CN117679868A - Leakage accurate positioning system and method for bag-type dust collector - Google Patents
Leakage accurate positioning system and method for bag-type dust collector Download PDFInfo
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Abstract
A leakage accurate positioning system and method for bag-type dust collector is disclosed. Firstly, acquiring granularity concentration values of each row of a monitored cabin at a plurality of preset time points in a preset time period, then, carrying out data preprocessing on the granularity concentration values of each row at a plurality of preset time points in the preset time period to obtain a plurality of row-arranged granularity concentration time sequence input vectors, then, carrying out time sequence analysis on the plurality of row-arranged granularity concentration time sequence input vectors to obtain a plurality of row-arranged granularity concentration time sequence characteristic vectors, and finally, determining the escape peak of the cloth bag dust remover of the row of the monitored cabin based on the sequence of the row-arranged granularity concentration time sequence characteristic vectors. Therefore, the leakage position can be found out in time, and related technicians are reminded to take corresponding measures so as to prevent non-stop caused by exceeding of the emission data of the particulate matters.
Description
Technical Field
The application relates to the field of bag-type dust collectors, and more particularly, to a leakage accurate positioning system and method for a bag-type dust collector.
Background
With the implementation of the national ultra-low emission strategy, the national and local disputes promulgate stricter emission standards. Bag dust removal has the highest efficiency in all dust removers, has absolute advantages in purifying PM2.5 of fine particles, and has become a mainstream technology for controlling fine particles of industrial flue gas and realizing ultra-low emission.
A plurality of cloth bag dust collectors are used in the cement production and manufacturing process. The large bag dust collector at the kiln head and the kiln tail is two most important devices in the cement production process. Meanwhile, the particle monitoring equipment is required to be installed on the kiln head and the kiln tail chimney so as to monitor the emission concentration of the particles in real time, and the particle monitoring equipment is used as the basis for examination of the environmental protection department. However, the event that the particulate matter monitoring data exceeds the standard and the kiln has to be forcibly stopped due to leakage of the large bag-type dust collector is frequent.
In the actual production process, the cloth bags in different cabins can be back blown simultaneously, and it is difficult to determine which cabin the escape peak after the back blowing of the currently leaked cloth bag comes from, and if a plurality of cabins are closed together by back blowing simultaneously, the safe operation of the kiln can be seriously influenced.
Therefore, a system and method for accurately positioning leakage of a bag-type dust collector are expected.
Disclosure of Invention
In view of this, the application provides a precise positioning system and a method for leakage of a bag-type dust collector, which can find the leakage position in time and remind relevant technicians to take corresponding measures so as to prevent non-stop caused by exceeding of particulate matter emission data.
According to an aspect of the present application, there is provided a leakage accurate positioning system of a bag-type dust collector, including a particle concentration monitor, a bag-type dust collector, a baffle valve, a data processor, a pulse valve sequence and a pulse valve relay control point, which are located at an outlet of a cabin, wherein the data processor includes:
the data acquisition module is used for acquiring particle size concentration values of each row of the monitored cabin at a plurality of preset time points in a preset time period;
the data preprocessing module is used for preprocessing the data of the granularity concentration values of each row at a plurality of preset time points in a preset time period to obtain a plurality of row-oriented granularity concentration time sequence input vectors;
the time sequence analysis module is used for performing time sequence analysis on the plurality of positioning granularity concentration time sequence input vectors to obtain a plurality of positioning granularity concentration time sequence characteristic vectors; and
and the escape peak analysis module is used for determining the number of rows of the cloth bag dust collectors in the monitored cabin to have escape peaks based on the sequence of the time sequence feature vectors of the positioning granularity concentration.
According to another aspect of the present application, there is provided a leakage accurate positioning method of a bag-type dust collector, including:
acquiring particle size concentration values of each row of monitored cabins at a plurality of preset time points in a preset time period;
performing data preprocessing on the granularity concentration values of the rows at a plurality of preset time points in a preset time period to obtain a plurality of row-oriented granularity concentration time sequence input vectors;
performing time sequence analysis on the plurality of positioning granularity concentration time sequence input vectors to obtain a plurality of positioning granularity concentration time sequence feature vectors; and
and determining the escape peak of the cloth bag dust collector in the row of the monitored cabin based on the sequence of the sequence feature vectors of the granularity concentration of each row.
According to the embodiment of the application, firstly, the granularity concentration values of each row of the monitored cabin at a plurality of preset time points in a preset time period are obtained, then, data preprocessing is carried out on the granularity concentration values of each row at a plurality of preset time points in the preset time period to obtain a plurality of row-positioned granularity concentration time sequence input vectors, then, time sequence analysis is carried out on the plurality of row-positioned granularity concentration time sequence input vectors to obtain a plurality of row-positioned granularity concentration time sequence characteristic vectors, and finally, the escape peak of the cloth bag dust collectors of the rows of the monitored cabin is determined based on the sequence of the row-positioned granularity concentration time sequence characteristic vectors. Therefore, the leakage position can be found out in time, and related technicians are reminded to take corresponding measures so as to prevent non-stop caused by exceeding of the emission data of the particulate matters.
