Intelligent pulse cloth bag dust collector

Abstract

This invention belongs to the field of dust collector technology, specifically an intelligent pulse bag dust collector, including a dust collector body, a dust removal system, a sensor monitoring network, and a control center. The dust collector body includes a dust hopper, a dust-laden air chamber with filter bags suspended inside, and a clean air chamber. A compartmentalized lifting valve is provided between the dust-laden air chamber and the clean air chamber. The dust removal system includes an air manifold, a pulse valve installed on the air manifold, and a blowpipe extending into the clean air chamber. The sensor monitoring network is distributed on the dust collector body and the dust removal system. The control center is configured to perform the following steps: acquiring the pulse valve coil current waveform characteristics through a current transmitter in the sensor monitoring network, and acquiring the ambient noise sound pressure level through a sound level meter. This solves the problem of the existing control logic being too flat, achieving a globally optimal synergy between dust removal efficiency, system energy consumption, and operational safety.

Description

Technical Field

[0001] This invention belongs to the field of dust collector technology, specifically an intelligent pulse bag dust collector. Background Technology

[0002] Most existing pulse jet baghouse dust collectors adopt a timed or differential pressure single-boundary cleaning control method. The control system usually triggers the pulse valves sequentially according to a preset fixed time period, or relies solely on the feedback of the inlet and outlet filter pressure difference exceeding the limit to start the cleaning. In terms of adjusting the jet pressure, existing equipment often uses fixed settings based on manual experience or extremely simple open-loop adjustment. The system is only equipped with basic differential pressure transmitters or air tank pressure gauges for single-point monitoring, lacking a deep understanding of the internal flow field of the dust collector and the overall operating status of the equipment.

[0003] However, the control logic in existing technologies is too flat, making it impossible to quantitatively assess the health of cleaning actuators such as pulse valve current waveforms and actual valve opening. This can easily mask potential mechanical jamming or electromagnetic faults. Secondly, it does not comprehensively consider leakage temperature drop and actual air consumption, failing to accurately reflect the overall energy consumption of the filtration status. This often leads to over-cleaning that damages the filter bags or insufficient cleaning that increases resistance. Not only is it extremely easy to cause safety accidents when approaching the explosion threshold, but it can also cause violent oscillations of the pressure regulating mechanism under complex and fluctuating operating conditions, making it impossible to achieve the optimal synergy between safety, energy saving, and cleaning efficiency.

[0004] Therefore, the present invention provides an intelligent pulse bag dust collector. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] The technical solution adopted by this invention to solve its technical problem is as follows: An intelligent pulse bag filter dust collector, comprising a dust collector body, a dust removal system, a sensor monitoring network, and a control center, wherein the dust collector body includes a dust hopper, a dust-laden air chamber with filter bags suspended inside, and a clean air chamber, with a compartmentalized lifting valve provided between the dust-laden air chamber and the clean air chamber; the dust removal system includes an air manifold, a pulse valve installed on the air manifold, and a blowpipe extending into the clean air chamber; the sensor monitoring network is distributed on the dust collector body and the dust removal system; the control center is configured to perform the following steps: A1: The pulse valve coil current waveform characteristics are obtained through the current transmitter in the sensor monitoring network, the sound pressure level of the dust cleaning environment is obtained through the sound level meter, the actual opening degree of the compartment lifting valve is obtained through the displacement sensor, and the dust cleaning execution health coefficient is calculated through the dust cleaning execution health model. A2: The differential pressure difference of each compartment is obtained through the differential pressure transmitter, the temperature difference between the clean air chamber and the dust-containing chamber is obtained through the temperature sensor, the instantaneous flow rate and cumulative consumption of compressed air are obtained through the gas flow meter, and the filtration status and energy consumption coefficient are calculated through the filtration status and energy consumption model. A3: Obtain the oxygen content of the inlet flue gas through an oxygen analyzer, obtain the dust electrostatic charge density through an electrostatic sensor, obtain the fan operating frequency through a communication interface, and calculate the operating safety and boundary coefficients through an operating safety and boundary model; A4: Based on the cleaning performance health coefficient, filtration status and energy consumption coefficient, instantaneous dynamic pressure drop of the air bag obtained by the air bag pressure transmitter and vibration intensity of the blowpipe obtained by the vibration sensor, the cleaning gas-solid efficiency coupling coefficient is calculated by the cleaning gas-solid efficiency coupling model. A5: Based on the dust removal gas-solid efficiency coupling coefficient, the current dust removal injection pressure, and the operation safety and boundary coefficient, the target dust removal injection pressure is output through the injection pressure optimization model, and the pressure regulation actuator of the dust removal system is controlled accordingly.

[0007] The beneficial effects of this invention are as follows: 1. The intelligent pulse bag filter of this invention introduces a cleaning execution health model based on an inverse polynomial deviation decay architecture to finely and quantitatively evaluate key characteristics such as pulse valve current waveform, environmental noise, and valve opening. This effectively avoids cleaning failure and blind pressure adjustment caused by potential mechanical jamming or electromagnetic faults. At the same time, combined with an algebraic saturated nonlinear negative feedback jet pressure optimization model, it achieves deep perception of the internal flow field and operating status of the equipment, fundamentally solving the problem of the overly flat existing control logic and achieving global optimal synergy of cleaning efficiency, system energy consumption, and operational safety.

[0008] 2. The intelligent pulse bag filter of this invention overcomes the serious defect of existing technologies that rely solely on single inlet and outlet filtration pressure difference feedback to initiate dust removal by quantifying the coupling relationship between the complex changes in filtration resistance and energy consumption within the dust removal system. It comprehensively considers multiple dimensions of data, such as the independent filtration pressure difference of each compartment, the temperature difference between the clean air chamber and the dust-laden air chamber, and the instantaneous flow rate and cumulative consumption of compressed air. The system remains stable under normal operating conditions and can sensitively reflect the deterioration trend when approaching the extreme operating conditions, thereby guiding precise pressure regulation. This completely avoids the problems of over-cleaning damaging the filter bags or insufficient cleaning increasing resistance, achieving a dynamic balance between energy saving and filtration efficiency.

[0009] 3. The intelligent pulse bag filter of the present invention uses real-time extraction of inlet flue gas oxygen content, dust electrostatic charge density and fan operating frequency as safety dimensions. When any parameter approaches the danger threshold, the coefficient output by the model will undergo a severe nonlinear decay. At this time, the pressure regulation step term of the control center is suppressed by the square exponential of the coefficient, limiting the sudden action of the electric proportional pressure reducing valve. This not only accurately avoids the risk of approaching the explosion red line, but also effectively prevents safety accidents caused by violent oscillation of the pressure regulating mechanism under complex fluctuating conditions. Attached Figure Description

[0010] The invention will now be further described with reference to the accompanying drawings.

[0011] Figure 1 This is a perspective view of the present invention; Figure 2 This is a schematic diagram of the structure of the jet pipe in this invention; Figure 3 This is a schematic diagram of the installation of the dust collector bag in this invention; Figure 4 This is the overall data flow and control flowchart of this invention; Figure 5 This is a flowchart of the injection pressure optimization calculation logic in this invention; Figure 6 This is a schematic flowchart illustrating the coupling of health diagnosis and efficacy in this invention; Figure 7 This is a flowchart of the safety limiting protection mechanism in this invention.

[0012] In the diagram: 1. Dust collector body; 2. Dust hopper; 3. Dust-laden air chamber; 4. Clean air chamber; 5. Pulse jet pipe; 6. Dust collector bag; 7. Air manifold; 8. Pulse valve. Detailed Implementation

[0013] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0014] An intelligent pulse bag filter dust collector according to an embodiment of the present invention includes a dust collector body 1, a dust removal system, a sensor monitoring network, and a control center. The dust collector body 1 includes a dust hopper 2, a dust-laden air chamber 3 with dust collector bags 6 suspended inside, and a clean air chamber 4. A compartment lifting valve is provided between the dust-laden air chamber 3 and the clean air chamber 4. The dust removal system includes an air tank 7, a pulse valve 8 installed on the air tank 7, and a blowpipe 5 extending into the clean air chamber 4. The sensor monitoring network is distributed on the dust collector body 1 and the dust removal system. The control center is configured to perform the following steps: A1: Obtain the current waveform characteristics of the pulse valve 8 coil through the current transmitter in the sensor monitoring network, obtain the sound pressure level of the dust cleaning environment noise through the sound level meter, obtain the actual opening degree of the compartment lifting valve through the displacement sensor, and calculate the dust cleaning execution health coefficient through the dust cleaning execution health model. A2: The differential pressure difference of each compartment is obtained through the differential pressure transmitter, the temperature difference between the clean air chamber 4 and the dust-containing air chamber is obtained through the temperature sensor, the instantaneous flow rate and cumulative consumption of compressed air are obtained through the gas flow meter, and the filtration status and energy consumption coefficient are calculated through the filtration status and energy consumption model. A3: Obtain the oxygen content of the inlet flue gas through an oxygen analyzer, obtain the dust electrostatic charge density through an electrostatic sensor, obtain the fan operating frequency through a communication interface, and calculate the operating safety and boundary coefficients through an operating safety and boundary model; A4: Based on the cleanup execution health coefficient, filtration status and energy consumption coefficient, instantaneous dynamic pressure drop of air manhole 7 obtained by the air manhole 7 pressure transmitter, and vibration intensity of blowpipe 5 obtained by the vibration sensor, the cleanup gas-solid efficiency coupling coefficient is calculated by the cleanup gas-solid efficiency coupling model. A5: Based on the air-solid coupling coefficient of dust removal, the current dust removal injection pressure, and the operational safety and boundary coefficients, the target dust removal injection pressure is output through the injection pressure optimization model, and the pressure regulation actuator of the dust removal system is controlled accordingly.

