CN110954536A - Fly ash carbon content online measurement device and method - Google Patents
Fly ash carbon content online measurement device and method Download PDFInfo
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 101
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 101
- 238000000034 method Methods 0.000 title claims abstract description 38
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- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 12
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- G01N1/02—Devices for withdrawing samples
- G01N1/22—Devices for withdrawing samples in the gaseous state
- G01N1/2202—Devices for withdrawing samples in the gaseous state involving separation of sample components during sampling
- G01N1/2211—Devices for withdrawing samples in the gaseous state involving separation of sample components during sampling with cyclones
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- G01N1/2258—Sampling from a flowing stream of gas in a stack or chimney
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- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
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Abstract
The invention relates to an online measuring device and method for carbon content in fly ash, belonging to the technical field of detection. The fly ash constant-speed sampling system comprises a sampling gun, a cyclone separator, an ejector, a flow controller, an exhaust pipe, an anti-blocking type backrest pipe, a differential pressure transmitter and a blanking valve, wherein one end of the sampling gun is positioned in a flue, the other end of the sampling gun is connected with the cyclone separator, the blanking valve is installed at the lower end of the cyclone separator, the upper end of the cyclone separator is connected with the ejector, the ejector is connected with the flow controller, one end of the exhaust pipe is connected with the flow controller, the other end of the exhaust pipe is positioned in the flue, the differential pressure transmitter is connected with the flow controller, one end of the anti-blocking type backrest pipe is connected with the differential pressure transmitter, and the other end of the anti-blocking type backrest pipe is positioned in the flue.
Description
Technical Field
The invention relates to an online measuring device and method for carbon content in fly ash, belonging to the technical field of detection.
Background
Coal is a main primary energy source in China, and a coal-fired boiler is a main mode and equipment for coal utilization and is widely applied to industries such as power generation, cement, steel, chemical engineering and the like. Boiler efficiency is a main technical index for measuring the performance and the economy of a coal-fired boiler. The coal burnout degree is one of the main factors influencing the boiler efficiency. The higher the coal burnout, the more sufficient the chemical energy release of the coal, and the higher the boiler efficiency. The coal burnout degree is directly reflected on the carbon content of fly ash and slag. In a pulverized coal furnace (the most specific boiler type in a coal-fired boiler), the amount of generated fly ash is much larger than that of slag, so that the carbon content of fly ash becomes a key index reflecting the combustion efficiency of the boiler.
The carbon content of the fly ash can be measured by a loss on ignition test, which is the most direct and accurate measurement method. However, the method needs to be detected in a laboratory after sampling, is complex in process, long in time consumption and manual operation, and is difficult to meet the urgent requirements of industrial production on measurement frequency, automation and the like.
The carbon content of the fly ash can be reflected in the aspects of the color depth, the particle morphology and the like of the fly ash. Generally, the higher the carbon content of fly ash, the darker the color of fly ash (higher gray scale), the higher the proportion of coke particles and irregularly shaped particles in the composition of fly ash particles, and the lower the proportion of spherical microbeads. This provides a physical basis for the (indirect) measurement of fly ash carbon content by image detection and identification. The invention is based on this principle.
The patent "a method for measuring the carbon content of fly ash in a coal-fired boiler and a kiln and an online detection device" (patent number CN200310109638, in an unauthorized state) provides a method for measuring the carbon content of fly ash by using a gray value of fly ash, but does not consider information such as the appearance of fly ash particles, and the like, and needs to manually calibrate the relationship between the gray value and the carbon content of fly ash in advance, and for uncalibrated coal types, the measurement accuracy is difficult to guarantee, and the method cannot adapt to the condition that the coal types and the coal quality of the existing coal-fired boiler are changeable.
The invention utilizes the detail information of the micro-morphology of the fly ash particles and the like to detect the gray level from more dimensions, improves the accuracy and the robustness of the method, can carry out on-line self-learning and self-calibration, and takes the current coal type for combustion as one of the input parameters by an informatization means, thereby being applicable to the detection of the carbon content of the fly ash under various coal types for combustion.
At present, the carbon content of fly ash in coal-fired boilers is mainly measured by two means, firstly, the fly ash is sent to a laboratory for a loss-on-ignition test after sampling, the fly ash is continuously oxidized in a high-temperature environment, the loss mass of the fly ash is measured, the ratio of the loss mass to the mass before loss-on-ignition is calculated, the carbon content of the fly ash can be obtained, secondly, the fly ash is measured on line by a microwave method, a fly ash sample is collected from a flue at the tail part of the boiler through a sampling device, and the carbon content of the fly ash is calculated based on a quantitative rule that the. The former has the disadvantages of multiple processes, long time consumption and serious dependence on manual operation, and the latter has the disadvantages of great influence by coal types and high cost.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide the device and the method for measuring the carbon content of the fly ash on line, which have reasonable structural design.
The technical scheme adopted by the invention for solving the problems is as follows: the fly ash carbon content online measuring device comprises a flue, a fly ash constant-speed sampling system, an image shooting and analyzing system and a fly ash cleaning and collecting system, wherein the flue is connected with the fly ash constant-speed sampling system, the fly ash cleaning and collecting system is positioned below the fly ash constant-speed sampling system, the fly ash constant-speed sampling system is matched with the image shooting and analyzing system, and the fly ash carbon content online measuring device is structurally characterized in that: fly ash constant speed sampling system includes sampling gun, cyclone, ejector, flow controller, blast pipe, prevents stifled formula back pipe, differential pressure transmitter and blanking valve, the one end of sampling gun is located the flue, the other end and the cyclone of sampling gun are connected, blanking valve is installed to cyclone's lower extreme, cyclone's upper end is connected with the ejector, the ejector is connected with flow controller, the one end and the flow controller of blast pipe are connected, the other end of blast pipe is located the flue, differential pressure transmitter is connected with flow controller, the one end and the differential pressure transmitter of preventing stifled formula back pipe are connected, the other end of preventing stifled formula back pipe is located the flue.
Further, the ejector comprises a compressed air source, an ejector body and a compressed air jet flow spray pipe, the upper end of the cyclone separator is connected with the ejector body, the ejector body is connected with the flow controller, and the ejector body is connected with the compressed air source through the compressed air jet flow spray pipe.
