Underground air dust removal equipment for phosphorite mining

Abstract

The invention relates to the technical field of air dust removal, and discloses phosphorite mining underground air dust removal equipment which comprises a dust removal box, a top shell, a cloth bag dust removal mechanism, a cleaning mechanism and an ash removal period regulation and control system. The ash removal period regulation and control system is based on the inlet dust mass concentration, the treatment air volume, the pressure difference of the two sides of a filter bag, the gas humidity and dew point margin, the pressure drop recovery rate after last round of ash removal, the initial pressure difference base line after ash removal and the resistance recovery rate. And calculating a dust load impact coefficient, a dynamic resistance coefficient of the filter bag, an environment adaptability degree and a dust cleaning potential coefficient of the filter bag in real time, and dynamically determining a target dust cleaning period according to the parameters. According to the invention, the self-adaptive precise regulation and control of the dust removal period under the instantaneous change of dust load and high-humidity environment is realized, excessive dust removal or insufficient dust removal is effectively avoided, the dust removal efficiency is improved, the energy consumption is reduced, and the service life of the filter bag is prolonged.

Description

Technical Field

[0001] This invention belongs to the field of air dust removal technology, and particularly relates to an air dust removal device for underground phosphate mining. Background Technology

[0002] Phosphate mining is a typical underground non-coal mining operation. During the mining process, drilling, blasting, crushing, and transportation all generate large amounts of mineral dust. This dust not only severely pollutes the underground working environment and endangers workers' occupational health (leading to pneumoconiosis, etc.), but also poses a certain explosion risk. Furthermore, high concentrations of suspended dust can reduce equipment visibility, affecting production safety and efficiency. Therefore, efficient dust removal from underground air is an indispensable part of safe phosphate mining production.

[0003] Baghouse dust collectors are widely used in underground mine air purification due to their high dust removal efficiency, stable operation, and ability to handle high concentrations of dust. Their working principle involves the dust-laden airflow passing through filter bags, where dust is trapped on the surface, forming a dust layer, and the purified gas is discharged. As the dust layer thickens, the equipment's operating resistance (pressure difference) increases, requiring periodic activation of the cleaning mechanism (such as pulse jet cleaning) to remove part of the dust layer, restoring the filter bags' permeability and ensuring the system continues to operate within a reasonable resistance range.

[0004] Currently, the dust removal control strategies for underground baghouse dust collectors are mostly simple and fixed, mainly divided into two categories: timed dust removal, which starts dust removal at preset fixed time intervals; and differential pressure dust removal, which starts dust removal when the pressure difference across the filter bag reaches a certain fixed set value. However, the working conditions in phosphate mines are complex and variable. Dust generation is instantaneous and intermittent (such as a sharp increase in concentration after an explosion), and the humidity in the underground environment is generally high, making the dust prone to moisture absorption and deliquescence. Existing dust removal control strategies have obvious limitations: timed dust removal cannot sense the actual dust load, resulting in energy waste and excessive wear of filter bags at low loads, while at high loads it may lead to insufficient dust removal and a sudden increase in resistance; differential pressure dust removal, although responding to the resistance signal, is a delayed control and does not consider the profound impact of environmental humidity on the dust removal effect and filter bag condition (for example, in high-temperature environments, frequent and forceful dust removal may actually cause dust to clump on the filter bag surface, resulting in irreversible damage such as "bag clogging").

[0005] Therefore, existing control methods struggle to achieve precise, economical, and adaptive regulation of dust removal operations in the complex and dynamic environment of underground phosphate mines, often facing significant contradictions between dust removal efficiency, operating energy consumption, filter bag lifespan, and system reliability. Developing a control system capable of comprehensively sensing multi-dimensional information such as dust load, equipment resistance status, and environmental conditions, and intelligently deciding on the dust removal cycle, is of great significance for improving the overall efficiency of dust removal equipment in underground phosphate mines, reducing overall operating costs, and ensuring long-term stable operation. This invention is proposed based on this practical need. Summary of the Invention

[0006] The purpose of this invention is to provide an underground air dust removal device for phosphate mining, which aims to solve the above-mentioned problems.

[0007] This invention is implemented as follows: an underground air dust removal device for phosphate mining includes a dust collection box and a top shell fixed to the top of the dust collection box. The side wall of the dust collection box and the top of the top shell are respectively provided with an inlet and an outlet. It also includes: a bag filter mechanism installed inside the dust collection box for removing dust from the underground air; a cleaning mechanism on the bag filter mechanism for cleaning the filter bags; and a cleaning cycle control system connected to the control terminal of the cleaning mechanism for real-time adjustment of the cleaning cycle. The configuration is as follows: Calculate the dust load impact coefficient based on the inlet dust mass concentration and the processing air volume; calculate the filter bag dynamic resistance coefficient based on the pressure difference across the filter bag; calculate the environmental adaptability based on the gas humidity and dew point margin, the dust load impact coefficient, and the filter bag dynamic resistance coefficient; calculate the filter bag cleaning potential coefficient based on the pressure drop recovery rate after the previous cleaning action, the initial pressure difference baseline after cleaning, and the initial resistance recovery rate after cleaning; and determine the target cleaning cycle based on the filter bag cleaning potential coefficient and the environmental adaptability.

[0008] A further technical solution involves calculating the dust load impact coefficient by: normalizing the inlet dust concentration and the processing air volume with their respective design maximum values ​​to obtain the inlet dust concentration index and the processing air volume index; and multiplying the inlet dust concentration index and the processing air volume index to obtain the dust load impact coefficient.

[0009] A further technical solution involves calculating the dynamic resistance coefficient of the filter bag as follows: obtaining the pressure difference across the filter bag and calculating its rate of change over time; normalizing the pressure difference and its rate of change across the filter bag with their respective allowable maximum values ​​to obtain the pressure difference index and the rate of change index of the pressure difference across the filter bag; and performing a weighted combination operation on the pressure difference index and the rate of change index of the pressure difference across the filter bag to obtain the dynamic resistance coefficient of the filter bag. The dynamic resistance coefficient of the filter bag is positively correlated with both the pressure difference index and the rate of change index of the pressure difference across the filter bag.