[0015] In terms of the health assessment of the dust removal process, a current transmitter monitors the current waveform of the pulse valve 8 coil in real time, a sound level meter monitors the ambient noise during dust removal, and a displacement sensor monitors the actual opening degree of the compartment lift valve. For example, if the rise time of the coil current of a certain pulse valve 8 is slightly prolonged, and the ambient noise sound pressure level during the dust removal period is slightly increased, and the closing speed of the corresponding compartment lift valve is slightly slowed down, the dust removal health model will integrate this information and calculate a dust removal health coefficient that is slightly lower than the normal value. This indicates that the dust removal system may have slight mechanical wear or electromagnetic performance degradation.

[0016] In terms of filtration status and energy consumption assessment, the differential pressure transmitter monitors the independent filtration differential pressure of each compartment, the temperature sensor monitors the temperature difference between the clean air compartment 4 and the dust-containing air compartment, and the gas flow meter monitors the instantaneous flow rate and cumulative consumption of compressed air. For example, when the independent filtration differential pressure of multiple compartments continues to rise, the temperature difference between the clean air compartment 4 and the dust-containing air compartment increases, and the instantaneous flow rate and cumulative consumption of compressed air are both higher than the average level, the filtration status and energy consumption model will calculate a lower filtration status and energy consumption coefficient, indicating that the filter bag may be clogged, the filtration resistance is increased, and the compressed air consumption is high, resulting in increased energy consumption.

[0017] In terms of operational safety and boundary assessment, the oxygen analyzer monitors the oxygen content of the inlet flue gas, the electrostatic sensor monitors the electrostatic charge density of the dust, and the communication interface obtains the fan operating frequency. For example, when the oxygen content of the inlet flue gas increases slightly due to process fluctuations, the electrostatic charge density of the dust also reaches a moderate level, and the fan operating frequency remains stable, the operational safety and boundary model will calculate an operational safety and boundary coefficient, indicating that the dust collector is currently in a safe operating state, but there are certain potential risk factors.

[0018] Subsequently, the air-solid efficiency coupling model for dust removal integrates the calculated dust removal health coefficient, filtration status and energy consumption coefficient, as well as the real-time instantaneous dynamic pressure drop of the air mantle 7 obtained through the pressure transmitter and the vibration intensity of the blowpipe 5 obtained through the vibration sensor. Through this model, a comprehensive air-solid efficiency coupling coefficient for dust removal is calculated. This coefficient comprehensively reflects the current health status, filtration performance, energy consumption level and overall performance of the dust removal system.

[0019] Finally, the jet pressure optimization model receives the dust removal gas-solid efficiency coupling coefficient, the current dust removal jet pressure, and the operational safety and boundary coefficients as inputs. Based on the preset target dust removal efficiency coupling coefficient, the model determines that the current dust removal efficiency is insufficient to achieve the target, and that there is room to increase the jet pressure within the current safety boundary. Therefore, the jet pressure optimization model calculates and outputs a new target dust removal jet pressure, for example, 0.42. The control center then sends a command to the pressure regulating actuator of the dust removal system, adjusting the blowing pressure to 0.42. Through this dynamic adjustment, the dust collector improves cleaning efficiency, reduces filtration resistance, and optimizes compressed air consumption while ensuring operational safety, achieving optimal synergy between cleaning efficiency, energy consumption, and operational safety.

[0020] By introducing a health model for dust removal, the current waveform characteristics of the pulse valve 8 coil, the sound pressure level of the dust removal environment, and the actual opening degree of the compartment lifting valve can be monitored and evaluated in a refined manner. This enables quantitative perception of the health status of the dust removal actuators and effectively avoids the dust removal failure problem caused by the lack of health assessment in traditional solutions.

[0021] Furthermore, traditional solutions often rely solely on feedback from the inlet and outlet filtration pressure difference exceeding the limit to initiate dust removal, failing to comprehensively consider leakage temperature drop and actual air consumption. Existing technologies only focus on the increase in filtration pressure difference, unable to identify energy consumption issues reflected by the increased temperature difference between the clean air chamber 4 and the dust-containing air chamber, as well as the high instantaneous flow rate and cumulative consumption of compressed air. This application constructs a filtration status and energy consumption model that comprehensively considers independent filtration pressure difference, the temperature difference between the clean air chamber 4 and the dust-containing air chamber, and the instantaneous flow rate and cumulative consumption of compressed air. This model can accurately reflect the comprehensive energy consumption of the filtration status, thereby avoiding the problems of excessive dust removal damaging the filter bag or insufficient dust removal increasing resistance and causing energy waste in traditional solutions.

[0022] This embodiment couples the cleaning performance health coefficient, filtration status and energy consumption coefficient, instantaneous dynamic pressure drop of air manifold 7, and vibration intensity of blowpipe 5 to obtain the cleaning gas-solid efficiency coupling coefficient. Based on this, and combined with the current cleaning blow pressure and operational safety and boundary coefficients, the target cleaning blow pressure is output through the blow pressure optimization model. This multi-dimensional, coupled optimization control enables the dust collector to dynamically balance cleaning efficiency, energy consumption, and operational safety under complex fluctuating operating conditions, achieving globally optimal collaborative control. This significantly improves the operating efficiency, safety, and economy of the dust collector, overcoming the limitations of existing technologies such as flat control logic, lack of deep perception, and inability to achieve globally optimal collaboration.

[0023] The dust removal execution health model is based on an inverse polynomial deviation decay architecture, and its specific calculation logic is as follows: Calculate the peak current of the pulse valve 8 coil, current rise time, effective energizing time, ambient noise level during dust removal, and dimensionless relative deviation of the actual opening of the lifting valve relative to its respective design reference value. Multiply the squares of each dimensionless relative deviation by the corresponding dimensionless sensitivity weighting coefficient and sum them to obtain the sum of squared deviations. The sum of squared deviations is added to constant 1 and the reciprocal is taken to obtain the dust removal performance health coefficient; among which, the sum of all dimensionless sensitivity weight coefficients is equal to constant 1.

[0024] The dust removal performance health model is based on an inverse polynomial deviation decay architecture, and its calculation formula is as follows: ; In the formula, The health coefficient for dust removal is a dimensionless, normalized value, and its range is limited to [value range missing]. Within the interval, The dimensionless sensitivity weight coefficients for each feature and satisfying ; These are the peak currents of the pulse valve 8 coil (unit: ), current rise time (unit: Effective power-on time (unit: ), ambient noise level during dust removal (unit: ) and the dimensionless relative deviation of the valve opening from its respective design reference value.

[0025] This dust removal execution health model aims to quantitatively evaluate the operating status of the pulse bag filter dust removal system. Its core function is to comprehensively transform multiple key physical quantities and electrical parameters involved in the dust removal process into a single indicator that can intuitively reflect the health status of the dust removal actuator through specific mathematical relationships. This model can identify potential faults or performance degradation of the dust removal actuator, providing basic data for the intelligent control of the dust collector. This model, based on an inverse polynomial deviation decay architecture, is characterized by the fact that when the input parameters deviate from their design baseline values, the health coefficient output by the model will respond in a non-linear, decaying manner. Specifically, the larger the deviation, the more significant the decay of the health coefficient, and the decay rate will accelerate as the deviation increases. This design makes the model highly sensitive to abnormal states, effectively amplifying the impact of small deviations on the system health, thereby achieving early warning of faults.

[0026] The above calculation formula is the specific mathematical expression of the dust removal performance health model. It calculates the dimensionless relative deviations of various characteristics. The total deviation is converted into a health coefficient by squaring and weighting the result, then mapping it through a function of inverse polynomial form. This ensured The output range is limited to Within the range, 1 represents a completely healthy state, while values ​​close to 0 indicate a severe abnormality. As the final output of the dust removal execution health model, its value directly reflects the overall health level of the dust removal actuator. The higher the value, the closer the operating status of the dust removal actuator is to the design benchmark and the better its performance; the lower the value, the more likely there are faults or performance degradation of varying degrees.

[0027] The dimensionless sensitivity weight coefficients for each feature and satisfying These weight coefficients are used to quantify the relative importance or sensitivity of different input features to the health of dust removal performance. For example, the deviation of certain features may have a greater impact on the dust removal effect, so they can be given higher weights. By adjusting these weights, the model can more accurately reflect the actual impact of various factors on the health of a specific dust collector or under specific working conditions. The sum of the weights is 1, which ensures the reasonable allocation of weights and the stability of the model.