Further, image capture analysis system includes expansion bend, telescopic link, tray, standard look board, optical camera, micro-camera, computer and workstation, the telescopic link is installed on the expansion bend, the tray is connected with the telescopic link and tray and workstation cooperation, the standard look board sets up on the workstation, optical camera and micro-camera are located the top and the below of workstation respectively, optical camera and micro-camera all are connected with the computer.
Furthermore, the image shooting and analyzing system also comprises an upper light source, a lower light source and a data cable, wherein the upper light source and the lower light source are respectively positioned above and below the workbench, and the optical camera and the microscopic camera are both connected with the computer through the data cable.
Further, fly ash clearance collection system includes scraper blade, compressed air sweeps device and hopper, the hopper is located the below of blanking valve, scraper blade and compressed air sweep the device all with the tray cooperation.
Further, the tray is a transparent carrier.
Further, another technical object of the present invention is to provide a measuring method of the fly ash carbon content online measuring device.
The technical purpose of the invention is realized by the following technical scheme.
A measuring method of a fly ash carbon content on-line measuring device is characterized in that: the measuring method comprises the following steps:
a) collecting a fly ash sample from the flue gas by constant-speed sampling, and preprocessing the fly ash sample to form a fly ash sample with a smooth surface on a transparent tray;
b) respectively obtaining a general picture image and a microscopic image of the fly ash sample by adopting conventional photographing and microscopic photographing;
c) analyzing, processing and identifying the fly ash image based on technologies such as image detection, machine vision and the like to obtain characteristic parameters of the fly ash;
d) acquiring characteristic parameters of boiler combustion from a unit operation database;
f) acquiring a carbon content measured value of a fly ash sample from routine tests of a unit, and establishing a correlation model between the measured value and characteristic parameters (fly ash characteristic parameters and boiler combustion characteristic parameters) by means of machine learning and the like;
g) and c) measuring and calculating the carbon content of the fly ash by using the correlation model established in the f) and taking the characteristic parameters in the steps c) and d) as model input, thereby realizing online measurement.
Further, the measuring method comprises a step e) between steps d) and f),
e) acquiring a carbon content measurement value of the fly ash samples in the same batch by adopting a conventional assay means, calibrating the characteristic parameters acquired in the steps c and d, and establishing a correlation model of the characteristic parameters and the carbon content of the fly ash;
g) and (4) measuring and calculating the carbon content of the fly ash by using the correlation model established by e) or f) and taking the characteristic parameters of the steps c) and d) as model input, thereby realizing online measurement.
Wherein step e) is not necessary.
Compared with the prior art, the invention has the following advantages: the accuracy rate is improved and the working efficiency is improved by the online measuring device for the carbon content of the fly ash. The method aims to realize the automatic online detection of the carbon content of the fly ash with high accuracy, high adaptability and high technical economy based on means and algorithms such as image detection, machine learning and the like. The measured value of the carbon content of the fly ash is obtained from the routine tests of the unit, and the machine algorithm and the model are trained to realize self-learning and continuous improvement. Conventional photography and photomicrography were performed on the same fly ash sample, making the photographs taken consistent with the sample. The microscopic photography is carried out aiming at the contact surface of the fly ash sample and the transparent carrier (tray), and a higher-quality photomicrograph (higher focusing quality and consistent depth of field at each position in a shooting area) can be obtained.
Drawings
FIG. 1 is a schematic view of an on-line measuring device for carbon content in fly ash according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of the method for on-line measurement of carbon content in fly ash according to the embodiment of the present invention.
In the figure: a flue 1, a fly ash constant-speed sampling system 2, an image shooting and analyzing system 3, a fly ash cleaning and collecting system 4,
2-1 parts of sampling gun, 2-2 parts of cyclone separator, 2-3 parts of compressed air source, 2-4 parts of ejector, 2-5 parts of ejector body, 2-6 parts of compressed air jet spray pipe, 2-7 parts of flow controller, 2-8 parts of exhaust pipe, 2-9 parts of anti-blocking type backrest pipe, 2-10 parts of differential pressure transmitter, 2-11 parts of blanking valve,
3-1 parts of expansion piece, 3-2 parts of expansion rod, 3-3 parts of tray, 3-4 parts of standard color plate, 3-5 parts of optical camera, 3-6 parts of upper light source, 3-7 parts of microscopic camera, 3-8 parts of lower light source, 3-9 parts of data cable, 3-10 parts of computer, 3-11 parts of workbench,
A scraper 4-1, a compressed air blowing device 4-2 and a hopper 4-3.
Detailed Description
The present invention will be described in further detail below by way of examples with reference to the accompanying drawings, which are illustrative of the present invention and are not to be construed as limiting the present invention.
Referring to fig. 1 to 2, it should be understood that the structures, ratios, sizes, and the like shown in the drawings attached to the present specification are only used for matching the disclosure of the present specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions that the present invention can be implemented, so that the present invention has no technical essence, and any modifications of the structures, changes of the ratio relationships, or adjustments of the sizes, should still fall within the scope of the present disclosure without affecting the functions and the achievable objectives of the present invention. In the present specification, the terms "upper", "lower", "left", "right", "middle" and "one" are used for clarity of description, and are not used to limit the scope of the present invention, and the relative relationship between the terms and the relative positions may be changed or adjusted without substantial technical changes.
Example 1.
The fly ash carbon content online measuring device in the embodiment comprises a flue 1, a fly ash constant-speed sampling system 2, an image shooting and analyzing system 3 and a fly ash cleaning and collecting system 4, wherein the flue 1 is connected with the fly ash constant-speed sampling system 2, the fly ash cleaning and collecting system 4 is positioned below the fly ash constant-speed sampling system 2, and the fly ash constant-speed sampling system 2 is matched with the image shooting and analyzing system 3.
The fly ash constant-speed sampling system 2 in the embodiment comprises a sampling gun 2-1, a cyclone separator 2-2, an ejector 2-4, a flow controller 2-7, an exhaust pipe 2-8, an anti-blocking type backrest pipe 2-9, a differential pressure transmitter 2-10 and a blanking valve 2-11, wherein one end of the sampling gun 2-1 is positioned in a flue 1, the other end of the sampling gun 2-1 is connected with the cyclone separator 2-2, a blanking valve 2-11 is arranged at the lower end of the cyclone separator 2-2, the upper end of the cyclone separator 2-2 is connected with the ejector 2-4, the ejector 2-4 is connected with the flow controller 2-7, one end of the exhaust pipe 2-8 is connected with the flow controller 2-7, and the other end of the exhaust pipe 2-8 is positioned in the flue 1, the differential pressure transmitter 2-10 is connected with the flow controller 2-7, one end of the anti-blocking type backrest pipe 2-9 is connected with the differential pressure transmitter 2-10, and the other end of the anti-blocking type backrest pipe 2-9 is positioned in the flue 1.
