Intelligent control system and method applied to bag-type dust collector

Abstract

This invention discloses an intelligent control system and method for baghouse dust collectors, belonging to the field of intelligent control technology. The invention first acquires initial flue gas parameters and system pressure difference in real time, and detects the dust mass in the ash hopper after cleaning. Then, it calculates the ratio of the pressure difference change rate to the dust mass change rate, thereby constructing a correlation model. Based on the current operating condition parameters grouped together, the ratio is compared with a preset normal range: if there is a deviation, an early warning is issued, and the pulse jet pressure and duration are dynamically adjusted according to the deviation to achieve adaptive optimization of the cleaning parameters. The cleaning effect is evaluated by calculating the pressure difference reduction per unit dust mass; if the effect is not up to standard, a second early warning is issued, and mechanical vibration cleaning is initiated as a supplementary measure, adjusting the vibration frequency according to the cleaning effect coefficient. This invention achieves intelligent sensing, precise judgment, and dynamic control of the cleaning process, effectively preventing filter bag clogging or damage, significantly reducing energy consumption, and extending the filter bag's service life.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically an intelligent control system and method applied to bag filters. Background Technology

[0002] Baghouse dust collectors have been continuously evolving to meet the ever-increasing demands for dust removal efficiency and equipment stability in the industrial sector. Their core performance has become one of the key indicators for measuring the level of industrial pollution control, driving related control technologies towards a more intelligent and adaptable direction.

[0003] In the current operation of baghouse dust collectors, due to the limited capacity of the filter bags, if dust collection parameters are not adjusted and controlled in a timely manner, problems such as filter bag adhesion, clogging, and bag burning may occur, affecting the dust collection efficiency and the service life of the filter bags. Most current mainstream baghouse dust collector cleaning control systems adopt a fixed-parameter mechanized control mode. However, in industrial production, operating parameters such as dust concentration, particle viscosity, flue gas temperature and humidity are often in a state of dynamic fluctuation, making it difficult for this mechanized control mode to adapt to changes in operating conditions in real time. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent control system and method for bag filters to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent control method for bag filters, the method comprising the following steps:

[0006] Step S1: Obtain the initial wind speed and initial temperature and humidity of the dust collector as initial parameter features, and obtain the pressure difference between the air inlet and outlet of the dust collector to establish a correlation dataset;

[0007] Furthermore, step S1 includes:

[0008] The dust collector is a bag filter.

[0009] Temperature and humidity sensors are installed at the air inlet of the dust collector to obtain the initial temperature and humidity of the gas to be dusted. The initial temperature and humidity include the initial temperature and the initial humidity. The gas to be dusted is the gas that enters the air inlet.

[0010] A wind speed sensor is installed at the air inlet of the dust collector to obtain the initial wind speed of the gas to be dusted. The initial wind speed is the airflow velocity passing through the cross-section of the air inlet per unit time.

[0011] Differential pressure transmitters are installed at the air inlet and air outlet of the dust collector to obtain the pressure difference between the air inlet and air outlet.

[0012] The temperature and humidity sensor, the wind speed sensor, and the differential pressure transmitter have the same sampling frequency, and a timestamp is recorded during sampling.

[0013] Using timestamps as indexes, the initial parameter features are mapped to pressure differences and stored in the associated dataset.

[0014] Step S2: When the pressure difference reaches the preset threshold, the filter bags on the dust collector are cleaned, and the cleaned dust falls into the ash hopper; after each cleaning, the dust mass in the ash hopper is detected, and the correlation model between dust mass, initial parameter characteristics and pressure difference is obtained.

[0015] Furthermore, step S2 includes:

[0016] The dust removal method is pulse jet cleaning; the dust hopper is a funnel-shaped, sealed cavity structure with a fixed volume, located directly below the filter bag. Pulse jet cleaning uses compressed air as the core power source, and achieves dust removal by instantaneously releasing high-pressure airflow through a pulse valve. The pulse controller triggers the pulse valve, injecting dry compressed air into the interior of the filter bag at high speed for a short time, forming a reverse airflow impact. The rapid expansion of the airflow causes the filter bag to inflate and vibrate instantly, peeling off the dust layer adhering to the outer surface of the filter bag. The dust falls into the dust hopper by gravity.

[0017] A preset waiting time interval is used to ensure that the dust settles fully into the ash hopper. The dust collector is considered to have completed the dust removal process after the dust collector has started cleaning and a waiting time interval has elapsed.

[0018] A pressure sensor is installed at the bottom of the ash hopper to obtain the dust mass in the ash hopper; the sampling frequency of the pressure sensor is consistent with that of the temperature and humidity sensor, the wind speed sensor, and the differential pressure transmitter.