[0010] A further technical solution involves calculating the environmental adaptability as follows: obtaining gas humidity and dew point margin; calculating an environmental condition coefficient based on the gas humidity and dew point margin, wherein the environmental condition coefficient characterizes the degree of environmental support for dust removal behavior; performing geometric averaging on the dust load impact coefficient and the filter bag dynamic resistance coefficient to obtain a dust removal demand coefficient; combining the dust load impact coefficient and the filter bag dynamic resistance coefficient to obtain a dust removal demand coefficient, wherein the dust removal demand coefficient characterizes the system's demand for dust removal behavior; and comparing the environmental condition coefficient and the dust removal demand coefficient to obtain the environmental adaptability, wherein the environmental adaptability value increases with the increase of the environmental condition coefficient and decreases with the increase of the dust removal demand coefficient.

[0011] A further technical solution involves calculating the environmental condition coefficient as follows: A humidity influence factor is calculated based on the gas humidity, whereby the humidity influence factor decreases as the gas humidity changes in an unfavorable direction; the dew point margin is ratioed to the safe dew point margin setpoint, and then a min function is used to limit the upper limit to 1 to obtain the dew point margin influence factor; the square root of the product of the humidity influence factor and the dew point margin influence factor is taken to obtain the environmental condition coefficient. .

[0012] A further technical solution involves calculating the filter bag cleaning potential coefficient as follows: The initial differential pressure baseline and the initial resistance recovery rate after cleaning are both subjected to maximum-minimum normalization to obtain the initial differential pressure baseline index and the initial resistance recovery rate index after cleaning. Based on the pressure drop recovery rate after the previous cleaning action, the initial differential pressure baseline index, and the initial resistance recovery rate index after cleaning, a comprehensive calculation is performed based on a preset weighting relationship to obtain the filter bag cleaning potential coefficient. The filter bag cleaning potential coefficient is positively correlated with the pressure drop recovery rate and negatively correlated with the initial differential pressure baseline index and the initial resistance recovery rate index after cleaning.

[0013] A further technical solution is that determining the target cleaning cycle specifically involves: based on a benchmark cleaning cycle, adjusting the benchmark cleaning cycle in the positive direction according to the filter bag cleaning potential coefficient, and adjusting the adjusted cycle in the reverse direction according to the environmental adaptability, thereby determining the target cleaning cycle.

[0014] A further technical solution is that the bag filter dust removal mechanism includes a horizontally fixed mounting plate inside the dust removal box, at least one filter bag is fixed on the mounting plate, the filter bag is located below the mounting plate, and a negative pressure fan is fixed inside the top housing.

[0015] A further technical solution is that the cleaning mechanism includes a flushing pipe fixed above the mounting plate inside the dust collection box, the flushing pipe being connected to an external air source, and at least one flushing head being provided on the flushing pipe, the flushing head being located directly above the filter bag.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] 1. Achieve adaptive and precise control of dust removal cycle: Through multi-parameter fusion sensing and intelligent decision-making, the lag and blindness of traditional timed or differential pressure dust removal methods are overcome, so that the dust removal frequency is dynamically matched with the actual dust load and environmental conditions.

[0018] 2. Improve dust removal efficiency and system stability: Respond promptly under high dust load conditions to prevent a sudden increase in resistance; reduce ineffective dust removal under low load conditions, maintain system operation within a reasonable resistance range, and ensure continuous and efficient dust removal capabilities.

[0019] 3. Significantly reduces operating energy consumption and filter bag wear: Avoids unnecessary dust cleaning, saves compressed air or electricity consumption, and reduces mechanical fatigue and damage to filter bags caused by frequent dust cleaning, thus extending their service life.

[0020] 4. Effectively avoids the risk of bag clogging in high humidity environments: Through environmental adaptability calculation, the cleaning cycle is automatically extended under high humidity or low dew point margin conditions to prevent moisture from being introduced during cleaning, which can lead to dust deliquescence and caking, thus avoiding irreversible bag clogging.

[0021] 5. Improve system reliability and intelligence: Integrate multiple sensors and intelligent algorithms to achieve self-sensing of operating conditions and self-adjustment of decisions, reduce reliance on manual intervention, and enhance the long-term operational reliability and maintenance convenience of equipment in complex downhole environments. Attached Figure Description

[0022] Figure 1 Flowchart of the dust removal cycle control system provided by the present invention;

[0023] Figure 2 This is a schematic diagram of the structure of an underground air dust removal device for phosphate mining provided by the present invention;

[0024] Figure 3 Provided by the present invention Figure 2 Schematic diagram of the internal structure of the dust collector;

[0025] Figure 4 Provided by the present invention Figure 2 A schematic diagram of the structure of the top shell.

[0026] In the attached diagram: 1. Dust collection box; 2. Top shell; 3. Inlet; 4. Outlet; 5. Mounting plate; 6. Filter bag; 7. Negative pressure fan; 8. Dust collection box; 9. Flushing pipeline; 10. Flushing head. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0028] Phosphate mining is a typical underground non-coal mining operation. During the mining process, drilling, blasting, crushing, and transportation all generate large amounts of mineral dust. This dust not only severely pollutes the underground working environment and endangers workers' occupational health (leading to pneumoconiosis, etc.), but also poses a certain explosion risk. Furthermore, high concentrations of suspended dust can reduce equipment visibility, affecting production safety and efficiency. Therefore, efficient dust removal from underground air is an indispensable part of safe phosphate mining production.

[0029] Baghouse dust collectors are widely used in underground mine air purification due to their high dust removal efficiency, stable operation, and ability to handle high concentrations of dust. Their working principle involves the dust-laden airflow passing through filter bags, where dust is trapped on the surface, forming a dust layer, and the purified gas is discharged. As the dust layer thickens, the equipment's operating resistance (pressure difference) increases, requiring periodic activation of the cleaning mechanism (such as pulse jet cleaning) to remove part of the dust layer, restoring the filter bags' permeability and ensuring the system continues to operate within a reasonable resistance range.

