Intelligent ash removal control method and device based on electrostatic fabric filter

Abstract

The embodiment of the invention provides an intelligent dust removal control method and device based on an electrostatic fabric filter. The method comprises the following steps: acquiring a customized sequence for storing dust removal parameters of the dust remover; performing real-time data acquisition on the dust remover based on the customized sequence to obtain sample data; when the data volume of the sample data reaches a preset data volume threshold value, performing iterative training on a to-be-trained dust accumulation rate model based on the sample data to obtain a trained dust accumulation rate model; the to-be-trained dust accumulation rate model is constructed through a customized sequence and is used for calculating the dust accumulation rate; performing dust accumulation rate calculation on the current measured value corresponding to the customized sequence by adopting a trained dust accumulation rate model to obtain a current dust accumulation rate; and dynamically adjusting the dust removal interval time based on the ratio of the maximum allowable dust accumulation amount of the filter bag to the current dust accumulation rate. According to the method, accurate dust removal can be realized, and energy consumption and filter bag loss are reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to an intelligent dust removal control method and device based on an electrostatic precipitator and bag filter. Background Technology

[0002] Most electrostatic precipitators and baghouse dust collectors employ either timed cleaning or differential pressure cleaning. Timed cleaning operates at preset fixed time intervals, which cannot be adjusted according to the actual dust load, easily leading to problems such as untimely or excessive cleaning. Differential pressure cleaning, on the other hand, performs cleaning when the pressure difference between the inlet and outlet of the dust collector reaches a set value. While it can reflect the dust accumulation to some extent, it still struggles to achieve precise cleaning when dust properties change significantly or operating conditions are unstable.

[0003] Excessive dust removal can shorten the lifespan of filter bags and increase operating costs; while untimely dust removal can increase the resistance of the dust collector, increase energy consumption, and may even affect the normal operation of the production process. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent dust removal control method and device based on an electrostatic precipitator and a bag filter. The specific technical solution adopted is as follows: In a first aspect, embodiments of the present invention provide an intelligent dust removal control method based on an electrostatic precipitator-baghouse hybrid dust collector, the method comprising: Obtain a customized sequence for storing the dust collector's cleaning parameters; Based on the customized sequence, real-time data is collected from the dust collector to obtain sample data; When the amount of sample data reaches a preset data threshold, the dust accumulation rate model to be trained is iteratively trained based on the sample data to obtain a trained dust accumulation rate model; wherein, the dust accumulation rate model to be trained is constructed through the customized sequence and is used to calculate the dust accumulation rate. The dust accumulation rate is calculated by using the trained dust accumulation rate model to calculate the dust accumulation rate of the current measured value corresponding to the customized sequence, thus obtaining the current dust accumulation rate. The dust collector's intelligent cleaning interval is dynamically adjusted based on the ratio between the maximum allowable dust accumulation on the filter bag and the current dust accumulation rate.

[0005] Secondly, an intelligent dust removal control device based on an electrostatic precipitator-baghouse hybrid dust collector is provided. The device includes: a data determination module, a dataset construction module, a fitting determination module, a dust accumulation rate calculation module, and a dust removal interval calculation module; wherein: The data determination module is used to obtain a customized sequence for storing the dust collector's cleaning parameters; The dataset construction module is used to collect real-time data from the dust collector based on the customized sequence to obtain sample data. The fitting determination module is used to iteratively train the dust accumulation rate model to be trained based on the sample data when the amount of sample data reaches a preset data amount threshold, so as to obtain a trained dust accumulation rate model; wherein, the dust accumulation rate model to be trained is constructed through the customized sequence and is used to calculate the dust accumulation rate. The dust accumulation rate calculation module is used to calculate the dust accumulation rate of the current measured value corresponding to the customized sequence using the trained dust accumulation rate model, so as to obtain the current dust accumulation rate. The dust removal interval calculation module is used to dynamically adjust the dust removal interval of the dust collector for intelligent dust removal based on the ratio between the maximum allowable dust accumulation amount of the filter bag and the current dust accumulation rate.

