CN117150677A - Method and device for determining type selection parameters of electric dust collector - Google Patents
Method and device for determining type selection parameters of electric dust collector Download PDFInfo
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Abstract
The embodiment of the application discloses a method and a device for determining type selection parameters of an electric dust collector, wherein the method comprises the following steps: constructing a prediction model taking flue gas parameters, coal quality parameters and ash parameters as input and selecting type parameters as output; inputting the input parameters collected in advance into a prediction model to obtain output data, wherein the output data comprises optimized approach speed and total dust collection area; and determining the type selection parameters of the electric dust collector by adopting an optimizing and matching algorithm based on the output data and specification parameter constraint data, wherein the specification parameter constraint data comprises application field space data, specification parameters of a dust collection substrate and process knowledge base data. According to the scheme, the prediction model is built through the historical data, the optimized approach speed and the total dust collection area are obtained in an auxiliary mode, the optimizing matching algorithm is further utilized, the type selection parameters of the electric dust collector are selected intelligently, therefore error problems possibly existing when the type selection parameters are determined through manual experience are reduced, and meanwhile efficiency of type selection design of the electric dust collector can be improved.
Description
Technical Field
The application relates to the technical field of flue gas treatment, in particular to a method and a device for determining type selection parameters of an electric dust collector.
Background
At present, the design of the electric dust collector at home and abroad depends on experience of technicians, namely, the design of the electric dust collector is firstly determined by the technicians according to experience, then the dust collection area is determined according to a required dust collection efficiency and a multi-Islamic formula or other improved electric dust collection efficiency calculation formula, and then the electric dust collector is arranged according to an aspect ratio. However, the electric dust collector designed in this way may have a large area, resulting in increased investment; and the area is possibly too small, the dust removal efficiency cannot be guaranteed, and the environment-friendly event of exceeding dust emission is easily caused.
Disclosure of Invention
In view of this, the present application provides the following technical solutions:
a method for determining type selection parameters of an electric dust collector comprises the following steps:
constructing a prediction model taking flue gas parameters, coal quality parameters and ash parameters as input and selecting type parameters as output;
inputting the input parameters collected in advance into the prediction model to obtain output data, wherein the output data comprises optimized approach speed and total dust collection area;
and determining the type selection parameters of the electric dust collector by adopting an optimizing and matching algorithm based on the output data and specification parameter constraint data, wherein the specification parameter constraint data comprises application field space data, specification parameters of a dust collection substrate and process knowledge base data.
Optionally, the prediction model is an optimized approach speed prediction model, and the inputting the input parameters collected in advance into the prediction model to obtain output data includes:
inputting the pre-collected coal quality parameters and ash parameters into the optimized approach speed prediction model to obtain an output value, wherein the output value is the optimized approach speed;
based on the pre-collected processed flue gas parameters and the optimized approach speed, a total dust collection area is determined.
Optionally, after determining the type-selecting parameters of the electric precipitator, the method further comprises:
based on the model selection parameters, a manufacturing and installing platform of the electric dust collector is built by adopting a digital twin technology, and the manufacturing and installing platform comprises an electric dust collector manufacturing and cooperating system and an electric dust collector installing and cooperating system.
Optionally, the building of the electric precipitator manufacturing collaboration system includes:
based on the process model and the model selection parameters of the electric dust collector, a three-dimensional modeling technology is applied to construct a three-dimensional model of the electric dust collector;
the construction of the electric dust collector installation cooperation system comprises the following steps:
the method comprises the steps that an electric dust collector installation cooperative system is built on the basis of an electric dust collector three-dimensional model, the installation cooperative system is used for carrying out three-dimensional disassembly and reduction on each component in the three-dimensional model, installation guiding data are obtained, an installation image of a first component is obtained, whether the installation of the first component meets the standard or not is checked through an image analysis algorithm, and the first component is any component in the three-dimensional model.
Optionally, after determining the type-selecting parameters of the electric precipitator, the method further comprises:
and a digital twin technology and a three-dimensional modeling technology are adopted to build a real-time state monitoring system of the electric dust collector.