[0028] These are the dimensionless relative deviations of the peak current of pulse valve 8 coil, current rise time, effective energizing time, ambient noise sound pressure level during dust removal, and the opening degree of the lifting valve relative to their respective design reference values. These deviations are the core inputs of the model, obtained by real-time monitoring of key physical quantities during the dust removal process and comparing them with preset design reference values. The dimensionless relative deviation of the peak current of pulse valve 8 coil is also included. It reflects the deviation between the maximum current value that the electromagnetic coil of pulse valve 8 can reach at the moment of energization and the design value. Its change may indicate problems such as coil aging, deterioration of drive circuit performance or increase of internal friction in valve body, which directly affects the opening force and speed of valve.

[0029] Dimensionless relative deviation of current rise time The deviation of the time required for the coil current of the pulse valve 8 to rise from its initial value to its peak value from the design value was measured. An excessively long rise time may indicate a slow coil response or a fault in the drive circuit, causing the valve to lag and affecting the accuracy of the injection timing. The dimensionless relative deviation of the effective energizing time was also measured. This indicates the difference between the actual energized duration of the pulse valve 8 coil and the design value. The deviation may be due to abnormal control signals or malfunctioning valve mechanical action, which directly affects the duration of the jetting airflow and energy release.

[0030] Dimensionless relative deviation of ambient noise sound pressure level during dust removal By monitoring the sound pressure level of the surrounding environment at the moment of dust removal pulse-jet, the intensity and uniformity of the pulse-jet airflow and the impact condition on the filter bags can be reflected. Abnormal noise levels (e.g., too low may indicate insufficient pulse-jet energy, too high may indicate excessive filter bag vibration or abnormal pulse-jet) can indicate potential problems with the dust removal effect. The dimensionless relative deviation of the valve opening can also be considered. The difference between the actual opening angle or stroke of the compartment lift valve and the design value was quantified. The accurate opening degree of the lift valve is crucial to ensuring effective isolation of the compartment and correct guidance of the dust removal airflow. Its deviation may indicate mechanical jamming, actuator failure or insufficient control precision, affecting the precise control of the dust removal area. These deviations are compared with their respective design reference values ​​and processed in a dimensionless manner, so that the deviations of different physical quantities can be compared and weighted on the same scale, thereby providing a unified input basis for health assessment.

[0031] By constructing a dust removal execution health model, the dimensionless relative deviations of key parameters such as the peak current of the pulse valve 8 coil, current rise time, effective energizing time, sound pressure level of the dust removal environment, and valve opening degree are analyzed. As input, these deviation values ​​are squared and then multiplied by a preset dimensionless sensitivity weighting coefficient. The results are then summed to form a comprehensive deviation term. Subsequently, this comprehensive deviation term is used to calculate the dust removal performance health coefficient through an inverse polynomial function mapping. This inverse polynomial bias decay architecture means that an increase in the bias of any one or more input parameters will lead to an increase in the health coefficient. The deviation decreases significantly, thus enabling sensitive reflection of early failures or performance degradation in the dust removal actuator. For example, when aging of the pulse valve 8 coil leads to a decrease in peak current or an increase in response time, the corresponding deviation will be reflected. It will increase, thus making The indicator shows a decrease in the health status of the dust removal process.

[0032] Similarly, when the lift valve becomes stuck, causing inaccurate opening, its deviation... The increase in size will also lead to The model reduces the decay of mechanical and electrical operating conditions, thus transforming the complex mechanical and electrical performance into a single, easily understood health coefficient. This dust removal performance health coefficient... Subsequently, as a crucial input to the control center of the intelligent pulse bag filter, it is fed into the dust removal gas-solid efficiency coupling model for further calculations. This, in turn, affects the output of the pulse jet pressure optimization model to the target dust removal pulse jet pressure, ultimately guiding the action of the pressure regulation actuator. This enables the dust collector to dynamically adjust its dust removal strategy based on real-time perception of the health status of the dust removal actuator, avoiding the problems of poor dust removal effect or increased energy consumption caused by dust removal actuator failure in traditional control methods.

[0033] By comprehensively considering the electromagnetic characteristics of the pulse valve 8, the noise of the dust cleaning environment, and the linkage accuracy of the mechanical actuators from multiple dimensions, the model can accurately identify early faults or performance degradation of the dust cleaning actuator. This avoids blindly adjusting the blowing pressure when there are problems with the dust cleaning actuator, thus ensuring the effectiveness of the dust cleaning strategy and the stability of the dust collector operation. In addition, by processing the multi-source heterogeneous features into dimensionless form and assigning sensitivity weights, and using an inverse polynomial function to apply a quadratic penalty to the deviation, the model can generate a sensitive decay response to any abnormal fluctuation of any single indicator, effectively avoiding misjudgments caused by the failure of a single sensor, and significantly improving the accuracy and reliability of the dust cleaning execution health assessment.

[0034] The filtering state and energy consumption model is based on the Cauchy rational distribution architecture, and its specific computational logic is as follows: Calculate the ratios of the measured independent filter pressure difference to the limit value, the measured temperature difference to the maximum value, the measured instantaneous compressed air flow rate to the maximum throughput, and the measured cumulative compressed air consumption to the highest cycle quota, respectively. After multiplying each ratio by its corresponding dimensionless influence factor, the weighted sum is obtained to obtain the comprehensive dimensionless load index. Multiply the square of the comprehensive dimensionless load index by the dimensionless decay rate constant, add a constant 1, and take the reciprocal to obtain the filtration state and energy consumption coefficient.

[0035] The filtering state and energy consumption model is based on the Cauchy rational distribution architecture, and its calculation formula is as follows: ; In the formula, The filter state and energy consumption coefficient are dimensionless normalized values, and their range is limited to [value range missing]. Within the range; For the comprehensive dimensionless load index; It is a dimensionless decay rate constant; It is a dimensionless influence factor; and These are the measured and limit values ​​of the independent filter differential pressure (unit: ); and These are the measured and maximum temperature differences (unit: ); and These are the measured instantaneous flow rate and maximum throughput of compressed air, respectively (unit: ); and These are the measured cumulative compressed air consumption and the highest periodic quota, respectively (unit: ).

[0036] This filtration status and energy consumption model aims to quantitatively evaluate the filtration efficiency and energy consumption of pulse bag filters during operation. The concept is to integrate multiple key physical parameters of the dust collector during operation and map them into a unified coefficient through mathematical relationships, thereby intuitively reflecting the overall operating load and health status of the dust collector. This model can serve as a core component of the intelligent control system for dust collectors, providing data support for the optimization of dust removal strategies.

[0037] The Cauchy rational distribution architecture is a nonlinear function form characterized by its gradual change when the input value is close to the center, and its rapid decay when the input value deviates from the center. When applied to the filtration status and energy consumption model, it can make the model insensitive to slight parameter fluctuations and maintain the relative stability of the coefficients under normal operating conditions of the dust collector. However, when the operating conditions deteriorate (such as excessive pressure difference or abnormal energy consumption), it can quickly and sensitively reflect the sharp decline in filtration status and energy consumption, thereby triggering early warnings or adjusting the dust removal strategy in a timely manner. This architecture can effectively capture the nonlinear transition of the system from normal to abnormal.

[0038] Comprehensive dimensionless load index It is a comprehensive indicator used to measure the current operating load of a dust collector. It is obtained by dimensionless processing and weighted summation of multiple key operating parameters (independent filtration pressure difference, temperature difference between clean air chamber 4 and dust-containing air chamber, instantaneous compressed air flow rate, and cumulative compressed air consumption). Its function is to unify parameters with different physical dimensions to a comparable scale, thereby comprehensively and objectively reflecting the overall performance of the dust collector in terms of filtration efficiency and energy consumption. The value of this index directly reflects the pressure or burden of the dust collector's current operation.

[0039] dimensionless decay rate constant It is a key parameter in the Cauchy rational distribution framework, used to adjust the model's response to the comprehensive dimensionless load index. The sensitivity to change and the rate of decay, The larger the value, the more sensitive the model is to the load index; when the load index... When added, the filter status and energy consumption coefficient The faster the decay, the better; conversely, The smaller the value, the smoother the decay. This can be achieved by adjusting... This allows the model to better adapt to the response characteristics of different types of dust collectors or different operating conditions, ensuring the accuracy and practicality of the model output.

[0040] Dimensionless Influence Factor It is used to quantify various input parameters (independent filter pressure difference, temperature difference between clean air chamber 4 and dust-containing air chamber, instantaneous compressed air flow rate, and cumulative compressed air consumption) in the comprehensive dimensionless load index. The coefficients representing the weight or degree of influence of various parameters are typically determined through experimental data, expert experience, or optimization algorithms to reflect the actual contribution of different parameters to the filtration status and energy consumption of the dust collector. For example, if the independent filtration differential pressure has the greatest impact on system performance, then its corresponding... The value will be relatively large. By setting these influencing factors reasonably, it can be ensured that the comprehensive load index can accurately reflect the actual operating status of the dust collector.