[0006] Thirdly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the method described in the first aspect.

[0007] Fourthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the method described in the first aspect.

[0008] This invention offers the following advantages: It acquires a customized sequence for storing the dust collector's cleaning parameters and performs real-time data acquisition on the dust collector according to this customized sequence, quickly obtaining relatively accurate and abundant sample data. Then, it determines whether the amount of sample data reaches a preset data volume threshold. If so, it iteratively trains the dust accumulation rate model to be trained based on the sample data, obtaining a trained dust accumulation rate model. This model is constructed using the customized sequence and is used to calculate the dust accumulation rate. Thus, training the model with abundant sample data improves its performance. Finally, the trained dust accumulation rate model is used to calculate the current measured value corresponding to the customized sequence, obtaining the current dust accumulation rate. Based on the ratio between the maximum allowable dust accumulation on the filter bag and the current dust accumulation rate, the cleaning interval for intelligent dust removal in the dust collector is dynamically adjusted. In this way, by combining the dust accumulation rate model with historical operating data and using artificial intelligence (AI) to dynamically adjust the dust cleaning interval, precise dust cleaning can be achieved, reducing energy consumption and filter bag wear. Attached Figure Description

[0009] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic diagram illustrating the implementation process of an intelligent dust removal control method based on an electrostatic precipitator and a bag filter, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of another implementation process of an intelligent dust removal control method based on an electrostatic precipitator and a bag filter provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the system framework of an intelligent dust removal control method based on an electrostatic precipitator and a bag filter, provided in an embodiment of the present invention. Figure 4 This is an application principle diagram of an intelligent dust removal control method based on an electrostatic precipitator and a bag filter, provided in an embodiment of the present invention. Figure 5 This is a schematic diagram of the composition structure of an intelligent dust removal control device based on an electrostatic precipitator and a bag filter, provided in an embodiment of the present invention. Figure 6 This is a schematic diagram of the structure of a computer block device provided in an embodiment of the present invention. Detailed Implementation

[0011] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an intelligent dust removal control method based on an electrostatic precipitator and baghouse dust collector proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments may be combined from any suitable form.

[0012] In the description of the embodiments of the present invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" means two or more.

[0013] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0014] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0015] This invention provides an intelligent dust removal control device based on an electrostatic precipitator-baghouse hybrid dust collector. The specific solution of the intelligent dust removal control method based on an electrostatic precipitator-baghouse hybrid dust collector provided by this invention is described in detail below with reference to the accompanying drawings. Please refer to... Figure 1 The diagram illustrates a flowchart of an intelligent dust removal control method based on an electrostatic precipitator-baghouse hybrid dust collector, according to an embodiment of the present invention. The method includes: 101, Obtain the customized sequence used to store the dust collector's cleaning parameters.

[0016] Here, a preset database for storing multiple dust removal indicators is first obtained. Then, in the preset database, the following parameters are selected: inlet dust concentration C1, outlet dust concentration C2, inlet flue gas velocity V1, outlet flue gas velocity V2, flue gas temperature T, flue gas humidity H, filter bag filtration area S, and inlet / outlet pressure difference ΔP. Based on the inlet dust concentration C1, outlet dust concentration C2, inlet flue gas velocity V1, outlet flue gas velocity V2, flue gas temperature T, flue gas humidity H, filter bag filtration area S, and inlet / outlet pressure difference ΔP, a vector sequence is constructed to obtain the customized sequence.

[0017] The preset database can be a custom database storing multiple dust removal parameters. In the data determination module, a parameter filtering interface is provided, allowing users to select parameters from the preset database for calculation and form a vector sequence, thus obtaining a customized sequence. In this way, by selecting multiple parameters from the preset database, a customized sequence can be accurately constructed, facilitating the subsequent construction of a dust accumulation rate model.

[0018] 102. Based on the customized sequence, real-time data acquisition is performed on the dust collector to obtain sample data.

