CN115452923A - Flue gas flow pattern recognition method and device based on electrostatic sensor - Google Patents
Flue gas flow pattern recognition method and device based on electrostatic sensor Download PDFInfo
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- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 title claims abstract description 54
- 239000003546 flue gas Substances 0.000 title claims abstract description 54
- 238000012567 pattern recognition method Methods 0.000 title claims abstract description 21
- 238000004364 calculation method Methods 0.000 claims abstract description 27
- 239000002245 particle Substances 0.000 claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 16
- 239000013618 particulate matter Substances 0.000 claims abstract description 13
- 239000000779 smoke Substances 0.000 claims abstract description 10
- 238000012512 characterization method Methods 0.000 claims description 8
- 230000006698 induction Effects 0.000 claims description 8
- 238000012821 model calculation Methods 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 5
- 230000005686 electrostatic field Effects 0.000 claims description 4
- 238000011144 upstream manufacturing Methods 0.000 claims description 4
- 230000007704 transition Effects 0.000 claims 1
- 238000005259 measurement Methods 0.000 abstract description 4
- 238000007418 data mining Methods 0.000 abstract description 3
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- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/60—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrostatic variables, e.g. electrographic flaw testing
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Abstract
The invention provides a flue gas flow pattern recognition method and device based on an electrostatic sensor, and relates to the technical field of flue gas emission measurement. The method comprises the following steps: collecting an output signal of the electrostatic sensor in a flue gas discharge pipeline; calculating the transit time of the output signal and an estimated velocity value of the particulate matter based on a cross-correlation operation; calculating a signal time domain model caused when the single charged particles pass through the sensitive field of the electrostatic sensor; carrying out data processing on the output signal by using the signal time domain model to restore an electrostatic signal; determining the speed fluctuation characteristics of the particles according to the transit time and the speed estimation value, selecting a calculation interval, and calculating the standard deviation of the electrostatic signal; and characterizing the smoke discharge flow pattern by using the standard deviation of the electrostatic signal. The invention can carry out data mining on the electrostatic signal to obtain the information of the smoke flow type. The flow-type changes are reflected more rapidly than with pitot tube sensors that employ differential pressure principles.
Description
Technical Field
The invention relates to the technical field of flue gas emission measurement, in particular to a flue gas flow pattern recognition method and device based on an electrostatic sensor.
Background
The electrostatic sensor is the most widely used sensor in the field of pipeline flow detection due to simple structure and low cost. Its electrode imbeds the pipeline inner wall, can with the direct contact of fluid in the pipeline, therefore the static signal mainly contains two parts: one part of the signal is generated by electrostatic induction of the charged fluid; the other part is from the transferred charge signal generated by the contact of the fluid and the electrode.
Disclosure of Invention
In view of this, the invention provides a flue gas flow pattern recognition method and device based on an electrostatic sensor, which can realize flue gas flow pattern recognition based on electrostatic induction space convolution signal restoration.
In order to achieve the above object, a first aspect of the present invention provides a flue gas flow pattern recognition method based on an electrostatic sensor, including:
collecting an output signal of the electrostatic sensor in a flue gas discharge pipeline;
calculating the transit time of the output signal and an estimated velocity value of the particulate matter based on a cross-correlation operation;
calculating a signal time domain model caused when the single charged particles pass through the sensitive field of the electrostatic sensor;
carrying out data processing on the output signal by using the signal time domain model to restore an electrostatic signal;
determining the speed fluctuation characteristics of the particles according to the transit time and the speed estimation value, selecting a calculation interval, and calculating the standard deviation of the electrostatic signal;
and characterizing the smoke discharge flow pattern by using the standard deviation of the electrostatic signal.
