severity and urgency of the dust concentration change. A low-level response corresponds to a slight concentration change, typically using a conventional damper adjustment strategy. A medium-level response corresponds to a moderate concentration change, requiring a larger damper adjustment range and a shorter response time. A high-level response corresponds to a severe concentration surge, requiring the maximum damper opening and the fastest response speed. The trigger condition for a low-level response is that the concentration change rate exceeds the basic threshold but does not reach the medium-level threshold. In this case, the system considers the dust concentration change to be within the normal fluctuation range and adopts a gradual adjustment strategy. The adjustment range of the damper angle is usually controlled within 5 degrees, and the adjustment speed is relatively slow to avoid impacting the production process. The trigger condition for the intermediate response level is that the concentration change rate exceeds the intermediate threshold but does not reach the advanced threshold. The system determines that a change in dust concentration requires timely intervention and adopts an aggressive adjustment strategy. The damper angle adjustment range can reach 10 to 15 degrees, and the adjustment speed is significantly faster, ensuring that the concentration increase trend is controlled within a short time. The trigger condition for the advanced response level is that the concentration change rate exceeds the advanced threshold. The system determines that a severe surge in dust concentration has occurred and requires immediate and strongest response measures. The damper angle can be adjusted to the maximum opening, and the adjustment speed reaches the maximum allowable value of the equipment. Simultaneously, backup dust removal equipment or alarm systems may be activated. In practical applications in non-ferrous metal smelters, the setting of graded response levels fully considers the characteristics of different processes. In the electrolysis workshop, because dust generation is relatively stable, the advanced threshold is set lower to ensure timely response to any abnormal changes. In the smelting workshop, due to the process characteristics leading to large variations in dust concentration, the advanced threshold is set relatively higher to avoid frequent triggering of the highest level response. S3, acquire the angular position information of the damper, and adjust the angle to a target value matching the graded response level using a control algorithm. In one embodiment, damper angle control is a core execution step in the dust removal system, directly affecting airflow distribution and dust removal efficiency. Accurate acquisition of angular position information is a prerequisite for precise control, requiring the use of high-precision angle sensors and reliable signal transmission methods. The selection of the control algorithm and parameter settings need to be optimized based on the dynamic characteristics of the damper and the system response requirements.