[0041] Independent filtration differential pressure This refers to the pressure difference in a single or specific compartment of a dust collector under independent filtration conditions. It directly reflects the thickness and permeability of the dust layer on the surface of the filter bag and is a key indicator for evaluating filter bag resistance, dust accumulation, and cleaning efficiency. This is the limit value of the independent filtration differential pressure, which is usually the maximum allowable differential pressure set according to the dust collector design specifications or safe operation requirements. Approaching or exceeding When the pressure difference is high, it indicates that the filter bag resistance is too high, requiring cleaning or indicating a risk of clogging. This parameter can be monitored in real time using a differential pressure transmitter. (The text also mentions temperature differences, but these seem unrelated to the main point about filter bag resistance and clogging.) This refers to the temperature difference between the clean air chamber 4 and the dust-laden air. Under normal circumstances, the temperature change inside the dust collector should be within a certain range. An abnormal temperature difference may indicate a variety of problems. For example, a large temperature difference may mean that there is air leakage causing cold air to enter, or that there is local overheating inside the dust collector. This will affect the filtration efficiency and filter bag life and increase energy consumption.

[0042] This is the maximum permissible temperature difference; exceeding this range is considered an abnormal operating condition. This parameter can be monitored in real time using a temperature sensor, along with the instantaneous compressed air flow rate. This refers to the amount of compressed air consumed by the cleaning system during the instantaneous pulse-jet cleaning process. It is one of the important indicators for measuring cleaning energy consumption. Excessively high instantaneous flow rate may indicate low cleaning efficiency or leakage, while excessively low flow rate may lead to incomplete cleaning.

[0043] This is the maximum compressed air throughput, representing the maximum instantaneous flow rate allowed by the system design. This parameter can be monitored in real time using a gas flow meter, and it also represents the cumulative compressed air consumption. This refers to the total amount of compressed air consumed by the dust removal system within a certain period (such as a shift, a day, or a dust removal cycle). It reflects the total energy consumption level of the dust collector over a period of time. By monitoring the cumulative consumption, the economic efficiency of the dust removal strategy can be evaluated. This is the maximum cycle quota, which is the maximum cumulative amount of compressed air allowed within a specific cycle. Exceeding this quota may indicate that dust removal is too frequent or inefficient. This parameter can be calculated cumulatively using a gas flow meter.

[0044] The control center first obtains the independent filter pressure difference of each compartment via a differential pressure transmitter. The temperature difference between the clean air chamber 4 and the dust-containing air chamber is obtained through a temperature sensor. The instantaneous flow rate of compressed air is obtained through a gas flow meter. With cumulative usage Subsequently, these measured values ​​will be compared with their respective limit values ​​or maximum values ​​( , , , The parameters are then processed to be dimensionless, and then multiplied by a preset dimensionless influence factor. Then sum them up to calculate the comprehensive dimensionless load index. Finally, the load index Substituting into the formula based on the Cauchy rational distribution framework, and combining it with the dimensionless decay rate constant... The final filtration state and energy consumption coefficient are calculated. This coefficient, as a comprehensive evaluation index of the dust collector's operating status, can overcome the limitations of single differential pressure monitoring, fully reflect the complex changes in filtration resistance inside the dust collector, and consider key energy consumption indicators such as leakage temperature drop and actual air consumption. The control center can accurately quantify the coupling relationship between filtration status and energy consumption, providing precise input for the calculation of the subsequent dust removal air-solid efficiency coupling model and the decision-making of the jet pressure optimization model. This makes the dust removal strategy no longer blind and can achieve a dynamic balance between energy saving and filtration efficiency.

[0045] By integrating multi-dimensional data such as independent filtration pressure difference, temperature difference between clean air chamber 4 and dust-laden air chamber, instantaneous flow rate and cumulative consumption of compressed air, the complex changes in filtration resistance and energy consumption within the dust collector can be reflected more comprehensively and accurately. This achieves precise quantification of the coupling relationship between filtration status and energy consumption, transforming multi-dimensional physical quantities into unified filtration status and energy consumption coefficients. This provides a reliable quantitative basis for formulating dust removal strategies. Furthermore, the adoption of a Cauchy rational distribution architecture ensures that the model remains stable under normal operating conditions, while sensitively reflecting the deterioration trend of energy consumption and filtration status when approaching extreme operating conditions, thus promptly identifying potential problems. This coefficient can serve as an important input to the jet pressure optimization model in the control center, thereby guiding the dust removal system to adjust the jet pressure, avoiding over-cleaning that damages the filter bags or insufficient cleaning that increases resistance. Ultimately, this achieves a dynamic balance between energy saving and filtration efficiency, improving the overall operational economy and stability of the dust collector.

[0046] The operational safety and boundary model is a continuously differentiable model based on higher-order rational barrier functions, and its specific operational logic is as follows: Calculate the ratios of the measured oxygen content of the inlet flue gas to the explosion-proof critical value, the measured dust electrostatic charge density to the discharge limit threshold, and the measured fan operating frequency to the maximum overload frequency. After performing higher-order even-number power operations on the above ratios, multiplying them by their respective dimensionless penalty scale constants, we obtain multiple boundary penalty values. Add all boundary penalty values ​​to a constant and take the reciprocal to obtain the operational safety and boundary coefficients.

[0047] The operational safety and boundary model is a continuously differentiable model based on higher-order rational barrier functions, and its calculation formula is as follows: ; In the formula, For operational safety and boundary coefficients, which are dimensionless normalized values, the range is limited to [value range missing]. Within the range; It is a dimensionless penalty scale constant; and These are the measured values ​​of oxygen concentration in flue gas and the explosion-proof critical value (unit: ); and These are the measured values ​​of dust electrostatic charge density and the discharge limit threshold, respectively (unit: ); and These are the measured values ​​of the fan frequency and the maximum overload frequency, respectively (unit: ).

[0048] This operational safety and boundary model aims to quantitatively assess the potential safety risks faced by intelligent pulse bag filters during operation and provide a comprehensive safety factor. It integrates multiple key safety parameters to reflect the degree to which the system approaches the danger boundary in a nonlinear manner. This model can be implemented as a software module in the control center. The higher-order rational barrier function is a mathematical function whose characteristic is that when the input variable approaches a certain critical value, the output value of the function will change rapidly and nonlinearly, forming a barrier effect.

[0049] In this application, a safety assessment model is constructed such that when any safety parameter (such as oxygen concentration, electrostatic charge density, or fan frequency) approaches its preset danger threshold, the safety factor output by the model will drop sharply, thereby providing a strong early warning signal. This function can be implemented on the processor of the control center through a numerical calculation library.

[0050] A continuously differentiable model is one whose function curve is continuous within its domain and has a derivative at every point. This means that the model's output value will not change abruptly, and its trend is smooth and predictable. In control systems, continuous differentiability is crucial for achieving smooth and precise control, and can avoid system oscillations or instability caused by abrupt changes in the model output. This is usually achieved by selecting appropriate mathematical functions (such as polynomials, exponential functions, rational functions, etc.) and ensuring that their parameters are set reasonably, for example, by optimizing the model parameters through the least squares method or gradient descent method.

[0051] Measured value of flue gas oxygen concentration This refers to the oxygen content in the flue gas at the inlet or inside of the dust collector, as monitored in real time by an oxygen analyzer; the explosion-proof critical value. This refers to the upper limit of oxygen content in flue gas set to prevent explosions in specific dust environments. When the measured oxygen concentration approaches or exceeds this critical value, the risk of explosion increases significantly. Monitoring and comparing these two values ​​are key indicators for assessing the explosion-proof safety of dust collectors.

[0052] Measured values ​​of dust electrostatic charge density This refers to the amount of static charge carried by dust particles inside the dust collector, measured in real time by an electrostatic sensor, and its discharge limit threshold. This refers to a point where, when the electrostatic charge density of dust reaches a certain value, the risk of electrostatic discharge between dust particles or between dust and equipment walls is extremely high, potentially igniting sparks and combustible dust. Monitoring and comparing these two values ​​is crucial for preventing fires and explosions caused by electrostatic sparks.

[0053] Actual measured value of fan frequency This refers to the current operating frequency and maximum overload frequency of the wind turbine, obtained from the wind turbine inverter via the communication interface. This refers to the highest operating frequency that a wind turbine can withstand under design or safe operating conditions. Exceeding this frequency may lead to wind turbine overload, increased vibration, bearing damage, or even motor burnout. Monitoring and comparing these two values ​​helps assess the wind turbine's operating load and mechanical safety, preventing equipment damage due to overload operation. (Dimensionless penalty scale constant) These constants are used to adjust the weights of various safety indicators in the model on the final safety factor. They are dimensionless, meaning they are unitless and are only used to adjust the relative importance or sensitivity of each indicator in the model. By adjusting these constants, the model's response to different safety risks can be flexibly configured according to actual working conditions, dust characteristics, or safety management requirements. For example, for flammable and explosive dust, the penalty scale constant related to oxygen concentration or electrostatic charge density can be increased to make its impact on the safety factor more significant.