[0030] Currently, the dust removal control strategies for underground baghouse dust collectors are mostly simple and fixed, mainly divided into two categories: timed dust removal, which starts dust removal at preset fixed time intervals; and differential pressure dust removal, which starts dust removal when the pressure difference across the filter bag reaches a certain fixed set value. However, the working conditions in phosphate mines are complex and variable. Dust generation is instantaneous and intermittent (such as a sharp increase in concentration after an explosion), and the humidity in the underground environment is generally high, making the dust prone to moisture absorption and deliquescence. Existing dust removal control strategies have obvious limitations: timed dust removal cannot sense the actual dust load, resulting in energy waste and excessive wear of filter bags at low loads, while at high loads it may lead to insufficient dust removal and a sudden increase in resistance; differential pressure dust removal, although responding to the resistance signal, is a delayed control and does not consider the profound impact of environmental humidity on the dust removal effect and filter bag condition (for example, in high-temperature environments, frequent and forceful dust removal may actually cause dust to clump on the filter bag surface, resulting in irreversible damage such as "bag clogging").

[0031] Therefore, existing control methods struggle to achieve precise, economical, and adaptive regulation of dust removal operations in the complex and dynamic environment of underground phosphate mines, often facing significant contradictions between dust removal efficiency, operating energy consumption, filter bag lifespan, and system reliability. Developing a control system capable of comprehensively sensing multi-dimensional information such as dust load, equipment resistance status, and environmental conditions, and intelligently deciding on the dust removal cycle, is of great significance for improving the overall efficiency of dust removal equipment in underground phosphate mines, reducing overall operating costs, and ensuring long-term stable operation. This invention is proposed based on this practical need.

[0032] In baghouse dust collection applications in phosphate mines, dust generation is instantaneous and intermittent, and the underground environment is generally humid, making it difficult for existing dust removal control strategies to achieve precise adaptive regulation of the dust removal cycle. Dynamic changes in dust load lead to energy waste and excessive filter bag wear under low-load conditions, while under high-load conditions, it results in insufficient dust removal and a sharp increase in system resistance. Simultaneously, the impact of ambient humidity on dust properties is not incorporated into the control logic; the constant pressure differential dust removal strategy only responds to hygroscopic resistance signals and cannot avoid the risk of filter bag caking caused by dust deliquescence under high humidity conditions. This, in turn, affects key performance indicators such as dust removal efficiency, system operating energy consumption, and filter bag lifespan.

[0033] For example, after blasting operations in a phosphate mine, dust concentration experiences a sudden surge. However, conventional timed dust removal systems continue to operate at fixed intervals, leading to frequent, ineffective dust removal actions during periods of low dust load, accelerating mechanical damage to the filter bags. When ambient humidity rises due to fluctuations in the ventilation system, the constant pressure differential dust removal strategy forces powerful blowing once a preset pressure differential threshold is reached. This causes hygroscopic dust to form a dense, caked layer on the filter bag surface, resulting in an irreversible decrease in air permeability. In this scenario, the dust removal actions are disconnected from the actual dust load and environmental conditions, causing abnormal fluctuations in system operating resistance, reduced equipment visibility, and threatening production safety.

[0034] If the above problems are not addressed, the filter bags will continue to be subjected to unnecessary dust removal impacts, accelerating the deterioration of their physical structural integrity and shortening their service life. Simultaneously, the "bag clogging" phenomenon caused by dust caking will lead to a continuous increase in system resistance, forcing frequent equipment shutdowns for maintenance and reducing overall operational reliability. Furthermore, the mismatch between dust removal timing and environmental conditions will further exacerbate energy consumption, affecting the sustained effectiveness of downhole air purification and posing a potential risk to the health of workers.

[0035] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0036] like Figure 1 and Figure 2As shown, an underground air dust removal device for phosphate mining, according to an embodiment of the present invention, includes a dust collection box 1 and a top shell 2 fixed to the top of the dust collection box 1. An inlet 3 and an outlet 4 are respectively provided on the side wall of the dust collection box 1 and the top of the top shell 2. The device also includes:

[0037] The bag filter system, installed inside the dust collection box, is used to remove dust from the underground air.

[0038] Cleaning equipment, specifically for cleaning the filter bags in baghouse dust collection systems;

[0039] A dust removal cycle control system, connected to the control terminal of the cleaning mechanism, is used to adjust the dust removal cycle in real time. The dust removal cycle control system is configured as follows:

[0040] Calculate the dust load impact coefficient based on the inlet dust mass concentration and the processing air volume;

[0041] The dust load impact coefficient is an indicator that quantifies the impact of inlet dust load on the system. It can be calculated by linearly proportionalizing the inlet dust mass concentration with a preset concentration threshold, such as by obtaining the relative impact value through division, or by determining the impact degree based on the moving average of concentration data. Its main purpose is to achieve a rapid response to instantaneous changes in dust load.

[0042] Calculate the dynamic resistance coefficient of the filter bag based on the pressure difference across the filter bag;

[0043] The dynamic resistance coefficient of a filter bag refers to a parameter that reflects the dynamic change trend of the pressure difference across the filter bag. It can be obtained by performing first-order differential processing on the pressure difference across the filter bag, such as calculating the pressure difference change per unit time, or by extracting the trend component of the pressure difference signal through low-pass filtering. Its main purpose is to achieve early detection of abnormal resistance trends.

[0044] Based on gas humidity and dew point margin, the dust load impact coefficient and the filter bag dynamic resistance coefficient, the environmental adaptability is calculated.

[0045] Environmental adaptability refers to a comprehensive index that assesses the suitability of environmental conditions for dust removal operations. It can be achieved by weighting the gas humidity and dew point margin, for example, by classifying risk levels based on humidity data, or by determining adaptability based on the mapping relationship between environmental parameters and historical dust removal effects. Its main purpose is to avoid the risk of bag clogging in high humidity environments.

[0046] Based on the pressure drop recovery rate after the previous cleaning action, the initial pressure difference baseline after cleaning, and the initial resistance recovery rate after cleaning, the filter bag cleaning potential coefficient is calculated.

[0047] The filter bag cleaning potential coefficient is a parameter that characterizes the performance recovery potential of the filter bag after cleaning. It can be obtained by exponential fitting of the initial pressure difference baseline after cleaning, such as estimating the resistance recovery curve by the least squares method, or calculating the potential index based on the functional relationship between pressure drop recovery rate and time. Its main purpose is to achieve a reasonable evaluation of the filter bag cleaning effect.

[0048] The target cleaning cycle is determined based on the filter bag cleaning potential coefficient and the environmental adaptability.