The ejector 2-4 in the embodiment comprises a compressed air source 2-3, an ejector body 2-5 and a compressed air jet flow spray pipe 2-6, the upper end of the cyclone separator 2-2 is connected with the ejector body 2-5, the ejector body 2-5 is connected with a flow controller 2-7, and the ejector body 2-5 is connected with the compressed air source 2-3 through the compressed air jet flow spray pipe 2-6.
The image shooting and analyzing system 3 in the embodiment comprises a telescopic device 3-1, a telescopic rod 3-2, a tray 3-3, a standard color plate 3-4, an optical camera 3-5, an upper light source 3-6, a microscopic camera 3-7, a lower light source 3-8, a data cable 3-9, a computer 3-10 and a workbench 3-11, wherein the telescopic rod 3-2 is installed on the telescopic device 3-1, the tray 3-3 is connected with the telescopic rod 3-2, the tray 3-3 is matched with the workbench 3-11, the standard color plate 3-4 is arranged on the workbench 3-11, the optical camera 3-5 and the microscopic camera 3-7 are respectively positioned above and below the workbench 3-11, the optical camera 3-5 and the microscopic camera 3-7 are both connected with the computer 3-10, normally, the upper light source 3-6 and the lower light source 3-8 are located above and below the work table 3-11, respectively, and the optical camera 3-5 and the microscopic camera 3-7 are connected to the computer 3-10 through the data cable 3-9.
The fly ash cleaning and collecting system 4 in the embodiment comprises a scraper 4-1, a compressed air blowing device 4-2 and a hopper 4-3, wherein the hopper 4-3 is positioned below a blanking valve 2-11, the scraper 4-1 and the compressed air blowing device 4-2 are matched with a tray 3-3, and the tray 3-3 is a transparent carrier.
The measuring method of the fly ash carbon content online measuring device in the embodiment comprises the following steps:
a) collecting a fly ash sample from the flue gas by constant-speed sampling, and preprocessing the fly ash sample to form a fly ash sample with a smooth surface on a transparent tray;
b) respectively obtaining a general picture image and a microscopic image of the fly ash sample by adopting conventional photographing and microscopic photographing;
c) analyzing, processing and identifying the fly ash image based on technologies such as image detection, machine vision and the like to obtain characteristic parameters of the fly ash;
d) acquiring characteristic parameters of boiler combustion from a unit operation database;
f) acquiring a carbon content measured value of a fly ash sample from routine tests of a unit, and establishing a correlation model between the measured value and characteristic parameters (fly ash characteristic parameters and boiler combustion characteristic parameters) by means of machine learning and the like;
g) and c) measuring and calculating the carbon content of the fly ash by using the correlation model established in the f) and taking the characteristic parameters in the steps c) and d) as model input, thereby realizing online measurement.
The working principle is as follows: sampling fly ash at constant speed, acquiring fly ash samples, shooting and storing fly ash images (including conventional images and microscopic images), acquiring fly ash images and morphological characteristics based on an image detection technology and a machine vision technology, and establishing, self-learning and actually measuring and calculating a fly ash carbon content prediction model based on a machine learning algorithm.
The fly ash constant-speed sampling system 2 utilizes a sampling gun 2-1 to extract smoke gas flow from a flue 1 at a constant speed, fly ash in the gas flow is separated by a cyclone separator 2-2, exhaust gas is returned to the flue 1 through an exhaust pipe 2-8, and gas flow of the fly ash constant-speed sampling system 2 is provided by an ejector 2-4 (the ejector 2-4 consists of a compressed air source 2-3, an ejector body 2-5 and a compressed air jet nozzle 2-6); the flue gas flow rate is measured by a flue gas flow rate measuring device consisting of anti-blocking type backrest pipes 2-9 and a differential pressure transmitter 2-10 and is transmitted to a flow controller 2-7 by an electrical signal.
The collected fly ash is collected at the lower part of the cyclone separator 2-2, the blanking valve 2-11 can control the blanking of the fly ash to make the fly ash fall on the tray 3-3 to form a material pile, then the tray 3-3 is controlled by the expansion device 3-1 to translate and pass under the scraper 4-1, the lower edge of the scraper 4-1 is parallel and slightly higher than the tray 3-3, when the fly ash passes under the tray 3-3, the material pile is scraped by the scraper 4-1 to form a flat upper surface, then the tray 3-3 continues to translate to the lower part of the optical camera 3-5 of the observation area and the upper part of the microscopic camera 3-7; the tray 3-3 is made of transparent material, and is respectively used for shooting general picture images by an optical camera 3-5 under the irradiation of an upper light source 3-6 and shooting microscopic images by a microscopic camera 3-7 under the irradiation of a lower light source 3-8, and the images are transmitted to a computer 3-10 for storage, processing and analysis by a data cable 3-9; and finally, the tray 3-3 is translated to the position below the blanking valve 2-11 again under the control of the expansion piece 3-1, before secondary blanking, the upper surface and the lower surface of the tray 3-3 are swept by the compressed air sweeping device 4-2 to remove fly ash on the surface, and the process is repeated to realize continuous automatic measurement.
The hopper 4-3 is used for collecting fly ash scattered during blanking, scraping and blowing.
The photographing of the optical camera 3-5 and the microscopic camera 3-7 is controlled by the computer 3-10, and both have an automatic focusing function.
The standard color plate 3-4 is used for white balance correction of the image taken by the optical camera 3-5.
The preferred magnification of the microscopic camera 3-7 is 5x-500 x.
As an alternative scheme, the ejector 2-4 can be replaced by a centrifugal fan or a vacuum pump, and the anti-blocking type backrest pipe 2-9 can be replaced by a hot wire type anemometer or a double-venturi anemometer.