[0054] The control center obtains the measured value of flue gas oxygen concentration through an oxygen analyzer. The measured value of dust electrostatic charge density was obtained through an electrostatic sensor. The measured value of the wind turbine frequency is obtained through the communication interface. These measured values ​​were then compared with the preset explosion-proof threshold. Discharge limit threshold and maximum overload frequency The comparisons are made, and the sixth power of their ratios is used as higher-order penalty terms, which are passed through a dimensionless penalty scale constant. Weighted terms are applied to form the decay factor of the higher-order rational barrier function. When any safety parameter approaches its critical threshold, the corresponding penalty term grows exponentially, affecting the operational safety and boundary coefficients of the model output. Rapid decay, this nonlinear decay characteristic enables the system to exhibit high sensitivity to changes in potentially dangerous states, thereby achieving early warning.

[0055] In the injection pressure optimization model, operational safety and boundary coefficients Suppressing the voltage regulation step size term in the form of a quadratic exponent means that when the system is in a good safety state ( When the pressure approaches 1), the blowing pressure can be actively adjusted according to the dust removal efficiency requirements; however, when the system safety condition deteriorates ( When the pressure drops significantly, the pressure adjustment step size will be strictly limited, thereby avoiding aggressive dust removal operations under dangerous conditions, effectively preventing safety accidents caused by dust removal actions, and ensuring that the dust collector always operates within the safety boundary. This synergistic effect enables the intelligent pulse bag dust collector to not only achieve efficient dust removal, but also maintain a high degree of operational safety in complex and ever-changing environments.

[0056] By introducing three key safety dimensions—flue gas oxygen concentration, dust electrostatic charge density, and fan operating frequency—and employing a high-order rational barrier function for nonlinear coupling evaluation, the system can accurately and in real-time detect the trend of the dust collector approaching its safety critical point. When any safety parameter approaches its limit threshold, the operational safety and boundary coefficients output by the model decay rapidly, providing a timely and strong early warning signal. This effectively avoids safety accidents caused by the failure or insufficient evaluation of a single indicator. Furthermore, because the model can quantify the safety state in a refined manner, it avoids the overprotection caused by traditional conservative strategies, thus maintaining the dust collector's high-efficiency dust removal performance while ensuring safe equipment operation.

[0057] The air-solid coupling model for dust removal efficiency is based on the Cobb-Douglas multivariate geometric features, and its specific computational logic is as follows: Using the health coefficient of dust removal, the filtration status and energy consumption coefficient, the ratio of the measured value of the instantaneous dynamic pressure drop of air tank 7 to its benchmark value, and the ratio of the measured value of the vibration intensity of blowpipe 5 to its benchmark value as the base, and the corresponding dimensionless elastic coefficient as the exponent, multiple power function terms are constructed. Multiply all the power function terms together to obtain the air-solid coupling coefficient for dust removal efficiency; among them, the sum of all dimensionless state elastic coefficients is equal to a constant.

[0058] The air-solid coupling model for dust removal efficiency is based on the Cobb-Douglas multivariable geometric features, and its calculation formula is as follows: ; In the formula, The gas-solid coupling coefficient for dust removal efficiency is a dimensionless normalized value, and its range is limited to [value range missing]. Within the range; The elastic coefficient is a dimensionless state and satisfies ; and These are the measured and baseline values ​​of the instantaneous dynamic pressure drop of air tank 7 (unit: ); and The measured and reference values ​​of the vibration intensity of blowpipe 5 are respectively (obtained from the vibration sensor; if an accelerometer is used, the recommended unit is...). If measuring speed, the recommended unit is [unit missing]. ).

[0059] The air-solid coupling model for dust removal efficiency combines multiple interrelated input variables in a product form and uses a dimensionless state elasticity coefficient in exponential form to characterize the nonlinear contribution of each variable to the overall efficiency. This model not only reflects the independent influence of each factor but also captures their synergistic effects, thus providing a more comprehensive and accurate assessment of dust removal efficiency. This model can be implemented by programming in the processor of the control center, for example, by using algorithm modules written in languages ​​such as Python, MATLAB, or C++ to receive data from the sensor monitoring network and perform real-time calculations. Alternatively, for higher computational efficiency and real-time performance, the model can also be implemented using dedicated hardware logic circuits, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), embedding complex exponentiation and multiplication operations into the hardware.

[0060] Air-solid coupling coefficient for dust removal efficiency It is the core indicator output by the model. It is a dimensionless comprehensive value used to quantitatively evaluate the synergistic efficiency of airflow dynamics and mechanical response during the cleaning process of a pulse bag filter. This coefficient directly reflects the efficiency of the cleaning system in converting compressed air energy into effective cleaning of the filter bags. Its value directly indicates the effectiveness of the current cleaning operation.

[0061] Dimensionless elasticity coefficient These are the key parameters in the model, representing the relative contributions of the cleaning performance health coefficient, filtration status and energy consumption coefficient, instantaneous dynamic pressure drop ratio of air manifold 7, and vibration intensity ratio of blowpipe 5 to the air-solid coupling coefficient of the cleaning process. The sum of these coefficients satisfies... This ensures that the model has a certain degree of scale return invariance. These elasticity coefficients can be determined in various ways. For example, they can be based on a large amount of historical operating data and be trained and optimized offline through regression analysis and machine learning algorithms (such as support vector machines and neural networks) to obtain the optimal weight allocation under different operating conditions.

[0062] Measured instantaneous dynamic pressure drop of air bag 7 Compared with the benchmark value It is an important parameter for evaluating the intensity of the jetting energy release, and the instantaneous dynamic pressure drop of the air tank 7. This refers to the magnitude of the rapid pressure drop within the air reservoir 7 at the instant of pulse valve 8's injection. It directly reflects the instantaneous release capacity of compressed air. This measured value can be monitored in real time using a high-frequency response pressure transmitter for the air reservoir 7. For example, a piezoresistive or capacitive pressure sensor can be used, which has a fast response time and can capture transient pressure changes. (Reference value) It can be preset as the average pressure drop value when the dust collector is in a healthy state or when a new device is being commissioned, or it can be obtained through self-learning after the system has been running stably for a period of time.

[0063] Measured values ​​of vibration intensity of blowpipe 5 Compared with the benchmark value It is a key indicator reflecting the mechanical effect of the blowing energy on the filter bag, and the vibration intensity of the blowing pipe 5. This refers to the amplitude or energy of the mechanical vibration of the blowpipe 5 caused by the airflow impact when the pulse valve 8 is in operation. This measured value can be obtained in real time using a vibration sensor, such as an accelerometer or piezoelectric sensor, which can be installed on the blowpipe 5 to monitor its vibration response at the moment of initiation. (Reference value) It can be set to the average vibration intensity value of the dust collector when it is in good condition or when a new device is being commissioned.

[0064] The control center first obtains the health coefficient of the dust removal process through the sensor monitoring network. and filtration status and energy consumption coefficient These two coefficients reflect the basic state of cleaning demand and cleaning capacity from the perspectives of the health status of the cleaning actuators and the operating load and energy consumption of the filtration system, respectively. Based on this, the control center further obtains the measured value of the instantaneous dynamic pressure drop of air manifold 7. and measured values ​​of vibration intensity of blowpipe 5 Instantaneous dynamic pressure drop of air bag 7 Compared with the benchmark value The ratio of these values ​​can effectively characterize the release intensity of compressed air during the injection, i.e., the dynamic characteristics of airflow impact; while the vibration intensity of the injection pipe 5... Compared with the benchmark value The ratio directly reflects the mechanical effect of the injection energy on the filter bag, i.e., the response characteristics of the solids. These key parameters are input into a dust removal gas-solid efficiency coupling model based on Cobb-Douglas multivariate geometric features. This model uses exponentiation to determine the dust removal performance health coefficient. Filtration status and energy consumption coefficient The instantaneous dynamic pressure drop ratio of air tank 7 and the vibration intensity ratio of blowpipe 5 are nonlinearly combined and determined by the dimensionless elastic coefficient. By adjusting the contribution weights of each factor, this product-like coupling method allows the model to fully consider the mutual influence and synergistic effect between the factors, rather than simply superimposing them, thus generating a comprehensive air-solid cleaning efficiency coupling coefficient. This coefficient It can accurately quantify the actual efficiency of the current dust removal operation, overcome the limitations of single index evaluation in traditional methods, and provide more accurate and comprehensive input for the subsequent pulse jet pressure optimization model. This enables the intelligent pulse bag dust collector to dynamically adjust the dust removal strategy according to the actual operating conditions, achieving more refined control.

[0065] By introducing a gas-solid efficiency coupling model for dust removal based on Cobb-Douglas multivariate geometric features, a deep integration and quantitative evaluation of multi-dimensional information such as the health of dust removal operation, filtration status and energy consumption, instantaneous dynamic pressure drop of air manifold 7, and vibration intensity of blowpipe 5 is achieved. This model can more comprehensively and accurately reflect the synergistic relationship between airflow dynamics and mechanical response during the dust removal process, and precisely capture the energy transfer efficiency at the moment of blow. This enables the intelligent pulse bag filter to perform more refined and intelligent dust removal pressure control according to actual working conditions, thereby avoiding damage to the filter bags from over-duration or increased system resistance due to insufficient dust removal, and significantly improving the operating efficiency of the dust collector and the service life of the filter bags.