Optionally, the monitoring operation of the real-time status monitoring system includes:
determining a first numerical relation between a real-time approach speed and the optimized approach speed, and determining the health state of the electric dust collector based on the first numerical relation;
and determining a second numerical relation between the real-time dust removal efficiency and the set dust removal efficiency, and determining whether to perform early warning or not based on the second numerical relation.
Optionally, after determining the type-selecting parameters of the electric precipitator, the method further comprises:
and constructing an optimal control model of the electric dust collector so as to optimally control the electric dust collector.
Optionally, the controlling of the optimizing control model includes:
carrying out integrity analysis on the operation parameters of the electric dust collector, and determining the data integrity of the electric dust collector;
and if the integrity is lower than a first set value, obtaining data corresponding to the missing part and supplementing the data into the operation parameters of the electric dust collector.
Optionally, the controlling of the optimizing control model includes:
determining the actual approach speed of the electric dust collector and the active power required by the smoke volume based on the optimized control model;
determining an electric field power value ratio through the electric field of the electric dust collector and the coefficient value of the dust outlet based on the active power;
and adjusting a secondary voltage and current based on the electric field power value ratio.
A device for determining type selection parameters of an electric precipitator, comprising:
the prediction model building model is used for building a prediction model taking flue gas parameters, coal quality parameters and ash parameters as inputs and selecting type parameters as outputs;
the output data obtaining module is used for inputting the input parameters which are collected and processed in advance into the prediction model to obtain output data, and the output data comprises optimized approach speed and total dust collection area;
the model selection parameter determining module is used for determining model selection parameters of the electric dust collector by adopting an optimizing matching algorithm based on the output data and specification parameter constraint data, wherein the specification parameter constraint data comprises application field space data, specification parameters of a dust collection substrate and process knowledge base data.
As can be seen from the above technical solution, the embodiment of the present application discloses a method and a device for determining a type selection parameter of an electric precipitator, where the method includes: constructing a prediction model taking flue gas parameters, coal quality parameters and ash parameters as input and selecting type parameters as output; inputting the input parameters collected in advance into the prediction model to obtain output data, wherein the output data comprises optimized approach speed and total dust collection area; and determining the type selection parameters of the electric dust collector by adopting an optimizing and matching algorithm based on the output data and specification parameter constraint data, wherein the specification parameter constraint data comprises application field space data, specification parameters of a dust collection substrate and process knowledge base data. According to the scheme, the prediction model is built through the historical data, the optimized approach speed and the total dust collection area are obtained in an auxiliary mode, the optimizing matching algorithm is further utilized, the type selection parameters of the electric dust collector are selected intelligently, therefore error problems possibly existing when the type selection parameters are determined through manual experience are reduced, and meanwhile efficiency of type selection design of the electric dust collector can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for determining type selection parameters of an electric precipitator, which is disclosed in an embodiment of the present application;
FIG. 2 is a flow chart of obtaining output data based on a predictive model according to an embodiment of the application;
FIG. 3 is a schematic diagram of the implementation principle of intelligent type selection, manufacturing and installation of the electric precipitator disclosed in the embodiment of the application;
FIG. 4 is a schematic diagram of a data integrity analysis flow disclosed in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a device for determining type-selecting parameters of an electric precipitator according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Fig. 1 is a flowchart of a method for determining type selection parameters of an electric precipitator according to an embodiment of the present application.
Referring to fig. 1, the method for determining the type-selection parameters of the electric precipitator may include:
step 101: and constructing a prediction model taking the flue gas parameters, the coal quality parameters and the ash parameters as inputs and the model selection parameters as outputs.
In the embodiment of the application, the model selection of the key parameters of the electric dust collector is guided by constructing a prediction model taking the flue gas parameters, the coal quality parameters and the ash parameters as inputs and the model selection parameters of the electric dust collector as outputs. Wherein the flue gas parameters may include, but are not limited to: the unit capacity, the moisture content, the flue gas quantity (Q), the flue gas temperature, the inlet concentration, the outlet concentration and the dust removal efficiency (eta); the coal quality parameters may include, but are not limited to: receiving a base carbon Car, receiving a base hydrogen Har, receiving a base oxygen Oar, receiving a base nitrogen Nar, receiving a base total sulfur Sar, receiving a base ash content Aar, receiving a base moisture Mar, drying an ash-free base volatile matter Vdaf, and receiving a base low-level heating value Qnet. Ar; ash parameters may include, but are not limited to: siO2, al2O3, fe2O3, caO, mgO, na2O, siO2, K2O, SO3, tiO2; the output parameter is optimized approach speed omega y Total dust collection area a.
