CN115452923A - Smoke flow pattern recognition method and device based on electrostatic sensor - Google Patents

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

Smoke flow pattern recognition method and device based on electrostatic sensor

technical field

The invention relates to the technical field of flue gas emission measurement, in particular to a method and device for identifying a flue gas flow pattern based on an electrostatic sensor.

Background technique

Electrostatic sensors, due to their simple structure and low cost, are the most widely used sensors in the field of pipeline flow detection. The electrodes are embedded in the inner wall of the pipeline and can be in direct contact with the fluid in the pipeline. Therefore, the electrostatic signal mainly includes two parts: one part comes from the signal generated by the electrostatic induction of the charged fluid; the other part comes from the transfer charge signal generated by the contact between the fluid and the electrode.

Contents of the invention

In view of this, the present invention provides a smoke flow pattern recognition method and device based on an electrostatic sensor, which can restore the smoke flow pattern based on electrostatic induction spatial convolution signal restoration.

In order to achieve the above purpose, the first aspect of the present invention provides a smoke flow pattern identification method based on an electrostatic sensor, including:

Collect the output signal of the electrostatic sensor in the flue gas discharge pipe;

calculating a transit time of the output signal and an estimated velocity of the particle based on a cross-correlation operation;

Calculating a time-domain model of a signal caused by a single charged particle passing through the sensitive field of the electrostatic sensor;

performing data processing on the output signal by using the time domain model of the signal to restore the electrostatic signal;

According to the transit time and the estimated velocity, determine the velocity fluctuation characteristics of the particulate matter and select a calculation interval to calculate the standard deviation of the electrostatic signal;

The smoke emission flow pattern is characterized using the standard deviation of the electrostatic signal.

The second aspect of the present invention provides a smoke flow pattern recognition device based on an electrostatic sensor, including:

The signal acquisition module is used to collect the output signal of the electrostatic sensor in the flue gas discharge pipe;

A velocity calculation module, configured to calculate the transit time of the output signal and the estimated velocity of the particulate matter based on a cross-correlation operation;

A signal model calculation module, which is used to calculate the time domain model of the signal caused by a single charged particle passing through the sensitive field of the electrostatic sensor;

A signal restoration module, configured to use the signal time-domain model to perform data processing on the output signal to restore the electrostatic signal;

A standard deviation calculation module, used to determine the velocity fluctuation characteristics of the particles and select a calculation interval to calculate the standard deviation of the electrostatic signal according to the transit time and the estimated velocity;

The flue gas flow pattern characterization module is used to characterize the flue gas discharge flow pattern by using the standard deviation of the electrostatic signal.

The third aspect of the present invention provides an electronic device, including: one or more processors; memory for storing one or more programs, wherein, when the one or more programs are processed by the one or more When the controller is executed, one or more processors are made to execute the above-mentioned smoke flow pattern identification method based on the electrostatic sensor.

Compared with the prior art, the smoke flow pattern recognition method and device based on the electrostatic sensor provided by the present invention has at least the following beneficial effects:

The invention can carry out data mining on the static signal to obtain the information of the smoke flow pattern. Compared with the Pitot tube sensor adopting the principle of differential pressure, the measurement result of the method adopted in the present invention can reflect the change of the flow pattern more rapidly.

Description of drawings

Through the following description of the embodiments of the present invention with reference to the accompanying drawings, the above-mentioned and other objects, features and advantages of the present invention will be more clear, in the accompanying drawings:

FIG. 1 schematically shows an application scenario diagram of a smoke flow pattern recognition method based on an electrostatic sensor according to an embodiment of the present invention;

FIG. 2 schematically shows a flow chart of a method for identifying smoke flow patterns based on an electrostatic sensor according to an embodiment of the present invention;

FIG. 3 schematically shows a streamline diagram of a signal time domain model according to an embodiment of the present invention;

Fig. 4 schematically shows a comparison effect diagram of the smoke flow pattern recognition result based on the electrostatic sensor and the differential pressure sensor according to an embodiment of the present invention;

Fig. 5 schematically shows a structural block diagram of an electrostatic sensor-based smoke flow pattern identification device according to an embodiment of the present invention;

Fig. 6 schematically shows a block diagram of an electronic device suitable for implementing a method for identifying a smoke flow pattern based on an electrostatic sensor according to an embodiment of the present invention.

detailed description

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to 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 "comprising", "comprising", etc. used herein indicate the presence of stated features, steps, operations and/or components, but do not exclude 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 meaning commonly understood by one of ordinary skill in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted to have a meaning consistent with the context of this specification, and not be interpreted in an idealized or overly rigid manner.