[0030] Optionally, this step further includes: step S31, acquiring the current angular position information of the damper using an angle sensor. In one embodiment, the angle sensor is a key device for measuring the rotation angle of the damper, and commonly used types include potentiometer-type, encoder-type, and magnetoresistive sensors. Potentiometer-type angle sensors determine the angular position by measuring changes in resistance, and have the advantages of simple structure and low cost, but their accuracy is relatively low and they are easily affected by wear. Encoder-type angle sensors output digital signals through photoelectric or magnetoelectric principles, and have the characteristics of high accuracy and high reliability, making them the mainstream choice for industrial applications. Magnetoresistive angle sensors utilize the magnetoresistive effect to measure angular changes, and have the advantages of non-contact measurement and strong anti-interference capabilities. The sensor internally contains a magnetoresistive element and a signal processing circuit. When the damper rotates, the direction of the magnetic field changes, and the resistance value of the magnetoresistive element changes accordingly. The angular position can be calculated by measuring the change in resistance value. In dusty environments, the angle sensor needs to have good protective performance. The sensor housing adopts a sealed design, and the protection level usually reaches IP65 or higher standards. The internal circuit adopts an anti-interference design, which can work stably in environments with strong electromagnetic interference. The sensor's installation location must avoid direct contact with dust and corrosive gases; it is typically installed on the damper bearing housing or at the reducer output end. The angle sensor's output signal is transmitted to the control system via a dedicated cable. To improve signal transmission reliability, differential signal transmission is usually employed, effectively suppressing common-mode interference. The signal cable is shielded, with the outer shielding mesh grounded to prevent electromagnetic interference. In long-distance transmission applications, signal amplifiers or signal converters can be used to improve signal quality. Step S32: Determine the target angle value based on the graded response level. Determining the target angle value requires comprehensive consideration of multiple factors, including the graded response level, current dust concentration, damper characteristics, and system load. Different response levels correspond to different target angle ranges; low-level responses typically correspond to smaller angle adjustments, medium-level responses to medium-amplitude angle adjustments, and high-level responses may require adjusting the damper to its maximum opening. The target angle value is calculated based on a pre-established response level-angle mapping table, which is determined through extensive experimental data and theoretical analysis. At low response levels, the target angle value is typically increased by 3 to 8 degrees from the current angle. This small adjustment gently increases the dust collection airflow, avoiding disruption to the production process. The specific adjustment range is fine-tuned based on the degree of dust concentration increase and historical response results. The system also considers the current position of the damper; if the damper is close to its maximum opening, the adjustment range will be reduced accordingly. The target angle adjustment range at the intermediate response level is typically between 10 and 20 degrees. The system selects an appropriate target angle within this range based on the specific concentration change rate. When the concentration change rate just exceeds the intermediate threshold, the target angle adjustment range is close to the lower limit. When the concentration change rate approaches the high threshold, the target angle adjustment range is close to the upper limit.This dynamic adjustment mechanism enables more precise control. At the advanced response level, the system typically sets the target angle value to the maximum allowable opening of the damper. In emergencies, the dust removal system needs to operate at maximum capacity to rapidly reduce dust concentration. Determining the maximum opening requires consideration of factors such as fan capacity, pipeline resistance, and structural safety. In certain special circumstances, the system may also activate backup dampers or auxiliary dust removal equipment. In the application of blast furnace tapping areas in steel enterprises, the target angle setting of the graded response reflects the process characteristics. At the initial stage of tapping, dust generation is relatively high, so the system presets a larger target angle value. During the middle stage of tapping, dust generation is relatively stable, and the target angle value is moderate. At the end of tapping, dust gradually decreases, and the target angle value decreases accordingly. This preset mechanism, combined with real-time response, achieves better dust removal results. Step S33: Using a proportional-integral-derivative (PID) control algorithm, the angle of the damper is adjusted to the target angle value. In one embodiment, the PID is a classic control method widely used in industrial control, achieving precise control through the coordinated action of proportional, integral, and derivative components. The proportional terminator generates a control output based on the magnitude of the current error, offering a fast response but potentially introducing