[0019] Using timestamps as indexes, dust quality is correlated with initial parameter features and pressure differences and stored in the associated dataset;

[0020] Based on the dust mass and pressure difference stored in the associated dataset according to the timestamp index, the rate of change of pressure difference over time and the rate of change of dust mass over time are calculated. The rate of change of pressure difference over time and the rate of change of dust mass over time are correlated according to the same initial parameter characteristics. The ratio of the rate of change of pressure difference over time to the rate of change of dust mass over time, R=A / B, is calculated as the association model, where A represents the rate of change of pressure difference over time and B represents the rate of change of dust mass over time. When the filter bags are clean and have good air permeability, the dust load increases steadily, and the system pressure difference also rises smoothly, with the R value remaining within a relatively stable normal range. If the R value is significantly high, it may mean that the pressure difference is increasing too rapidly while the amount of ash falling from the ash hopper is insufficient, suggesting that the filter bags may be clogged or caked, indicating insufficient cleaning or high dust stickiness. If the R value is significantly low, it may mean that the pressure difference is increasing slowly but the amount of ash falling from the ash hopper is abnormally high, suggesting that the filter bags may be damaged, causing dust to penetrate directly, or that excessive cleaning is causing energy waste. Therefore, the ratio R is an effective indicator for judging whether the working condition of the filter bags and the cleaning system are matched with the operating conditions.

[0021] Step S3: Determine whether to issue an early warning based on the association model. If an early warning is issued, adjust the dust removal parameters; if no early warning is issued, evaluate the dust removal effect.

[0022] Furthermore, step S3 includes:

[0023] Preset initial temperature threshold T0, initial humidity threshold H0, and initial wind speed threshold V0. Based on the relationship between the initial parameter characteristics and the corresponding T0, H0, and V0, the ratio R of the rate of change of pressure difference over time to the rate of change of dust mass over time is grouped to obtain several parameter characteristic groups. For each parameter characteristic group, a normal range for the corresponding ratio R is preset, denoted as [R]. min,g R max,g ], where R min,g R represents the minimum value of the normal interval in the g-th parameter feature group. max,g This represents the maximum value of the normal interval in the g-th parameter feature group;

[0024] At the end of each dust removal process, if the ratio of the rate of change of pressure difference over time to the rate of change of dust mass over time, R > R... max,g Or R <R min,g If the dust removal fails, an early warning will be issued; otherwise, the dust removal process will end and the dust removal effect will be evaluated.

[0025] The dust removal parameters include the pulse valve blowing pressure and the pulse valve blowing duration. The pulse valve is a device used by the dust collector to spray compressed air to clean the filter bags.

[0026] The process of adjusting the dust removal parameters includes:

[0027] Calculate the deviation of the ratio R (the rate of change of pressure difference over time to the rate of change of dust mass over time) from the normal range:

[0028] ;

[0029] in R represents the deviation, and R represents the ratio of the rate of change of pressure difference over time to the rate of change of dust mass over time; when R is greater than the maximum value in the normal range, This represents the maximum value within the normal range; when R is less than the minimum value within the normal range, This represents the minimum value within the normal range;

[0030] Adjust the dust removal parameters according to the deviation:

[0031] ;

[0032] Where C new This represents the adjusted dust removal parameters. C0 represents the preset baseline dust removal parameters, including the pulse valve injection baseline pressure and pulse valve injection baseline duration. k represents the preset weighting coefficient, with different weighting coefficients corresponding to different dust removal parameters. Represents the degree of deviation;

[0033] After adjusting the cleaning parameters, the filter bags are cleaned repeatedly. An alarm is issued based on the ratio R of the rate of change of the new pressure difference over time to the rate of change of the dust mass over time. Cleaning continues until no alarms are issued, or the number of cleaning cycles exceeds a preset threshold. The cleaning effect is then evaluated. The evaluation process includes:

[0034] The evaluation results include whether the dust removal effect meets the standard or not.

[0035] If the number of times the filter bag is cleaned exceeds the preset cleaning frequency threshold, the cleaning effect is considered to be substandard.

[0036] Obtain the change in pressure difference ΔP and the change in dust mass Δm before and after dust removal. ΔP = P0 - P1, where P0 represents the pressure difference at the start of dust removal and P1 represents the pressure difference at the end of dust removal; Δm = m1 - m0, where m1 represents the dust mass at the start of dust removal and m0 represents the dust mass at the end of dust removal.

[0037] Calculate the pressure difference reduction rate K per unit dust mass based on the change in pressure difference ΔP and the change in dust mass Δm, where K = ΔP / Δm; preset a pressure difference reduction rate threshold K0. When the pressure difference reduction rate K per unit dust mass is greater than the pressure difference reduction rate threshold K0, it is regarded that the cleaning effect meets the standard, otherwise it is regarded that the cleaning effect does not meet the standard. The pressure difference reduction rate K per unit dust mass is a key indicator for evaluating the cleaning efficiency, and its meaning is the system ventilation capacity restored by removing dust of unit mass. The higher the value of K, the higher the cost performance of the cleaning action, that is, a smaller amount of dust removal is exchanged for a larger reduction in pressure difference, indicating that the air permeability of the filter bag is restored well and the cleaning effect is remarkable. The preset pressure difference reduction rate threshold K0 represents the acceptable minimum cleaning efficiency standard. If K < K0, it means that the cleaning effect does not meet the standard. Even after cleaning, the residual resistance of the filter bag is still too high, and secondary cleaning may be required or the status of the filter bag needs to be checked.

[0038] Step S4: Issue a secondary warning according to the evaluation result. When the secondary warning is issued, perform secondary cleaning on the filter bags of the dust collector; when the secondary warning is not issued, save the evaluation result.