The invention provides a smoke flow type recognition device based on an electrostatic sensor, which comprises:
the signal acquisition module is used for acquiring the output signal of the electrostatic sensor in the smoke discharge pipeline;
the speed calculation module is used for calculating the transit time of the output signal and the speed estimation value of the particulate matter based on cross-correlation operation;
the signal model calculation module is used for calculating a signal time domain model caused when the single charged particles pass through the sensitive field of the electrostatic sensor;
the signal restoration module is used for carrying out data processing on the output signal by using the signal time domain model to restore an electrostatic signal;
the standard deviation calculation module is used for determining the speed fluctuation characteristics of the particles according to the transit time and the speed estimation value, selecting a calculation interval and calculating the standard deviation of the electrostatic signal;
and the flue gas flow type characterization module is used for characterizing the flue gas discharge flow type by using the standard deviation of the electrostatic signal.
A third aspect of the present invention provides an electronic device comprising: one or more processors; memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the electrostatic sensor-based flue gas flow pattern recognition method described above.
Compared with the prior art, the flue gas flow pattern recognition method and device based on the electrostatic sensor, provided by the invention, at least have the following beneficial effects:
the invention can carry out data mining on the electrostatic signal to obtain the information of the smoke flow type. Compared with a pitot tube sensor adopting a differential pressure principle, the method provided by the invention has the advantage that the measurement result can reflect the flow type change more quickly.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent from the following description of embodiments of the present invention with reference to the accompanying drawings, in which:
fig. 1 is a schematic diagram showing an application scenario of a flue gas flow pattern recognition method based on an electrostatic sensor according to an embodiment of the invention;
FIG. 2 schematically illustrates a flow chart of a flue gas flow pattern recognition method based on an electrostatic sensor according to an embodiment of the invention;
FIG. 3 schematically shows a flow diagram of a signal time domain model according to an embodiment of the invention;
FIG. 4 is a graph schematically illustrating the comparison effect between flue gas flow pattern recognition results based on an electrostatic sensor and a differential pressure sensor according to an embodiment of the invention;
FIG. 5 is a block diagram schematically illustrating the structure of a flue gas flow pattern recognition device based on an electrostatic sensor according to an embodiment of the invention;
fig. 6 schematically illustrates a block diagram of an electronic device suitable for implementing a method for electrostatic sensor-based flue gas flow pattern recognition, in accordance with an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to specific embodiments and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Fig. 1 is a schematic diagram showing an application scenario of a flue gas flow pattern recognition method based on an electrostatic sensor according to an embodiment of the invention. It should be noted that fig. 1 is only an example of an application scenario in which the embodiment of the present invention may be applied to help those skilled in the art understand the technical content of the present invention, and does not mean that the embodiment of the present invention may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, an application scenario according to this embodiment may be a flue gas discharge flow pattern detection system 100, where the system 100 specifically includes an electrostatic sensor 101, a signal conditioning circuit 102, and a data processing device 103.
The electrostatic sensor 101 is installed in the flue gas discharge duct, and specifically includes two electrostatic electrodes upstream and downstream. The signal conditioning circuit 102 draws out and amplifies the electrostatic induction signals on the two electrostatic electrodes, and outputs the signals to be sensor output signals. The data processing device 103 identifies the flue gas emission flow pattern through an algorithm based on the sensor output signals.
It should be understood that the number of electrostatic sensors, signal conditioning circuits and data processing devices in fig. 1 is merely illustrative. There may be any number of electrostatic sensors, signal conditioning circuitry, and data processing devices, as desired for implementation.
The method of the embodiment of the present invention will be described in detail with fig. 2 to 4 based on the application scenario described in fig. 1.
Fig. 2 schematically shows a flow chart of a flue gas flow pattern recognition method based on an electrostatic sensor according to an embodiment of the invention. Fig. 3 schematically shows a flow diagram of a signal time domain model according to an embodiment of the invention.
As shown in fig. 2, the flue gas flow pattern recognition method based on the electrostatic sensor according to the embodiment may include operations S210 to S260.
In operation S210, an output signal of the electrostatic sensor is collected in the flue gas discharge duct.
In the embodiment of the invention, the electrostatic sensor is composed of two electrostatic electrodes arranged on the upstream and downstream of the flue gas discharge pipeline. The two electrostatic electrodes output electrostatic induction signals, and the electrostatic induction signals are led out and amplified to obtain output signals of the electrostatic sensor.