steady-state error. The integral term generates a control output based on the accumulated error, eliminating steady-state error but potentially causing system oscillations. The derivative term generates a control output based on the rate of change of the error, improving the system's dynamic performance and stability. The proportional gain directly affects the system's response speed and stability. A proportional gain that is too small will result in a slow system response, failing to track changes in the target angle in a timely manner. A proportional gain that is too large may cause system oscillations or even instability. In valve control applications, the proportional gain is typically set based on the valve's moment of inertia, driving torque, and load characteristics. For large valves, due to their larger moment of inertia, the proportional gain can be set relatively small. For small valves, a larger proportional gain can be set to improve response speed. The integral time constant determines the strength of the integral terminator's action. A smaller integral time constant results in a stronger integral action, quickly eliminating steady-state error but easily causing overshoot and oscillations. A larger integral time constant results in a weaker integral action, offering good system stability but slower elimination of steady-state error. In practical applications, the integral time constant setting needs to find a balance between speed and stability. The derivative time constant affects the system's ability to predict error changes. An appropriate derivative action can predict error trends in advance, improving the system's dynamic performance. However, the derivative element is sensitive to noise; an excessively strong derivative action may amplify measurement noise and affect control effectiveness. In dusty environments, due to various interference factors, the derivative time constant is usually set relatively small, or an incomplete derivative form is used to reduce noise impact. In the application case of a cement plant kiln tail dust removal system, the parameters of the proportional-integral-derivative (PID) control algorithm were carefully tuned. The proportional coefficient was set to 0.8, which ensures response speed while avoiding excessive oscillation.The integral time constant is set to 15 seconds, which effectively eliminates steady-state error without causing significant overshoot. The derivative time constant is set to 2 seconds, providing moderate predictive capability without excessively amplifying noise. Step S34 involves real-time monitoring of angle changes during adjustment to ensure matching with the graded response level. In one embodiment, real-time monitoring is crucial for ensuring control effectiveness. By continuously tracking the change in the damper angle, the system can promptly detect anomalies and take corresponding measures. Monitoring includes multiple indicators such as angle change rate, adjustment accuracy, response time, and stability. Real-time evaluation of these indicators helps determine the execution effect of the control algorithm and the health status of the system. Monitoring the angle change rate is achieved by calculating the difference between angle values ​​at adjacent moments. Under normal circumstances, the angle change rate should be within the allowable range of the equipment; it should not be too slow, affecting the response effect, nor too fast, causing mechanical shock. When an abnormal angle change rate is detected, the system analyzes possible causes, such as drive failure, mechanical jamming, or improper control parameters. Monitoring the adjustment accuracy is achieved by comparing the deviation between the actual angle and the target angle. Under steady-state conditions, the angle deviation should be controlled within the allowable range, typically 1% to 3% of the target angle. If the deviation continues to exceed the allowable range, the system will check factors such as sensor accuracy, control algorithm parameters, and actuator performance. Response time monitoring begins from receiving the target angle command and continues until the actual angle reaches 95% of the target value. Different response levels correspond to different response time requirements; high-level response requires the shortest response time, while low-level response allows for a relatively longer response time. The response time monitoring results are used to evaluate the system's dynamic performance and optimize control parameters. In an application example in a non-ferrous metal smelter, the real-time monitoring system detected slight oscillations in the angle adjustment of the damper in high-level response mode. Through analysis of the monitoring data, technicians found that this was caused by an excessively large differential coefficient. After adjusting the differential coefficient, the oscillations disappeared, and the system operated more smoothly. S4, a stability index is extracted from the adjusted angle value, and the stable state is monitored and locked through a feedback loop. In one embodiment, the stability index is an important parameter for evaluating the performance of the damper control system, reflecting the system's stability and anti-interference capability after reaching the target angle. Extracting the stability index requires statistical analysis of the time series of angle changes to identify the system's dynamic characteristics and steady-state properties. The design of the feedback loop ensures that the system can automatically maintain a stable state and resist the influence of external disturbances.