[0019] Here, the dataset construction module connects to the data determination module, collects real-time data from the customized sequence at preset time intervals (e.g., 1 minute), generates structured sample data (e.g., [timestamp, C1, V1, T, ΔP, ...]), and stores it as a historical dataset.

[0020] 103. When the amount of sample data reaches a preset data amount threshold, the dust accumulation rate model to be trained is iteratively trained based on the sample data to obtain the trained dust accumulation rate model.

[0021] The dust accumulation rate model to be trained is constructed using the customized sequence and is used to calculate the dust accumulation rate. The preset data volume threshold can be a custom threshold, for example, a preset data volume threshold of 1000.

[0022] Here, when the amount of sample data reaches the preset data volume threshold, the inlet dust concentration C1, outlet dust concentration C2, inlet flue gas velocity V1, outlet flue gas velocity V2, flue gas temperature T, flue gas humidity H, filter bag filtration area S, inlet and outlet pressure difference ΔP, and corresponding weighting factors in the customized sequence are fused to construct a dust accumulation rate model to be trained. In this way, a dust accumulation rate model to be trained can be quickly built using the inlet dust concentration C1, outlet dust concentration C2, inlet flue gas velocity V1, outlet flue gas velocity V2, flue gas temperature T, flue gas humidity H, filter bag filtration area S, and inlet and outlet pressure difference ΔP in the customized sequence.

[0023] After the dust accumulation rate model to be trained is built, the least squares method is used to fit the data of the dust accumulation rate model to be trained, and the target values ​​of each weight factor are obtained. Thus, a high-precision trained dust accumulation rate model is obtained through the target values ​​of the weight factors.

[0024] In the fitting determination module, an embedded linear regression algorithm (least squares method) is used. When the amount of historical data (i.e., the amount of sample data) reaches a preset data threshold (e.g., 1000 records), a dust accumulation rate model is constructed based on a customized sequence: R = k1 * inlet dust concentration C1 + k2 * outlet dust concentration C2 + k3 * inlet flue gas velocity V1 + k4 * outlet flue gas velocity V2 + k5 * flue gas temperature T + k6 * flue gas humidity H + k7 * filter bag filtration area S + k8 * inlet and outlet pressure difference + b, where C1, C2, V1, V2, etc. are selected parameters. k1, k2, k3, k4, k5, k6, k7, k8 and b are calculated by fitting historical data to optimize the model accuracy and obtain the trained dust accumulation rate model.

[0025] In some embodiments, after determining whether the amount of sample data has reached a preset data amount threshold, if the amount of sample data has not reached the preset data amount threshold, the dust removal interval time can be adjusted through the following steps: The first step is to determine the rate of change of the real-time operating pressure difference of the dust collector if the amount of sample data does not reach the preset data amount threshold or the dust accumulation rate model to be trained has not been completed.

[0026] Here, when the amount of sample data does not reach a preset data volume threshold, the maximum allowable emission concentration and the target operating pressure difference are obtained. If the outlet dust concentration is less than or equal to the maximum allowable emission concentration at a preset ratio, the rate of change of the real-time operating pressure difference relative to the target operating pressure difference is determined within a preset time period. The preset time period can be a custom time period. Within this preset time period, the difference between the real-time operating pressure difference and the target operating pressure difference is calculated in real time. The rate of change is obtained by taking this difference within the preset time period. Thus, when the amount of sample data does not reach the preset data volume threshold, the rate of change of the real-time operating pressure difference relative to the target operating pressure difference can be accurately analyzed by using the maximum allowable emission concentration and the target operating pressure difference as standards.

[0027] The second step is to adjust the dust removal interval based on the rate of change of the real-time operating pressure difference of the dust collector, so that the real-time operating pressure difference meets the target operating pressure difference.