[0039] Further, step S4 includes:

[0040] When the cleaning effect does not meet the standard, issue a secondary warning.

[0041] 2]The secondary cleaning is mechanical vibration cleaning, including a preset vibration frequency, a preset amplitude, and a preset vibration duration; after a waiting time interval elapses after the secondary cleaning ends, it is regarded that the secondary cleaning is completed, and the vibration frequency is adjusted: Mechanical vibration cleaning is a cleaning method with mechanical vibration as the core power. The filter bag is driven by a vibration device to generate periodic vibration to achieve cleaning. A vibration mechanism such as a motor or an eccentric wheel drives the filter bag frame or the filter bag itself. The inertial force generated by the vibration overcomes the adhesion force between the dust and the surface of the filter bag, causing the dust layer to fall off and fall into the ash hopper.

[0042] After the secondary cleaning is completed, calculate the pressure difference reduction rate K1 per unit dust mass before and after the secondary cleaning, and obtain the cleaning effect coefficient according to the pressure difference reduction rate threshold K0 = K1 / K0, and adjust the vibration frequency according to the cleaning effect coefficient:

[0043] ;

[0044] where represents the adjusted vibration frequency, represents the vibration frequency before adjustment, represents the cleaning effect coefficient, represents the adjustment factor, which is used to control the amplitude of vibration adjustment. When < 1, it means that the cleaning effect is insufficient, and the cleaning strength is enhanced by increasing the vibration frequency; when When the value is ≥1, it indicates that the dust removal effect meets the standard. Reduce or maintain the vibration frequency to avoid excessive vibration damaging the filter bag.

[0045] An intelligent control system for bag filters, comprising a parameter acquisition module, a dust removal modeling module, an early warning and parameter adjustment module, and a secondary dust removal module;

[0046] The parameter acquisition module is used to obtain the initial parameter characteristics of the dust collector and the pressure difference between the air inlet and the air outlet, and to establish a related dataset;

[0047] The dust removal modeling module is used to clean the filter bags, obtain the dust mass in the dust hopper, and establish a correlation model between dust mass, initial parameter characteristics, and pressure difference.

[0048] The early warning parameter adjustment module is used to determine whether to issue an early warning based on the correlation model, adjust the dust removal parameters, and evaluate the dust removal effect.

[0049] The secondary cleaning module is used to issue a secondary warning based on the cleaning effect evaluation result and perform secondary cleaning on the filter bags.

[0050] The output of the parameter acquisition module is connected to the input of the dust removal modeling module; the output of the dust removal modeling module is connected to the input of the early warning parameter adjustment module; and the output of the early warning parameter adjustment module is connected to the input of the secondary dust removal module.

[0051] The parameter acquisition module also includes an initial parameter acquisition unit and an associated dataset construction unit;

[0052] The initial parameter acquisition unit is used to acquire the initial temperature and humidity of the gas to be dusted through a temperature and humidity sensor, acquire the initial wind speed through a wind speed sensor, and acquire the pressure difference between the air inlet and the air outlet through a differential pressure transmitter.

[0053] The associated dataset construction unit is used to store the initial parameter features and pressure difference in the associated dataset using timestamps as indexes.

[0054] The dust removal modeling module also includes a pulse dust removal execution unit and an association model establishment unit;

[0055] The pulse cleaning execution unit is used to perform cleaning by pulse jet cleaning when the pressure difference reaches a preset threshold.

[0056] The association model building unit is used to store dust quality and initial parameter features and pressure difference with timestamp as index, and calculates the ratio R of the rate of change of pressure difference with time to the rate of change of dust quality with time as the association model.

[0057] The early warning parameter adjustment module also includes an early warning judgment unit and a dust removal effect evaluation unit;

[0058] The first warning judgment unit is used to group the preset ratio R into normal ranges according to the initial parameter characteristics. If R exceeds the range, a warning is issued and the dust removal parameters are adjusted according to the deviation.

[0059] The dust removal effect evaluation unit is used to determine whether the dust removal effect meets the standard by whether the number of dust removals exceeds the threshold or whether the pressure difference reduction K per unit dust mass exceeds the threshold.

[0060] The secondary dust removal module also includes a secondary early warning judgment unit and a mechanical dust removal execution unit;

[0061] The secondary warning judgment unit is used to issue a secondary warning when the dust removal effect is not up to standard;

[0062] The mechanical cleaning unit is used to perform secondary cleaning by means of mechanical vibration cleaning, according to a preset vibration frequency, preset amplitude and preset vibration duration, and to adjust the vibration frequency.

[0063] Compared with the prior art, the beneficial effects of the present invention are:

[0064] 1. This invention pre-sets normal ranges for R values ​​based on initial temperature, humidity, and wind speed, and dynamically adjusts the pulse jet pressure and duration based on deviation. This avoids the problem of fixed parameters being unable to adapt to fluctuations in operating conditions, effectively preventing filter bag clogging or excessive dust removal, improving the equipment's adaptability to dynamic operating conditions, and extending the service life of filter bags.