The process of processing, analyzing and measuring the carbon content of the profile image and the microscopic image by the computer 3-10 and the internal algorithm is as follows:
1. obtaining fly ash samples
2. Obtaining characteristic parameters of fly ash samples based on image recognition
2.1, carrying out image analysis on the general picture image to obtain gray level parameters
2.2, carrying out image recognition and analysis on the microscopic images to obtain the morphological characteristic parameters (particle size, slenderness ratio, color and the like) of a certain amount of monomer fly ash particles
3. Obtaining the accurate value of the carbon content of the fly ash
3.1, acquiring the carbon content (called as a calibration reference value) of the fly ash in the same batch in the step 2 based on a traditional standard measurement method of the carbon content of the fly ash, wherein the step is only carried out when the correlation between the carbon content of the fly ash and the characteristic parameters needs to be specially calibrated;
3.2, acquiring a plant-level reference value of the carbon content of the fly ash from a daily routine test of the boiler (the test of the carbon content of the fly ash is routine operation, but the test frequency is not high);
3.3, inputting the calibration reference value and the plant-level reference value of the carbon content of the fly ash, the fly ash characteristic parameters obtained in the step 2 and boiler combustion characteristic parameter (coal quality, coal powder fineness, furnace temperature, oxygen content of discharged smoke, boiler evaporation capacity and the like) data obtained from a plant-level information system (such as a plant-level information monitoring system (SIS)) into a database of a machine learning algorithm of the computer 3-10;
4. repeating the steps 1, 2 and 3 under different working conditions such as coal quality, coal powder fineness, combustion condition and the like, and establishing a database with enough samples and covering enough working conditions
5. Establishing a correlation model of the fly ash carbon content and the characteristic parameters through machine learning and other related algorithms:
5.1, establishing a correlation model based on the calibration reference value of the carbon content of the fly ash, the fly ash characteristic parameters and the boiler combustion characteristic parameters (the step is only carried out in a calibration stage);
5.2, establishing a correlation model based on the plant-level reference value of the carbon content of the fly ash, the fly ash characteristic parameters and the boiler combustion characteristic parameters, wherein the step can be continuously carried out, so that the learning process is continuous, the established correlation model can be adjusted and improved along with the change of the actual running condition, and a long-short term memory (LSTM) neural network can be adopted for machine learning in the step;
5.3, description: 5.1, 5.2, but is beneficial for improving the scientificity and accuracy of the correlation model, and the steps 5.1 and 5.2 can be carried out independently;
6. and (3) actual measurement stage:
6.1, sampling the fly ash on line according to the step 1, and obtaining the characteristic parameters of the specific fly ash sample through the step 2
6.2, acquiring the boiler combustion characteristic parameters through a plant-level information system
6.3, inputting the characteristic parameters obtained in the steps 6.1 and 6.2 into the association model established in the step 5, and calculating to obtain the carbon content of the fly ash sample
7. Continuously improving and correcting the association model established in the step 5 through measurement (step 6) and feedback (step 3):
7.1, recording the calculated value of the carbon content of the fly ash obtained in the step 6
7.2 obtaining the plant-level reference value of the carbon content of the fly ash from the daily routine test which is closest to the boiler operation condition and parameter during the collection of the fly ash sample and has the smallest time interval as possible
The deviation of the carbon content (i.e., [ measured value-plant-level reference value ]) obtained at 7.1 and 7.2 is used as an error and fed back to step 5 to correct and improve the parameters of the algorithm and the correlation model.
The device is placed outside a downstream flue of an air preheater, fly ash is collected from flue gas at the outlet of the air preheater, trays 3-3 are rectangular flat glass made of colorless transparent wear-resistant glass, and the method comprises the following steps:
a) the expansion piece 3-1 controls the tray 3-3 to move to the lower part of the blanking valve 2-11 (the blanking valve 2-11 is in a closed state at the moment);
b) starting a compressed air blowing device 4-2 to blow the upper surface and the lower surface of the tray 3-3, wherein blown fly ash falls into a hopper 4-3, the blowing strength is gradually increased from low to high, and the blowing is stopped after the duration is about 10 s;
c) the fly ash constant-speed sampling system 2 extracts air from the flue gas for 30-60s to obtain 1-2g of fly ash samples in a small cyclone separator 2-2;
d) the blanking valve 2-11 is opened, and the fly ash of the cyclone separator 2-2 falls on the lower tray 3-3;
e) the expansion piece 3-1 moves the tray 3-3 to pass below the scraper 4-1, the upper surface of the fly ash sample is scraped to be flat by the scraper 4-1, and scattered fly ash still falls into the hopper 4-3;
f) the tray 3-3 is continuously moved to an observation position, and the optical camera 3-5 automatically focuses the fly ash sample on the tray 3-3 under the illumination of the upper light source 3-6, shoots a general picture image and transmits the general picture image to the computer 3-10;
g) the upper light source 3-6 is turned off, the lower light source 3-8 is turned on, and the micro-camera 3-7 automatically focuses the fly ash sample on the tray 3-3 under the illumination of the lower light source 3-8, shoots a micro-image and transmits the micro-image to the computer 3-10;
h) the computer 3-10 analyzes, processes and identifies the fly ash images obtained in the steps f and g to obtain fly ash characteristic parameters;
i) the computer 3-10 obtains the boiler combustion characteristic parameters (coal quality, coal powder fineness, furnace temperature, oxygen content of smoke exhaust, boiler evaporation capacity and the like) from a plant-level information system;
j) after the above steps are completed, (a-i) is repeated again at an interval of 3 mins;
k) collecting the fly ash accumulated in the hopper 4-3 at intervals of 15mins, carrying out routine test on the carbon content of the fly ash, and inputting the obtained carbon content data of the fly ash into a computer 3-10 (the step belongs to an unnecessary calibration stage);
l) the computer 3-10 obtains the routine test data of the carbon content of the fly ash from the plant-level information system;
m) learning and improving of the model: the computer 3-10 takes the fly ash characteristic parameter (obtained in step h), the boiler combustion characteristic parameter (obtained in step i) and the actual value of the fly ash carbon content (obtained in step k and/or step l) which are gradually accumulated along with the time as known parameters, and performs supervised learning based on machine learning means such as a neural network and the like to establish or improve a correlation model of the fly ash characteristic parameter, the boiler combustion characteristic parameter → the fly ash carbon content;
n) prediction (calculation) of fly ash carbon content: the computer 3-10 takes the fly ash characteristic parameter (obtained in the step h) and the boiler combustion characteristic parameter (obtained in the step i) as input parameters of the correlation model established in the step m, predicts the carbon content of the fly ash and outputs a numerical value;
o) in the step m, the unit is required to operate for more than hundreds of hours to complete the learning process, the prediction precision reaches a higher level, and then a self-learning and continuous improvement stage of the model is entered (at this time, the long-short term memory LSTM neural network can be adopted for machine learning);
p) in steps m and n, boiler combustion characteristic parameters are not necessary;
q) step k is not necessary, but step k can speed up the establishment of the correlation model, without step k, a longer learning process (step o) is required to establish the correlation model.