[0028] Here, the difference between the real-time operating pressure difference and the target operating pressure difference is determined. Using the constraint that the difference between the real-time operating pressure difference and the target operating pressure difference is less than a preset difference, the dust removal interval is dynamically adjusted based on the rate of change. The preset difference can be a custom difference, meaning that the constraint is that the real-time operating pressure difference stabilizes near the target operating pressure difference. By analyzing the rate of change, the dust removal interval is increased or decreased to ensure that the real-time operating pressure difference remains stable near the target operating pressure difference. Thus, in the initial stage of system operation, by dynamically adjusting the dust removal interval by analyzing the rate of change of the real-time operating pressure difference, the real-time operating pressure difference is stabilized near the target operating pressure difference, thereby improving dust removal efficiency.

[0029] In some possible implementations, the mechanism control module is initialized in the early stage of system commissioning (when the model parameters are not determined) with the maximum allowable emission concentration (C_max) and the target operating pressure difference (ΔP_target) as the benchmark: when the outlet dust concentration is ≤80% of C_max, the real-time operating pressure difference ΔP is compared with ΔP_target in real time, and the injection interval is automatically adjusted in combination with the previous pressure difference change rate (ΔP_rate) to keep ΔP stable near ΔP_target.

[0030] 104. The dust accumulation rate is calculated by using the trained dust accumulation rate model to calculate the dust accumulation rate of the current measured value corresponding to the customized sequence, so as to obtain the current dust accumulation rate.

[0031] In some possible implementations, by acquiring the calculation function describing the trained dust accumulation rate model and determining the data format requirements of the calculation function for the input data, the current measured values ​​corresponding to the customized sequence are adjusted according to these data format requirements to ensure that the adjusted data meets the requirements. Finally, the adjusted data is input into the calculation function to accurately calculate the current dust accumulation rate. The process of calculating the current dust accumulation rate can be implemented through a dust accumulation rate calculation module. In this module, the measured values ​​of the current customized sequence are acquired in real time, substituted into the model output by the fitting determination module, and the current dust accumulation rate R (unit: kg / h) is calculated.

[0032] 105. Based on the ratio between the maximum allowable dust accumulation on the filter bag and the current dust accumulation rate, the cleaning interval of the dust collector for intelligent cleaning is dynamically adjusted.

[0033] Here, by calculating the ratio between the maximum allowable dust accumulation on the filter bag and the current dust accumulation rate, the cleaning interval can be adjusted adaptively according to this ratio.

[0034] In some possible implementations, step 105 above can be achieved by... Figure 2 The steps shown are to be implemented as follows: 201, obtain the working condition correction coefficient, filter bag correction coefficient, and dust removal system correction coefficient.

[0035] The operating condition correction coefficient is used to correct the dust content in the flue gas, the filter bag correction coefficient is used to correct the filter bag usage time, and the dust removal system correction coefficient is used to correct the pulse-jet pressure.

[0036] 202. Multiply the working condition correction coefficient, filter bag correction coefficient, and dust removal system correction coefficient by the ratio to obtain the dust removal interval time.

[0037] Steps 201 and 202 above can be implemented through the dust removal interval calculation module. In this dust removal interval calculation module, the dust removal interval time T is calculated based on R, and the formula is: T=n1*n2*n3*(M / R), where: M is the maximum allowable dust accumulation of the filter bag (kg); n1 is the working condition correction coefficient (such as the dust content correction of flue gas); n2 is the filter bag correction coefficient (such as the filter bag usage time correction); n3 is the dust removal system correction coefficient (such as the pulse jet pressure correction).

[0038] In this embodiment of the invention, real-time data acquisition from the dust collector according to a customized sequence enables the rapid acquisition of relatively accurate and abundant sample data. Then, it is determined whether the amount of sample data reaches a preset data volume threshold. If the amount of sample data reaches the preset data volume threshold, the dust accumulation rate model to be trained is iteratively trained based on the sample data to obtain a trained dust accumulation rate model. The dust accumulation rate model to be trained is constructed using the customized sequence and is used to calculate the dust accumulation rate. Thus, training the dust accumulation rate model constructed from the customized sequence using abundant sample data improves the model's performance. Finally, the trained dust accumulation rate model is used to calculate the dust accumulation rate of the current measured value corresponding to the customized sequence, obtaining the current dust accumulation rate. Based on the ratio between the maximum allowable dust accumulation amount of the filter bag and the current dust accumulation rate, the cleaning interval of the dust collector for intelligent cleaning is dynamically adjusted. In this way, by combining the dust accumulation rate model with historical operating data and using AI to dynamically adjust the cleaning interval, precise cleaning is achieved, reducing energy consumption and filter bag wear.