[0031] Optionally, this step further includes: step S41, calculating a stability index from the adjusted angle value, the index including angle offset and response time. In one embodiment, angle offset is the core index for measuring the stability of the damper angle, reflecting the degree of deviation between the actual angle and the target angle. The calculation of offset needs to consider the selection of a time window, and a sliding window method is usually used to calculate the average offset and maximum offset within a certain time period. The average offset reflects the overall accuracy level of the system, and the maximum offset reflects the worst-case performance of the system. When calculating the angle offset, the system first determines the steady-state judgment condition. When the fluctuation amplitude of the damper angle near the target value is less than a preset fluctuation threshold and the duration exceeds the minimum stabilization time, the system judges that it has entered a steady state. The fluctuation threshold for steady-state judgment is usually set to 0.5% to 2% of the target angle, and the minimum stabilization time is set to 5 to 15 seconds. These parameters need to be adjusted according to the specific application scenario and control requirements. Statistical analysis of angle offset includes statistics such as mean, standard deviation, maximum value, and minimum value. The mean reflects whether there is a systematic deviation in the system, and ideally the mean should be close to zero. The standard deviation reflects the degree of angle fluctuation; a smaller standard deviation indicates a more stable system. The maximum and minimum values ​​reflect the system's extreme performance and are used to assess system reliability. Response time is calculated from the issuance of the control command until the system reaches steady state. Response time includes three components: rise time, settling time, and settling time. Rise time is the time required to rise from 10% to 90% of the target value, reflecting the system's speed. Settling time is the time required from the start of control to entering the allowable error range, reflecting the system's overall responsiveness. Settling time is the time required for the system to remain stable within the allowable error range, reflecting the system's stability. In dust removal systems used in coal-fired power plants in the power industry, the calculated stability index shows a clear load correlation. Under high load conditions, due to the large and frequent changes in dust generation, the angle offset is relatively large, and the response time is correspondingly prolonged. Under low load conditions, the system operates relatively smoothly, and the stability index performs better. Step S42 involves real-time monitoring of the angle offset through the feedback loop. In one embodiment, the feedback loop is an important component of the control system, achieving closed-loop control by continuously monitoring the system output and comparing it with the expected value. In damper angle control, the feedback loop includes an angle sensor, a signal processing unit, a comparator, and a controller. The angle sensor measures the actual angle value, the signal processing unit filters and calibrates the sensor signal, the comparator calculates the deviation between the actual and target values, and the controller generates a control output based on the deviation. Real-time monitoring requires high-frequency data acquisition and processing capabilities. The system typically acquires angle data at frequencies between 10 and 100 Hz to ensure timely detection of angle deviations. Simultaneously with data acquisition, the system performs real-time signal quality checks, including indicators such as signal amplitude, noise level, and transmission delay.When an abnormal signal quality is detected, the system initiates a fault diagnosis procedure to identify possible causes of the fault. In one embodiment, the real-time calculation of the angle offset uses a recursive algorithm to avoid redundant calculations and improve processing efficiency. The system maintains a fixed-length data buffer, updating the buffer contents and recalculating statistical indicators each time new data arrives. This method can reduce the computational load while ensuring calculation accuracy and meeting real-time requirements. In one embodiment, the feedback loop also includes an adaptive adjustment function, dynamically adjusting monitoring parameters according to the system's operating status. During the system startup phase, due to the more intense dynamic process, the monitoring threshold is set relatively loosely. During steady-state operation, the monitoring threshold is tightened to improve detection accuracy. In environments with strong external interference, the system increases the filtering intensity to reduce false alarms. In the application case of a catalytic cracking unit in a petrochemical enterprise, the real-time monitoring function of the feedback loop plays an important role. After equipment maintenance, the system detected an abnormal increase in the angle offset, which was found to be caused by a loose angle sensor installation. Timely detection and resolution of the problem prevented potential control failure. Step S43: If the angle offset is greater than a preset angle offset threshold, a locking mechanism is activated. In one embodiment, the locking mechanism is a safety measure to ensure stable system operation. When an angular offset exceeding the normal range is detected, the system automatically activates a protection program to prevent further deterioration. The design of the locking mechanism needs to balance security and availability, effectively preventing system malfunction while avoiding over-protection that could affect normal operation. The preset angular offset threshold is determined based on the system's design specifications and operational experience. Setting the angular offset threshold too low can lead to frequent malfunctions, affecting normal system operation. Setting it too high may prevent timely detection of genuine anomalies. Typically, the angular offset threshold is set to 3 to 5 times the standard deviation of the angular offset during normal operation, thus covering the normal fluctuation range while effectively identifying anomalies. In one embodiment, the locking mechanism includes multiple layers of protection. The first layer is software limitation; when an offset exceeding the limit is detected, the system limits the amplitude and rate of change of the control output to prevent overly aggressive control actions. The second layer is hardware protection, providing independent protection functions through hardware devices such as limit switches and safety relays. The third layer is manual intervention; the system issues an alarm signal to notify the operator, and can switch to manual control mode if necessary. When the locking mechanism is activated, the system records detailed event information, including trigger time, offset value, system status, and possible cause analysis. This information is invaluable for fault diagnosis and system optimization. The system also automatically executes pre-defined emergency procedures, such as switching to standby control mode, activating alarm devices, or notifying maintenance personnel. In practical applications of cement kiln head dust removal systems in the building materials industry, the locking mechanism successfully prevented a potential equipment damage incident. At that time, due to wear on mechanical transmission components causing a continuous increase in angular offset, the system promptly activated the locking mechanism and issued an alarm.Maintenance personnel quickly located and resolved the problem based on the alarm information, preventing more serious consequences. Step S44: The locking mechanism restricts further changes in the valve angle, generating the stable state. In one embodiment, the locking mechanism achieves angle change restriction through various methods. The most direct method is to stop the control output, keeping the valve at its current angle. This method is simple and effective, but may affect dust removal efficiency. A more intelligent method is to limit the amplitude and speed of the control output, allowing small adjustments but preventing large changes. Implementing angle change restriction requires adding constraints to the control algorithm. The system calculates the expected value of the control output in real time and then checks whether the output will cause the angle change to exceed the limit. If it exceeds the limit, the system will limit the control output within the allowable range. This restriction method can maintain a certain level of control capability while ensuring safety. Generating a stable state is the ultimate goal of the locking mechanism, allowing the system to converge to a relatively stable operating point by restricting angle changes. The criteria for judging a stable state include indicators such as the amplitude, speed, and duration of angle changes. When these indicators meet the stability requirements, the system confirms that it has entered a stable state. In a stable state, the system continues to monitor changes in various indicators to ensure its maintenance. If external conditions change and disrupt the stable state, the system restarts the control process to find a new stable point. This adaptive capability allows the system to cope with changes in various operating conditions. The locking mechanism also includes setting release conditions, enabling the system to automatically resume normal control mode once the abnormal situation is resolved. Release conditions typically include requirements such as the offset returning to the normal range, the system running time reaching a minimum, and manual confirmation. These conditions ensure that the system restores normal function as quickly as possible while ensuring safety.