Example 2.
The fly ash carbon content online measuring device in the embodiment comprises a flue 1, a fly ash constant-speed sampling system 2, an image shooting and analyzing system 3 and a fly ash cleaning and collecting system 4, wherein the flue 1 is connected with the fly ash constant-speed sampling system 2, the fly ash cleaning and collecting system 4 is positioned below the fly ash constant-speed sampling system 2, and the fly ash constant-speed sampling system 2 is matched with the image shooting and analyzing system 3.
The fly ash constant-speed sampling system 2 in the embodiment comprises a sampling gun 2-1, a cyclone separator 2-2, an ejector 2-4, a flow controller 2-7, an exhaust pipe 2-8, an anti-blocking type backrest pipe 2-9, a differential pressure transmitter 2-10 and a blanking valve 2-11, wherein one end of the sampling gun 2-1 is positioned in a flue 1, the other end of the sampling gun 2-1 is connected with the cyclone separator 2-2, a blanking valve 2-11 is arranged at the lower end of the cyclone separator 2-2, the upper end of the cyclone separator 2-2 is connected with the ejector 2-4, the ejector 2-4 is connected with the flow controller 2-7, one end of the exhaust pipe 2-8 is connected with the flow controller 2-7, and the other end of the exhaust pipe 2-8 is positioned in the flue 1, the differential pressure transmitter 2-10 is connected with the flow controller 2-7, one end of the anti-blocking type backrest pipe 2-9 is connected with the differential pressure transmitter 2-10, and the other end of the anti-blocking type backrest pipe 2-9 is positioned in the flue 1.
The ejector 2-4 in the embodiment comprises a compressed air source 2-3, an ejector body 2-5 and a compressed air jet flow spray pipe 2-6, the upper end of the cyclone separator 2-2 is connected with the ejector body 2-5, the ejector body 2-5 is connected with a flow controller 2-7, and the ejector body 2-5 is connected with the compressed air source 2-3 through the compressed air jet flow spray pipe 2-6.
The image shooting and analyzing system 3 in the embodiment comprises a telescopic device 3-1, a telescopic rod 3-2, a tray 3-3, a standard color plate 3-4, an optical camera 3-5, an upper light source 3-6, a microscopic camera 3-7, a lower light source 3-8, a data cable 3-9, a computer 3-10 and a workbench 3-11, wherein the telescopic rod 3-2 is installed on the telescopic device 3-1, the tray 3-3 is connected with the telescopic rod 3-2, the tray 3-3 is matched with the workbench 3-11, the standard color plate 3-4 is arranged on the workbench 3-11, the optical camera 3-5 and the microscopic camera 3-7 are respectively positioned above and below the workbench 3-11, the optical camera 3-5 and the microscopic camera 3-7 are both connected with the computer 3-10, normally, the upper light source 3-6 and the lower light source 3-8 are located above and below the work table 3-11, respectively, and the optical camera 3-5 and the microscopic camera 3-7 are connected to the computer 3-10 through the data cable 3-9.
The fly ash cleaning and collecting system 4 in the embodiment comprises a scraper 4-1, a compressed air blowing device 4-2 and a hopper 4-3, wherein the hopper 4-3 is positioned below a blanking valve 2-11, the scraper 4-1 and the compressed air blowing device 4-2 are matched with a tray 3-3, and the tray 3-3 is a transparent carrier.
The measuring method of the fly ash carbon content online measuring device in the embodiment comprises the following steps:
a) collecting a fly ash sample from the flue gas by constant-speed sampling, and preprocessing the fly ash sample to form a fly ash sample with a smooth surface on a transparent tray;
b) respectively obtaining a general picture image and a microscopic image of the fly ash sample by adopting conventional photographing and microscopic photographing;
c) analyzing, processing and identifying the fly ash image based on technologies such as image detection, machine vision and the like to obtain characteristic parameters of the fly ash;
d) acquiring characteristic parameters of boiler combustion from a unit operation database;
e) acquiring a carbon content measurement value of the fly ash samples in the same batch by adopting a conventional assay means, calibrating the characteristic parameters acquired in the steps c and d, and establishing a correlation model of the characteristic parameters and the carbon content of the fly ash;
f) acquiring a carbon content measured value of a fly ash sample from routine tests of a unit, and establishing a correlation model between the measured value and characteristic parameters (fly ash characteristic parameters and boiler combustion characteristic parameters) by means of machine learning and the like;
g) and (4) measuring and calculating the carbon content of the fly ash by using the correlation model established by e) or f) and taking the characteristic parameters of the steps c) and d) as model input, thereby realizing online measurement.
The working principle is as follows: sampling fly ash at constant speed, acquiring fly ash samples, shooting and storing fly ash images (including conventional images and microscopic images), acquiring fly ash images and morphological characteristics based on an image detection technology and a machine vision technology, and establishing, self-learning and actually measuring and calculating a fly ash carbon content prediction model based on a machine learning algorithm.
The fly ash constant-speed sampling system 2 utilizes a sampling gun 2-1 to extract smoke gas flow from a flue 1 at a constant speed, fly ash in the gas flow is separated by a cyclone separator 2-2, exhaust gas is returned to the flue 1 through an exhaust pipe 2-8, and gas flow of the fly ash constant-speed sampling system 2 is provided by an ejector 2-4 (the ejector 2-4 consists of a compressed air source 2-3, an ejector body 2-5 and a compressed air jet nozzle 2-6); the flue gas flow rate is measured by a flue gas flow rate measuring device consisting of anti-blocking type backrest pipes 2-9 and a differential pressure transmitter 2-10 and is transmitted to a flow controller 2-7 by an electrical signal.