[0049] The target cleaning cycle refers to a dynamically determined cleaning interval. It can be achieved by multiplying the filter bag cleaning potential coefficient with the environmental adaptability, for example, by adjusting the baseline cycle through a scaling factor, or by selecting the optimal cycle based on a decision tree model. The main purpose is to dynamically optimize the cleaning frequency. This application uses a cleaning cycle control system to comprehensively consider multi-dimensional information such as dust load, equipment resistance, and environmental conditions. It calculates the dust load impact coefficient, filter bag dynamic resistance coefficient, environmental adaptability, and filter bag cleaning potential coefficient in real time, and determines the target cleaning cycle accordingly. This overcomes the limitations of traditional fixed-cycle or constant-pressure-difference cleaning in the instantaneous, intermittent, and high-humidity environments of phosphate mines, avoiding energy waste, excessive filter bag wear, insufficient cleaning, and the risk of filter bag clogging. As a preferred implementation, this system can dynamically integrate equipment status and environmental constraints based on parameters such as inlet dust mass concentration, processing air volume, pressure difference across the filter bag, gas humidity, and dew point margin, ensuring dust removal efficiency and extending filter bag life.

[0050] In practical implementation, the inlet dust concentration can be monitored and obtained in real time by a dust concentration sensor, and the processing air volume can be measured by an airflow meter. For example, the calculation of the dust load impact coefficient involves multiplying the inlet dust concentration and processing air volume by the maximum design value respectively to quantify the instantaneous impact of the dust load on the system. Therefore, the system can dynamically adjust the dust removal strategy by comprehensively considering multi-dimensional information such as dust load, equipment resistance status, and environmental conditions.

[0051] This technical solution effectively addresses the challenge of precisely controlling the cleaning cycle of baghouse dust collectors in phosphate mines under conditions of instantaneous, intermittent dust loads and high humidity. Under low dust load conditions, it avoids energy waste and excessive filter bag wear caused by indiscriminate cleaning. Under high dust load conditions, it responds promptly to prevent insufficient cleaning due to sudden increases in resistance. In high humidity environments, it automatically extends the cleaning cycle to mitigate the risk of dust deliquescence and prevent irreversible damage such as bag clogging. This ensures stable dust removal efficiency, extends filter bag lifespan, and reduces system operational risks.

[0052] like Figure 1 As shown, in a preferred embodiment of the present invention, the calculation of the dust load impact coefficient is specifically as follows:

[0053] The dust mass concentration and processing air volume at inlet 3 are acquired in real time as basic data. These parameters directly capture the instantaneous and intermittent working conditions of dust in the well.

[0054] The inlet dust mass concentration and the processing air volume are compared with the maximum design dust concentration and the maximum design processing air volume, respectively. Then, the inlet dust mass concentration index and the processing air volume index are obtained by using the min function with an upper limit of 1.

[0055] Ratio processing refers to converting actual measured values ​​into dimensionless proportional values ​​relative to the system design limits. This can be achieved through software algorithms for normalization calculations or through pre-set proportional circuits for signal conversion. This processing method eliminates the influence of differences in equipment size, making load assessments of dust removal equipment of different specifications comparable. Its purpose is to map raw parameters into standardized indicators directly related to the system's capacity. Min function limiting refers to imposing an upper limit constraint mechanism on the calculation results. This can be understood as embedding limiting logic in the program code or implemented through clamping elements in analog circuits. This processing method prevents the exponent from increasing infinitely when actual operating parameters exceed the design maximum value. Its purpose is to ensure the numerical stability of coefficients under extreme dust impacts and avoid control decision errors caused by distorted calculation results.

[0056] The dust load impact coefficient is obtained by multiplying the inlet dust mass concentration index by the processing air volume index, with the value ranging from 0 to 1. Multiplying the concentration index and the air volume index means using a multiplication operation to integrate the load effects of two dimensions, which can be understood as a mathematical representation of the dust mass flow rate. This processing method can accurately reflect the dynamic impact intensity of dust on the filter bag, and its purpose is to make the coefficient directly reflect the characteristics of the total dust load, providing accurate input for environmental adaptability calculation.

[0057] As a specific implementation method, the solution of this application is implemented as follows: An optical dust concentration sensor and a thermal air volume meter are installed at the inlet 3 of the air dust removal equipment in the phosphate mine to collect the inlet dust mass concentration and processing air volume signals in real time; these signals are transmitted to the dust removal cycle control system, which divides the inlet dust mass concentration by the preset maximum design dust concentration threshold, and then limits the amplitude through software to ensure that the result does not exceed 1, generating an inlet dust mass concentration index; simultaneously, the processing air volume is divided by the maximum design processing air volume threshold and limited to generate a processing air volume index; the control unit then multiplies the two indices to calculate the dust load impact coefficient, which is strictly limited to a value between 0 and 1, and this coefficient is transmitted in real time to the dust removal cycle decision module for dynamically adjusting the dust removal action interval.

[0058] Through the above scheme, this application can ensure that the dust load impact coefficient stably represents the actual dust load state under various downhole working conditions, avoid excessive dust cleaning caused by coefficient calculation distortion under low load conditions, effectively reduce energy waste and mechanical wear of filter bag 6; at the same time, it prevents insufficient dust cleaning caused by coefficient exceeding the reasonable range under high load conditions, significantly reduces the risk of sudden increase in system resistance and filter bag blockage, thereby achieving precise adaptive control of the dust cleaning cycle.

[0059] like Figure 1 As shown, in a preferred embodiment of the present invention, the calculation of the dynamic resistance coefficient of the filter bag is specifically as follows:

[0060] Obtain the pressure difference across the filter bag; divide the difference between the pressure difference across the filter bag at the previous moment and the current moment by the sampling time interval to obtain the rate of change of the pressure difference across the filter bag.

[0061] The pressure difference across the filter bag refers to the pressure difference between the air inlet and outlet sides of the filter bag. It can be achieved using a differential pressure sensor, with the aim of directly reflecting the current filter bag resistance level. The rate of change of the pressure difference across the filter bag refers to the rate of change of the pressure difference across the filter bag over time. It can be achieved by performing differential calculations on continuously collected pressure difference data, with the aim of capturing the instantaneous trend of dust load changes.

[0062] The pressure difference across the filter bag and the rate of change of pressure difference across the filter bag are compared with the maximum allowable pressure difference and the rate of change of pressure difference, respectively. Then, the upper limit of the filter bag pressure difference index and the rate of change of pressure difference across the filter bag are obtained by using the min function with an upper limit of 1.