Fig. 1 schematically shows an application scene diagram of a smoke flow pattern recognition method based on an electrostatic sensor according to an embodiment of the present invention. It should be noted that Figure 1 is only an example of an application scenario where the embodiment of the present invention can be applied to help those skilled in the art understand the technical content of the present invention, but it does not mean that the embodiment of the present invention cannot be used in other device, system, environment or scenario.

As shown in FIG. 1 , the application scenario according to this embodiment may be a smoke discharge flow pattern detection system 100 , and the system 100 specifically includes an electrostatic sensor 101 , a signal conditioning circuit 102 and a data processing device 103 .

Wherein, the electrostatic sensor 101 is installed in the flue gas discharge pipe, specifically including two electrostatic electrodes upstream and downstream. The signal conditioning circuit 102 extracts and amplifies the electrostatic induction signals on the two electrostatic electrodes, and outputs them as sensor output signals. The data processing device 103 realizes the recognition of the smoke discharge flow pattern through an algorithm based on the above-mentioned sensor output signal.

It should be understood that the number of electrostatic sensors, signal conditioning circuits, and data processing means in Figure 1 is illustrative only. There may be any number of electrostatic sensors, signal conditioning circuits, and data processing devices according to implementation requirements.

Based on the application scenario described in FIG. 1 , the method in the embodiment of the present invention will be described in detail with reference to FIGS. 2 to 4 .

Fig. 2 schematically shows a flow chart of a method for identifying a smoke flow pattern based on an electrostatic sensor according to an embodiment of the present invention. Fig. 3 schematically shows a streamline diagram of a signal time domain model according to an embodiment of the present invention.

As shown in FIG. 2 , the method for identifying smoke flow patterns based on electrostatic sensors according to this embodiment may include operation S210 to operation S260 .

In operation S210, an output signal of an electrostatic sensor is collected in a smoke exhaust pipe.

In the embodiment of the present invention, the electrostatic sensor is composed of two electrostatic electrodes arranged upstream and downstream of the smoke discharge pipe. Wherein, the two electrostatic electrodes output electrostatic induction signals, and the electrostatic induction signals are extracted and amplified to obtain output signals of the electrostatic sensor.

Optionally, the distance between the two electrostatic electrodes is 50 mm.

In operation S220, based on the cross-correlation operation, the transit time of the output signal and the velocity estimation value of the particulate matter are calculated.

It can be understood that if the signal sent by the sound source is x(t), and the signal received by the sound wave receiving device is y(t), then the cross-correlation function of the two signals can be calculated by the following formula:

Among them, 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, so that the transit time τ of the sound wave from the sound source to the signal receiving device can be obtained.

Then, through the cross-correlation operation, the velocity estimation value of the particle can be obtained.

In operation S230, a time-domain model of a signal caused by a single charged particle passing through the sensitive field of the electrostatic sensor is calculated.

There are two main calculation methods for the signal time-domain model F(t), one can calculate the induced charge caused by the point charge on the inner surface of the electrode through the electrostatic field CFD, or it can be calculated according to the empirical formula.

In some embodiments, the induced charge caused by the point charge on the inner surface of the two electrostatic electrodes can be calculated by electrostatic field CFD to obtain a time domain model of the signal. Specifically, Computational Fluid Dynamics (CFD) software (Computational Fluid Dynamics, CFD) can be used to simulate the electrostatic field on the electrodes of the electrostatic sensor, and the amplitude of the output signal of the electrostatic sensor caused by the charged particles at different positions can be obtained to form a series of time series, The time domain model of the signal can be obtained.