The collected fly ash is collected at the lower part of the cyclone separator 2-2, the blanking valve 2-11 can control the blanking of the fly ash to make the fly ash fall on the tray 3-3 to form a material pile, then the tray 3-3 is controlled by the expansion device 3-1 to translate and pass under the scraper 4-1, the lower edge of the scraper 4-1 is parallel and slightly higher than the tray 3-3, when the fly ash passes under the tray 3-3, the material pile is scraped by the scraper 4-1 to form a flat upper surface, then the tray 3-3 continues to translate to the lower part of the optical camera 3-5 of the observation area and the upper part of the microscopic camera 3-7; the tray 3-3 is made of transparent material, and is respectively used for shooting general picture images by an optical camera 3-5 under the irradiation of an upper light source 3-6 and shooting microscopic images by a microscopic camera 3-7 under the irradiation of a lower light source 3-8, and the images are transmitted to a computer 3-10 for storage, processing and analysis by a data cable 3-9; and finally, the tray 3-3 is translated to the position below the blanking valve 2-11 again under the control of the expansion piece 3-1, before secondary blanking, the upper surface and the lower surface of the tray 3-3 are swept by the compressed air sweeping device 4-2 to remove fly ash on the surface, and the process is repeated to realize continuous automatic measurement.
The hopper 4-3 is used for collecting fly ash scattered during blanking, scraping and blowing.
The photographing of the optical camera 3-5 and the microscopic camera 3-7 is controlled by the computer 3-10, and both have an automatic focusing function.
The standard color plate 3-4 is used for white balance correction of the image taken by the optical camera 3-5.
The preferred magnification of the microscopic camera 3-7 is 5x-500 x.
As an alternative scheme, the ejector 2-4 can be replaced by a centrifugal fan or a vacuum pump, and the anti-blocking type backrest pipe 2-9 can be replaced by a hot wire type anemometer or a double-venturi anemometer.
The process of processing, analyzing and measuring the carbon content of the profile image and the microscopic image by the computer 3-10 and the internal algorithm is as follows:
1. obtaining fly ash samples
2. Obtaining characteristic parameters of fly ash samples based on image recognition
2.1, carrying out image analysis on the general picture image to obtain gray level parameters
2.2, carrying out image recognition and analysis on the microscopic images to obtain the morphological characteristic parameters (particle size, slenderness ratio, color and the like) of a certain amount of monomer fly ash particles
3. Obtaining the accurate value of the carbon content of the fly ash
3.1, acquiring the carbon content (called as a calibration reference value) of the fly ash in the same batch in the step 2 based on a traditional standard measurement method of the carbon content of the fly ash, wherein the step is only carried out when the correlation between the carbon content of the fly ash and the characteristic parameters needs to be specially calibrated;
3.2, acquiring a plant-level reference value of the carbon content of the fly ash from a daily routine test of the boiler (the test of the carbon content of the fly ash is routine operation, but the test frequency is not high);
3.3, inputting the calibration reference value and the plant-level reference value of the carbon content of the fly ash, the fly ash characteristic parameters obtained in the step 2 and boiler combustion characteristic parameter (coal quality, coal powder fineness, furnace temperature, oxygen content of discharged smoke, boiler evaporation capacity and the like) data obtained from a plant-level information system (such as a plant-level information monitoring system (SIS)) into a database of a machine learning algorithm of the computer 3-10;
4. repeating the steps 1, 2 and 3 under different working conditions such as coal quality, coal powder fineness, combustion condition and the like, and establishing a database with enough samples and covering enough working conditions
5. Establishing a correlation model of the fly ash carbon content and the characteristic parameters through machine learning and other related algorithms:
5.1, establishing a correlation model based on the calibration reference value of the carbon content of the fly ash, the fly ash characteristic parameters and the boiler combustion characteristic parameters (the step is only carried out in a calibration stage);
5.2, establishing a correlation model based on the plant-level reference value of the carbon content of the fly ash, the fly ash characteristic parameters and the boiler combustion characteristic parameters, wherein the step can be continuously carried out, so that the learning process is continuous, the established correlation model can be adjusted and improved along with the change of the actual running condition, and a long-short term memory (LSTM) neural network can be adopted for machine learning in the step;
5.3, description: 5.1, 5.2, but is beneficial for improving the scientificity and accuracy of the correlation model, and the steps 5.1 and 5.2 can be carried out independently;
6. and (3) actual measurement stage:
6.1, sampling the fly ash on line according to the step 1, and obtaining the characteristic parameters of the specific fly ash sample through the step 2
6.2, acquiring the boiler combustion characteristic parameters through a plant-level information system
6.3, inputting the characteristic parameters obtained in the steps 6.1 and 6.2 into the association model established in the step 5, and calculating to obtain the carbon content of the fly ash sample
7. Continuously improving and correcting the association model established in the step 5 through measurement (step 6) and feedback (step 3):
7.1, recording the calculated value of the carbon content of the fly ash obtained in the step 6
7.2 obtaining the plant-level reference value of the carbon content of the fly ash from the daily routine test which is closest to the boiler operation condition and parameter during the collection of the fly ash sample and has the smallest time interval as possible
The deviation of the carbon content (i.e., [ measured value-plant-level reference value ]) obtained at 7.1 and 7.2 is used as an error and fed back to step 5 to correct and improve the parameters of the algorithm and the correlation model.