[0065] 2. This invention compares the unit dust mass pressure difference reduction K with a preset threshold and evaluates the dust removal effect by combining the dust removal frequency threshold. If the standard is not met, mechanical vibration is activated for secondary dust removal to ensure that the dust removal efficiency meets the standard, reduce the energy consumption of ineffective dust removal, and at the same time avoid excessive residual dust in the filter bag from affecting the dust removal effect, and ensure the stable output purification capacity of the equipment.

[0066] 3. This invention constructs a dataset by associating initial parameters, pressure difference, and dust quality with timestamps, establishes an R-value correlation model to achieve a primary early warning, and combines a secondary early warning to form a closed-loop control of data collection, modeling, early warning, parameter adjustment, and evaluation. This replaces traditional mechanized control, achieves intelligent decision-making, reduces the cost of manual intervention, and improves the intelligence and reliability of bag filter operation. Attached Figure Description

[0067] Figure 1 This is a flowchart illustrating an intelligent control method for a bag filter according to the present invention.

[0068] Figure 2 This is a schematic diagram of the structure of an intelligent control system for a bag filter according to the present invention. Detailed Implementation

[0069] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0070] Example 1: As Figure 1 As shown, the present invention provides a technical solution, an intelligent control method for bag filters, the method comprising the following steps:

[0071] Step S1: Obtain the initial wind speed and initial temperature and humidity of the dust collector as initial parameter features, and obtain the pressure difference between the air inlet and outlet of the dust collector to establish a correlation dataset;

[0072] Step S1 includes:

[0073] The dust collector is a bag filter.

[0074] Temperature and humidity sensors are installed at the air inlet of the dust collector to obtain the initial temperature and humidity of the gas to be dusted. The initial temperature and humidity include the initial temperature and the initial humidity. The gas to be dusted is the gas that enters the air inlet.

[0075] A wind speed sensor is installed at the air inlet of the dust collector to obtain the initial wind speed of the gas to be dusted. The initial wind speed is the airflow velocity passing through the cross-section of the air inlet per unit time.

[0076] Differential pressure transmitters are installed at the air inlet and air outlet of the dust collector to obtain the pressure difference between the air inlet and air outlet.

[0077] The temperature and humidity sensor, the wind speed sensor, and the differential pressure transmitter have the same sampling frequency, and a timestamp is recorded during sampling.

[0078] Using timestamps as indexes, the initial parameter features are mapped to pressure differences and stored in the associated dataset.

[0079] Step S2: When the pressure difference reaches the preset threshold, the filter bags on the dust collector are cleaned, and the cleaned dust falls into the ash hopper; after each cleaning, the dust mass in the ash hopper is detected, and the correlation model between dust mass, initial parameter characteristics and pressure difference is obtained.

[0080] Step S2 includes:

[0081] The dust removal method is pulse jet cleaning; the dust hopper is a funnel-shaped, sealed cavity structure with a fixed volume, located directly below the filter bag. Pulse jet cleaning uses compressed air as the core power source, and achieves dust removal by instantaneously releasing high-pressure airflow through a pulse valve. The pulse controller triggers the pulse valve, injecting dry compressed air into the interior of the filter bag at high speed for a short time, forming a reverse airflow impact. The rapid expansion of the airflow causes the filter bag to inflate and vibrate instantly, peeling off the dust layer adhering to the outer surface of the filter bag. The dust falls into the dust hopper by gravity.

[0082] A preset waiting time interval is used to ensure that the dust settles fully into the ash hopper. The dust collector is considered to have completed the dust removal process after the dust collector has started cleaning and a waiting time interval has elapsed.

[0083] A pressure sensor is installed at the bottom of the ash hopper to obtain the dust mass in the ash hopper; the sampling frequency of the pressure sensor is consistent with that of the temperature and humidity sensor, the wind speed sensor, and the differential pressure transmitter.

[0084] Using timestamps as indexes, dust quality is correlated with initial parameter features and pressure differences and stored in the associated dataset;

[0085] Based on the dust mass and pressure difference stored in the associated dataset according to the timestamp index, the rate of change of pressure difference over time and the rate of change of dust mass over time are calculated. The rate of change of pressure difference over time and the rate of change of dust mass over time are correlated according to the same initial parameter characteristics. The ratio of the rate of change of pressure difference over time to the rate of change of dust mass over time, R=A / B, is calculated as the association model, where A represents the rate of change of pressure difference over time and B represents the rate of change of dust mass over time. When the filter bags are clean and have good air permeability, the dust load increases steadily, and the system pressure difference also rises smoothly, with the R value remaining within a relatively stable normal range. If the R value is significantly high, it may mean that the pressure difference is increasing too rapidly while the amount of ash falling from the ash hopper is insufficient, suggesting that the filter bags may be clogged or caked, indicating insufficient cleaning or high dust stickiness. If the R value is significantly low, it may mean that the pressure difference is increasing slowly but the amount of ash falling from the ash hopper is abnormally high, suggesting that the filter bags may be damaged, causing dust to penetrate directly, or that excessive cleaning is causing energy waste. Therefore, the ratio R is an effective indicator for judging whether the working condition of the filter bags and the cleaning system are matched with the operating conditions.