Step 102: and inputting the input parameters which are collected and processed in advance into the prediction model to obtain output data, wherein the output data comprises the optimized approach speed and the total dust collection area.
The input parameters of the pre-collection process may be historical data, including historical flue gas parameters, historical coal quality parameters, historical ash parameters, and the like. Hundreds of dust collector flue gas, coal quality, ash parameters and electric dust collector equipment parameters which are put into operation can be collected in advance. Calculating actual measurement approach speed omega of each dust remover k =ln (1- η) ×q/a, the process principle and the calculation formula can determine if ω k If the design approach speed omega of the dust remover is longer than the design approach speed omega of the dust remover, the dust collection area of the dust remover is too small, namely omega k -ω>θ b ，θ b Is a threshold constant; if omega k The long-term approach speed omega which is greatly smaller than the dust removal design represents the selection of the dust removerThe dust collecting area is too large, i.e. omega k -ω<θ s ，θ s For the threshold constant, this part of the data with unreasonable design needs to be screened out, i.e. the obtained data needs to be cleaned before being used.
Step 103: and determining the type selection parameters of the electric dust collector by adopting an optimizing and matching algorithm based on the output data and specification parameter constraint data, wherein the specification parameter constraint data comprises application field space data, specification parameters of a dust collection substrate and process knowledge base data.
After the total dust collection area A is obtained, the parameters of the dust collection polar plate, the electric field number, the electric field channel number and the like can be intelligently selected by utilizing an optimizing matching algorithm according to the length-width-height limiting parameters of the field, the expert process knowledge base, the dust collection polar plate specification parameter base and the like, so that the optimal parameter model selection of the dust collector is realized.
According to the method for determining the type selection parameters of the electric dust collector, the prediction model is established through historical data, the optimized approach speed and the total dust collection area are obtained in an auxiliary mode, the type selection parameters of the electric dust collector are selected intelligently by further utilizing the optimizing matching algorithm, so that error problems possibly existing due to the fact that the type selection parameters are determined through manual experience are reduced, and meanwhile efficiency of type selection design of the electric dust collector can be improved.
In the foregoing embodiment, the prediction model may be an optimized approach speed prediction model, and the specific process of inputting the input parameters collected in advance into the prediction model to obtain the output data may refer to fig. 2, and as shown in fig. 2, the process of obtaining the output data may include:
step 201: and inputting the pre-collected coal quality parameters and ash parameters into the optimized approach speed prediction model to obtain an output value, wherein the output value is the optimized approach speed.
Specifically, the coal quality parameters can be screened and determined according to the numerical correlation analysis: receiving a base carbon Car, receiving a base hydrogen Har, receiving a base oxygen Oar, collecting nitrogen Nar, receiving a base total sulfur Sar, receiving a base ash content Aar, receiving a base moisture Mar, drying an ash-free base volatile matter Vdaf, and receiving a base low-grade heating value Qnet. Ar; ash parameters SiO2, al2O3, fe2O3,CaO, mgO, na2O, siO, K2O, SO3, tiO2 as model input variables; the approach speed ω is designed as an output variable. Determining a loss functionWherein omega di The approach speed is designed for the dust remover, +.>And (5) a dust remover approach speed predicted value output by the model. Based on the cleaned data set, a random gradient descent algorithm and a back propagation algorithm are used for reducing the Loss value, the model weight parameters are optimized in an iterative mode, and training of the weight parameters is stopped when the Loss value area is converged. The optimal approach speed omega required by the electric dust remover is predicted by coal quality and ash content parameters y 。
Step 202: based on the pre-collected processed flue gas parameters and the optimized approach speed, a total dust collection area is determined.
At the time of obtaining the optimized approach speed omega y After that, the flue gas parameters can be combined: the unit capacity, the moisture content, the smoke quantity (Q), the smoke temperature, the inlet concentration, the outlet concentration and the dust collection efficiency (eta) are calculated for the total dust collection area (A) of the dust collector. Computing application of the multi-iEQie equation η=1-e -ω*A/Q Logarithmically converting to obtain a= -Q xn (1- η) ×ω y 。
The above details the specific implementation of obtaining an optimized approach speed and total dust collection area, which is helpful for those skilled in the art to better implement the solution of the present application; after the optimized approach speed and the total dust collection area are obtained, the optimization matching algorithm can be adopted to determine the type selection parameters of the electric dust collector.