The device is placed outside a downstream flue of an air preheater, fly ash is collected from flue gas at the outlet of the air preheater, trays 3-3 are rectangular flat glass made of colorless transparent wear-resistant glass, and the method comprises the following steps:
a) the expansion piece 3-1 controls the tray 3-3 to move to the lower part of the blanking valve 2-11 (the blanking valve 2-11 is in a closed state at the moment);
b) starting a compressed air blowing device 4-2 to blow the upper surface and the lower surface of the tray 3-3, wherein blown fly ash falls into a hopper 4-3, the blowing strength is gradually increased from low to high, and the blowing is stopped after the duration is about 10 s;
c) the fly ash constant-speed sampling system 2 extracts air from the flue gas for 30-60s to obtain 1-2g of fly ash samples in a small cyclone separator 2-2;
d) the blanking valve 2-11 is opened, and the fly ash of the cyclone separator 2-2 falls on the lower tray 3-3;
e) the expansion piece 3-1 moves the tray 3-3 to pass below the scraper 4-1, the upper surface of the fly ash sample is scraped to be flat by the scraper 4-1, and scattered fly ash still falls into the hopper 4-3;
f) the tray 3-3 is continuously moved to an observation position, and the optical camera 3-5 automatically focuses the fly ash sample on the tray 3-3 under the illumination of the upper light source 3-6, shoots a general picture image and transmits the general picture image to the computer 3-10;
g) the upper light source 3-6 is turned off, the lower light source 3-8 is turned on, and the micro-camera 3-7 automatically focuses the fly ash sample on the tray 3-3 under the illumination of the lower light source 3-8, shoots a micro-image and transmits the micro-image to the computer 3-10;
h) the computer 3-10 analyzes, processes and identifies the fly ash images obtained in the steps f and g to obtain fly ash characteristic parameters;
i) the computer 3-10 obtains the boiler combustion characteristic parameters (coal quality, coal powder fineness, furnace temperature, oxygen content of smoke exhaust, boiler evaporation capacity and the like) from a plant-level information system;
j) after the above steps are completed, (a-i) is repeated again at an interval of 3 mins;
k) collecting the fly ash accumulated in the hopper 4-3 at intervals of 15mins, carrying out routine test on the carbon content of the fly ash, and inputting the obtained carbon content data of the fly ash into a computer 3-10 (the step belongs to an unnecessary calibration stage);
l) the computer 3-10 obtains the routine test data of the carbon content of the fly ash from the plant-level information system;
m) learning and improving of the model: the computer 3-10 takes the fly ash characteristic parameter (obtained in step h), the boiler combustion characteristic parameter (obtained in step i) and the actual value of the fly ash carbon content (obtained in step k and/or step l) which are gradually accumulated along with the time as known parameters, and performs supervised learning based on machine learning means such as a neural network and the like to establish or improve a correlation model of the fly ash characteristic parameter, the boiler combustion characteristic parameter → the fly ash carbon content;
n) prediction (calculation) of fly ash carbon content: the computer 3-10 takes the fly ash characteristic parameter (obtained in the step h) and the boiler combustion characteristic parameter (obtained in the step i) as input parameters of the correlation model established in the step m, predicts the carbon content of the fly ash and outputs a numerical value;
o) in the step m, the unit is required to operate for more than hundreds of hours to complete the learning process, the prediction precision reaches a higher level, and then a self-learning and continuous improvement stage of the model is entered (at this time, the long-short term memory LSTM neural network can be adopted for machine learning);
p) in steps m and n, boiler combustion characteristic parameters are not necessary;
q) step k is not necessary, but step k can speed up the establishment of the correlation model, without step k, a longer learning process (step o) is required to establish the correlation model.
In addition, it should be noted that the specific embodiments described in the present specification may be different in the components, the shapes of the components, the names of the components, and the like, and the above description is only an illustration of the structure of the present invention. Equivalent or simple changes in the structure, characteristics and principles of the invention are included in the protection scope of the patent. Various modifications, additions and substitutions for the specific embodiments described may be made by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
Claims (8)
1. The on-line measuring device for the carbon content of the fly ash comprises a flue (1), a fly ash constant-speed sampling system (2), an image shooting and analyzing system (3) and a fly ash cleaning and collecting system (4), wherein the flue (1) is connected with the fly ash constant-speed sampling system (2), the fly ash cleaning and collecting system (4) is positioned below the fly ash constant-speed sampling system (2), the fly ash constant-speed sampling system (2) is matched with the image shooting and analyzing system (3), and the on-line measuring device is characterized in that: the fly ash constant-speed sampling system (2) comprises a sampling gun (2-1), a cyclone separator (2-2), an ejector (2-4), a flow controller (2-7), an exhaust pipe (2-8), an anti-blocking backrest pipe (2-9), a differential pressure transmitter (2-10) and a blanking valve (2-11), one end of the sampling gun (2-1) is located in a flue (1), the other end of the sampling gun (2-1) is connected with the cyclone separator (2-2), the blanking valve (2-11) is installed at the lower end of the cyclone separator (2-2), the upper end of the cyclone separator (2-2) is connected with the ejector (2-4), and the ejector (2-4) is connected with the flow controller (2-7), one end of exhaust pipe (2-8) is connected with flow controller (2-7), the other end of exhaust pipe (2-8) is located flue (1), differential pressure transmitter (2-10) is connected with flow controller (2-7), the one end of anti-blocking type back pipe (2-9) is connected with differential pressure transmitter (2-10), the other end of anti-blocking type back pipe (2-9) is located flue (1).
2. The fly ash carbon content on-line measuring device of claim 1, characterized in that: the ejector (2-4) comprises a compressed air source (2-3), an ejector body (2-5) and a compressed air jet flow spray pipe (2-6), the upper end of the cyclone separator (2-2) is connected with the ejector body (2-5), the ejector body (2-5) is connected with the flow controller (2-7), and the ejector body (2-5) is connected with the compressed air source (2-3) through the compressed air jet flow spray pipe (2-6).
3. The fly ash carbon content on-line measuring device of claim 1, characterized in that: the image shooting analysis system (3) comprises a telescopic device (3-1), a telescopic rod (3-2), a tray (3-3), a standard color plate (3-4), an optical camera (3-5), a micro-camera (3-7), a computer (3-10) and a workbench (3-11), wherein the telescopic rod (3-2) is installed on the telescopic device (3-1), the tray (3-3) is connected with the telescopic rod (3-2), the tray (3-3) is matched with the workbench (3-11), the standard color plate (3-4) is arranged on the workbench (3-11), the optical camera (3-5) and the micro-camera (3-7) are respectively positioned above and below the workbench (3-11), and the optical camera (3-5) and the micro-camera (3-7) are both matched with the computer (3- 10) And (4) connecting.
4. The fly ash carbon content on-line measuring device of claim 3, characterized in that: the image shooting and analyzing system (3) further comprises an upper light source (3-6), a lower light source (3-8) and a data cable (3-9), the upper light source (3-6) and the lower light source (3-8) are respectively located above and below the workbench (3-11), and the optical camera (3-5) and the micro-camera (3-7) are connected with the computer (3-10) through the data cable (3-9).
5. The fly ash carbon content on-line measuring device of claim 1, characterized in that: the fly ash cleaning and collecting system (4) comprises a scraper (4-1), a compressed air purging device (4-2) and a hopper (4-3), wherein the hopper (4-3) is positioned below a blanking valve (2-11), and the scraper (4-1) and the compressed air purging device (4-2) are matched with a tray (3-3).
6. The fly ash carbon content on-line measuring device of claim 3, characterized in that: the tray (3-3) is a transparent carrier.