In some other embodiments, the signal time domain model can also be calculated according to the following empirical formula:

F(x,θ)＝[(0.5D) ² +x ² -Dxcosθ] ^1/2

Among them, Is(t) is the induced current; t is the time; q is the induced charge; D is the inner diameter of the two electrostatic electrodes; W is the width of the two electrostatic electrodes; V _s is the particle velocity; θ is the particle scattering angle; In the cross-section of the electrostatic electrode, the distance of the charged particles from the center of the electrode; F(x, θ) is an intermediate variable, which is a function of x and θ.

Fig. 3 shows a streamline diagram of a signal time-domain model in a specific embodiment, and it can be seen that at different speeds, there are different signal time-domain model streamlines.

In operation S240, data processing is performed on the output signal by using the signal time-domain model to restore the electrostatic signal.

Specifically, firstly, according to the signal time domain model F(t), the deconvolution transformation function F ⁻¹ (τ) is solved. Then use the deconvolution transformation function F ^-1 (τ) to inversely transform the output signal to restore the electrostatic signal.

In operation S250, according to the transit time and the velocity estimation value, the velocity fluctuation characteristics of the particles are determined and a calculation interval is selected to calculate the standard deviation of the electrostatic signal.

In the embodiment of the present invention, according to the transit time and the estimated velocity, the velocity fluctuation characteristics of the particles are determined and the calculation interval is selected, which specifically includes: using the transit time and the estimated velocity, calculating the distance required for the particles to flow through the preset pipe diameter Time, which is determined as the calculation interval.

Optionally, the preset pipe diameter is 100mm. That is to say, the estimated value of particle velocity can be obtained by cross-correlation calculation, for example, the value is 7m/s, and then the estimated value of particle velocity is used to calculate the time required for particles to flow through a pipe diameter of 100m, for example, the time is 1.4 s, determine the time as the calculation interval, and calculate the standard deviation of the electrostatic signal.

In operation S260, the smoke emission flow pattern is characterized by using the standard deviation of the electrostatic signal.

Therefore, the characterization of the smoke emission flow pattern is finally realized by using the standard deviation of the electrostatic signal.

Fig. 4 schematically shows a comparison effect diagram of the smoke flow pattern recognition result based on the electrostatic sensor and the differential pressure sensor according to an embodiment of the present invention.

As shown in Figure 4, it can be seen that both the electrode electrostatic signal and the differential pressure sensor signal can reflect the change of particle mass flow rate, and have the same change trend. Compared with the signal of the differential pressure sensor, the electrostatic signal of the electrode adopting the method of the present invention fluctuates violently. In addition, although the differential pressure sensor is installed about 1.5m upstream of the electrostatic electrode, the change of the differential pressure signal lags behind the change of the electrode electrostatic signal. This is because the differential pressure taken by the differential pressure sensor is the differential pressure on both sides of the 1.8m straight pipe section, and the slightly long pressure guiding pipe causes the lag and smoothness of the differential pressure signal.

Through the embodiment of the present invention, data mining can be performed on the electrostatic signal to obtain the information of the smoke flow pattern. Compared with the Pitot tube sensor adopting the principle of differential pressure, the measurement result of the method adopted in the present invention can reflect the change of the flow pattern more rapidly.

Based on the method disclosed above, the present invention also provides a smoke flow pattern identification device based on an electrostatic sensor, which will be described in detail below with reference to FIG. 5 .

Fig. 5 schematically shows a structural block diagram of an electrostatic sensor-based smoke flow pattern identification device according to an embodiment of the present invention.

As shown in Figure 5, the device 500 for identifying smoke flow patterns based on electrostatic sensors according to this embodiment includes a signal acquisition module 510, a speed calculation module 520, a signal model calculation module 530, a signal restoration module 540, a standard deviation calculation module 550 and Smoke flow pattern characterization module 560 .

The signal collection module 510 is used to collect the output signal of the electrostatic sensor in the flue gas discharge pipe.

The velocity calculation module 520 is configured to calculate the transit time of the output signal and the velocity estimation value of the particle based on the cross-correlation calculation.