[0063] The pressure difference index across the filter bag refers to the dimensionless value after normalizing the actual pressure difference and the maximum allowable pressure difference. It can be calculated by ratio and limited to eliminate dimensional differences and ensure that the value is within a safe range. The pressure difference change rate index across the filter bag refers to the dimensionless value after normalizing the actual pressure difference change rate and the maximum allowable pressure difference change rate. It can be calculated by ratio and limited to standardize the change rate index.

[0064] The dynamic resistance coefficient of the filter bag is obtained by weighted combination calculation of the pressure difference index and the pressure difference change rate index on both sides of the filter bag. The dynamic resistance coefficient of the filter bag is positively correlated with both the pressure difference index and the pressure difference change rate index on both sides of the filter bag. The dynamic resistance coefficient of the filter bag is used to comprehensively characterize the resistance state and change trend of the filter bag.

[0065] The weighted combination operation specifically involves importing the pressure difference index across the filter bag and the rate of change of pressure difference across the filter bag into the formula. Obtain the dynamic resistance coefficient of the filter bag , The value range is 0-1, where, This refers to the pressure difference index across the filter bag. The index represents the rate of change of pressure difference across the filter bag. This is the pressure difference weight.

[0066] In one specific implementation, the pressure difference across the filter bag can be monitored in real time by a differential pressure transmitter installed in the dust collector; the calculation of the differential pressure change rate can be performed by the microprocessor in the control system periodically collecting differential pressure data and performing differential operations; the normalization of the differential pressure index and the differential pressure change rate index can be achieved in the control system software by setting the maximum allowable value and performing ratio calculation; the filter bag dynamic resistance coefficient refers to a parameter used to adjust the importance of different factors in decision-making, which can be assigned a value through a preset strategy or dynamic algorithm.

[0067] Through the above scheme, this application can dynamically integrate the static value and trend of filter bag resistance, accurately quantify the filter bag resistance status, avoid decision lag caused by relying solely on static pressure difference, promptly start dust cleaning to prevent a sudden increase in resistance when dust concentration increases sharply, and reduce unnecessary dust cleaning actions at low loads, thereby reducing energy consumption and filter bag wear, and improving the overall operating efficiency and stability of the system.

[0068] like Figure 1 As shown, in a preferred embodiment of the present invention, the computing environment adaptability specifically refers to:

[0069] Obtaining gas humidity and dew point margin; gas humidity and dew point margin are key parameters reflecting the state of the downhole air environment. They can be obtained using capacitive humidity sensors and dew point sensors, with the aim of capturing environmental humidity fluctuations and condensation risk thresholds in real time.

[0070] Importing gas humidity into the formula Obtaining humidity influencing factors ,in, For gas humidity, The optimal operating humidity threshold (below this value, humidity will not affect dust removal). The maximum permissible operating humidity threshold (above this value is considered an extremely harsh environment) is defined as the optimal operating humidity threshold and the maximum permissible operating humidity threshold. These thresholds can be determined using historical operating data and can be dynamically updated based on the equipment's historical operating database. The humidity impact factor is a core parameter that quantifies the inhibitory effect of gas humidity on the dust removal process. It can be implemented using a threshold-based linear decay model. When the gas humidity is below the optimal operating humidity threshold, the factor remains at 1, indicating that humidity has no negative impact. When the humidity exceeds the threshold, the factor linearly decays to 0 as the humidity increases. The purpose is to accurately capture the inhibitory effect of humidity changes on dust removal and avoid introducing additional moisture during dust removal under high humidity conditions.

[0071] The dew point margin is calculated by comparing the dew point margin with the safe dew point margin setpoint, and then the upper limit of the dew point margin is limited to 1 using the min function to obtain the dew point margin influence factor.

[0072] The dew point margin impact factor can be understood as a dynamic indicator reflecting the level of condensation risk. It can be achieved by normalizing the ratio of the actual dew point margin to the safety setpoint and limiting it to the range of 0-1. The purpose is to transform discrete dew point margin data into continuous risk assessment values. The smaller the margin, the lower the factor, which directly warns of a decline in environmental safety.

[0073] The environmental condition coefficient is obtained by taking the square root of the product of the humidity influence factor and the dew point margin influence factor. ;

[0074] The square root operation of the product is used to balance the contribution weights of humidity and dew point margin factors, avoiding the dominance of a single factor in the calculation results, thereby providing a stable and reliable input basis for environmental adaptability; the environmental condition coefficient is used to characterize the degree of environmental support for the dust removal behavior, and automatically extends the dust removal cycle when the humidity exceeds the standard or the risk of condensation increases, effectively avoiding the risk of moisture introduction and filter bag condensation and clogging.

[0075] The dust removal demand coefficient is obtained by taking the square root of the product of the dust load impact coefficient and the filter bag dynamic resistance coefficient; the dust removal demand coefficient is used to characterize the degree of system demand for dust removal behavior.

[0076] Import the environmental condition coefficient and the dust removal requirement coefficient into the formula. Obtain environmental adaptability , Output range 0-1, The lower the value, the less the current environment can support the dust removal requirements that the system should meet. Therefore, it is necessary to reduce the actual dust removal frequency by "extending the dust removal cycle" to avoid risks (such as reducing moisture introduction and preventing condensation and bag clogging). For environmental conditions coefficient, This is the dust removal demand coefficient. Demand-environment sensitivity coefficient, A value greater than 0 indicates environmental adaptability, which can be understood as an indicator of the degree of matching between environmental conditions and dust removal requirements. It links environmental condition coefficients with dust removal requirement coefficients through a specific proportional function, aiming to dynamically adjust dust removal frequency to mitigate the risks of high-humidity environments. (Requirement-Environment Sensitivity Coefficient) It refers to the configurable parameter that adjusts the weight of environmental factors. It can be set to a fixed empirical value or dynamically adjusted through an adaptive algorithm. The purpose is to flexibly control the influence of environmental factors on ash removal decisions based on the actual working conditions downhole.