[0039] This invention provides an intelligent dust removal control method based on an electrostatic precipitator-baghouse hybrid dust collector, which can... Figure 3 The system comprises a data determination module, an initialization mechanism control module, a dataset construction module, a fitting determination module, a dust accumulation rate calculation module, and a dust removal interval calculation module. The data determination module selects the parameters for calculation. Then, a judgment module checks whether the collected sample data reaches a preset data volume threshold. If not, the initialization mechanism control module initiates temporary dust removal logic. The dataset construction module periodically collects and stores parameters to form a historical database. Once the historical data volume reaches the threshold, the fitting determination module automatically trains the dust accumulation rate model and continuously iterates to optimize the parameters. Finally, the dust accumulation rate calculation module outputs the current R, and the dust removal interval calculation module generates T accordingly, dynamically adjusting the dust removal timing. In this way, by continuously fitting and improving the model using the dust accumulation rate model and historical operating data, precise dust removal control is achieved.

[0040] Figure 4 This is a schematic diagram illustrating the application principle of an intelligent dust removal control method based on an electrostatic precipitator and a bag filter, as provided in an embodiment of the present invention. Figure 4 As shown, the data determination module is used to combine models, i.e., to build the dust accumulation rate model to be trained; the dataset construction module is used to acquire datasets and historical data; and the fitting determination module is used to enable the self-training and continuous fitting of the dust accumulation rate model to be trained, thereby dynamically adjusting the blowing interval. In this way, the data determination module flexibly generates application models to adapt to different working conditions, autonomously and periodically trains and fits data, continuously optimizes model parameters, and improves control accuracy.

[0041] This invention provides an intelligent dust removal control device based on an electrostatic precipitator and a bag filter hybrid dust collector, such as... Figure 5 As shown, the device 500 includes: a data determination module 501, a dataset construction module 502, a fitting determination module 503, a dust accumulation rate calculation module 504, and a dust removal interval calculation module 505; The data determination module 501 is used to obtain a customized sequence for storing the dust collector's cleaning parameters; The dataset construction module 502 is used to collect real-time data from the dust collector based on the customized sequence to obtain sample data; The fitting determination module 503 is used to iteratively train the dust accumulation rate model to be trained based on the sample data when the amount of sample data reaches a preset data amount threshold, so as to obtain a trained dust accumulation rate model; wherein, the dust accumulation rate model to be trained is constructed through the customized sequence and is used to calculate the dust accumulation rate. The dust accumulation rate calculation module 504 is used to calculate the dust accumulation rate of the current measured value corresponding to the customized sequence using the trained dust accumulation rate model, so as to obtain the current dust accumulation rate. The dust removal interval calculation module 505 is used to dynamically adjust the dust removal interval time of the dust collector for intelligent dust removal based on the ratio between the maximum allowable dust accumulation amount of the filter bag and the current dust accumulation rate.

[0042] In some possible implementations, the device further includes: The initialization mechanism control module 506 is used to determine the rate of change of the real-time operating pressure difference of the dust collector when the amount of sample data does not reach the preset data amount threshold or the dust accumulation rate model to be trained has not been trained; and to adjust the dust removal interval time based on the rate of change of the real-time operating pressure difference of the dust collector so that the real-time operating pressure difference meets the target operating pressure difference.