After the type selection parameters of the electric dust collector are determined, the electric dust collector is further designed to be manufactured and installed. In the current field, the manufacture and installation of electric dust collectors are generally managed according to a flow process; because the electric dust collector has a great number of parts, the manufacturing and installation process is easy to make mistakes, and the production efficiency is low.
In the embodiment of the application, after the type selection parameters of the electric dust collector are determined, the method further comprises the following steps: based on the model selection parameters, a manufacturing and installing platform of the electric dust collector is built by adopting a digital twin technology, and the manufacturing and installing platform comprises an electric dust collector manufacturing and cooperating system and an electric dust collector installing and cooperating system.
Wherein, for the construction of the electric dust collector manufacturing cooperative system: based on the process model and the model selection parameters of the electric dust collector, a three-dimensional modeling technology is applied to construct a three-dimensional model of the electric dust collector.
And constructing an electric dust removal manufacturing cooperative system, wherein the system is based on a process model (the process flow of the electric dust remover), and the electric dust remover model selection parameters, a three-dimensional model of the electric dust remover is constructed by applying a three-dimensional modeling technology, and a 3D model is disassembled to obtain a 2D equipment production diagram so as to facilitate the subsequent production and manufacturing of auxiliary equipment parts based on the 2D equipment production diagram. Meanwhile, the scheduling optimization strategy is formulated based on the order situation of factory equipment and scheduling experience, so that the scheduling of intelligent equipment is realized. The system realizes the production scheduling and production progress node tracking of the whole manufacturing process of the electric dust remover.
Building a cooperative system for electric dust collector installation: the method comprises the steps that an electric dust collector installation cooperative system is built on the basis of an electric dust collector three-dimensional model, the installation cooperative system is used for carrying out three-dimensional disassembly and reduction on each component in the three-dimensional model, installation guiding data are obtained, an installation image of a first component is obtained, whether the installation of the first component meets the standard or not is checked through an image analysis algorithm, and the first component is any component in the three-dimensional model.
And constructing an electric dust collector installation cooperative system, wherein the system is constructed based on the three-dimensional model of electric dust collector equipment. According to the production drawing, the system guides an installer to quickly familiarize with the installation process by carrying out three-dimensional disassembly and reduction on each part; meanwhile, the system is provided with an installation checking system, an installation image of the key part of the equipment is required to be uploaded, whether the installation meets the standard or not is checked through a system image analysis algorithm, and the installation quality is improved. The installation checking analysis algorithm flow is as follows:
A. device installation 3D standard installation template generation
And generating a three-dimensional virtual image of the key installation part of the electric dust collector by utilizing a data twinning technology and a three-dimensional modeling technology, and recording corresponding installation key parameters of equipment according to equipment installation specification files of related equipment.
B. Image preprocessing, angle matching
And matching the shooting image angle of the field installation image uploaded by the installer with the angle of the 3D standard installation template by using algorithms such as image preprocessing, morphological change, multiplying power estimation and the like.
C. Template matching
Dividing the image into a plurality of areas according to the installation specification file, matching the images with the corresponding template images area by area, evaluating the installation effect according to the matching degree, and re-confirming the installation when the matching degree is lower than a certain threshold value.
Based on the foregoing embodiment, fig. 3 shows a schematic diagram of the implementation principle of intelligent type selection, manufacturing and installation of the electric precipitator, and the description of the corresponding parts can be understood in conjunction with fig. 3.
In the embodiment, a digital twin technology is applied to build an intelligent manufacturing and mounting platform of the electric dust collector, wherein the platform comprises an electric dust collector manufacturing cooperative system and an electric dust collector mounting cooperative system, so that intelligent manufacturing and mounting of the electric dust collector are realized; and intelligent production scheduling, visual guidance for 3D installation of the dust remover, intelligent verification for the installation of the dust remover and the like in the manufacturing and installation processes of the electric dust remover can be realized.