[0077] Specifically, the solution in this application achieves closed-loop control of environmental perception and dust removal decision-making through a hierarchical quantification mechanism. First, gas humidity and dew point margin are collected in real time as basic environmental parameters, and their values ​​directly determine the calculation benchmark for subsequent environmental condition coefficients. Based on this, the environmental condition coefficients are normalized to transform the original parameters into standardized indices within the range of 0-1, making environmental parameters of different magnitudes comparable. At the same time, the dust load impact coefficient reflects the inlet dust impact intensity, and the filter bag dynamic resistance coefficient characterizes the trend of system resistance change. The two are fused by taking the square root to generate the dust removal demand coefficient. This processing method effectively suppresses the interference of extreme fluctuations of a single parameter on demand assessment. Subsequently, the environmental adaptability formula links the environmental condition coefficient and the dust removal demand coefficient through a proportional relationship. When the environmental condition coefficient is low (such as in a high humidity state) and the dust removal demand coefficient is high, the environmental adaptability value decreases significantly, triggering a dust removal cycle extension mechanism. Finally, the environmental adaptability summary formula integrates underlying parameters such as humidity influencing factors and dew point margin influencing factors to form a complete logical chain from environmental perception to decision output, enabling dust removal control to respond to changes in dust load while avoiding the risk of condensation caused by moisture introduction.

[0078] As a specific implementation method, the solution of this application is implemented as follows: gas humidity is monitored in real time by a capacitive humidity sensor installed at the inlet 3 of the dust collector 1, and the dew point margin is obtained by joint calculation of temperature sensor and humidity sensor data; the environmental condition coefficient adopts a neural network model to replace the traditional linear calculation, and the model uses historical environmental data as the training set to output an environmental suitability score; the dust removal demand coefficient calculation module is integrated into the dust removal cycle control system, which uses a field-programmable gate array to realize the hardware acceleration of square root operation; the environmental adaptability calculation unit inputs the environmental condition coefficient and the dust removal demand coefficient into a proportional function, and the output signal is transmitted to the control terminal of the cleaning mechanism after analog-to-digital conversion to dynamically adjust the start and stop sequence of the flushing pipeline 9; the demand-environment sensitivity coefficient λ is preset to 0.8 according to the humidity characteristics of the underground area during the system initialization stage, and is finely adjusted according to the historical events of bag clogging during operation through an online learning algorithm.

[0079] Through the above technical solution, this application effectively avoids the risk of dust condensation and bag clogging caused by the introduction of additional moisture during the ash cleaning operation in the high humidity environment of phosphate mines. At the same time, it ensures that the ash cleaning action is performed in a timely manner when the environmental conditions are suitable to maintain the stability of the system resistance, thereby solving the problem of operational reliability caused by the failure to incorporate environmental parameters into the ash cleaning decision-making logic.

[0080] like Figure 1 As shown, in a preferred embodiment of the present invention, the calculation of the filter bag cleaning potential coefficient is specifically as follows:

[0081] Obtain the pressure drop recovery rate after the previous cleaning action, the initial differential pressure baseline after cleaning, and the initial resistance recovery rate after cleaning;

[0082] Pressure drop recovery rate refers to the speed at which the pressure difference across the filter bag recovers to its working state after the dust removal action. It can be achieved by monitoring the pressure difference change across the filter bag in real time and calculating the pressure difference recovery ratio per unit time. The purpose is to objectively quantify the dust removal efficiency and avoid the lag defect of relying solely on a single pressure difference value. Initial pressure difference baseline after dust removal refers to the reference value of the pressure difference across the filter bag at the instant the dust removal action is completed. It can be obtained by sampling with a high-precision differential pressure sensor during the stable phase after the dust removal pulse ends. The purpose is to characterize the degree of dust removal on the filter bag surface. The lower the initial pressure difference, the better the cleaning effect. Initial resistance recovery rate after dust removal refers to the dynamic trend index of the filter bag resistance changing over time after dust removal. It can be achieved by continuously collecting differential pressure data and calculating the difference value between adjacent time points. The purpose is to predict the subsequent dust accumulation rate. The lower the resistance recovery rate, the less the subsequent operating burden of the filter bag.

[0083] The initial differential pressure baseline and the initial resistance recovery rate after cleaning were both subjected to maximum-minimum normalization to obtain the initial differential pressure baseline index and the initial resistance recovery rate index after cleaning.

[0084] Maximum-minimum normalization refers to linearly transforming parameter values ​​into a 0-1 standardized range by using the extreme value range determined by historical operating data. This can be achieved by dynamically updating the maximum and minimum thresholds based on the equipment's historical operating database. The purpose is to eliminate the dimensional differences and magnitude fluctuations between different parameters and ensure that each parameter is comparable in comprehensive calculations.

[0085] The pressure drop recovery rate after the previous cleaning operation, the initial pressure difference baseline index after cleaning, and the initial resistance recovery rate index after cleaning are imported into the formula. Obtain the filter bag cleaning potential coefficient , Output range 0-1.

[0086] in, This represents the pressure drop recovery rate after the previous dust removal operation. The baseline index of the initial differential pressure after dust removal. The initial resistance recovery rate index after dust removal. , and All are weighting coefficients with values ​​ranging from 0 to 1, and The filter bag cleaning potential coefficient is positively correlated with the pressure drop recovery rate and negatively correlated with the initial differential pressure baseline index after cleaning and the initial resistance recovery rate index after cleaning. The filter bag cleaning potential coefficient is a quantitative index that comprehensively reflects the recovery state of the filter bag after cleaning. It integrates normalized multi-dimensional parameters in the form of weighted index, with the aim of providing adaptive input for cleaning cycle decision-making, so that the calculation results can dynamically respond to complex downhole working conditions.

[0087] As a specific implementation method, the solution of this application is implemented as follows: During the operation of the air dust removal equipment in the phosphate mine, after the dust removal action is completed, the system collects the pressure difference data on both sides of the filter bag in real time through the differential pressure sensor installed in the dust collection box 1, and calculates the pressure difference recovery ratio within 30 seconds after dust removal as the pressure drop recovery rate; at the same time, the stable pressure difference value 5 seconds after the end of the dust removal pulse is recorded as the initial pressure difference baseline after dust removal, and the initial resistance recovery rate after dust removal is obtained by calculating the pressure difference change rate over 10 consecutive seconds. Subsequently, the system calls the maximum-minimum threshold of the filter bag of this model stored in the equipment's historical operation database, performs normalization processing on the initial pressure difference baseline and resistance recovery rate, and generates corresponding index values. Finally, the pressure drop recovery rate, the normalized initial pressure difference baseline index, and the resistance recovery rate index are input into the calculation module, combined with preset weighting coefficients (e.g., set under high dust load conditions). =0.5、 =0.3、 =0.2), the filter bag cleaning potential coefficient calculation formula is executed by the embedded microcontroller, and the quantitative result in the range of 0-1 is output for cleaning cycle decision.