Other features and aspects of the present application will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments, features and aspects of the present application and together with the description, serve to explain the principles of the present application.
Fig. 1 shows a block diagram of the data processor in a leakage accurate positioning system of a bag-type dust collector according to an embodiment of the present application.
Fig. 2 shows a block diagram of the escape peak analysis module in a leakage accurate positioning system of a bag-type dust collector according to an embodiment of the application.
Fig. 3 shows a block diagram of the semantic weight calculation unit in the leakage accurate positioning system of the bag-type dust collector according to an embodiment of the present application.
Fig. 4 shows a flow chart of a method of accurately locating leakage of a bag-type dust collector according to an embodiment of the application.
Fig. 5 shows a schematic architecture diagram of a leakage accurate positioning method of a bag-type dust collector according to an embodiment of the application.
Fig. 6 shows an application scenario diagram of a leakage accurate positioning system of a bag-type dust collector according to an embodiment of the application.
Fig. 7 shows a schematic structural diagram of a bag-type dust collector leakage accurate positioning system.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some, but not all embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, are also within the scope of the present application.
As used in this application and in the claims, the terms "a," "an," "the," and/or "the" are not specific to the singular, but may include the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
Various exemplary embodiments, features and aspects of the present application will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. Although various aspects of the embodiments are illustrated in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
In addition, numerous specific details are set forth in the following detailed description in order to provide a better understanding of the present application. It will be understood by those skilled in the art that the present application may be practiced without some of these specific details. In some instances, methods, means, elements, and circuits have not been described in detail as not to unnecessarily obscure the present application.
A plurality of cloth bag dust collectors are used in the cement production and manufacturing process. The large bag dust collector at the kiln head and the kiln tail is two most important devices in the cement production process. Meanwhile, the particle monitoring equipment is required to be installed on the kiln head and the kiln tail chimney so as to monitor the emission concentration of the particles in real time, and the particle monitoring equipment is used as the basis for examination of the environmental protection department. However, the event that the particulate matter monitoring data exceeds the standard and the kiln has to be forcibly stopped due to leakage of the large bag-type dust collector is frequent.
Taking a kiln tail large cloth bag dust collector as an example, the kiln tail large cloth bag is composed of hundreds of rows of bags, and a chamber is formed by several rows or tens of rows of bags; in order to reduce the resistance of the dust remover, most of the dust removers adopt an online back blowing mode; in order to reduce the cloth bag ash removal time interval, a certain row of cloth bags in two or more chambers are often back-blown at the same time. When the surface area of the cloth bag reaches a certain thickness, the pulse ash removing device is started, the arrangement bag shares a back blowing air pipe to instantly inject compressed air into the cloth bag, the cloth bag instantly and rapidly expands, the particles accumulated on the surface of the cloth bag shake off, the pores of the cloth bag are fully cleaned under the back blowing of high-pressure air, and the cleaning and regeneration of the cloth bag are completed. The regenerated cloth bag loses the filtering effect of the particulate matter filter cake layer, and has a short particulate matter escape process. If the cloth bag has holes, a faster and larger particle escape peak value will be generated after the cloth bag is cleaned, and the peak value starts to drop after the holes are gradually covered by the newly built filter cake layer.
In the actual production process, the bags in different cabins can be back blown at the same time, and it is difficult to judge where the escape peak of the currently leaked bag after back blowing comes from. If several compartments of the blowback are closed simultaneously, the safe operation of the kiln is seriously affected.
Aiming at the technical problems, the technical concept of the application is to analyze the granularity concentration change time sequence characteristics of each row of the monitored cabin and determine the escape peak of the cloth bag dust remover of the row of the monitored cabin based on the sequence of the granularity concentration change time sequence characteristics of each row. Therefore, the leakage position can be found out in time, and relevant technicians are reminded to take corresponding measures so as to prevent non-stop caused by exceeding of the emission data of the particulate matters.