[0043] In some possible implementations, the initialization mechanism control module 506 is further configured to obtain the maximum allowable emission concentration and the target operating pressure difference when the amount of sample data does not reach a preset data amount threshold. When the outlet dust concentration is less than or equal to the maximum allowable emission concentration of a preset ratio, the rate of change of the real-time operating pressure difference relative to the target operating pressure difference is determined within a preset time period.

[0044] In some possible implementations, the initialization mechanism control module 506 is further configured to determine the difference between the real-time operating pressure difference and the target operating pressure difference; and dynamically adjust the dust removal interval time based on the rate of change, with the difference between the real-time operating pressure difference and the target operating pressure difference being less than a preset difference as a constraint condition.

[0045] In some possible implementations, the dust removal interval calculation module 505 is also used to obtain the operating condition correction coefficient, the filter bag correction coefficient, and the dust removal system correction coefficient; wherein, the operating condition correction coefficient is used to correct the dust content in the flue gas, the filter bag correction coefficient is used to correct the filter bag usage time, and the dust removal system correction coefficient is used to correct the pulse jet pressure. The cleaning interval time is obtained by multiplying the working condition correction coefficient, filter bag correction coefficient, and cleaning system correction coefficient by the ratio.

[0046] In some possible implementations, the data determination module 501 is further configured to acquire a preset database for storing multiple dust removal indicators; in the preset database, select inlet dust concentration, outlet dust concentration, inlet flue gas velocity, outlet flue gas velocity, flue gas temperature, flue gas humidity, flue gas temperature, flue gas humidity, filter bag filtration area, and inlet / outlet pressure difference; based on the inlet dust concentration, outlet dust concentration, inlet flue gas velocity, outlet flue gas velocity, flue gas temperature, flue gas humidity, flue gas temperature, flue gas humidity, filter bag filtration area, and inlet / outlet pressure difference, construct a vector sequence to obtain the customized sequence.

[0047] In some possible implementations, the fitting determination module 503 is further configured to, when the amount of sample data reaches the preset data amount threshold, fuse the inlet dust concentration, outlet dust concentration, inlet flue gas velocity, outlet flue gas velocity, flue gas temperature, flue gas humidity, flue gas temperature, flue gas humidity, filter bag filtration area, inlet and outlet pressure difference, and corresponding weighting factors in the customized sequence to construct the dust accumulation rate model to be trained.

[0048] In some possible implementations, the fitting determination module 503 is further configured to perform data fitting on the dust accumulation rate model to be trained using the least squares method to obtain the target value of the weight factor; and obtain the trained dust accumulation rate model based on the target value of the weight factor.

[0049] In some possible implementations, the dust accumulation rate calculation module 504 is used to determine the data format requirements of the input data based on the calculation function describing the trained dust accumulation rate model; based on the data format requirements, adjust the current measured value corresponding to the customized sequence to obtain adjusted data; and input the adjusted data into the calculation function describing the trained dust accumulation rate model to obtain the current dust accumulation rate. Optionally, the transmission medium can be a wired link (e.g., but not limited to, coaxial cable, optical fiber, and Digital Subscriber Line (DSL)) or a wireless link (e.g., but not limited to, Wireless Fidelity (WIFI), Bluetooth, and mobile block device networks). It should be noted that the control block device provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer block device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the method embodiments provided above belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0050] Figure 6 This is a schematic diagram of the structure of a computer block device provided in an embodiment of the present invention. For example, as shown... Figure 6 As shown, the computer block device 600 includes: a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602. When the processor 602 executes the computer program 603, the computer block device can execute any of the intelligent dust removal control methods based on the electrostatic precipitator-baghouse hybrid dust collector described above.