7. A measurement method based on the fly ash carbon content online measurement device of any one of claims 1 to 6 is characterized in that: the measuring method comprises the following steps:
a) collecting a fly ash sample from the flue gas by constant-speed sampling, and preprocessing the fly ash sample to form a fly ash sample with a smooth surface on a transparent tray;
b) respectively obtaining a general picture image and a microscopic image of the fly ash sample by adopting conventional photographing and microscopic photographing;
c) analyzing, processing and identifying the fly ash image based on image detection and machine vision technology to obtain characteristic parameters of the fly ash;
d) acquiring characteristic parameters of boiler combustion from a unit operation database;
f) acquiring a carbon content measured value of a fly ash sample from routine tests of a unit, and establishing a correlation model of the measured value and characteristic parameters by a machine learning means;
g) and c) measuring and calculating the carbon content of the fly ash by using the correlation model established in the f) and taking the characteristic parameters in the steps c) and d) as model input, thereby realizing online measurement.
8. The method for measuring the fly ash carbon content online measuring device according to claim 7, characterized in that: the measuring method further comprises a step e) between steps d) and f),
e) acquiring a carbon content measurement value of the fly ash samples in the same batch by adopting a conventional assay means, calibrating the characteristic parameters acquired in the steps c and d, and establishing a correlation model of the characteristic parameters and the carbon content of the fly ash;
g) and (4) measuring and calculating the carbon content of the fly ash by using the correlation model established by e) or f) and taking the characteristic parameters of the steps c) and d) as model input, thereby realizing online measurement.
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| CN114965164A (en) * | 2022-06-02 | 2022-08-30 | 连云港同顺电力节能设备有限公司 | Power production fly ash monitoring system, method and storage medium |
| CN115683751A (en) * | 2022-10-31 | 2023-02-03 | 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) | Device and method for synchronizing fly ash sampling and rapid detection of fly ash conveying system |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1992008965A1 (en) * | 1990-11-13 | 1992-05-29 | Powergen Plc | Method and apparatus for measurement of carbon in ash |
| DE10211410A1 (en) * | 2002-03-15 | 2003-10-02 | Rag Ag | Determination of the slag content in coal by passing a sample through a measurement path in which both particle size distribution and average gray value of reflected light are determined and then used to determine slag content |
| CN1542432A (en) * | 2003-11-06 | 2004-11-03 | 华北电力大学 | Method for measuring carbon content in coal-burning boiler and kiln fly ash and on line detection apparatus therefor |
| CN201965095U (en) * | 2011-01-06 | 2011-09-07 | 长沙市亿泰科技有限公司 | Online monitoring device for carbon content in fly ash |
| CN102778538A (en) * | 2012-07-06 | 2012-11-14 | 广东电网公司电力科学研究院 | Soft measuring method based on improved SVM (Support Vector Machine) for measuring boiler unburned carbon content in fly ash |
| CN202886143U (en) * | 2012-11-26 | 2013-04-17 | 山东电力集团公司电力科学研究院 | Permanent flying ash sampling device of coal-fired boiler |
| CN103822880A (en) * | 2014-02-26 | 2014-05-28 | 清华大学 | Method and system for measuring fly ash carbon content |
| CN204202951U (en) * | 2014-11-13 | 2015-03-11 | 东方电气集团东方锅炉股份有限公司 | Online constant speed flyash sampler |
| CN104502284A (en) * | 2014-12-16 | 2015-04-08 | 西安西热锅炉环保工程有限公司 | Online detection device for carbon content in flue dust of coal fired power plant |
| CN107133460A (en) * | 2017-04-26 | 2017-09-05 | 中国能源建设集团广东省电力设计研究院有限公司 | A kind of online dynamic prediction method of boiler flyash carbon content |
| CN107356598A (en) * | 2017-06-08 | 2017-11-17 | 中国计量大学 | Ash content carbon online test method based on digital image processing techniques |
| CN211927711U (en) * | 2019-12-03 | 2020-11-13 | 华电电力科学研究院有限公司 | Fly ash carbon content on-line measuring device |
-
2019
- 2019-12-03 CN CN201911218193.7A patent/CN110954536B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1992008965A1 (en) * | 1990-11-13 | 1992-05-29 | Powergen Plc | Method and apparatus for measurement of carbon in ash |
| DE10211410A1 (en) * | 2002-03-15 | 2003-10-02 | Rag Ag | Determination of the slag content in coal by passing a sample through a measurement path in which both particle size distribution and average gray value of reflected light are determined and then used to determine slag content |
| CN1542432A (en) * | 2003-11-06 | 2004-11-03 | 华北电力大学 | Method for measuring carbon content in coal-burning boiler and kiln fly ash and on line detection apparatus therefor |
| CN201965095U (en) * | 2011-01-06 | 2011-09-07 | 长沙市亿泰科技有限公司 | Online monitoring device for carbon content in fly ash |
| CN102778538A (en) * | 2012-07-06 | 2012-11-14 | 广东电网公司电力科学研究院 | Soft measuring method based on improved SVM (Support Vector Machine) for measuring boiler unburned carbon content in fly ash |
| CN202886143U (en) * | 2012-11-26 | 2013-04-17 | 山东电力集团公司电力科学研究院 | Permanent flying ash sampling device of coal-fired boiler |
| CN103822880A (en) * | 2014-02-26 | 2014-05-28 | 清华大学 | Method and system for measuring fly ash carbon content |
| CN204202951U (en) * | 2014-11-13 | 2015-03-11 | 东方电气集团东方锅炉股份有限公司 | Online constant speed flyash sampler |
| CN104502284A (en) * | 2014-12-16 | 2015-04-08 | 西安西热锅炉环保工程有限公司 | Online detection device for carbon content in flue dust of coal fired power plant |
| CN107133460A (en) * | 2017-04-26 | 2017-09-05 | 中国能源建设集团广东省电力设计研究院有限公司 | A kind of online dynamic prediction method of boiler flyash carbon content |
| CN107356598A (en) * | 2017-06-08 | 2017-11-17 | 中国计量大学 | Ash content carbon online test method based on digital image processing techniques |
| CN211927711U (en) * | 2019-12-03 | 2020-11-13 | 华电电力科学研究院有限公司 | Fly ash carbon content on-line measuring device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114965164A (en) * | 2022-06-02 | 2022-08-30 | 连云港同顺电力节能设备有限公司 | Power production fly ash monitoring system, method and storage medium |
| CN115683751A (en) * | 2022-10-31 | 2023-02-03 | 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) | Device and method for synchronizing fly ash sampling and rapid detection of fly ash conveying system |
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