In another implementation, after determining the type selection parameters of the electric precipitator, the method may further include: and a digital twin technology and a three-dimensional modeling technology are adopted to build a real-time state monitoring system of the electric dust collector.
Wherein, the monitoring work of the real-time state monitoring system can comprise: determining a first numerical relation between a real-time approach speed and the optimized approach speed, and determining the health state of the electric dust collector based on the first numerical relation; and determining a second numerical relation between the real-time dust removal efficiency and the set dust removal efficiency, and determining whether to perform early warning or not based on the second numerical relation.
In particular, by using digital twin technology and three-dimensional modeling technology,building a 3D real-time state monitoring system of the electric dust collector, and monitoring key operation parameters such as real-time approaching speed omega k, real-time dust collection efficiency eta, operation current and voltage of each electric field, real-time active power of the electric dust collector, real-time smoke quantity Q of electric dust collection inlet dust, electric dust collection outlet dust and the like of the electric dust collector in real time. Design approach speed omega y And designing fixed-value parameters such as dust removal efficiency and the like. Wherein the design approaches speed omega y The design dust removal efficiency is calculated according to the inlet dust estimation condition and the outlet dust requirement value.
The electric dust collector state monitoring method specifically comprises the following steps:
calculating real-time approach speed omega k =ln (1- η) ×q/a, where the real-time dust removal efficiency η= (inlet dust concentration-outlet dust concentration)/inlet dust concentration×100%, Q is the real-time smoke amount is the variation, and a is the designed dust collection area is constant. By comparing real-time approach speeds omega k Approaching speed omega to design y And determining the health status of the electric precipitator.
If (real-time approach speed omega) k Design approach speed ω y )>0, the electric precipitator is in an unhealthy state.
If (real-time approach speed omega) k Design approach speed ω y )<0, the electric precipitator is in a healthy state.
At the same time, the multi-isk formula η=1-e is applied -ω*A/Q Establishing f (Q) =eta function, wherein A and omega are both design value constants, calculating real-time dust removal efficiency eta through real-time smoke quantity Q, and monitoring the real-time dust removal efficiency eta and the dust removal efficiency design value eta of the dust remover 1 Alignment, when eta is close to eta 1 And the system automatically gives an early warning.
According to the embodiment, the health state monitoring system of the electric dust collector is intelligently judged by monitoring the real-time approach speed omega and the real-time dust collection efficiency eta.
In a further implementation, after determining the type selection parameters of the electric dust collector, the method further comprises the following steps: and constructing an optimal control model of the electric dust collector so as to optimally control the electric dust collector. The method specifically comprises the following two aspects, namely historical data integrity analysis and application of building an optimal control model.
The controlling of the optimal control model may include: carrying out integrity analysis on the operation parameters of the electric dust collector, and determining the data integrity of the electric dust collector; and if the integrity is lower than a first set value, obtaining data corresponding to the missing part and supplementing the data into the operation parameters of the electric dust collector.
The data integrity refers to the degree of all conditions in the actual operation of the data containing equipment, and if the data integrity does not meet the requirement, the optimization control model may be reduced in the accuracy of part of the scene, and even completely fail. The proposal determines a data change interval based on the data mechanism characteristics, then quantifies the distribution condition of data in the change interval through frequency analysis, and finally, the integrity degree is qualitatively. If the data integrity of the equipment operation parameter change is insufficient, a missing parameter disturbance scheme is required to be formulated, and a disturbance experiment is carried out to obtain data; in case the data integrity of the condition change is insufficient, more historical data can be collected to complement the missing condition information. The integrity analysis flow is shown in fig. 4. The data mechanism refers to a priori knowledge of the data according to the process and equipment conditions. For example, the power plant load variation interval may determine a rough range based on a priori knowledge, and if there is no such data in some ranges, it may be necessary to supplement such data.
The controlling of the optimal control model may include: determining the actual approach speed of the electric dust collector and the active power required by the smoke volume based on the optimized control model; determining an electric field power value ratio through the electric field of the electric dust collector and the coefficient value of the dust outlet based on the active power; and adjusting a secondary voltage and current based on the electric field power value ratio.