Optionally, the spacing of the two electrostatic electrodes is 50mm.
In operation S220, a transit time of the output signal and an estimated velocity of the particulate matter are calculated based on the cross-correlation operation.
It can be understood that if the signal emitted by the sound source is x (t) and the signal received by the sound wave receiving device is y (t), the cross-correlation function of the two signals can be calculated by the following formula:
where τ is the transit time. After the cross-correlation operation is performed by the above formula, the time corresponding to the peak point of the cross-correlation function can be obtained, and thus the transit time τ of the sound wave from the sound source to the signal receiving device can be obtained.
Then, through cross-correlation operation, the velocity estimation value of the particulate matter can be obtained.
In operation S230, a time domain model of a signal caused when a single charged particle passes through a sensitive field of the electrostatic sensor is calculated.
The signal time domain model F (t) mainly has two calculation methods, i.e., the induced charge caused by the point charge on the inner surface of the electrode can be calculated by the electrostatic field CFD, or can be calculated according to an empirical formula.
In some embodiments, the induced charges caused by the point charges on the inner surfaces of the two electrostatic electrodes can be calculated through the electrostatic field CFD to obtain a signal time domain model. Specifically, the electrostatic field simulation may be performed on the electrostatic sensor electrode by using Computational Fluid Dynamics (CFD), and amplitudes of the electrostatic sensor output signals caused by the charged particles at different positions are obtained to form a series of time series, so as to obtain a signal time domain model.
In some other embodiments, the signal time domain model may also be calculated according to the following empirical formula:
F(x，θ)＝[(0.5D) 2 +x 2 -Dxcosθ] 1/2
wherein Is (t) Is induction current; t is time;q is an induced charge; d is the inner diameter of the two electrostatic electrodes; w is the width of two electrostatic electrodes; v s Is the particle velocity; theta is the particle scattering angle; x is the distance of the charged particles from the center of the electrode in the cross section of the electrostatic electrode; f (x, θ) is an intermediate variable that is a function of x and θ.
Fig. 3 shows a flow chart of a signal time domain model in a specific embodiment, and it can be seen that there are different signal time domain model flow lines at different speeds.
In operation S240, the output signal is subjected to data processing using the signal time domain model to restore the electrostatic signal.
Specifically, firstly, according to a signal time domain model F (t), a deconvolution transformation function F is solved -1 (τ) is calculated. Reuse of the deconvolution transform function F -1 And (tau) performing inverse transformation on the output signal to restore an electrostatic signal.
In operation S250, a velocity fluctuation characteristic of the particulate matter is determined according to the transit time and the velocity estimation value, and a calculation interval is selected to calculate a standard deviation of the electrostatic signal.
In the embodiment of the invention, the speed fluctuation characteristics of the particulate matters are determined and the calculation interval is selected according to the transit time and the estimated speed value, and the method specifically comprises the following steps: and calculating the time required for the particles to flow through the preset pipe diameter distance by using the transit time and the estimated speed value, and determining the time as a calculation interval.
Optionally, the preset pipe diameter is 100mm. That is, an estimated value of the velocity of the particulate matter, for example, the value is 7m/s, can be obtained by a cross-correlation operation, and then the estimated value of the velocity of the particulate matter is used to calculate the time required for the particles to flow through a pipe diameter distance of 100m, for example, the time is 1.4s, and the time is determined as a calculation interval to calculate the standard deviation of the electrostatic signal.
In operation S260, the flue gas emission flow pattern is characterized using the standard deviation of the electrostatic signal.
Therefore, the standard deviation of the electrostatic signal is utilized to finally realize the characterization of the smoke discharge flow pattern.
Fig. 4 is a graph schematically showing the effect of flue gas flow pattern recognition based on an electrostatic sensor in comparison with a differential pressure sensor according to an embodiment of the present invention.