[0086] Step S3: Determine whether to issue an early warning based on the association model. If an early warning is issued, adjust the dust removal parameters; if no early warning is issued, evaluate the dust removal effect.

[0087] Step S3 includes:

[0088] Preset initial temperature threshold T0, initial humidity threshold H0, and initial wind speed threshold V0. Based on the relationship between the initial parameter characteristics and the corresponding T0, H0, and V0, the ratio R of the rate of change of pressure difference over time to the rate of change of dust mass over time is grouped to obtain several parameter characteristic groups. For each parameter characteristic group, a normal range for the corresponding ratio R is preset, denoted as [R]. min,g R max,g ], where R min,g R represents the minimum value of the normal interval in the g-th parameter feature group. max,g This represents the maximum value of the normal interval in the g-th parameter feature group;

[0089] At the end of each dust removal process, if the ratio of the rate of change of pressure difference over time to the rate of change of dust mass over time, R > R... max,g Or R <R min,g If the dust removal fails, an early warning will be issued; otherwise, the dust removal process will end and the dust removal effect will be evaluated.

[0090] The dust removal parameters include the pulse valve blowing pressure and the pulse valve blowing duration. The pulse valve is a device used by the dust collector to spray compressed air to clean the filter bags.

[0091] The process of adjusting the dust removal parameters includes:

[0092] Calculate the deviation of the ratio R (the rate of change of pressure difference over time to the rate of change of dust mass over time) from the normal range:

[0093] ;

[0094] in R represents the deviation, and R represents the ratio of the rate of change of pressure difference over time to the rate of change of dust mass over time; when R is greater than the maximum value in the normal range, This represents the maximum value within the normal range; when R is less than the minimum value within the normal range, This represents the minimum value within the normal range;

[0095] Adjust the dust removal parameters according to the deviation:

[0096] ;

[0097] Where C new This represents the adjusted dust removal parameters. C0 represents the preset baseline dust removal parameters, including the pulse valve injection baseline pressure and pulse valve injection baseline duration. k represents the preset weighting coefficient, with different weighting coefficients corresponding to different dust removal parameters. Represents the degree of deviation;

[0098] After adjusting the dust cleaning parameters, repeat the dust cleaning of the filter bag, and judge whether to issue a primary warning according to the ratio R of the change rate of the new differential pressure over time to the change rate of the dust mass over time, until no primary warning is issued, or the number of times of repeating the dust cleaning of the filter bag exceeds the preset dust cleaning times threshold, then end the dust cleaning and evaluate the dust cleaning effect; the process of evaluating the dust cleaning effect includes:

[0099] The evaluation results include that the dust cleaning effect meets the standard and the dust cleaning effect does not meet the standard;

[0100] If the number of times of repeating the dust cleaning of the filter bag exceeds the preset dust cleaning times threshold, it is regarded that the dust cleaning effect does not meet the standard;

[0101] Obtain the change amount ΔP of the differential pressure before and after dust cleaning and the change amount Δm of the dust mass, ΔP = P0 - P1, where P0 represents the differential pressure at the start of dust cleaning and P1 represents the differential pressure at the end of dust cleaning; Δm = m1 - m0, where m1 represents the dust mass at the start of dust cleaning and m0 represents the dust mass at the end of dust cleaning;

[0102] Calculate the differential pressure drop K per unit dust mass according to the change amount ΔP of the differential pressure and the change amount Δm of the dust mass, where K = ΔP / Δm; preset a differential pressure drop threshold K0. When the differential pressure drop K per unit dust mass is greater than the differential pressure drop threshold K0, it is regarded that the dust cleaning effect meets the standard, otherwise it is regarded that the dust cleaning effect does not meet the standard. The differential pressure drop K per unit dust mass is a key index for evaluating the dust cleaning efficiency, and its meaning is the system ventilation capacity restored by removing unit mass of dust. The higher the K value, the higher the cost performance of the dust cleaning action, that is, a larger differential pressure reduction is obtained by removing a smaller amount of dust, indicating that the air permeability of the filter bag is restored well and the dust cleaning effect is remarkable. The preset differential pressure drop threshold K0 represents the acceptable minimum dust cleaning efficiency standard. If K < K0, it means that the dust cleaning effect does not meet the standard. Even after dust cleaning, the residual resistance of the filter bag is still too high, and secondary dust cleaning or inspection of the filter bag status may be required.

[0103] Step S4: Issue a secondary warning according to the evaluation result. When the secondary warning is issued, perform secondary dust cleaning on the filter bag of the dust collector; when the secondary warning is not issued, save the evaluation result;

[0104] Step S4 includes:

[0105] When the dust cleaning effect does not meet the standard, issue a secondary warning;

[0106] The secondary cleaning is mechanical vibration cleaning, which includes a preset vibration frequency, a preset amplitude, and a preset vibration duration. After a waiting time interval, the secondary cleaning is considered complete, and the vibration frequency is adjusted. Mechanical vibration cleaning uses mechanical vibration as the core power source. The vibration device drives the filter bag to generate periodic vibration to achieve cleaning. The motor or eccentric wheel and other vibration mechanisms drive the filter bag frame or the filter bag itself. The inertial force generated by the vibration overcomes the adhesion between the dust and the surface of the filter bag, causing the dust layer to fall off and into the ash hopper.