[0032] S5. Optimize the airflow distribution based on the angular position under steady-state conditions to generate enhanced dust removal performance indicators. In one embodiment, firstly, filtration effect parameters, including dust removal rate, are calculated based on the angular position under steady-state conditions. Then, the airflow distribution is optimized using a ventilation efficiency model based on fluid dynamics principles. Next, the enhanced dust removal performance indicators are calculated based on the optimized airflow distribution. Finally, the degree of system performance improvement is determined by comparing the enhanced dust removal performance indicators with the expected values.

[0033] If the performance improvement does not meet expectations, the system will automatically adjust the valve angle and re-enter the optimization process. Through multiple iterative adjustments, the dust removal performance indicators are ensured to gradually approach the optimal value. Furthermore, during the optimization process, the system records the parameter changes and corresponding performance results for each adjustment, accumulating data to provide a reference for subsequent operations. This data-driven dynamic optimization method not only improves the system's adaptability but also significantly enhances the stability and reliability of dust removal efficiency.

[0034] S6. Update the control algorithm parameters according to the enhanced dust removal performance index to form a closed-loop control loop.

[0035] In one embodiment, the proportional coefficient and integral time of the proportional-integral-derivative control algorithm are first adjusted according to the enhanced dust removal performance index. Then, the adjusted parameters are applied to the dust concentration data processing of the next cycle. The changes in the dust removal performance index are continuously monitored through the closed-loop control loop. If the dust removal performance index is lower than expected, the control algorithm parameters are iteratively updated.

[0036] S6. Update the control algorithm parameters according to the enhanced dust removal performance index to form a closed-loop control loop.

[0037] In one embodiment, the proportional coefficient and integral time of the proportional-integral-derivative (PID) control algorithm are first comprehensively and meticulously adjusted based on the precisely calculated enhanced dust removal performance indicators. This adjustment process is not a simple numerical modification but requires comprehensive consideration of multiple factors. For example, it is necessary to deeply analyze the current dust concentration distribution, including differences in dust concentration in different areas, the size and properties of dust particles, etc. Simultaneously, it is also necessary to combine the system's historical operating data to understand the influence of the proportional coefficient and integral time on the dust removal effect under different operating conditions. Only through such comprehensive and in-depth consideration can the proportional coefficient and integral time be accurately adjusted to ensure that the control algorithm can better adapt to actual dust removal needs.