Building an optimal control model:
1. active power sum prediction model
Determining a set of data set input variables X d ＝[ωk，Q]Output variable Y d ＝[P]Wherein omega k Is the real-time approach speed, Q is the sum of the smoke quantity and the active powerWherein n is the number of electric fields of the dust remover, u 2i And I 2i Representing the secondary voltage and secondary current of each electric field.
Determining a loss functionWherein P is i Is the sum of real active power, P i p And predicting the sum of the active power output by the model. And (3) reducing the Loss value by using a random gradient descent algorithm and a back propagation algorithm, iteratively optimizing the model weight parameters, and stopping training the weight parameters when the Loss value area is converged. The sum P of active power is predicted by real-time approach speed and smoke quantity.
2. Automatic electric field power distribution
And (3) analyzing and calculating the correlation coefficient of each electric field power P and the dust discharge outlet S, wherein the calculation formula of the correlation coefficient is as follows:
wherein (P) i ，S i ) For each sample point of electric field power and dust discharge outlet concentration S, P b And S is equal to b Respectively X i And Y is equal to i N is the number of sample points. And obtaining a correlation coefficient table of each electric field power and the dust discharge outlet concentration through calculation:
and distributing electric field power values by utilizing the correlation coefficient values of each electric field and the dust outlet, so as to realize optimal power distribution.
Comprehensive intelligent optimization control for setting up electric dust collectorModeling, namely calculating the current approach speed omega of the electric dust collector through the model k And the secondary voltage and current are automatically adjusted according to the power value ratio of the electric field and the electric field distributed by the relevant coefficient value of the electric field and the dust outlet, so as to realize optimal power distribution.
The embodiment of the application provides an implementation scheme of an intelligent electric precipitator by using artificial intelligence and digital twin technology in the whole flow, which adopts the artificial intelligence technology from design, manufacturing installation and operation adjustment, guides technicians to apply the electric precipitator in the whole flow, reduces manual errors, improves design, manufacturing, installation and debugging efficiency, saves energy and reduces emission.
For the foregoing method embodiments, for simplicity of explanation, the methodologies are shown as a series of acts, but one of ordinary skill in the art will appreciate that the present application is not limited by the order of acts, as some steps may, in accordance with the present application, occur in other orders or concurrently. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
The method is described in detail in the embodiments disclosed in the present application, and the method can be implemented by using various types of devices, so that the present application also discloses a device, and specific embodiments are given below for details.
Fig. 5 is a schematic structural diagram of a device for determining type-selecting parameters of an electric precipitator according to an embodiment of the present application. Referring to fig. 5, the determining device 50 for the type-selection parameters of the electric precipitator may include:
the prediction model construction model 501 is used for constructing a prediction model taking flue gas parameters, coal quality parameters and ash parameters as input and selecting type parameters as output.
The output data obtaining module 502 is configured to input the input parameters collected in advance into the prediction model, and obtain output data, where the output data includes an optimized approach speed and a total dust collection area.
The model selection parameter determining module 503 is configured to determine a model selection parameter of the electric precipitator by adopting an optimizing matching algorithm based on the output data and specification parameter constraint data, where the specification parameter constraint data includes application field space data, specification parameters of a dedusting substrate, and process knowledge base data.
According to the device for determining the type selection parameters of the electric dust collector, the prediction model is established through historical data, the optimized approach speed and the total dust collection area are obtained in an auxiliary mode, the type selection parameters of the electric dust collector are selected intelligently by further utilizing the optimizing matching algorithm, so that error problems possibly existing due to the fact that the type selection parameters are determined through manual experience are reduced, and meanwhile efficiency of type selection design of the electric dust collector can be improved.
In one implementation, the prediction model is an optimized approach speed prediction model, and the output data obtaining module is specifically configured to: inputting the pre-collected coal quality parameters and ash parameters into the optimized approach speed prediction model to obtain an output value, wherein the output value is the optimized approach speed; based on the pre-collected processed flue gas parameters and the optimized approach speed, a total dust collection area is determined.
In one implementation, the apparatus may further include: the platform building module is used for building a manufacturing and installing platform of the electric dust collector by adopting a digital twin technology based on the type selection parameters after the type selection parameters of the electric dust collector are determined, and the manufacturing and installing platform comprises an electric dust collector manufacturing and cooperating system and an electric dust collector installing cooperating system.