Based on this, the application provides a precise positioning system of revealing of sack cleaner, including particle concentration monitor, sack cleaner, baffle valve, data processor 100, pulse valve sequence and pulse valve relay control point that is located the export in cabin. Wherein fig. 1 shows a block diagram schematic of the data processor 100 in a leakage accurate positioning system of a bag-type dust collector according to an embodiment of the present application. As shown in fig. 1, the data processor 100 includes: a data acquisition module 110, configured to acquire particle size concentration values of each row of the monitored cabin at a plurality of predetermined time points within a predetermined time period; a data preprocessing module 120, configured to perform data preprocessing on the granularity concentration values of the rows at a plurality of predetermined time points within a predetermined time period to obtain a plurality of aligned granularity concentration time sequence input vectors; a timing analysis module 130, configured to perform timing analysis on the plurality of aligned granularity concentration timing input vectors to obtain a plurality of aligned granularity concentration timing feature vectors; and an escape peak analysis module 140, configured to determine, based on the ordering of the individual row-aligned granularity concentration time sequence feature vectors, what row of bag-type dust collectors in the monitored cabin has an escape peak.
It should be appreciated that the data acquisition module 110 is responsible for collecting the particle size concentration data for the different rows within the chamber from the monitoring device. The data preprocessing module 120 functions to process the particle concentration values for the rows at a plurality of predetermined time points over a predetermined period of time to obtain a plurality of row-oriented particle concentration time series input vectors, which may include data cleaning, denoising, interpolation or other data processing steps to prepare the data for subsequent analysis. The timing analysis module 130 is configured to perform timing analysis on the plurality of aligned granularity concentration timing input vectors to obtain a plurality of aligned granularity concentration timing feature vectors, and at this stage, the module may perform time series analysis, such as trend analysis, periodicity analysis, or other time-dependent feature extraction. The escape peak analysis module 140 is configured to determine, based on the order of the time sequence feature vectors of the set of aligned granularity concentration, which row of the bag-type dust collectors of the monitored cabin has an escape peak, where the escape peak refers to an abnormally high concentration value, which may indicate that the particulate matter is not effectively captured or processed in some cases.
Specifically, the encoding process of the data processor includes: first, particle size concentration values at a plurality of predetermined time points in each row of the monitored compartment within a predetermined period of time are obtained. It should be appreciated that during operation of the bag house, when the bag is perforated or damaged, the bag house has a higher concentration of particulates around it. By acquiring the granularity concentration values of each row of monitored cabins at a plurality of preset time points in a preset time period, the granularity concentration change condition of each row of bag-type dust collectors at different time points can be excavated, and the information has important significance for positioning the leaked bag-type dust collectors.
Then, respectively arranging the granularity concentration values of the rows at a plurality of preset time points in a preset time period into input vectors according to a time dimension to obtain a plurality of row-positioned granularity concentration time sequence input vectors; and the plurality of positioning granularity concentration time sequence input vectors respectively pass through a granularity concentration time sequence feature extractor based on a one-dimensional convolution layer to obtain a plurality of positioning granularity concentration time sequence feature vectors. That is, the granularity concentration time sequence feature extractor is constructed based on a one-dimensional convolution layer to capture the granularity concentration time sequence change feature distribution of each row expressed by the plurality of row-oriented granularity concentration time sequence input vectors so as to reflect the running state and the leakage condition of the bag-type dust collector.
Accordingly, the data preprocessing module 120 is configured to: and respectively arranging the granularity concentration values of the rows at a plurality of preset time points in a preset time period into input vectors according to a time dimension to obtain a plurality of row-positioned granularity concentration time sequence input vectors.
Accordingly, the timing analysis module 130 is configured to: and performing feature extraction on the plurality of positioning granularity concentration time sequence input vectors by using a deep learning network model to obtain the plurality of positioning granularity concentration time sequence feature vectors.
The deep learning network model is a granularity concentration time sequence feature extractor based on a one-dimensional convolution layer; the granularity concentration time sequence feature extractor based on the one-dimensional convolution layer comprises an input layer, the one-dimensional convolution layer, a pooling layer, an activation layer and an output layer.
Specifically, the timing analysis module 130 is configured to: and respectively enabling the plurality of positioning granularity concentration time sequence input vectors to pass through the granularity concentration time sequence feature extractor based on the one-dimensional convolution layer to obtain the plurality of positioning granularity concentration time sequence feature vectors.
It is worth mentioning that a one-dimensional convolution layer is a neural network layer commonly used in deep learning, and is used for processing a one-dimensional data sequence, such as time sequence data or signal data, and functions similarly to a two-dimensional convolution layer, but performs a convolution operation on one-dimensional data. The one-dimensional convolution layer has the following functions and advantages when processing time sequence data: 1. feature extraction: the one-dimensional convolution layer can extract local features in the input data in a sliding window mode, and the features can help the network learn patterns and rules in the data. 2. Parameter sharing: similar to the two-dimensional convolution layer, the one-dimensional convolution layer also adopts a parameter sharing mode, which means that the convolution kernel slides on the whole input sequence, so that the number of parameters to be learned is reduced, and the risk of overfitting is reduced. 3. Key features of dimension reduction and extraction: through convolution and pooling operations, the one-dimensional convolution layer can reduce the dimension of input data and extract key features in the input data, which is helpful for reducing the computational complexity of a subsequent network layer and improving the generalization capability of a model. In this example, a one-dimensional convolutional layer is used as a granularity concentration time series feature extractor, which functions to extract features from a plurality of aligned granularity concentration time series input vectors, which can help a deep learning network model learn and understand patterns and rules in the input data, so as to better perform subsequent prediction or classification tasks.