[0107] After the secondary dust removal is completed, calculate the pressure difference reduction K1 per unit dust mass before and after the secondary dust removal, and obtain the dust removal effect coefficient based on the pressure difference reduction threshold K0. =K1 / K0, adjust the vibration frequency according to the dust removal effect coefficient:

[0108] ;

[0109] in This represents the adjusted vibration frequency. This represents the vibration frequency before adjustment. Represents the dust removal efficiency coefficient. This represents the adjustment factor, used to control the amplitude of vibration modulation. When When the value is less than 1, it indicates insufficient dust removal effect; the dust removal force can be enhanced by increasing the vibration frequency. When the value is ≥1, it indicates that the dust removal effect meets the standard. Reduce or maintain the vibration frequency to avoid excessive vibration damaging the filter bag.

[0110] For example:

[0111] Parameter acquisition and dataset construction:

[0112] Temperature and humidity sensors and wind speed sensors are installed at the air inlet of the bag filter, and differential pressure transmitters are installed at the inlet and outlet. The sampling frequency is 1 time / minute, and the timestamp is recorded during sampling.

[0113] Initial parameters were collected at a certain moment: initial temperature T=55℃, initial humidity H=72%, initial wind speed V=1.3m / s; initial pressure difference between the air inlet and outlet P=1200Pa.

[0114] Using timestamps as indexes, (T=55℃, H=72%, V=1.3m / s) are stored in correspondence with P=1200Pa to construct an associated dataset.

[0115] Dust removal execution and correlation model establishment:

[0116] After running for 30 minutes, the pressure difference rose to 1450Pa, which did not reach the threshold of 1500Pa, but the cleaning cycle ended and pulse jet cleaning was started.

[0117] After 5 minutes of dust cleaning, it is regarded as the completion of dust cleaning. The pressure sensor at the bottom of the ash hopper detects that the dust mass at the start of dust cleaning is m1 = 8.5 kg, and the dust mass at the end of dust cleaning is m0 = 3.2 kg.

[0118] Calculate the parameters of the correlation model:

[0119] During the period before dust cleaning, the differential pressure change is ΔP = 1450 Pa - 1200 Pa = 250 Pa;

[0120] The rate of change of differential pressure with time A = 250 Pa / 30 min ≈ 8.33 Pa / min;

[0121] The change in dust mass = 8.5 kg - 5.0 kg (residual after the previous dust cleaning) = 3.5 kg;

[0122] The rate of change of dust mass with time B = 3.5 kg / 30 min ≈ 0.117 kg / min;

[0123] The correlation model ratio R = A / B = 8.33 / 0.117 ≈ 71.2.

[0124] Primary warning judgment and evaluation of dust cleaning effect:

[0125] Parameter matching: T = 55 °C < T0 = 60 °C, H = 72% > H0 = 70%, V = 1.3 m / s < V0 = 1.5 m / s, and the corresponding parameter feature group g = 2, with the normal range of its R being [55, 65];

[0126] Warning judgment: R = 71.2 > R max,g = 65, issue a primary warning and initiate adjustment of dust cleaning parameters;

[0127] Calculation of deviation: δ = |R - R max,g | / R max,g = |71.2 - 65| / 65 ≈ 0.095.

[0128] Adjustment of dust cleaning parameters:

[0129] Adjusted injection pressure:

[0130] C new，1 = C 0，1 ×(1 + k1×δ) = 0.5×(1 + 0.3×0.095) ≈ 0.514 MPa; <00003​​​​​​​​​​

[0133] Repeated dust cleaning: Pulse jet dust cleaning is carried out again according to the adjusted parameters and completed after 5 minutes. The newly calculated R = 63.8, which falls within the range of [55, 65], and repeated dust cleaning is stopped.

[0134] Evaluation of dust cleaning effect: The differential pressure P0 before dust cleaning is 1450 Pa, the differential pressure P1 after dust cleaning is 820 Pa, ΔP = 1450 - 820 = 630 Pa; Δm = 8.5 - 3.2 = 5.3 kg; the differential pressure reduction per unit dust mass K = ΔP / Δm = 630 / 5.3 ≈ 118.9 Pa / kg.

[0135] Secondary warning judgment and secondary dust cleaning:

[0136] Compliance judgment: K = 118.9 Pa / kg > K0 = 100 Pa / kg, the dust cleaning effect meets the standard, and no secondary warning is issued;

[0137] Result storage: Save the evaluation results such as the dust cleaning parameters (0.514 MPa, 0.102 s), R value of 63.8, and K value of 118.9 Pa / kg this time; <s

[0138] If K = 95 Pa / kg < K0 = 100 Pa / kg in step S3 and the dust cleaning does not meet the standard, a secondary warning is triggered:

[0139] Start mechanical vibration dust cleaning and execute according to the preset parameters;

[0140] Detection after secondary dust cleaning: ΔP = 1450 - 900 = 550 Pa, Δm = 8.5 - 3.0 = 5.5 kg, K1 = 550 / 5.5 = 100 Pa / kg, and the dust cleaning effect coefficient α = K1 / K0 = 1.0.