[0066] The injection pressure optimization model is based on algebraic saturated nonlinear negative feedback, and its calculation formula is as follows: ; In the formula, Target cleaning pulse pressure (unit: ); The current measured dust removal pulse pressure (unit: ); The maximum physical step size allowed by the hardware for a single voltage adjustment (unit: ); This is a dimensionless error amplification gain; The coupling coefficient for the set target dust removal efficiency, a dimensionless value; This is the air-solid coupling coefficient for dust removal efficiency calculated so far. These are the operational safety and boundary coefficients calculated so far.

[0067] The core function of the jet pressure optimization model is to dynamically calculate and output an optimal jet pressure for dust removal based on the current operating status of the dust collector and the dust removal efficiency requirements. This model aims to replace the traditional fixed pressure or simple linear adjustment method to achieve more precise and intelligent dust removal control. It can be a software module that runs on the processor of the control center.

[0068] The algebraic saturated nonlinear negative feedback model refers to a model that uses a nonlinear mathematical function to handle the deviation in cleaning efficiency and adjusts it using a negative feedback mechanism. Here, "algebraic" means that it is calculated based on a specific mathematical formula; "saturated" means that when the deviation is too large, the output adjustment will be limited to a reasonable range to avoid over-adjustment; "nonlinear" means that the adjustment response is no longer a simple proportional relationship, which can better adapt to complex working conditions; and "negative feedback" ensures that the system can automatically correct the deviation and make the cleaning efficiency approach the target value. This model can effectively avoid the overshoot or oscillation problems that may occur in traditional PID control under certain working conditions, and provide a smoother and more robust control effect.

[0069] Target dust removal jet pressure This refers to the ideal pressure value that the system should apply to the pressure regulating actuator of the dust removal system after calculation by the jet pressure optimization model. This pressure value is dynamically changing, aiming to ensure that the dust collector's dust removal efficiency reaches the preset target. The currently measured jet pressure for dust removal... This refers to the actual blowing pressure of the dust removal system, which is monitored in real time by a pressure sensor (such as the pressure transmitter of the air tank 7). This value is input as a feedback signal into the blowing pressure optimization model for comparison and adjustment with the target pressure.

[0070] Maximum single voltage regulation physical step size allowed by the hardware This refers to the maximum pressure adjustment allowed by the physical structure or control algorithm of the pressure regulating actuator in the dust removal system within one adjustment cycle. Setting this step size is to prevent the pressure regulating actuator from making violent movements due to receiving excessive adjustment commands, thereby protecting the equipment and avoiding mechanical shock or system instability. This step size can be preset according to the response characteristics of the actuator and the system stability requirements.

[0071] Dimensionless error amplification gain This is a parameter used to adjust the influence of cleaning efficiency deviation on the pulse-jet pressure regulation. This gain coefficient can amplify or reduce the impact of the efficiency deviation, thereby adjusting the system's response speed and sensitivity. A larger gain coefficient... A higher value makes the system more sensitive to deviations and responds faster, but may increase the risk of oscillations; a smaller value... A higher value makes the system response smoother, but may lead to a slower response.

[0072] The set target dust removal efficiency coupling coefficient The target value refers to the ideal dust removal efficiency level that the system expects to achieve. This target value can be set according to the dust collector's design requirements, operating experience, filter bag life, emission standards, or higher-level production optimization strategies. It is the benchmark for negative feedback control of the jet pressure optimization model.

[0073] The calculated air-solid coupling coefficient for dust removal efficiency is as follows It is a quantitative index calculated by a comprehensive air-solid efficiency coupling model based on multiple parameters such as the current dust collector cleaning health coefficient, filtration status and energy consumption coefficient, instantaneous dynamic pressure drop of air manifold 7 and vibration intensity of blowpipe 5. This coefficient reflects the actual effect and efficiency of the current dust cleaning process and is a key input for feedback control of the blowpipe pressure optimization model.

[0074] The operational safety and boundary coefficients calculated so far This coefficient is a quantitative indicator calculated using a comprehensive operational safety and boundary model based on parameters such as the oxygen content of the flue gas at the dust collector inlet, the electrostatic charge density of the dust particles, and the operating frequency of the fan. It reflects the safety margin of the dust collector's current operating state and is a safety constraint that the pulse-jet pressure optimization model must consider when adjusting the pressure.

[0075] By introducing a jet pressure optimization model, deviations in cleaning efficiency can be effectively addressed and transformed into reasonable pressure adjustment commands. The control center first obtains the current measured cleaning jet pressure. Meanwhile, the system continuously monitors and calculates the current air-solid coupling coefficient for dust removal. and operational safety and boundary factors .

[0076] Air-solid coupling coefficient for dust removal efficiency This comprehensively reflects the health status of the dust removal actuators, the energy consumption performance of the filtration system, and the actual efficiency of the gas-solid two-phase flow, while also considering operational safety and boundary coefficients. This quantifies the safety margins of the dust collector in terms of explosion prevention, static electricity prevention, and fan overload protection.

[0077] The jet pressure optimization model receives the set target cleaning efficiency coupling coefficient. and the results of the current calculation Calculate the deviation between the two, this deviation term First, the gain is amplified using dimensionless error. Adjustments are made to enhance or reduce the system's sensitivity to deviations. Subsequently, the gain-adjusted deviation term is fed into an algebraically saturated nonlinear function. The nonlinear function is used to process the pressure regulating actuator. When the deviation is small, the adjustment amount is approximately linear; when the deviation is large, the adjustment amount tends to saturate, thereby avoiding excessively violent action of the pressure regulating actuator due to excessive deviation, effectively suppressing overshoot and ensuring the stability of the regulation process.

[0078] Building on this, the model further incorporates operational safety and boundary coefficients. The squared term is used as the multiplier of the adjustment amount. This design allows the system's safety state to directly participate in the pressure regulation decision. When the dust collector operates near the safety boundary, i.e. When the value is low, its squared term will decrease significantly, thereby suppressing the adjustment range of the jetting pressure. Even if the dust removal efficiency deviates significantly, the system will prioritize operational safety and avoid exceeding safety limits in pursuit of dust removal efficiency. Conversely, when the system has sufficient operational safety margin... As the value approaches 1, the inhibitory effect on the adjustment amount weakens, allowing the system to more actively adjust the blowing pressure to optimize dust removal efficiency. Ultimately, the adjustment amount, after nonlinear saturation processing and safety factor weighting, is related to the maximum single pressure adjustment physical step size allowed by the hardware. Multiplying these yields a constrained, safe pressure adjustment increment, which is proportional to the currently measured dust removal pulse-jet pressure. Adding them together yields the final target dust removal jet pressure. The target pressure is then sent to the pressure regulating actuator of the dust removal system, such as an electro-proportional pressure reducing valve, to achieve precise control of the blowing pressure. This not only allows for dynamic adjustment of the blowing pressure based on the actual dust removal efficiency, avoiding the blindness of traditional control methods, but also effectively solves the problems of regulation overshoot, response lag, and safety risks that may occur under complex working conditions by introducing nonlinear saturation characteristics and dynamic suppression of safety boundary coefficients. This achieves a dynamic balance and optimization between dust removal efficiency, energy consumption, and operational safety.

[0079] By introducing an algebraic saturated nonlinear negative feedback mechanism, the adjustment amount of the jet pressure can be adaptively adjusted according to the magnitude of the dust removal efficiency deviation. This avoids the situation where traditional linear control adjusts too aggressively when the deviation is large or too weakly when the deviation is small, thus achieving refined closed-loop control of the dust removal jet pressure. Simultaneously, the square term of the operational safety factor and boundary coefficient is introduced into the adjustment logic, directly coupling the system's safety state into the pressure adjustment process. When the system's operational safety factor decreases, the pressure adjustment action is automatically suppressed, effectively ensuring the stability and safety of the dust collector under complex operating conditions.

[0080] The output ranges of the cleaning performance health coefficient, filtration status and energy consumption coefficient, operational safety and boundary coefficient, and cleaning gas-solid efficiency coupling coefficient are all normalized and limited to [specific range]. Within the range.

[0081] Among them, the output value range of the dust removal execution health coefficient, filtration status and energy consumption coefficient, operation safety and boundary coefficient, and dust removal gas-solid efficiency coupling coefficient is normalized and restricted to within 1000 rpm. Within an interval, this refers to ensuring that the values ​​of these coefficients always fall within the range of greater than 0 and less than or equal to 1 through specific mathematical transformations or post-processing mechanisms. This eliminates the potential influence caused by differences in physical dimensions and numerical ranges between different coefficients, ensuring uniform weight and comparability in subsequent calculations and decisions. Various methods can be used to achieve this normalization constraint. For example, it can be achieved through min-max normalization (…). The method is to convert the original coefficients. Mapping to the target interval, i.e. ,in and These are the theoretical minimum and maximum values ​​of the coefficient, respectively. To ensure the results are strictly within the range... Intervals can be checked and truncated after calculation, for example, values ​​less than or equal to 0 can be corrected to a very small positive number, and values ​​greater than 1 can be corrected to 1.