As shown in fig. 4, it can be seen that both the electrode electrostatic signal and the differential pressure sensor signal can reflect the change of the particle mass flow rate, and have the same change trend. Compared with the differential pressure sensor signal, the electrode electrostatic signal adopting the method of the invention has violent fluctuation. In addition, although the differential pressure sensor is installed approximately 1.5m upstream of the electrostatic electrode, the change in the differential pressure signal lags behind the change in the electrode electrostatic signal. This is because the differential pressure sensor takes the differential pressure of 1.8m straight tube section both sides differential pressure, and its impulse tube is slightly long, leads to the lag and the smoothness of differential pressure signal.
Through the embodiment of the invention, the data mining can be carried out on the electrostatic signal to obtain the information of the smoke flow type. Compared with a pitot tube sensor adopting a differential pressure principle, the method provided by the invention has the advantage that the measurement result can reflect the flow type change more quickly.
Based on the method disclosed above, the invention also provides a flue gas flow pattern recognition device based on the electrostatic sensor, which will be described in detail below with reference to fig. 5.
Fig. 5 is a block diagram schematically illustrating the structure of a flue gas flow type recognition apparatus based on an electrostatic sensor according to an embodiment of the present invention.
As shown in fig. 5, the flue gas flow pattern recognition apparatus 500 based on the electrostatic sensor according to the embodiment includes a signal acquisition module 510, a speed calculation module 520, a signal model calculation module 530, a signal reduction module 540, a standard deviation calculation module 550 and a flue gas flow pattern characterization module 560.
And the signal acquisition module 510 is used for acquiring the output signal of the electrostatic sensor in the flue gas discharge pipeline.
A velocity calculation module 520 for calculating the transit time of the output signal and an estimate of the velocity of the particulate matter based on the cross-correlation operation.
And the signal model calculating module 530 is used for calculating a signal time domain model caused when the single charged particles pass through the sensitive field of the electrostatic sensor.
And the signal restoring module 540 is configured to perform data processing on the output signal by using the signal time domain model, and restore the electrostatic signal.
And a standard deviation calculation module 550, configured to determine a velocity fluctuation characteristic of the particulate matter according to the transit time and the velocity estimation value, select a calculation interval, and calculate a standard deviation of the electrostatic signal.
And the flue gas flow pattern characterization module 560 is used for characterizing the flue gas emission flow pattern by using the standard deviation of the electrostatic signal.
It should be noted that the embodiment of the apparatus portion is similar to the embodiment of the method portion, and the achieved technical effects are also similar, and for specific details, reference is made to the embodiment of the method described above, and details are not repeated here.
According to the embodiment of the present invention, any multiple of the signal acquisition module 510, the velocity calculation module 520, the signal model calculation module 530, the signal reduction module 540, the standard deviation calculation module 550 and the flue gas flow type characterization module 560 may be combined and implemented in one module, or any one of the modules may be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present invention, at least one of the signal acquisition module 510, the velocity calculation module 520, the signal model calculation module 530, the signal reduction module 540, the standard deviation calculation module 550, and the flue gas flow type characterization module 560 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or by a suitable combination of any of them. Alternatively, at least one of the signal acquisition module 510, the velocity calculation module 520, the signal model calculation module 530, the signal restoration module 540, the standard deviation calculation module 550 and the flue gas flow type characterization module 560 may be at least partially implemented as a computer program module that, when executed, may perform a corresponding function.
Fig. 6 schematically illustrates a block diagram of an electronic device suitable for implementing a method for electrostatic sensor-based flue gas flow pattern recognition, in accordance with an embodiment of the present invention.
As shown in fig. 6, an electronic device 600 according to an embodiment of the present invention includes a processor 601 which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. Processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 601 may also include onboard memory for caching purposes. Processor 601 may include a single processing unit or multiple processing units for performing the different actions of the method flows according to embodiments of the present invention.
In the RAM603, various programs and data necessary for the operation of the electronic apparatus 600 are stored. The processor 601, the ROM 602, and the RAM603 are connected to each other via a bus 604. The processor 601 performs various operations of the method flow according to an embodiment of the present invention by executing programs in the ROM 602 and/or RAM 603. It is to be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also perform various operations of method flows according to embodiments of the present invention by executing programs stored in the one or more memories.