[0051] Furthermore, embodiments of the present invention also protect a control block device, which may include a memory and a processor. The memory stores executable program code, and the processor is used to call and execute the executable program code to perform the intelligent dust removal control method based on an electrostatic precipitator and baghouse dust collector provided in this embodiment of the present invention. Embodiments of the present invention can divide the control block device into functional modules according to the above method examples. For example, each module may correspond to a specific function, or two or more functions may be integrated into a processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment of the present invention is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced to the functional description of the corresponding functional module, and will not be repeated here. It should be understood that the control block device provided in this embodiment of the present invention is used to execute the above-mentioned intelligent dust removal control method based on an electrostatic precipitator and baghouse dust collector, and therefore can achieve the same effect as the above-mentioned implementation method. When using integrated units, the control block device may include a processing module and a storage module. When the control block device is applied to a block device, the processing module can be used to control and manage the actions of the block device. The storage module can be used to support the block device in executing mutual program code, etc. The processing module can be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of Digital Signal Processing (DSP) and a microprocessor, etc., and the storage module can be a memory.

[0052] Furthermore, the control block device provided in the embodiments of the present invention may specifically be a chip, component, or module. The chip may include a connected processor and a memory. The memory stores instructions, and when the processor calls and executes the instructions, the chip can execute the intelligent dust removal control method based on an electrostatic precipitator and baghouse dust collector provided in the above embodiments. The embodiments of the present invention also provide a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the aforementioned method steps to implement the intelligent dust removal control method based on an electrostatic precipitator and baghouse dust collector provided in the above embodiments.

[0053] This invention also provides a computer program product. When the computer program product is run on a computer, it causes the computer to execute the aforementioned related steps to realize the intelligent dust removal control method based on an electrostatic precipitator and baghouse dust collector provided in the above embodiments. The control block device, computer-readable storage medium, computer program product, or chip provided in this invention are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they achieve can be referred to in the beneficial effects of the corresponding methods provided above, and will not be repeated here. Through the description of the above embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the control block device can be divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by this invention, it should be understood that the disclosed control block device and method can be implemented in other ways. For example, the control block device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another control block device, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings, direct couplings, or communication connections may be through some interfaces; indirect couplings or communication connections between control block devices or units may be electrical, mechanical, or other forms.

[0054] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multiple task processing and parallel processing are possible or may be advantageous. The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. An intelligent ash cleaning control method based on an electric bag composite dust collector, characterized in that, The method includes: Obtain a customized sequence for storing the dust collector's cleaning parameters; Based on the customized sequence, real-time data is collected from the dust collector to obtain sample data; When the amount of sample data reaches a preset data threshold, the dust accumulation rate model to be trained is iteratively trained based on the sample data to obtain a trained dust accumulation rate model; wherein, the dust accumulation rate model to be trained is constructed through the customized sequence and is used to calculate the dust accumulation rate. The trained dust accumulation rate model is used to calculate the dust accumulation rate of the current measured value corresponding to the customized sequence, so as to obtain the current dust accumulation rate. The dust collector's intelligent cleaning interval is dynamically adjusted based on the ratio between the maximum allowable dust accumulation on the filter bag and the current dust accumulation rate.

2. The method of claim 1, wherein, The method further includes: When the amount of sample data does not reach the preset data amount threshold or the dust accumulation rate model to be trained is not trained, the rate of change of the real-time operating pressure difference of the dust collector is determined. The dust removal interval is adjusted based on the rate of change of the real-time operating differential pressure of the dust collector so that the real-time operating differential pressure meets the target operating differential pressure.

3. The method of claim 2, wherein, When the amount of sample data does not reach a preset data volume threshold or the dust accumulation rate model to be trained has not been completed, determining the rate of change of the real-time operating pressure difference of the dust collector includes: When the amount of sample data does not reach the preset data amount threshold, obtain the maximum allowable emission concentration and the target operating pressure difference; When the outlet dust concentration is less than or equal to the maximum allowable emission concentration of a preset ratio, the rate of change of the real-time operating pressure difference relative to the target operating pressure difference is determined within a preset time period.

4. The method of claim 2, wherein, The method of adjusting the dust removal interval time based on the rate of change of the real-time operating pressure difference of the dust collector, so as to ensure that the real-time operating pressure difference meets the target operating pressure difference, includes: Determine the difference between the real-time operating pressure difference and the target operating pressure difference; The dust removal interval time is dynamically adjusted based on the rate of change, with the constraint that the difference between the real-time operating differential pressure and the target operating differential pressure is less than a preset difference.