[0082] By uniformly normalizing the cleaning performance health coefficient, filtration status and energy consumption coefficient, operational safety and boundary coefficient, and cleaning gas-solid efficiency coupling coefficient calculated by the control center of the intelligent pulse bag filter, a unified quantitative benchmark is constructed. During the operation of the intelligent pulse bag filter, the control center acquires multi-source heterogeneous data through a sensor monitoring network and calculates the corresponding coefficients based on the cleaning performance health model, filtration status and energy consumption model, operational safety and boundary model, and cleaning gas-solid efficiency coupling model. Before normalization, these coefficients may have vastly different numerical ranges and dimensions due to differences in their physical meaning and calculation methods. By mapping all these coefficients to... The range ensures that all input parameters are within the same mathematical weight space when the control center executes the pulse jet pressure optimization model. This effectively avoids control command deviations caused by excessively large or small values ​​of a single indicator, and ensures that each evaluation dimension can play its expected regulatory role in comprehensive decision-making. As a result, the action of the pressure regulating actuator is smoother and more precise, avoiding nonlinear oscillations in the control system caused by mismatched parameter dimensions or excessively large numerical ranges. This significantly improves the stability and controllability of the intelligent pulse jet bag filter under complex working conditions.

[0083] The displacement sensor is specifically a pull-string displacement sensor or an angle encoder; the gas flow meter is specifically a thermal gas mass flow meter; the oxygen analyzer is specifically a zirconia oxygen analyzer; and the pressure regulating actuator is an electro-proportional pressure reducing valve controlled by the control center.

[0084] A displacement sensor is a device used to measure changes in the position or distance of an object. One implementation can be a drawstring displacement sensor, which uses the extension and retraction of a drawstring to drive an internal encoder or potentiometer, converting linear displacement into an electrical signal output.

[0085] A gas flow meter is an instrument used to measure the volume or mass of gas passing through a pipe per unit time. Among them, the thermal gas mass flow meter uses the principle of heat conduction to directly measure the gas mass flow by measuring the relationship between the heat dissipation of the heating element and the gas flow rate.

[0086] An oxygen analyzer is an instrument used to measure the oxygen concentration in a gas mixture. The zirconia oxygen analyzer utilizes the property that zirconia solid electrolyte is conductive to oxygen ions at high temperatures, and determines the oxygen content by measuring the potential difference generated by the difference in oxygen concentration.

[0087] Pressure regulating actuators are devices that adjust fluid pressure according to control signals. Electro-proportional pressure reducing valves receive electrical signals and drive the valve core through an internal proportional electromagnet to achieve continuous and precise regulation of the outlet pressure.

[0088] By specifically selecting key sensors and pressure regulating actuators in the sensing and monitoring network, highly reliable data input and high-precision execution guarantees are provided for the aforementioned intelligent control model. When the control center executes its intelligent control steps, the displacement sensor (pull-rope displacement sensor or angle encoder) can accurately capture the mechanical opening of the compartment lifting valve, ensuring that the valve position data obtained by the control center truly reflects the physical state, thereby providing accurate input parameters for the calculation of the dust removal execution health model.

[0089] Gas flow meters (thermal gas mass flow meters) directly measure the mass flow rate of compressed air using the principle of heat conduction, avoiding measurement deviations caused by pressure and temperature fluctuations in traditional volumetric flow meters, and ensuring the accuracy of flow data in filtration status and energy consumption models.

[0090] The oxygen analyzer (zirconia oxygen analyzer) utilizes its fast response speed and high measurement accuracy in high-temperature flue gas environments to provide a reliable basis for real-time monitoring of flue gas oxygen content in operational safety and boundary models, effectively supporting explosion-proof safety early warning.

[0091] The pressure regulating actuator (electric proportional pressure reducing valve) can continuously and smoothly regulate the compressed air pressure according to the target dust removal and blowing pressure signal output by the control center. This avoids the pressure sudden changes caused by traditional on / off solenoid valves and ensures that the target pressure calculated by the blowing pressure optimization model can be accurately executed, thereby achieving a fine balance between dust removal efficiency and system energy consumption.

[0092] Through the above technical solutions, the displacement sensor ensures that the valve opening data obtained by the cleaning execution health model is true and reliable, avoiding misjudgments caused by mechanical jamming or electromagnetic failure, thereby improving the accuracy of the cleaning execution health coefficient. The gas flow meter provides high-precision compressed air mass flow data, enabling the filtration status and energy consumption model to more accurately assess the filtration status and energy consumption, avoiding the drawbacks of traditional flow meters being affected by temperature and pressure fluctuations, thereby achieving more refined energy consumption management and reducing over-cleaning or under-cleaning.

[0093] The oxygen analyzer monitors the oxygen content of flue gas in real time and accurately, providing reliable explosion-proof early warning data for operational safety and boundary models. This significantly improves the operational safety of the dust collector under complex conditions and effectively avoids safety accidents when approaching the explosion threshold. The pressure regulating actuator enables continuous, smooth, and precise adjustment of the cleaning jet pressure, ensuring that the target pressure output by the jet pressure optimization model can be accurately executed. This avoids pressure surges and system oscillations caused by traditional on / off valves, thereby effectively reducing energy consumption and extending filter bag life while ensuring cleaning efficiency. These optimized configurations significantly improve the perception and execution accuracy of the entire intelligent control system, making the calculation results of models such as cleaning health, filtration status and energy consumption, operational safety and boundary, and cleaning gas-solid efficiency coupling more reliable. Ultimately, this achieves the global optimal synergy of cleaning efficiency, energy consumption, and operational safety.

[0094] The control center has a built-in safety limiting strategy: When the operational safety and boundary coefficients decay as a single monitoring parameter approaches its limit threshold, the pressure regulation step size term in the injection pressure optimization model is subsequently suppressed by the quadratic exponential of the operational safety and boundary coefficients, thereby limiting the abrupt action of the pressure regulating actuator.

[0095] Safety limiting strategy refers to a preset mechanism in the control system, which limits or adjusts the amplitude of the output command to prevent the system from generating excessive, rapid or unstable responses under specific conditions. This strategy can be implemented by embedding condition judgment and correction modules in the software algorithm of the control center.

[0096] The operational safety and boundary coefficient is an indicator that quantifies the operational safety status of a dust collector. Its value range is usually normalized to reflect the degree to which the system deviates from the safety boundary. As an indicator of safety risk, this coefficient guides the control system to take corresponding safety protection actions. For example, when key parameters such as flue gas oxygen concentration, dust electrostatic charge density, or fan frequency approach their safety critical values, this coefficient will decrease accordingly.

[0097] In the injection pressure optimization model, the pressure adjustment step size term is a parameter used to determine the magnitude of each pressure adjustment. Its function is to control the precision and response speed of pressure regulation. Quadratic exponential suppression is a nonlinear suppression mechanism. Its suppression effect is proportional to the square of a certain input parameter and exhibits an exponential decay trend. The function of this suppression method is to rapidly and significantly reduce the pressure adjustment step size when safety risks increase, thereby providing strong safety protection. Limiting the abrupt action of the pressure regulating actuator refers to preventing the pressure regulating actuator, such as an electric proportional pressure reducing valve, from making large or drastic adjustments in a short period of time. Its function is to avoid mechanical shock and system oscillation, thereby improving the operational stability and safety of the equipment.

[0098] The control center continuously monitors the operating safety and boundary coefficients calculated by the operating safety and boundary model. These coefficients comprehensively reflect the safety status of the dust collector in terms of oxygen concentration, dust electrostatic charge density, and fan frequency. When any monitored parameter approaches its limit threshold, the operating safety and boundary coefficients decrease accordingly, indicating that the system is in a high-risk operating state. At this time, the built-in safety limiting strategy is activated. It intervenes in the calculation process of the target cleaning jet pressure by the jet pressure optimization model. The core of this strategy is to multiply the pressure adjustment step term used to adjust the pressure in the jet pressure optimization model by the square of the operating safety and boundary coefficient. Since the square value of the operating safety and boundary coefficient decreases more sharply as the coefficient decreases, this quadratic exponential suppression mechanism can ensure that the pressure adjustment step term is rapidly and significantly reduced when the safety risk increases. Therefore, even if the jet pressure optimization model calculates that a large pressure adjustment is required based on the goal of optimal cleaning efficiency, the actual pressure adjustment command output to the pressure regulating actuator will be significantly smoothed and limited, thereby effectively avoiding drastic sudden actions by the pressure regulating actuator. This mechanism enables the dust collector to prioritize operational safety while pursuing high dust removal efficiency, avoiding mechanical shocks, system oscillations, and even safety accidents that may be caused by excessive pursuit of efficiency, and significantly improving the operational stability and reliability of the dust collector under complex working conditions.