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise. Furthermore, the word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A flue gas flow pattern recognition method based on an electrostatic sensor is characterized by comprising the following steps:
collecting an output signal of the electrostatic sensor in a flue gas discharge pipeline;
calculating the transit time of the output signal and an estimated velocity value of the particulate matter based on a cross-correlation operation;
calculating a signal time domain model caused when the single charged particles pass through the sensitive field of the electrostatic sensor;
carrying out data processing on the output signal by using the signal time domain model to restore an electrostatic signal;
determining the speed fluctuation characteristics of the particles according to the transit time and the speed estimation value, selecting a calculation interval, and calculating the standard deviation of the electrostatic signal;
and characterizing the smoke discharge flow pattern by using the standard deviation of the electrostatic signal.
2. The flue gas flow pattern recognition method based on the electrostatic sensor is characterized in that the electrostatic sensor is composed of two electrostatic electrodes arranged on the upstream and downstream of the flue gas discharge pipeline;
and the two electrostatic electrodes output electrostatic induction signals, and the electrostatic induction signals are led out and amplified to obtain output signals of the electrostatic sensor.
3. The flue gas flow pattern recognition method based on the electrostatic sensor as claimed in claim 2, wherein the calculating of the signal time domain model caused when the singly charged particles pass through the sensitive field of the electrostatic sensor comprises:
and calculating induced charges caused by point charges on the inner surfaces of the two electrostatic electrodes through an electrostatic field CFD, and solving the signal time domain model.
4. The flue gas flow pattern recognition method based on the electrostatic sensor, according to claim 2, wherein the calculating of the signal time domain model caused when the singly charged particles pass through the sensitive field of the electrostatic sensor further comprises calculating the signal time domain model according to the following empirical formula:
wherein Is(t) is an induced current; t is time; q is an induced charge; d is the inner diameter of the two electrostatic electrodes; w is the width of two electrostatic electrodes; v s Is the particle velocity; theta is the particle scattering angle; x is the distance of the charged particles from the center of the electrode in the cross section of the electrostatic electrode; f (x, theta) is an intermediate variable.
5. The flue gas flow pattern recognition method based on the electrostatic sensor according to claim 1, wherein the signal time domain model is used for performing data processing on the output signal to restore an electrostatic signal, and specifically comprises:
solving a deconvolution transformation function according to the signal time domain model;
and performing inverse transformation on the output signal by using the deconvolution transformation function to restore an electrostatic signal.
6. The electrostatic sensor-based flue gas flow pattern recognition method according to claim 2, wherein the distance between the two electrostatic electrodes is 50mm.
7. The flue gas flow pattern recognition method based on the electrostatic sensor as claimed in claim 1, wherein the determining of the velocity fluctuation characteristics of the particulate matter and the selection of the calculation interval according to the transit time and the velocity estimation value specifically comprises:
and calculating the time required for the particles to flow through a preset pipe diameter distance by using the transition time and the estimated speed value, and determining the time as the calculation interval.
8. The flue gas flow pattern recognition method based on the electrostatic sensor as claimed in claim 7, wherein the preset pipe diameter is 100mm.
9. A flue gas flow type recognition device based on an electrostatic sensor is characterized by comprising:
the signal acquisition module is used for acquiring the output signal of the electrostatic sensor in the smoke discharge pipeline;
the speed calculation module is used for calculating the transit time of the output signal and the speed estimation value of the particulate matter based on cross-correlation operation;
the signal model calculation module is used for calculating a signal time domain model caused when the single charged particles pass through the sensitive field of the electrostatic sensor;
the signal restoration module is used for carrying out data processing on the output signal by using the signal time domain model to restore an electrostatic signal;
the standard deviation calculation module is used for determining the speed fluctuation characteristics of the particles according to the transit time and the speed estimation value, selecting a calculation interval and calculating the standard deviation of the electrostatic signal;
and the flue gas flow type characterization module is used for characterizing the flue gas discharge flow type by using the standard deviation of the electrostatic signal.
10. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-8.
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