5. The method of claim 1, wherein, The method of dynamically adjusting the dust collector's intelligent cleaning interval based on the ratio between the maximum allowable dust accumulation on the filter bag and the current dust accumulation rate includes: Obtain the operating condition correction coefficient, filter bag correction coefficient, and dust removal system correction coefficient; wherein, the operating condition correction coefficient is used to correct the dust content in the flue gas, the filter bag correction coefficient is used to correct the filter bag usage time, and the dust removal system correction coefficient is used to correct the pulse jet pressure; The cleaning interval time is obtained by multiplying the working condition correction coefficient, filter bag correction coefficient, and cleaning system correction coefficient by the ratio.

6. The method of claim 1, wherein, The process of obtaining the customized sequence for storing the dust collector's cleaning parameters includes: Obtain the preset database used to store multiple dust removal indicators; In the preset database, select the inlet dust concentration, outlet dust concentration, inlet flue gas velocity, outlet flue gas velocity, flue gas temperature, flue gas humidity, flue gas temperature, flue gas humidity, filter bag filtration area, and inlet / outlet pressure difference. Based on the inlet dust concentration, outlet dust concentration, inlet flue gas velocity, outlet flue gas velocity, flue gas temperature, flue gas humidity, filter bag filtration area, and inlet / outlet pressure difference, a vector sequence is constructed to obtain the customized sequence.

7. The method of claim 6, wherein, The method further includes: When the amount of sample data reaches the preset data amount threshold, the inlet dust concentration, outlet dust concentration, inlet flue gas velocity, outlet flue gas velocity, flue gas temperature, flue gas humidity, flue gas temperature, flue gas humidity, filter bag filtration area, inlet and outlet pressure difference, and corresponding weighting factors in the customized sequence are fused to construct the dust accumulation rate model to be trained.

8. The method of claim 6, wherein, When the amount of sample data reaches the preset data amount threshold, the dust accumulation rate model to be trained is iteratively trained based on the sample data to obtain a trained dust accumulation rate model, including: The least squares method is used to fit the data to the dust accumulation rate model to be trained, and the target values ​​of the weight factors are obtained. Based on the target value of the weighting factor, the trained dust accumulation rate model is obtained.

9. The method of claim 1, wherein, The step of using the trained dust accumulation rate model to calculate the dust accumulation rate of the current measured value corresponding to the customized sequence to obtain the current dust accumulation rate includes: Based on the calculation function describing the trained dust accumulation rate model, the required data format for the input data is determined. Based on the data format requirements, the current measured values ​​corresponding to the customized sequence are adjusted to obtain the adjusted data; The adjusted data is input into the calculation function describing the trained dust accumulation rate model to obtain the current dust accumulation rate.

10. An intelligent ash cleaning control device based on an electric bag composite dust collector, characterized in that, The device includes: a data determination module, a dataset construction module, a fitting determination module, a dust accumulation rate calculation module, and a dust removal interval calculation module; wherein: The data determination module is used to obtain a customized sequence for storing the dust collector's cleaning parameters; The dataset construction module is used to collect real-time data from the dust collector based on the customized sequence to obtain sample data. The fitting determination module is used to iteratively train the dust accumulation rate model to be trained based on the sample data when the amount of sample data reaches a preset data amount threshold, so as to obtain a trained dust accumulation rate model; wherein, the dust accumulation rate model to be trained is constructed through the customized sequence and is used to calculate the dust accumulation rate. The dust accumulation rate calculation module is used to calculate the dust accumulation rate of the current measured value corresponding to the customized sequence using the trained dust accumulation rate model, so as to obtain the current dust accumulation rate. The dust removal interval calculation module is used to dynamically adjust the dust removal interval of the dust collector for intelligent dust removal based on the ratio between the maximum allowable dust accumulation amount of the filter bag and the current dust accumulation rate.

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