[0038] Then, the carefully adjusted parameters are applied to the dust concentration data processing in the next cycle. During application, a rigorous data acquisition and processing workflow must be established. High-precision sensors are used to collect dust concentration data in real time, and advanced data transmission technology is used to transmit the data quickly and accurately to the control system. In the control system, complex algorithms are used to process and analyze the data, ensuring its accuracy and reliability. Only in this way can the adjusted parameters achieve optimal results in actual data processing.

[0039] The closed-loop control system continuously monitors changes in dust removal performance indicators. This monitoring process requires a high degree of continuity and accuracy. The system records various data points of the dust removal performance indicators in real time and compares them with expected values. Once abnormal fluctuations in the indicators are detected, the system immediately activates an early warning mechanism, prompting operators to conduct further checks and analysis. If the dust removal performance indicators are lower than expected, the system quickly initiates an iterative update process for the control algorithm parameters. During the iterative update process, previous adjustment strategies are reviewed and summarized to identify potential problems and shortcomings. Then, based on the latest monitoring data and analysis results, parameters such as the proportional coefficient and integral time are readjusted and applied again to subsequent dust concentration data processing. Through this continuous iterative update, the control algorithm can be continuously optimized, gradually improving the performance of the dust removal system and ensuring its stable and efficient operation.

[0040] During the adjustment process, the system dynamically analyzes the fluctuation trend of dust concentration and predicts the possible range of future changes based on historical data. This analysis and prediction process requires the use of advanced data analysis techniques and models. The system mines and analyzes a large amount of historical data to identify the patterns and trends in dust concentration fluctuations. Simultaneously, it establishes a complex prediction model by combining current environmental factors such as temperature, humidity, and wind speed. Through this model, the system can predict the range of dust concentration changes over a future period with relatively high accuracy. By introducing an adaptive mechanism, the control algorithm can automatically correct deviations based on real-time monitoring results, thereby reducing the need for manual intervention. This adaptive mechanism acts like an intelligent "regulator," automatically adjusting according to the actual operating conditions of the system. When abnormal changes in dust concentration are detected, the adaptive mechanism quickly analyzes the cause and automatically adjusts the parameters of the control algorithm, enabling the system to quickly return to a stable operating state. Furthermore, to further improve the system's response speed, the algorithm also integrates a fast convergence module, which can complete parameter optimization and output stable results in a short time. This fast convergence module uses advanced optimization algorithms and techniques, significantly shortening the parameter optimization time while ensuring optimization effectiveness. It rapidly searches and optimizes the parameters of the control algorithm, and through continuous iteration and adjustment, enables the parameters to quickly converge to the optimal value. This design not only shortens the system's adjustment cycle but also effectively reduces the risk of performance fluctuations caused by external interference. Whether it's a sudden change in dust concentration or a sudden change in environmental factors, the system can react quickly and maintain stable dust removal performance.

[0041] like Figure 2 As shown, in a second aspect, the present invention provides a dust removal control system based on a three-layer air valve, employing the method described above for dust removal control, the system further comprising:

[0042] The data acquisition module A1 is used to collect dust concentration data in the industrial environment through sensors and generate a purified concentration value sequence using a filtering algorithm; the grading module A2 is used to calculate the concentration change rate based on the concentration value sequence and determine the graded response level based on the change rate; the matching module A3 is used to obtain the angle position information of the air valve and adjust the angle to a target value that matches the graded response level using a control algorithm; the monitoring module A4 is used to extract stability indicators from the adjusted angle values ​​and monitor and lock the stable state through a feedback loop; the generation module A5 is used to optimize the airflow distribution based on the angle position in the stable state and generate enhanced dust removal performance indicators; the control module A6 is used to update the control algorithm parameters based on the dust removal performance indicators to form a closed-loop control loop.