Next, calculating the semantic weights of the respective alignment granularity concentration time sequence feature vectors relative to the whole of the plurality of alignment granularity concentration time sequence feature vectors to obtain a plurality of semantic weight values. That is, the contribution degree of each row of bag-type dust collectors to the overall particle size concentration is evaluated by calculating the semantic weight of the individual row-positioned particle size concentration timing feature vector relative to the overall of the plurality of rows-positioned particle size concentration timing feature vector. Specifically, the higher the semantic weight value, the greater the contribution of the cloth bag dust collector of the row to the overall granularity concentration; the lower the semantic weight value, the less the bag-type dust collector of the row contributes to the overall particle size concentration, thereby determining the location of the leak.
In one specific example of the present application, the encoding process for calculating the semantic weights of the respective positional-granularity-concentration-timing feature vectors with respect to the entirety of the plurality of positional-granularity-concentration-timing feature vectors to obtain a plurality of semantic weight values includes: cascading the plurality of positioning granularity concentration time sequence feature vectors to obtain cascading feature vectors; and calculating the semantic weights of the alignment granularity concentration time sequence feature vectors relative to the cascade feature vectors to obtain the semantic weight values.
And then, determining what row of cloth bag dust collectors of the monitored cabin has escape peaks based on the ordering of the semantic weight values. In a specific example of the present application, the plurality of semantic weight values are ordered in descending order such that the plurality of semantic weight values are arranged from high to low. In the technical scheme of the application, the bag-type dust remover corresponding to the higher semantic weight value is considered as the bag-type dust remover with higher possibility of leakage. The location of the leak is located in such a way as to allow repair and maintenance operations to be performed.
Accordingly, as shown in fig. 2, the escape peak analyzing module 140 includes: a semantic weight calculating unit 141, configured to calculate semantic weights of the aligned granularity concentration time-series feature vectors with respect to the whole of the aligned granularity concentration time-series feature vectors to obtain a plurality of semantic weight values; and an analysis unit 142, configured to determine, based on the ordering of the plurality of semantic weight values, what row of bag-type dust collectors of the monitored cabin has an escape peak.
It should be understood that the escape peak analyzing module includes two main parts of the semantic weight calculating unit 141 and the analyzing unit 142. The role of the semantic weight calculation unit 141 is to calculate the importance or weight of each feature vector in the whole from the semantic information of the feature vectors, which may be done for the purpose of determining which feature vectors are more critical for the understanding of the overall system state. The analysis unit 142 is used for analyzing each bag collector arranged in the monitored cabin according to the sorting of the semantic weight values to determine whether an escape peak exists, wherein the escape peak is usually referred to as an abnormal or sudden concentration peak, and may represent abnormal conditions of some parts in the system, and further detection and processing are required. In summary, the semantic weight calculating unit is used for evaluating the importance of the feature vector, and the analyzing unit uses the weight values to determine whether an abnormal situation, i.e. an escape peak, exists in the system. These steps, in combination, can help the monitoring system make accurate decisions and responses to abnormal situations.
As shown in fig. 3, the semantic weight calculating unit 141 includes: a cascade subunit 1411, configured to cascade the plurality of aligned granularity concentration time sequence feature vectors to obtain a cascade feature vector; a feature distribution optimizing subunit 1412, configured to perform feature distribution optimization on the cascade feature vector to obtain an optimized cascade feature vector; and a calculating subunit 1413 configured to calculate semantic weights of the aligned granularity concentration time-series feature vectors relative to the optimized cascade feature vector to obtain the plurality of semantic weight values.
It should be appreciated that the semantic weight calculating unit 141 includes three sub-units, which have different roles, respectively. In the concatenation subunit 1411, concatenation refers to concatenating a plurality of feature vectors in a sequence to form a longer feature vector, which may be done to integrate information of different time periods or different features into a longer feature vector for subsequent processing and analysis. In the feature distribution optimization subunit 1412, feature distribution optimization may involve normalization, or other optimization processing of the feature vectors to ensure that the distribution of the feature vectors conforms to some desired form or to make the feature vectors more suitable for subsequent computation or analysis. The computation subunit 1413 is configured to compute, based on the optimized cascade feature vectors, a semantic weight value of each original feature vector relative to the optimized feature vector, where the purpose of this may be to determine the importance of each original feature vector in the overall feature, or to find the portion that most does not conform to the overall feature. These subunits act together in the processing and computation of feature vectors to derive a semantic weight value for each original feature vector relative to the overall feature vector to help determine the importance of each feature vector in the overall.