In one implementation, the platform building module may be specifically configured to: based on the process model and the model selection parameters of the electric dust collector, a three-dimensional modeling technology is applied to construct a three-dimensional model of the electric dust collector; the construction of the electric dust collector installation cooperation system comprises the following steps: the method comprises the steps that an electric dust collector installation cooperative system is built on the basis of an electric dust collector three-dimensional model, the installation cooperative system is used for carrying out three-dimensional disassembly and reduction on each component in the three-dimensional model, installation guiding data are obtained, an installation image of a first component is obtained, whether the installation of the first component meets the standard or not is checked through an image analysis algorithm, and the first component is any component in the three-dimensional model.
In one implementation, the apparatus may further include: the system building module is used for building a real-time state monitoring system of the electric dust collector by adopting a digital twin technology and a three-dimensional modeling technology.
In one implementation, the system building module is specifically usable for: determining a first numerical relation between a real-time approach speed and the optimized approach speed, and determining the health state of the electric dust collector based on the first numerical relation; and determining a second numerical relation between the real-time dust removal efficiency and the set dust removal efficiency, and determining whether to perform early warning or not based on the second numerical relation.
In one implementation, the apparatus may further include: the model building module is used for building an optimal control model of the electric dust collector so as to optimally control the electric dust collector.
In one implementation, the controlling of the optimal control model may include: carrying out integrity analysis on the operation parameters of the electric dust collector, and determining the data integrity of the electric dust collector; and if the integrity is lower than a first set value, obtaining data corresponding to the missing part and supplementing the data into the operation parameters of the electric dust collector.
In another implementation, the controlling of the optimized control model includes: determining the actual approach speed of the electric dust collector and the active power required by the smoke volume based on the optimized control model; determining an electric field power value ratio through the electric field of the electric dust collector and the coefficient value of the dust outlet based on the active power; and adjusting a secondary voltage and current based on the electric field power value ratio.
The specific implementation of the above-mentioned determining device for the type-selecting parameter of the electric precipitator, each module included in the determining device, and other modules possibly included in the determining device may be referred to the content description of the corresponding parts in the method embodiment, and the detailed description is not repeated here.
The device for determining the model selection parameters of any electric dust collector in the above embodiment includes a processor and a memory, where the prediction model building model, the output data obtaining module, the model selection parameter determining module and the like in the above embodiment are all stored as program modules in the memory, and the processor executes the program modules stored in the memory to implement corresponding functions.
The processor comprises a kernel, and the kernel fetches the corresponding program module from the memory. The kernel can be provided with one or more kernels, and the processing of the return visit data is realized by adjusting kernel parameters.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
In an exemplary embodiment, a computer readable storage medium is also provided, which can be directly loaded into an internal memory of a computer, and contains software codes, and the computer program can implement the steps shown in any embodiment of the method for determining the type-selection parameters of the electric dust collector after being loaded and executed by the computer.
In an exemplary embodiment, a computer program product is also provided, which can be directly loaded into an internal memory of a computer, and contains software codes, and the computer program can implement the steps shown in any embodiment of the method for determining the type-selecting parameters of the electric dust collector after being loaded and executed by the computer.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The method for determining the type selection parameters of the electric dust collector is characterized by comprising the following steps of:
constructing a prediction model taking flue gas parameters, coal quality parameters and ash parameters as input and selecting type parameters as output;
inputting the input parameters collected in advance into the prediction model to obtain output data, wherein the output data comprises optimized approach speed and total dust collection area;
and determining the type selection parameters of the electric dust collector by adopting an optimizing and matching algorithm based on the output data and specification parameter constraint data, wherein the specification parameter constraint data comprises application field space data, specification parameters of a dust collection substrate and process knowledge base data.
2. The method for determining a type-selection parameter of an electric precipitator according to claim 1, wherein the prediction model is an optimized approach speed prediction model, and the inputting the input parameter collected in advance into the prediction model to obtain output data comprises:
inputting the pre-collected coal quality parameters and ash parameters into the optimized approach speed prediction model to obtain an output value, wherein the output value is the optimized approach speed;
based on the pre-collected processed flue gas parameters and the optimized approach speed, a total dust collection area is determined.