The signal model calculation module 530 is used to calculate the time domain model of the signal caused by the single charged particle passing through the sensitive field of the electrostatic sensor.

The signal restoration module 540 is configured to perform data processing on the output signal by using the signal time-domain model to restore the electrostatic signal.

The standard deviation calculation module 550 is used to determine the velocity fluctuation characteristics of the particulate matter and select a calculation interval to calculate the standard deviation of the electrostatic signal according to the transit time and the velocity estimation value.

The smoke flow pattern characterization module 560 is used to characterize the smoke discharge flow pattern by using the standard deviation of the electrostatic signal.

It should be noted that the embodiment of the device part is similar to the embodiment of the method, and the achieved technical effect is also correspondingly similar. For details, please refer to the above-mentioned method embodiment, and will not be repeated here.

According to the embodiment of the present invention, any number of the signal acquisition module 510, velocity calculation module 520, signal model calculation module 530, signal restoration module 540, standard deviation calculation module 550 and smoke flow pattern characterization module 560 can be combined in one module, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules may be combined with at least part of the functions of 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 speed calculation module 520, the signal model calculation module 530, the signal restoration module 540, the standard deviation calculation module 550 and the smoke flow pattern characterization module 560 can be at least partially Implemented as a hardware circuit such as a Field Programmable Gate Array (FPGA), Programmable Logic Array (PLA), System on a Chip, System on a Substrate, System on a Package, Application Specific Integrated Circuit (ASIC), or can be implemented by integrating the circuit or any other reasonable way of encapsulation, such as hardware or firmware, or any one of the three implementations of software, hardware and firmware, or an appropriate combination of any of them. Alternatively, at least one of the signal acquisition module 510, velocity calculation module 520, signal model calculation module 530, signal restoration module 540, standard deviation calculation module 550 and smoke flow pattern characterization module 560 can be at least partially implemented as a computer program module , when the computer program module is executed, the corresponding function can be performed.

Fig. 6 schematically shows a block diagram of an electronic device suitable for implementing a method for identifying a smoke flow pattern based on an electrostatic sensor according to 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 be loaded into a random access memory (RAM) 603 according to a program stored in a read-only memory (ROM) 602 or from a storage section 608. Various appropriate actions and processing are performed by the program. Processor 601 may include, for example, a general-purpose microprocessor (eg, a CPU), an instruction set processor and/or a related chipset, and/or a special-purpose microprocessor (eg, an application-specific integrated circuit (ASIC)), and the like. Processor 601 may also include on-board memory for caching purposes. The processor 601 may include a single processing unit or multiple processing units for executing different actions of the method flow according to the embodiment of the present invention.

In the RAM 603, various programs and data necessary for the operation of the electronic device 600 are stored. The processor 601 , ROM 602 and RAM 603 are connected to each other through a bus 604 . The processor 601 executes the programs in the ROM 602 and/or the RAM 603 to perform various operations according to the method flow of the embodiment of the present invention. It should be noted that the program may also be stored in one or more memories other than ROM 602 and RAM 603 . The processor 601 may also execute various operations of the method flow according to the embodiment of the present invention by executing the programs stored in the one or more memories.

According to an embodiment of the present invention, the electronic device 600 may further include an input/output (I/O) interface 605 which is also connected to the bus 604 . The electronic device 600 may also include one or more of the following components connected to the I/O interface 605: an input section 606 including a keyboard, a mouse, etc.; including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. An output section 607 of a speaker or the like; a storage section 608 including a hard disk or the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc. is mounted on the drive 610 as necessary so that a computer program read therefrom is installed into the storage section 608 as necessary.

Some block diagrams and/or flowcharts are shown in the figures. It will be understood that some or combinations of blocks in the block diagrams and/or flowcharts can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, so that these instructions, when executed by the processor, can be created to implement the functions illustrated in these block diagrams and/or flowcharts /operated device.

In addition, the terms "first" and "second" are used for descriptive purposes only, and cannot be interpreted as indicating or implying relative importance or implicitly specifying the quantity of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of these features. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise specifically defined. Furthermore, the word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements.

The specific embodiments described above have further described the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above descriptions are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within 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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