[0099] This application employs a multi-source sensor network to collect real-time multi-dimensional physical parameters of the dust collector, including electrical, acoustic, mechanical, thermal, and gas parameters. These parameters are then input into an intelligent model at the control center for calculation and regulation. First, the system uses a cleaning execution health model, a filtration status and energy consumption model, and an operational safety and boundary model to perform deep nonlinear quantitative sensing of the equipment's mechanical and electromagnetic health status, comprehensive energy consumption and filtration load, as well as explosion-proof and overload protection safety margins. Subsequently, the system uses a cleaning gas-solid efficiency coupling model to exponentially couple the above parameters with real-time gas-solid dynamic response characteristics, outputting a comprehensive efficiency coefficient. Finally, the system uses a jet pressure optimization model based on algebraic saturated nonlinear negative feedback to calculate the target jet pressure and directly drive the electric proportional pressure reducing valve, achieving continuous, smooth, and closed-loop control of the dust removal system's cleaning pressure.

[0100] Compared with the existing single-boundary, flat open-loop control method that relies only on timing or constant pressure difference, this application has achieved outstanding and innovative technical effects: precise health diagnosis and prevention of blind adjustment, overcoming the defects of existing technologies that easily mask mechanical jamming or electromagnetic faults. Through the inverse polynomial deviation attenuation architecture, the actuator is finely evaluated, and early faults are sensitively captured, fundamentally avoiding blind pressure adjustment operations in the state of dust removal failure.

[0101] It breaks through the limitations of traditional dust removal triggered solely by inlet and outlet pressure difference feedback. It comprehensively considers leakage temperature drop and actual compressed air consumption, accurately identifies the trend of system energy consumption deterioration, and completely avoids the problems of filter bag damage due to excessive dust removal or increased system resistance due to insufficient dust removal.

[0102] To address the potential hazards of severe oscillations and near-explosion limits in pressure regulating mechanisms under complex and fluctuating operating conditions, a safety limiting strategy and a quadratic exponential suppression mechanism are introduced. When any monitored indicator, such as oxygen concentration or static electricity, approaches the safety limit, the system automatically and significantly limits the pressure regulating step size, forcibly suppressing the sudden action of the pressure regulating mechanism. Thus, under the premise of ensuring absolute safety, the system achieves the optimal synergy between cleaning efficiency, system energy consumption, and operational safety.

[0103] The terms "front," "back," "left," "right," "top," and "bottom" all refer to the figures in the accompanying drawings. Figure 1 Based on the perspective of the observer, the side of the device facing the observer is defined as the front, the left side of the observer is defined as the left, and so on.

[0104] In the description of this invention, it should be understood that the terms "center", "longitudinal", "lateral", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting the scope of protection of this invention.

[0105] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent pulse bag filter dust collector, comprising a dust collector body (1), a dust removal system, a sensor monitoring network, and a control center, wherein the dust collector body (1) includes a dust hopper (2), a dust-laden air chamber (3) with dust collector bags (6) suspended inside, and a clean air chamber (4), wherein a compartmentalized lifting valve is provided between the dust-laden air chamber (3) and the clean air chamber (4); the dust removal system includes an air manifold (7), a pulse valve (8) installed on the air manifold (7), and a blowpipe (5) extending into the clean air chamber (4); the sensor monitoring network is distributed on the dust collector body (1) and the dust removal system; characterized in that, The control center is configured to perform the following steps: A1: Obtain the current waveform characteristics of the pulse valve (8) coil through the current transmitter in the sensor monitoring network, obtain the sound pressure level of the cleaning environment noise through the sound level meter, obtain the actual opening degree of the compartment lifting valve through the displacement sensor, and calculate the cleaning execution health coefficient through the cleaning execution health model. A2: Obtain the independent filtration pressure difference of each compartment through the differential pressure transmitter, obtain the temperature difference between the clean air chamber (4) and the dust-containing air chamber through the temperature sensor, obtain the instantaneous flow rate and cumulative consumption of compressed air through the gas flow meter, and calculate the filtration status and energy consumption coefficient through the filtration status and energy consumption model. A3: Obtain the oxygen content of the inlet flue gas through an oxygen analyzer, obtain the dust electrostatic charge density through an electrostatic sensor, obtain the fan operating frequency through a communication interface, and calculate the operating safety and boundary coefficients through an operating safety and boundary model; A4: Based on the cleaning performance health coefficient, filtration status and energy consumption coefficient, instantaneous dynamic pressure drop of air bag (7) obtained by the air bag (7) pressure transmitter and vibration intensity of blowpipe (5) obtained by the vibration sensor, the cleaning gas-solid efficiency coupling coefficient is calculated by the cleaning gas-solid efficiency coupling model. A5: Based on the dust removal gas-solid efficiency coupling coefficient, the current dust removal injection pressure, and the operation safety and boundary coefficient, the target dust removal injection pressure is output through the injection pressure optimization model, and the pressure regulation actuator of the dust removal system is controlled accordingly.

2. The intelligent pulse bag filter dust collector according to claim 1, characterized in that: The dust removal execution health model is based on an inverse polynomial deviation decay architecture, and its specific calculation logic is as follows: Calculate the peak current of the pulse valve (8) coil, current rise time, effective energization time, noise level of the dust removal environment, and dimensionless relative deviation of the actual opening of the lifting valve relative to their respective design reference values. Multiply the squares of each dimensionless relative deviation by the corresponding dimensionless sensitivity weighting coefficient and sum them to obtain the sum of squared deviations. The sum of squared deviations is added to a constant and the reciprocal is taken to obtain the cleanliness coefficient; wherein, the sum of all dimensionless sensitivity weight coefficients is equal to a constant.

3. The intelligent pulse bag filter according to claim 1, characterized in that: The filtering state and energy consumption model is based on the Cauchy rational distribution architecture, and its specific operation logic is as follows: Calculate the ratios of the measured independent filter pressure difference to the limit value, the measured temperature difference to the maximum value, the measured instantaneous compressed air flow rate to the maximum throughput, and the measured cumulative compressed air consumption to the highest cycle quota, respectively. After multiplying each ratio by its corresponding dimensionless influence factor, the weighted sum is obtained to obtain the comprehensive dimensionless load index. Multiply the square of the comprehensive dimensionless load index by the dimensionless decay rate constant, add a constant 1, and take the reciprocal to obtain the filtration state and energy consumption coefficient.

4. The intelligent pulse bag filter according to claim 1, characterized in that: The operational safety and boundary model is a continuously differentiable model based on a higher-order rational barrier function, and its specific operational logic is as follows: Calculate the ratios of the measured oxygen content of the inlet flue gas to the explosion-proof critical value, the measured dust electrostatic charge density to the discharge limit threshold, and the measured fan operating frequency to the maximum overload frequency, respectively. After performing higher-order even-number power operations on the above ratios, multiplying them by their respective dimensionless penalty scale constants, we obtain multiple boundary penalty values. The operational safety and boundary coefficients are obtained by adding all boundary penalty values ​​to a constant and taking the reciprocal.

5. The intelligent pulse bag filter dust collector according to claim 1, characterized in that: The dust removal gas-solid efficiency coupling model is a model based on Cobb-Douglas multivariate geometric features, and its specific computational logic is as follows: Using the health coefficient of the dust removal operation, the filtration status and energy consumption coefficient, the ratio of the measured value of the instantaneous dynamic pressure drop of the air bag (7) to its reference value, and the ratio of the measured value of the vibration intensity of the blowpipe (5) to its reference value as the base, and the corresponding dimensionless state elastic coefficient as the exponent, multiple power function terms are constructed. The coupling coefficient of the air-solid efficiency of the dust removal is obtained by multiplying all the power function terms together; wherein, the sum of all dimensionless state elastic coefficients is equal to a constant.

6. The intelligent pulse bag filter dust collector according to claim 1, characterized in that: The injection pressure optimization model is based on algebraic saturated nonlinear negative feedback, and its calculation formula is as follows: ； In the formula, The target dust removal jet pressure; This is the current measured dust removal pulse pressure; This is the maximum physical step size for a single voltage adjustment allowed by the hardware. This is a dimensionless error amplification gain; The coupling coefficient for the set target dust removal efficiency; This is the air-solid coupling coefficient for dust removal efficiency calculated so far. These are the operational safety and boundary coefficients calculated so far.

7. The intelligent pulse bag filter according to claim 1, characterized in that: The output ranges of the cleaning performance health coefficient, filtration status and energy consumption coefficient, operational safety and boundary coefficient, and cleaning gas-solid efficiency coupling coefficient are all normalized and limited. Within the range.

8. The intelligent pulse bag filter dust collector according to claim 1, characterized in that: The displacement sensor is specifically a pull-wire displacement sensor or an angle encoder; the gas flow meter is specifically a thermal gas mass flow meter; the oxygen analyzer is specifically a zirconia oxygen analyzer; and the pressure regulating actuator is an electro-proportional pressure reducing valve controlled by the control center.

9. The intelligent pulse bag filter according to claim 1, characterized in that: The control center has a built-in safety limiting strategy: When the operational safety and boundary coefficients decay as a single monitoring parameter approaches its limit threshold, the pressure adjustment step term in the injection pressure optimization model is subsequently suppressed by the quadratic exponential of the operational safety and boundary coefficients, thereby limiting the abrupt action of the pressure regulating actuator.

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