[0088] Through the above technical solution, this application realizes the accurate quantification and adaptive processing of the filter bag state parameters after cleaning, so that the filter bag cleaning potential coefficient can truly reflect the cleaning efficiency and subsequent operating burden of the filter bag, effectively avoiding the decision deviation of the cleaning cycle caused by non-standardized or non-comprehensive consideration of parameters, thereby solving the problems of increased energy consumption and filter bag wear caused by premature cleaning, as well as the sudden increase in resistance and risk of bag clogging caused by late cleaning.

[0089] like Figure 1 As shown, in a preferred embodiment of the present invention, determining the target dust removal cycle specifically involves:

[0090] Based on a baseline cleaning cycle, the baseline cleaning cycle is positively adjusted according to the filter bag cleaning potential coefficient, and the adjusted cycle is negatively adjusted according to the environmental adaptability, thereby determining the target cleaning cycle.

[0091] The specific calculation formula is as follows: ,in, The target cleaning cycle refers to the interval between cleaning actions that is dynamically adjusted according to the downhole working conditions. It can be calculated using a mathematical model based on filter bag state parameters and environmental constraint parameters, with the aim of matching the cleaning frequency with the actual dust load and environmental risks. The baseline cleaning cycle refers to the basic cleaning time interval set under standard operating conditions. It can be a preset fixed time value or an average cycle calculated based on historical operating data. The purpose is to provide a stable reference benchmark for dynamic adjustment. The filter bag cleaning potential coefficient. For environmental adaptability.

[0092] In this embodiment of the invention, specifically, the solution of this application achieves dynamic optimization of the cleaning cycle by mathematically coupling the baseline cleaning cycle with the filter bag cleaning potential coefficient and environmental adaptability. In the formula... The target cleaning cycle is extended as the filter bag cleaning potential coefficient increases. When the pressure drop recovery rate after filter bag cleaning is high, the initial pressure difference baseline is low, and the resistance recovery rate is slow, it indicates that the filter bag has a longer cleaning maintenance capability. In this case, extending the cleaning cycle can reduce unnecessary cleaning actions. The formula significantly extends the target cleaning cycle as environmental adaptability decreases. When high gas humidity or insufficient dew point margin leads to reduced environmental adaptability, extending the cleaning cycle can avoid the risk of condensation and bag clogging caused by cleaning in high humidity environments. Overall, this formula organically integrates filter bag state parameters and environmental constraint parameters through a product form, enabling the cleaning cycle to respond to both the actual load on the filter bag and mitigate environmental risks. This achieves precise adaptive control of the cleaning frequency in complex scenarios such as intermittent dust impacts and humidity fluctuations in phosphate mines.

[0093] As a specific implementation method, the solution of this application is implemented as follows: During the operation of the air dust removal equipment in the phosphate mine, the dust removal cycle control system acquires the filter bag dust removal potential coefficient and environmental adaptability in real time. When the system detects that the pressure drop recovery rate after filter bag dust removal is high, the initial pressure difference baseline is low, and the resistance recovery rate is slow, and the gas humidity is close to the dew point margin safety threshold, it automatically calculates a high filter bag dust removal potential coefficient and a low environmental adaptability, thereby significantly extending the target dust removal cycle; while when the filter bag is in good condition and the environmental conditions are suitable, the system only slightly adjusts the dust removal cycle to ensure a balance between dust removal efficiency and equipment lifespan.

[0094] Through the above solution, this application can dynamically adjust the cleaning cycle according to the actual condition of the filter bag and environmental conditions, avoid energy waste and filter bag wear caused by excessive cleaning when the dust load fluctuates, and prevent condensation and bag clogging caused by cleaning in high humidity environment, thereby improving the operational stability and reliability of dust removal equipment in complex working conditions in phosphate mines.

[0095] like Figures 2-4 As shown, in a preferred embodiment of the present invention, the bag dust removal mechanism includes a mounting plate 5 horizontally fixed inside the dust collection box 1, at least one bag 6 fixed on the mounting plate 5, the bag 6 being located below the mounting plate 5, a negative pressure fan 7 fixed inside the top housing 2, and a dust collection box 8 being pulled out inside the dust collection box 1.

[0096] The filter bag 6 can be a filter element made of filter media, such as polyester fiber or glass fiber filter media, with the purpose of intercepting dust particles through surface filtration and promoting dust settling by gravity; the negative pressure fan 7 refers to the airflow drive device installed in the top housing 2, which can be a centrifugal fan or an axial fan, with the purpose of generating a negative pressure airflow from bottom to top to optimize the gas flow path; the dust collection box 8 refers to a pull-out dust collection container, which can be implemented with a sliding drawer structure or a snap-on quick-release design, with the purpose of facilitating quick dust cleaning without interrupting equipment operation.

[0097] In this embodiment of the invention, the mounting plate 5 is horizontally fixed inside the dust collection box 1, providing a stable mounting reference surface for the filter bag 6; the filter bag 6 is fixed below the mounting plate 5, and the dust-laden air enters through the inlet 3 and passes through the surface of the filter bag 6 from bottom to top, where the dust is trapped to form a filter layer; the negative pressure fan 7 is installed inside the top housing 2, and drives the purified gas to be discharged through the outlet 4 through suction, while maintaining the negative pressure state inside the box; the dust collection box 8 is located in the bottom area of ​​the dust collection box 1, directly below the filter bag 6, and is used to receive the dust particles that settle by gravity. Its pull-out structure allows it to be directly pulled out for cleaning during equipment operation intervals, avoiding secondary dust re-entrainment.

[0098] like Figure 2 and Figure 3 As shown, in a preferred embodiment of the present invention, the cleaning mechanism includes a flushing pipe 9 fixed above the mounting plate 5 inside the dust collection box 1. The flushing pipe 9 is connected to an external air source. At least one flushing head 10 is provided on the flushing pipe 9, and the flushing head 10 is located directly above the cloth bag 6.