[0141] Vibration frequency adjustment:

[0142] f new = f0×[1 - (1 - α)×β] = 50 Hz×[1 - (1 - 1.0)×0.1] = 50 Hz, maintain the reference frequency to avoid excessive vibration damage to the filter bag.

[0143] Embodiment 2: As Figure 2 )]]shown, the present invention provides an intelligent control system applied to a bag filter, and the system includes a parameter acquisition module, a dust cleaning modeling module, a warning parameter adjustment module, and a secondary dust cleaning module;

[0144] The parameter acquisition module is used to obtain the initial parameter characteristics of the dust collector and the differential pressure between the air inlet and the air outlet, and establish an associated data set;

[0145] The dust cleaning modeling module is used to clean the filter bag, obtain the dust mass in the ash hopper, and establish an associated model between the dust mass, the initial parameter characteristics, and the differential pressure;

[0146] The early warning parameter adjustment module is used to determine whether to issue an early warning based on the correlation model, adjust the dust removal parameters, and evaluate the dust removal effect.

[0147] The secondary cleaning module is used to issue a secondary warning based on the cleaning effect evaluation result and perform secondary cleaning on the filter bags.

[0148] The output of the parameter acquisition module is connected to the input of the dust removal modeling module; the output of the dust removal modeling module is connected to the input of the early warning parameter adjustment module; and the output of the early warning parameter adjustment module is connected to the input of the secondary dust removal module.

[0149] The parameter acquisition module also includes an initial parameter acquisition unit and an associated dataset construction unit;

[0150] The initial parameter acquisition unit is used to acquire the initial temperature and humidity of the gas to be dusted through a temperature and humidity sensor, acquire the initial wind speed through a wind speed sensor, and acquire the pressure difference between the air inlet and the air outlet through a differential pressure transmitter.

[0151] The associated dataset construction unit is used to store the initial parameter features and pressure difference in the associated dataset using timestamps as indexes.

[0152] The dust removal modeling module also includes a pulse dust removal execution unit and an association model establishment unit;

[0153] The pulse cleaning execution unit is used to perform cleaning by pulse jet cleaning when the pressure difference reaches a preset threshold.

[0154] The association model building unit is used to store dust quality and initial parameter features and pressure difference with timestamp as index, and calculates the ratio R of the rate of change of pressure difference with time to the rate of change of dust quality with time as the association model.

[0155] The early warning parameter adjustment module also includes an early warning judgment unit and a dust removal effect evaluation unit;

[0156] The first warning judgment unit is used to group the preset ratio R into normal ranges according to the initial parameter characteristics. If R exceeds the range, a warning is issued and the dust removal parameters are adjusted according to the deviation.

[0157] The dust removal effect evaluation unit is used to determine whether the dust removal effect meets the standard by whether the number of dust removals exceeds the threshold or whether the pressure difference reduction K per unit dust mass exceeds the threshold.

[0158] The secondary dust removal module also includes a secondary early warning judgment unit and a mechanical dust removal execution unit;

[0159] The secondary warning judgment unit is used to issue a secondary warning when the dust removal effect is not up to standard;

[0160] The mechanical cleaning unit is used to perform secondary cleaning by means of mechanical vibration cleaning, according to a preset vibration frequency, preset amplitude and preset vibration duration, and to adjust the vibration frequency.

[0161] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An intelligent control method for bag filters, characterized in that: The method includes the following steps: Step S1: Obtain the initial wind speed and initial temperature and humidity of the dust collector as initial parameter features, and obtain the pressure difference between the air inlet and outlet of the dust collector to establish a correlation dataset; Step S2: When the pressure difference reaches the preset threshold, the filter bags on the dust collector are cleaned, and the cleaned dust falls into the ash hopper. After each cleaning, the dust quality in the ash hopper is detected, and the correlation model between dust quality, initial parameter features and pressure difference is obtained. Using the timestamp as the index, the dust quality is correlated with the initial parameter features and pressure difference and stored in the correlation dataset. Based on the dust mass and pressure difference stored in the associated dataset according to the timestamp index, calculate the rate of change of pressure difference over time and the rate of change of dust mass over time. Assign the rate of change of pressure difference over time and the rate of change of dust mass over time according to the same initial parameter features, and calculate the ratio R=A / B of the rate of change of pressure difference over time to the rate of change of dust mass over time as the association model, where A represents the rate of change of pressure difference over time and B represents the rate of change of dust mass over time. A pressure sensor is installed at the bottom of the ash hopper to obtain the dust mass in the ash hopper; the sampling frequency of the pressure sensor is consistent with that of the temperature and humidity sensor, wind speed sensor, and differential pressure transmitter. Step S3: Determine whether to issue an early warning based on the association model. If an early warning is issued, adjust the dust removal parameters; if no early warning is issued, evaluate the dust removal effect. Preset initial temperature threshold T0, initial humidity threshold H0, and initial wind speed threshold V0. Based on the relationship between the initial parameter characteristics and the corresponding T0, H0, and V0, the ratio R of the rate of change of pressure difference over time to the rate of change of dust mass over time is grouped to obtain several parameter characteristic groups. For each parameter characteristic group, a normal range for the corresponding ratio R is preset, denoted as [R]. min,g R max,g ], where R min,g R represents the minimum value of the normal interval in the g-th parameter feature group. max,g This represents the maximum value of the normal interval in the g-th parameter feature group; At the end of each dust removal process, if the ratio of the rate of change of pressure difference over time to the rate of change of dust mass over time, R > R... max,g Or R <R min,g If the dust removal fails, an early warning will be issued; otherwise, the dust removal process will end and the dust removal effect will be evaluated. The process of adjusting the dust removal parameters includes: Calculate the deviation of the ratio R (the rate of change of pressure difference over time to the rate of change of dust mass over time) from the normal range: ； in R represents the deviation, and R represents the ratio of the rate of change of pressure difference over time to the rate of change of dust mass over time; when R is greater than the maximum value in the normal range, This represents the maximum value within the normal range; when R is less than the minimum value within the normal range, This represents the minimum value within the normal range; Adjust the dust removal parameters according to the deviation: ； Where C new This represents the adjusted dust removal parameters. C0 represents the preset baseline dust removal parameters, including the pulse valve injection baseline pressure and pulse valve injection baseline duration. k represents the preset weighting coefficient, with different weighting coefficients corresponding to different dust removal parameters. Represents the degree of deviation; After adjusting the cleaning parameters, the filter bag is cleaned repeatedly. The ratio R of the new pressure difference change rate over time to the dust mass change rate over time is used to determine whether to issue an early warning. The cleaning process continues until no early warning is issued or the number of times the filter bag is cleaned exceeds the preset cleaning number threshold. The cleaning effect is then evaluated. Step S4: Issue a secondary warning based on the evaluation results. When a secondary warning is issued, perform secondary cleaning of the filter bags on the dust collector. When no secondary warning is issued, save the evaluation results.