[0043] The above description discloses only preferred embodiments of the present invention and should not be construed as limiting the scope of the invention. Those skilled in the art will understand that implementing all or part of the above embodiments and making equivalent changes in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A dust removal control method based on a three-layer air valve, characterized in that, include: S1. Collect dust concentration data in the industrial environment using sensors, and generate a purified concentration value sequence using a filtering algorithm; S2. Calculate the concentration change rate based on the concentration value sequence, and determine the graded response level based on the change rate; S3. Obtain the angle position information of the air valve, and adjust the angle to a target value matching the graded response level using a control algorithm; S4. Extract a stability index from the adjusted angle value, and monitor and lock the stable state through a feedback loop; S5. Optimize the airflow distribution based on the angle position in the stable state to generate an enhanced dust removal performance index; S6. Update the control algorithm parameters based on the enhanced dust removal performance index to form a closed-loop control loop.

2. The method as described in claim 1, characterized in that, Step S1 further includes: Step S11, the sensor includes a dust concentration sensor, which collects dust concentration data in the industrial environment in real time; Step S12, the filtering algorithm uses Kalman filtering or mean filtering to remove noise interference in the dust concentration data; Step S13, the purified concentration value sequence is generated from the noise-removed data.

3. The method as described in claim 2, characterized in that, Step S12 further includes: simultaneously using Kalman filtering and mean filtering for data processing.

4. The method as described in claim 1, characterized in that, Step S2 further includes: Step S21, obtaining the concentration change rate by performing time differentiation calculation on the purified concentration value sequence; Step S22, if the concentration change rate exceeds a preset change rate threshold, it is determined to be a high-concentration sudden scenario; Step S23, determining the graded response level based on the high-concentration sudden scenario.

5. The method as described in claim 4, characterized in that, Step S22 further includes setting the change rate threshold to 2 to 3 times the standard deviation of the concentration change rate under normal conditions.

6. The method as described in claim 5, characterized in that, Step S23 further includes: the graded response level includes three levels: low, medium and high.

7. The method as described in claim 1, characterized in that, Step S3 further includes: step S31, obtaining the current angle position information of the air valve through an angle sensor; step S32, determining the target angle value according to the graded response level; step S33, adjusting the angle of the air valve to the target angle value using a proportional-integral-derivative control algorithm; and step S34, monitoring the angle change in real time during the adjustment process to ensure matching with the graded response level.

8. The method as described in claim 7, characterized in that, Step S32 further includes: setting different target angle values ​​according to different response levels. The target angle value for a low response level is set to increase by 3 to 8 degrees from the current angle, the target angle value for a medium response level is set to increase by 10 to 20 degrees from the current angle, and the target angle value for a high response level is set to the maximum allowable opening of the damper.

9. The method as described in claim 1, characterized in that, Step S4 further includes: Step S41, calculating a stability index from the adjusted angle value, the stability index including angle offset and response time; Step S42, monitoring the angle offset in real time through the feedback loop; Step S43, if the angle offset is greater than a preset angle offset threshold, activating a locking mechanism; Step S44, the locking mechanism restricts further changes in the damper angle to generate a stable state.

10. A dust removal control system based on a three-layer air valve, characterized in that, The dust removal control system, employing the method described in any one of claims 1-9, further includes: The data acquisition module (A1) collects dust concentration data from the industrial environment using sensors and generates a purified concentration value sequence using a filtering algorithm. The grading module (A2) calculates the concentration change rate based on the concentration value sequence and determines the graded response level based on the change rate. The matching module (A3) acquires the angle position information of the air valve and adjusts the angle to a target value that matches the graded response level using a control algorithm. The monitoring module (A4) extracts stability indicators from the adjusted angle values ​​and monitors and locks the stable state through a feedback loop. The generation module (A5) optimizes the airflow distribution based on the angle position in the stable state and generates enhanced dust removal performance indicators. The control module (A6) updates the control algorithm parameters based on the dust removal performance indicators to form a closed-loop control loop.

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