[0099] In this embodiment of the invention, the flushing pipeline 9 refers to the gas delivery channel fixed above the mounting plate 5. It can be made of stainless steel or corrosion-resistant engineering plastic to provide a rigid support structure and prevent the displacement deviation of the dust removal components caused by the vibration of the downhole equipment. The connection to the external gas source means that the flushing pipeline 9 is connected to the dry gas supply system, which can be implemented by a compressed air dryer or a nitrogen storage tank. The purpose is to replace the liquid medium with dry gas to fundamentally avoid the introduction of additional moisture. The flushing head 10 refers to the gas injection unit set on the flushing pipeline 9. It can be implemented by a Venturi effect nozzle or a vortex nozzle to achieve uniform dust removal coverage on the surface of the filter bag 6. Located directly above the filter bag 6 means that the installation position of the flushing head 10 is aligned with the axial center line of the filter bag 6. It can be implemented by vertical downward installation to guide the airflow to directionally impact the dust layer along the axial direction of the filter bag.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dust removal device for underground air in phosphate mining, comprising a dust collection box and a top shell fixed to the top of the dust collection box, wherein an inlet and an outlet are respectively provided on the side wall of the dust collection box and the top of the top shell, characterized in that, Also includes: The bag filter system, installed inside the dust collection box, is used to remove dust from the underground air. Cleaning equipment, specifically for cleaning the filter bags in baghouse dust collection systems; A dust removal cycle control system, connected to the control terminal of the cleaning mechanism, is used to adjust the dust removal cycle in real time. The dust removal cycle control system is configured as follows: Calculate the dust load impact coefficient based on the inlet dust mass concentration and the processing air volume; Calculate the dynamic resistance coefficient of the filter bag based on the pressure difference across the filter bag; Based on gas humidity and dew point margin, the dust load impact coefficient and the filter bag dynamic resistance coefficient, the environmental adaptability is calculated. Based on the pressure drop recovery rate after the previous cleaning action, the initial pressure difference baseline after cleaning, and the initial resistance recovery rate after cleaning, the filter bag cleaning potential coefficient is calculated. The target cleaning cycle is determined based on the filter bag cleaning potential coefficient and the environmental adaptability.

2. The underground air dust removal equipment for phosphate mining according to claim 1, characterized in that, The calculation of the dust load impact coefficient is specifically as follows: The inlet dust mass concentration and the processing air volume are normalized with their corresponding design maximum values ​​to obtain the inlet dust mass concentration index and the processing air volume index. The dust load impact coefficient is obtained by multiplying the inlet dust mass concentration index by the processing air volume index.

3. The underground air dust removal equipment for phosphate mining according to claim 2, characterized in that, The calculation of the dynamic resistance coefficient of the filter bag is specifically as follows: Obtain the pressure difference across the filter bag and calculate its rate of change over time; The pressure difference and its rate of change across the filter bag are normalized with the corresponding maximum allowable values ​​to obtain the pressure difference index and the rate of change index of the pressure difference across the filter bag. The dynamic resistance coefficient of the filter bag is obtained by weighted combination calculation of the pressure difference index and the pressure difference change rate index on both sides of the filter bag. The dynamic resistance coefficient of the filter bag is positively correlated with both the pressure difference index and the pressure difference change rate index on both sides of the filter bag.

4. The underground air dust removal equipment for phosphate mining according to claim 3, characterized in that, The computing environment adaptability specifically refers to: Obtain gas humidity and dew point margin; Environmental condition coefficients are calculated based on gas humidity and dew point margin. These environmental condition coefficients are used to characterize the degree of environmental support for dust removal activities. The dust load impact coefficient and the filter bag dynamic resistance coefficient are geometrically averaged to obtain the dust removal demand coefficient. The dust load impact coefficient and the filter bag dynamic resistance coefficient are combined to obtain the dust removal demand coefficient, which is used to characterize the system's demand for dust removal behavior. The environmental condition coefficient and the dust removal requirement coefficient are compared and calculated to obtain the environmental adaptability. The environmental adaptability value increases with the increase of the environmental condition coefficient and decreases with the increase of the dust removal requirement coefficient.

5. The underground air dust removal equipment for phosphate mining according to claim 4, characterized in that, The calculation of environmental condition coefficients specifically refers to: The humidity influence factor is calculated based on the gas humidity, and the humidity influence factor decreases as the gas humidity changes in an unfavorable direction; The dew point margin is calculated by comparing the dew point margin with the safe dew point margin setpoint, and then the upper limit of the dew point margin is limited to 1 using the min function to obtain the dew point margin influence factor. The environmental condition coefficient is obtained by taking the square root of the product of the humidity influence factor and the dew point margin influence factor. .

6. The underground air dust removal equipment for phosphate mining according to claim 1, characterized in that, The calculation of the filter bag cleaning potential coefficient is specifically as follows: The initial differential pressure baseline and the initial resistance recovery rate after cleaning were both subjected to maximum-minimum normalization to obtain the initial differential pressure baseline index and the initial resistance recovery rate index after cleaning. The filter bag cleaning potential coefficient is obtained by comprehensively calculating the pressure drop recovery rate after the previous cleaning action, the initial pressure difference baseline index after cleaning, and the initial resistance recovery rate index after cleaning, based on a preset weight relationship. The filter bag cleaning potential coefficient is positively correlated with the pressure drop recovery rate and negatively correlated with the initial pressure difference baseline index after cleaning and the initial resistance recovery rate index after cleaning.

7. The underground air dust removal equipment for phosphate mining according to claim 1, characterized in that, The specific determination of the target dust removal cycle is as follows: Based on a baseline cleaning cycle, the baseline cleaning cycle is positively adjusted according to the filter bag cleaning potential coefficient, and the adjusted cycle is negatively adjusted according to the environmental adaptability, thereby determining the target cleaning cycle.

8. The underground air dust removal equipment for phosphate mining according to claim 1, characterized in that, The bag filter dust removal mechanism includes a horizontally fixed mounting plate inside the dust removal box, at least one filter bag fixed on the mounting plate, the filter bag being located below the mounting plate, and a negative pressure fan fixed inside the top housing.

9. The underground air dust removal equipment for phosphate mining according to claim 1, characterized in that, The cleaning mechanism includes a flushing pipe fixed above the mounting plate inside the dust collection box. The flushing pipe is connected to an external air source. At least one flushing head is provided on the flushing pipe, and the flushing head is located directly above the filter bag.

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