Further, the computing subunit 1413 is configured to: calculating the semantic weights of the each positioning granularity concentration time sequence feature vector relative to the optimized cascade feature vector by using the following semantic weight formula to obtain a plurality of semantic weight values; wherein, the semantic weight formula is:
s i ＝σ(A*h i +B*V ′ )
wherein A is 1 XN w Vector of N w Is the dimension, h, of the alignment granularity concentration time sequence feature vector i Is the i-th position granularity concentration time sequence characteristic vector, V ′ Is the optimized cascade feature vector, B is 1 XN h Vector of N h Is the dimension of the concatenated feature vector, σ is the Sigmoid function, s i Is the i-th said semantic weight value.
In the above-described technical solution, each of the plurality of set-position granularity concentration time series feature vectors expresses a local time series correlation feature of the granularity concentration value of the row of the corresponding monitored cabin, but, in consideration of a source data distribution pattern difference in the time series direction of the granularity concentration value of each row of the monitored cabin, after the local time series correlation feature extraction, the plurality of set-position granularity concentration time series feature vectors as a whole may have inconsistency and instability of the time series feature distribution, that is, there may be an abnormal local distribution that diverges with respect to the overall feature distribution, thereby affecting the accuracy of calculating the plurality of semantic weight values of the respective set-position granularity concentration time series feature vectors with respect to the overall semantic weight of the plurality of set-position granularity concentration time series feature vectors. Based on this, the applicant of the present application optimizes the plurality of alignment granularity concentration time series feature vectors as a whole.
Accordingly, in one example, the feature distribution optimization subunit 1412 is further configured to: performing feature distribution optimization on the cascade feature vector by using the following optimization formula to obtain the optimized cascade feature vector; wherein, the optimization formula is:
wherein v is i Is a cascade feature vector obtained by cascading the plurality of alignment granularity concentration time sequence feature vectors, for example, the i-th feature value marked as V, II is II 1 And II 2 Respectively the 1-norm and 2-norm of the cascade feature vector V, L is the length of the cascade feature vector V, exp (·) represents the exponential operation of a value that represents the calculation of the natural exponential function value raised to the power of the value, and α is the sum of V i Related weight superparameter, v ′i Is the i-th eigenvalue of the optimized cascade eigenvector.
Here, the global feature distribution of the cascade feature vector V is made to have a certain repeatability with respect to local mode changes by the structural consistency and stability representation of the global feature distribution of the cascade feature vector V obtained after cascade of the plurality of alignment granularity concentration time-series feature vectors under the rigid structure of absolute distance and the non-rigid structure of spatial distance, respectively, so that when the semantic representation weights of the alignment granularity concentration time-series feature vector with respect to the global feature distribution of the cascade feature vector are calculated, the scale and rotation changes of the weight representation are made to have robustness, thereby improving the accuracy of the plurality of semantic weight values.
In summary, the leakage accurate positioning system of the bag-type dust collector based on the embodiment of the application is clarified, and can find out the leakage position in time and remind relevant technicians to take corresponding measures so as to prevent non-stop caused by exceeding of particulate matter emission data.
As described above, the leakage accurate positioning system of the bag-type dust collector according to the embodiment of the application can be implemented in various terminal devices, such as a server with a leakage accurate positioning algorithm of the bag-type dust collector, and the like. In one example, the leak accurate positioning system of the bag house may be integrated into the terminal device as a software module and/or a hardware module. For example, the leakage accurate positioning system of the bag-type dust collector may be a software module in the operating system of the terminal device, or may be an application developed for the terminal device; of course, the precise positioning system for leakage of the bag-type dust collector can be one of a plurality of hardware modules of the terminal equipment.
Alternatively, in another example, the leakage accurate positioning system of the bag-type dust collector and the terminal device may be separate devices, and the leakage accurate positioning system of the bag-type dust collector may be connected to the terminal device through a wired and/or wireless network, and transmit the interaction information according to a agreed data format.
Fig. 4 shows a flow chart of a method of accurately locating leakage of a bag-type dust collector according to an embodiment of the application. Fig. 5 shows a schematic diagram of a system architecture of a method for precisely locating leakage of a bag-type dust collector according to an embodiment of the application. As shown in fig. 4 and 5, a leakage accurate positioning method of a bag-type dust collector according to an embodiment of the present application includes: s110, acquiring particle size concentration values of each row of monitored cabins at a plurality of preset time points in a preset time period; s120, carrying out data preprocessing on the granularity concentration values of a plurality of preset time points of each row in a preset time period to obtain a plurality of row-positioned granularity concentration time sequence input vectors; s130, performing time sequence analysis on the plurality of positioning granularity concentration time sequence input vectors to obtain a plurality of positioning granularity concentration time sequence feature vectors; and S140, determining what row of bag-type dust collectors of the monitored cabin has escape peaks based on the ordering of the time sequence feature vectors of the positioning granularity concentration.