2. The intelligent control method for a bag filter according to claim 1, characterized in that: Step S1 includes: The dust collector is a bag filter. Temperature and humidity sensors are installed at the air inlet of the dust collector to obtain the initial temperature and humidity of the gas to be dusted. The initial temperature and humidity include the initial temperature and the initial humidity. The gas to be dusted is the gas that enters the air inlet. A wind speed sensor is installed at the air inlet of the dust collector to obtain the initial wind speed of the gas to be dusted. The initial wind speed is the airflow velocity passing through the cross-section of the air inlet per unit time. Differential pressure transmitters are installed at the air inlet and air outlet of the dust collector to obtain the pressure difference between the air inlet and air outlet. The temperature and humidity sensor, the wind speed sensor, and the differential pressure transmitter have the same sampling frequency, and a timestamp is recorded during sampling. Using timestamps as indexes, the initial parameter features are mapped to pressure differences and stored in the associated dataset.

3. The intelligent control method for a bag filter according to claim 2, characterized in that: Step S2 includes: The dust removal is pulse jet cleaning; the dust hopper is a funnel-shaped closed cavity structure with a fixed volume, located directly below the filter bag. A preset waiting time interval is set. After the dust collector starts cleaning, the cleaning process is considered complete after one waiting time interval has elapsed.

4. The intelligent control method for a bag filter according to claim 3, characterized in that: Step S3 includes: The dust removal parameters include the pulse valve blowing pressure and the pulse valve blowing duration. The pulse valve is a device used by the dust collector to spray compressed air to clean the filter bags. The process of evaluating the dust removal effect includes: The evaluation results include whether the dust removal effect meets the standard or not. If the number of times the filter bag is cleaned exceeds the preset cleaning frequency threshold, the cleaning effect is considered to be substandard. Obtain the change in pressure difference ΔP and the change in dust mass Δm before and after dust removal. ΔP = P0 - P1, where P0 represents the pressure difference at the start of dust removal and P1 represents the pressure difference at the end of dust removal; Δm = m1 - m0, where m1 represents the dust mass at the start of dust removal and m0 represents the dust mass at the end of dust removal. The pressure drop K per unit dust mass is calculated based on the change in pressure difference ΔP and the change in dust mass Δm, where K = ΔP / Δm. A preset pressure drop threshold K0 is set. When the pressure drop K per unit dust mass is greater than the pressure drop threshold K0, the dust removal effect is considered to be up to standard; otherwise, the dust removal effect is considered to be down to standard.

5. The intelligent control method for a bag filter according to claim 4, characterized in that: Step S4 includes: A secondary warning is issued when the dust removal effect fails to meet the standard. The secondary cleaning is a mechanical vibration cleaning process, including a preset vibration frequency, a preset amplitude, and a preset vibration duration. After a waiting time interval following the completion of the secondary cleaning, it is considered that the secondary cleaning is complete, and the vibration frequency is adjusted accordingly. After the secondary cleaning is completed, calculate the pressure difference reduction K1 per unit dust mass before and after the secondary cleaning, and obtain the cleaning effect coefficient based on the pressure difference reduction threshold K0. =K1 / K0, adjust the vibration frequency according to the dust removal effect coefficient: ； in This represents the adjusted vibration frequency. This represents the vibration frequency before adjustment. Represents the dust removal efficiency coefficient. This represents the adjustment factor, used to control the amplitude of vibration regulation.

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