3. The method for determining a type-selection parameter of an electric precipitator according to claim 1, further comprising, after determining the type-selection parameter of the electric precipitator:
based on the model selection parameters, a manufacturing and installing platform of the electric dust collector is built by adopting a digital twin technology, and the manufacturing and installing platform comprises an electric dust collector manufacturing and cooperating system and an electric dust collector installing and cooperating system.
4. A method of determining a type-selection parameter of an electric precipitator according to claim 3, in which the construction of the electric precipitator manufacturing co-system comprises:
based on the process model and the model selection parameters of the electric dust collector, a three-dimensional modeling technology is applied to construct a three-dimensional model of the electric dust collector;
the construction of the electric dust collector installation cooperation system comprises the following steps:
the method comprises the steps that an electric dust collector installation cooperative system is built on the basis of an electric dust collector three-dimensional model, the installation cooperative system is used for carrying out three-dimensional disassembly and reduction on each component in the three-dimensional model, installation guiding data are obtained, an installation image of a first component is obtained, whether the installation of the first component meets the standard or not is checked through an image analysis algorithm, and the first component is any component in the three-dimensional model.
5. The method for determining a type-selection parameter of an electric precipitator according to claim 1, further comprising, after determining the type-selection parameter of the electric precipitator:
and a digital twin technology and a three-dimensional modeling technology are adopted to build a real-time state monitoring system of the electric dust collector.
6. The method for determining a type-selection parameter of an electric precipitator according to claim 5, wherein said monitoring operation of said real-time status monitoring system comprises:
determining a first numerical relation between a real-time approach speed and the optimized approach speed, and determining the health state of the electric dust collector based on the first numerical relation;
and determining a second numerical relation between the real-time dust removal efficiency and the set dust removal efficiency, and determining whether to perform early warning or not based on the second numerical relation.
7. The method for determining the type-selection parameters of the electric dust collector according to claim 5, further comprising, after determining the type-selection parameters of the electric dust collector:
and constructing an optimal control model of the electric dust collector so as to optimally control the electric dust collector.
8. The method for determining a type-selection parameter of an electric precipitator according to claim 7, wherein said optimizing control model comprises:
carrying out integrity analysis on the operation parameters of the electric dust collector, and determining the data integrity of the electric dust collector;
and if the integrity is lower than a first set value, obtaining data corresponding to the missing part and supplementing the data into the operation parameters of the electric dust collector.
9. The method for determining a type-selection parameter of an electric precipitator according to claim 7, wherein said optimizing control model comprises:
determining the actual approach speed of the electric dust collector and the active power required by the smoke volume based on the optimized control model;
determining an electric field power value ratio through the electric field of the electric dust collector and the coefficient value of the dust outlet based on the active power;
and adjusting a secondary voltage and current based on the electric field power value ratio.
10. A device for determining type selection parameters of an electric dust collector, comprising:
the prediction model building model is used for building a prediction model taking flue gas parameters, coal quality parameters and ash parameters as inputs and selecting type parameters as outputs;
the output data obtaining module is used for inputting the input parameters which are collected and processed in advance into the prediction model to obtain output data, and the output data comprises optimized approach speed and total dust collection area;
the model selection parameter determining module is used for determining model selection parameters of the electric dust collector by adopting an optimizing matching algorithm based on the output data and specification parameter constraint data, wherein the specification parameter constraint data comprises application field space data, specification parameters of a dust collection substrate and process knowledge base data.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117339759A (en) * | 2023-12-04 | 2024-01-05 | 浙江大维高新技术股份有限公司 | Digital twin system and method for dust remover |
| CN118050995A (en) * | 2024-04-16 | 2024-05-17 | 中国标准化研究院 | Self-adaptive parameter adjusting method of electric dust collector based on concentration distribution of particulate matters |
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2023
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117339759A (en) * | 2023-12-04 | 2024-01-05 | 浙江大维高新技术股份有限公司 | Digital twin system and method for dust remover |
| CN117339759B (en) * | 2023-12-04 | 2024-04-09 | 浙江大维高新技术股份有限公司 | Digital twin system and method for dust remover |
| CN118050995A (en) * | 2024-04-16 | 2024-05-17 | 中国标准化研究院 | Self-adaptive parameter adjusting method of electric dust collector based on concentration distribution of particulate matters |
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