In one possible implementation, the data preprocessing is performed on the granularity concentration values of the rows at a plurality of predetermined time points within a predetermined time period to obtain a plurality of row-oriented granularity concentration time sequence input vectors, including: and respectively arranging the granularity concentration values of the rows at a plurality of preset time points in a preset time period into input vectors according to a time dimension to obtain a plurality of row-positioned granularity concentration time sequence input vectors.
Here, it will be understood by those skilled in the art that the specific operation of each step in the above-described leakage accurate positioning method of the bag-type dust collector has been described in detail in the above description of the leakage accurate positioning system of the bag-type dust collector with reference to fig. 1 to 3, and thus, repetitive descriptions thereof will be omitted.
Fig. 6 shows an application scenario diagram of a leakage accurate positioning system of a bag-type dust collector according to an embodiment of the application. As shown in fig. 6, in this application scenario, first, particle size concentration values of each row of the monitored compartment at a plurality of predetermined time points within a predetermined time period (for example, D illustrated in fig. 6) are acquired, and then, the particle size concentration values of each row at a plurality of predetermined time points within a predetermined time period are input to a server (for example, S illustrated in fig. 6) in which a leakage accurate positioning algorithm of a bag-type dust collector is deployed, wherein the server is capable of processing the particle size concentration values of each row at a plurality of predetermined time points within a predetermined time period using the leakage accurate positioning algorithm of the bag-type dust collector to obtain a escape peak determining what row of the monitored compartment has.
More specifically, in one embodiment of the present application, as shown in fig. 7, the precise positioning system for leakage of the bag-type dust collector includes a continuous online particulate monitor 1 located at a main exhaust port, a particulate monitor 2 of an ac coupling technology, a baffle valve at an outlet of each bag-type bin, and a bag-type bin. The particle monitor 2 of the alternating current coupling technology is positioned at the outlet of all chambers of the bag-type dust collector, the pulse valve blowback pipes 51, 52, 53, 54, 55, 56, 57 and 58 correspond to each row of bags, and the data acquisition processor acquires real-time data of the particle continuous on-line monitor 1 and the particle monitor 2 of the alternating current coupling technology, and a current ash cleaning pulse valve sequence of the bag-type dust collector and the chambers to which the pulse valves belong. The arithmetic unit collected by the data collection processor can be a part or a system with data processing and arithmetic capability such as a PLC, a singlechip, an ARM chip, and the like, and also comprises a display unit, an alarm unit, a signal output unit, and the like.
The continuous on-line particulate matter monitor 1 is positioned at the chimney discharge port, monitors the discharge concentration of particulate matters at the chimney discharge port in real time, and uploads the particulate matters to an environment-friendly monitoring department. The particle monitor of the alternating current coupling technology is positioned at the outlets of all the chambers of the bag-type dust collector, and is used for monitoring escape peaks generated after the bag is blown back in real time. The baffle valve of the outlet of each cloth bag bin is positioned at the exhaust port of the single cloth bag bin, and the baffle valve can be closed through the actuating mechanism, so that the cloth bag bin connected with the baffle valve is isolated. The cloth bag chambers are arranged in an ordered array, and the pulse valve blowback pipes correspond to each row of cloth bags. The pulse valve sequence refers to the current row of the pulse valve of the bin. The data acquisition processor acquires data of the continuous online particulate matter monitor 1, data of the particulate matter monitor 2 of an alternating current coupling technology and pulse valve sequence signals; the current escape peak is determined from those rows and those bins based on the particulate monitor 2 and pulse valve sequence signals of the ac coupling technique. And closely monitoring escape peaks after each bag blowback, and recording data. The data acquisition processor drives the executing mechanism of the baffle valve at the outlet of the bin of the current related cloth bag according to the size of the escape peak and the source of the bin after the ash removal of the cloth bag, sequentially stops the phase Guan Cangshi, determines the escape peak from the bin, and records the escape peak. When the data of the continuous online particulate matter monitor reaches 50% -110% of the environmental emission limit, the data acquisition processor automatically closes the bin with a large escape peak so as to achieve the aim of reducing the particulate matter emission concentration at the discharge outlet of the chimney. Further, in order to better protect the cloth bag, larger damage is prevented, after the escape peak is determined to come from the bin, the data acquisition processor closes the pulse valve of the current row, and further ash removal is prevented from being carried out to aggravate the damage of the cloth bag; furthermore, when the pulse valve fails, the cloth bag cannot remove ash effectively, no escape peak occurs, and when the cloth bag leaks, the particulate matter monitor 2 of the AC coupling technology changes the baseline value; when the baseline value of the particulate matter monitor 2 of the AC coupling technology exceeds the normal baseline value (the normal baseline value is generally considered to be 10-100, dimensionless), and no special high escape peak exists in the whole cloth bag ash cleaning period; at this time, the data acquisition processor sequentially stops all chambers of the whole bag-type dust collector by an actuating mechanism of a baffle valve at the outlet of the chamber of the bag-type dust collector, and the chamber with the obviously reduced baseline value of the particulate matter monitor 2 of the AC coupling technology is a leakage chamber. The data acquisition processor records the bin number, and closes the leakage bin when the data of the continuous online particulate matter monitor 1 reaches 50% -100% of the environmental emission limit, so that the stable standard emission of the unit is realized.
The input parameters of the data acquisition processor comprise data of a continuous online particulate matter monitor, a particulate matter monitor of an alternating current coupling technology, a current ash removal pulse valve sequence and a bin to which a pulse valve belongs; and outputting an actuating mechanism comprising baffle valves at the outlets of all cloth bag chambers and a pulse valve relay driving point. The digital data interface is provided with a network, RS485, RS232 and the like.
After the positioning of the leakage bin is accurate, environmental protection data is out of standard, and the positioning bin is maintained at fixed points by using shutdown opportunities in a production field.
Correspondingly, the bin in which the cloth bag leaks can be accurately positioned through the cloth bag leakage accurate positioning system, and the accurate maintenance on site can be guided; the accurate positioning system for the leakage of the cloth bags can close the leakage bin at fixed points when the data of the continuous online particulate matter monitor reaches or exceeds the environmental protection limit value, so that the data is prevented from being penalized due to exceeding of standard; the accurate positioning system for the leakage of the cloth bags can close the leakage bin at fixed points when the data of the continuous online monitor of the particulate matters reach or exceed the environmental protection limit value, so that the machine set can be stopped step by step in a time sharing way, and the serious loss caused by the emergency stop is avoided; the accurate positioning system can accurately control the running state of the current bag-type dust collector through the bag leakage, and intelligent safe running of the cement kiln dust collection system is realized.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The embodiments of the present application have been described above, the foregoing description is exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. The utility model provides a accurate positioning system of revealing of sack cleaner, includes granularity concentration monitor, sack cleaner, baffle valve, data processor, pulse valve sequence and pulse valve relay control point that is located the export in cabin, its characterized in that, data processor includes:
the data acquisition module is used for acquiring particle size concentration values of each row of the monitored cabin at a plurality of preset time points in a preset time period;
the data preprocessing module is used for preprocessing the data of the granularity concentration values of each row at a plurality of preset time points in a preset time period to obtain a plurality of row-oriented granularity concentration time sequence input vectors;
the time sequence analysis module is used for performing time sequence analysis on the plurality of positioning granularity concentration time sequence input vectors to obtain a plurality of positioning granularity concentration time sequence characteristic vectors; and
and the escape peak analysis module is used for determining the number of rows of the cloth bag dust collectors in the monitored cabin to have escape peaks based on the sequence of the time sequence feature vectors of the positioning granularity concentration.
2. The precise positioning system for leakage of a bag-type dust collector according to claim 1, wherein the data preprocessing module is configured to:
and respectively arranging the granularity concentration values of the rows at a plurality of preset time points in a preset time period into input vectors according to a time dimension to obtain a plurality of row-positioned granularity concentration time sequence input vectors.
3. The precise positioning system for leakage of a bag-type dust collector according to claim 2, wherein the timing analysis module is configured to:
and performing feature extraction on the plurality of positioning granularity concentration time sequence input vectors by using a deep learning network model to obtain the plurality of positioning granularity concentration time sequence feature vectors.
4. The precise positioning system for leakage of the bag-type dust remover according to claim 3, wherein the deep learning network model is a granularity concentration time sequence feature extractor based on a one-dimensional convolution layer;
the granularity concentration time sequence feature extractor based on the one-dimensional convolution layer comprises an input layer, the one-dimensional convolution layer, a pooling layer, an activation layer and an output layer.
5. The precise positioning system for leakage of a bag-type dust collector according to claim 4, wherein the timing analysis module is configured to:
and respectively enabling the plurality of positioning granularity concentration time sequence input vectors to pass through the granularity concentration time sequence feature extractor based on the one-dimensional convolution layer to obtain the plurality of positioning granularity concentration time sequence feature vectors.
6. The precise positioning system for leakage of a bag-type dust collector according to claim 5, wherein the escape peak analysis module comprises:
the semantic weight calculating unit is used for calculating the semantic weight of the whole of each positioning granularity concentration time sequence feature vector relative to the plurality of positioning granularity concentration time sequence feature vectors so as to obtain a plurality of semantic weight values; and
and the analysis unit is used for determining the row of the bag dust collectors in the monitored cabin to have escape peaks based on the ordering of the semantic weight values.
7. The precise positioning system for leakage of a bag-type dust collector according to claim 6, wherein the semantic weight calculating unit comprises:
a cascade subunit, configured to cascade the plurality of positioning granularity concentration time sequence feature vectors to obtain a cascade feature vector;
the feature distribution optimizing subunit is used for carrying out feature distribution optimization on the cascade feature vectors to obtain optimized cascade feature vectors; and
and the calculating subunit is used for calculating the semantic weights of the positioning granularity concentration time sequence feature vectors relative to the optimized cascade feature vectors so as to obtain the semantic weight values.
8. The precise positioning system for leakage of a bag-type dust collector of claim 7, wherein the computing subunit is configured to:
calculating the semantic weights of the each positioning granularity concentration time sequence feature vector relative to the optimized cascade feature vector by using the following semantic weight formula to obtain a plurality of semantic weight values;
wherein, the semantic weight formula is:
s i ＝σ(A*h i +B*V，)
wherein A is 1 XN w Vector of N w Is the dimension, h, of the alignment granularity concentration time sequence feature vector i Is the ith alignment granularity concentration time sequence feature vector, V is the optimized cascade feature vector, and B is 1 XN h Vector of N h Is said cascade ofFeature vector dimension, σ is Sigmoid function, s i Is the i-th said semantic weight value.
9. The leakage accurate positioning method of the bag-type dust remover is characterized by comprising the following steps of:
acquiring particle size concentration values of each row of monitored cabins at a plurality of preset time points in a preset time period;
performing data preprocessing on the granularity concentration values of the rows at a plurality of preset time points in a preset time period to obtain a plurality of row-oriented granularity concentration time sequence input vectors;
performing time sequence analysis on the plurality of positioning granularity concentration time sequence input vectors to obtain a plurality of positioning granularity concentration time sequence feature vectors; and
and determining the escape peak of the cloth bag dust collector in the row of the monitored cabin based on the sequence of the sequence feature vectors of the granularity concentration of each row.
10. The method for precisely positioning leakage of a bag-type dust collector according to claim 9, wherein the step of performing data preprocessing on the particle size concentration values of the rows at a plurality of predetermined time points within a predetermined time period to obtain a plurality of row-positioned particle size concentration time sequence input vectors comprises:
and respectively arranging the granularity concentration values of the rows at a plurality of preset time points in a preset time period into input vectors according to a time dimension to obtain a plurality of row-positioned granularity concentration time sequence input vectors.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105107302A (en) * | 2015-09-18 | 2015-12-02 | 上海西重所重型机械成套有限公司 | Cloth bag leakage detecting device for cloth bag dust collector and leakage detecting method of cloth bag leakage detecting device |
| CN109580263A (en) * | 2017-09-29 | 2019-04-05 | 上海金艺检测技术有限公司 | The on-line monitoring method of Environmental-protecting dust-removing system running state |
| CN116651099A (en) * | 2023-07-12 | 2023-08-29 | 上海勃创环保科技有限公司 | Method for evaluating running state of bag-type dust collector |
| CN116688658A (en) * | 2023-08-01 | 2023-09-05 | 苏州协昌环保科技股份有限公司 | Bag-leakage positioning method, device and medium for bag type dust collector |
| CN117085431A (en) * | 2023-09-11 | 2023-11-21 | 德清众新环保设备有限公司 | Control system and method for bag-type dust collector |
-
2023
- 2023-12-08 CN CN202311678801.9A patent/CN117679868B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105107302A (en) * | 2015-09-18 | 2015-12-02 | 上海西重所重型机械成套有限公司 | Cloth bag leakage detecting device for cloth bag dust collector and leakage detecting method of cloth bag leakage detecting device |
| CN109580263A (en) * | 2017-09-29 | 2019-04-05 | 上海金艺检测技术有限公司 | The on-line monitoring method of Environmental-protecting dust-removing system running state |
| CN116651099A (en) * | 2023-07-12 | 2023-08-29 | 上海勃创环保科技有限公司 | Method for evaluating running state of bag-type dust collector |
| CN116688658A (en) * | 2023-08-01 | 2023-09-05 | 苏州协昌环保科技股份有限公司 | Bag-leakage positioning method, device and medium for bag type dust collector |
| CN117085431A (en) * | 2023-09-11 | 2023-11-21 | 德清众新环保设备有限公司 | Control system and method for bag-type dust collector |
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