CN113222244A - Online boiler combustion optimization method based on flame combustion image judgment - Google Patents
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- 238000002485 combustion reaction Methods 0.000 title claims abstract description 105
- 238000005457 optimization Methods 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000012360 testing method Methods 0.000 claims abstract description 9
- 238000012549 training Methods 0.000 claims abstract description 8
- 238000000926 separation method Methods 0.000 claims description 25
- 238000007781 pre-processing Methods 0.000 claims description 11
- 239000003245 coal Substances 0.000 claims description 9
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 7
- 229910052799 carbon Inorganic materials 0.000 claims description 7
- 239000010881 fly ash Substances 0.000 claims description 5
- 125000001475 halogen functional group Chemical group 0.000 claims description 5
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 3
- 239000000428 dust Substances 0.000 claims description 3
- 229910052760 oxygen Inorganic materials 0.000 claims description 3
- 239000001301 oxygen Substances 0.000 claims description 3
- 239000002893 slag Substances 0.000 claims description 3
- 239000000779 smoke Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 description 5
- 238000001914 filtration Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 150000004767 nitrides Chemical class 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G—PHYSICS
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- G06T2207/20081—Training; Learning
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- G—PHYSICS
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Abstract
An online boiler combustion optimization method based on flame combustion image judgment comprises the following steps: s1: acquiring a flame combustion image and extracting a characteristic value through the flame combustion image, wherein the characteristic value comprises the area ratio of the flame center and the eccentricity of the flame center, and the flame combustion image comprises a training image and a testing image; s2: modeling is carried out aiming at the characteristic values extracted from the training chart to obtain a flame combustion image recognition model; s3: identifying the characteristic values extracted from the test chart by adopting a flame combustion image identification model to obtain a model identification result; s4: and optimizing and adjusting through a combustion optimization model according to the model identification result. The invention reduces the influence of subjective factors and experience and improves the real-time performance and reliability of boiler combustion optimization.
Description
Technical Field
The invention relates to the field of boiler combustion optimization, in particular to an online boiler combustion optimization method based on flame combustion image judgment.
Background
At present, boiler combustion optimization for thermal power generation has become a key research field of intelligent power plants and is applied to a certain extent. However, the combustion effect obtained through the sensor data is acquired from the end of the whole process, and the combustion effect in the hearth cannot be reflected in real time, so that the whole combustion optimization control is triggered, and therefore accurate and real-time online optimization is difficult to achieve, and the optimization effect cannot reach the expected level.
In the conventional boiler combustion control, an experienced engineer often observes the condition of flame combustion in a furnace through deploying in a furnace video monitoring device, so as to judge whether control parameters need to be adjusted. However, in general, engineers have difficulty in watching video observation at all times, and the judgment of the combustion state depends on subjective factors and experiences of different engineers in different teams and groups.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an online boiler combustion optimization method based on flame combustion image judgment, so that the influence of subjective factors and experience is reduced, and the instantaneity and reliability of boiler combustion optimization are improved.
The purpose of the invention is realized by the following technical scheme:
an online boiler combustion optimization method based on flame combustion image judgment comprises the following steps:
s1: acquiring a flame combustion image and extracting a characteristic value through the flame combustion image, wherein the characteristic value comprises the area ratio of the flame center and the eccentricity of the flame center, and the flame combustion image comprises a training image and a testing image;
s2: modeling is carried out aiming at the characteristic values extracted from the training chart to obtain a flame combustion image recognition model;
s3: identifying the characteristic values extracted from the test chart by adopting a flame combustion image identification model to obtain a model identification result;
s4: and optimizing and adjusting through a combustion optimization model according to the model identification result.
Further, the step S1 includes the following sub-steps:
s101: collecting a flame combustion image;
s102: extracting a characteristic value of the flame combustion image;
s103: preprocessing the flame combustion image;
s104: carrying out channel separation on the preprocessed flame combustion image and extracting to obtain a single channel image;
s105: converting the single channel image into a binary image;
s106: calculating the central area ratio of the flame through the binary image;
s107: calculating the eccentricity of the flame center through the binary image;
s108: and combining the area ratio of the flame center and the eccentricity of the flame center into a characteristic value vector.
Further, the channel separation method in step S104 is one of RGB channel separation, HSV channel separation, YcbCr channel separation, and L a b channel separation.
Further, the channel separation method in step S104 is RGB channel separation, and the single-channel image is a B-channel grayscale image.
Further, in step S105, a threshold is set to perform conversion from the mono-channel image to the binary image.
Further, the threshold is a multilayer fixed threshold T, and the extracted central area of the flame is the central area of the multilayer flame; the central area ratio of the flame is the central area ratio of the multilayer flame.
Further, in the step S103, the flame combustion image is subjected to preprocessing for denoising and suppressing an over-bright halo background.
Further, the eccentricity in step S107 is the distance between the flame center and the furnace center.
Further, the step S4 includes the following sub-steps:
s401: extracting input data and output data of the combustion optimization model, and preprocessing the input data and the output data;
s402: establishing a combustion optimization model according to input data and output data;
s403: and optimizing the input parameters through the combustion optimization model according to the model identification result.
Further, the input data comprises load, oxygen amount of a hearth outlet, primary air speed, air direction pressure difference, secondary air door opening, over-fire air, coal quality and coal feeding amount of a coal mill;
the output data comprises the carbon content of fly ash, the carbon content of slag, a furnace outlet Nox, a furnace outlet SO2 and the smoke dust content of the furnace outlet.
The invention has the beneficial effects that:
the influence of subjective factors and experience is reduced, and the real-time performance and the reliability of boiler combustion optimization are improved.
Drawings
FIG. 1 is a diagram illustrating a hardware system according to the present invention;
FIG. 2 is a schematic view of the present invention;
FIG. 3 is a schematic diagram of the differences in central flame area when different threshold values are selected;
FIG. 4 is a schematic illustration of eccentricity.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
The first embodiment is as follows:
as shown in fig. 1 to 4, an online boiler combustion optimization method based on flame combustion image judgment includes the following steps:
s1: acquiring a flame combustion image and extracting a characteristic value through the flame combustion image, wherein the characteristic value comprises the area ratio of the flame center and the eccentricity of the flame center, and the flame combustion image comprises a training image and a testing image;
s101: collecting a flame combustion image;
s102: extracting a characteristic value of the flame combustion image;
s103: preprocessing the flame combustion image;
in the step S103, the flame combustion image is subjected to pre-processing of denoising and suppressing an over-bright halo background;
since the actual arrangement position of the camera for acquiring the flame combustion image is far away from the flame, a large amount of image noise (such as fly ash) and an excessively bright halo background occur, so that preprocessing for denoising and suppressing the excessively bright halo background is performed. The preprocessing method comprises Gaussian filtering, mid-pass filtering, mean value filtering and the like.
S104: carrying out channel separation on the preprocessed flame combustion image and extracting to obtain a single channel image;
the method of channel separation in step S104 is one of RGB channel separation, HSV channel separation, YcbCr channel separation, and L a b channel separation.
By comparing the four methods, the RGB channel separation works best for center flame identification.
The channel separation method in step S104 is RGB channel separation, and the single channel image is a B-channel grayscale image.
S105: converting the single channel image into a binary image;
in step S105, a threshold is set to convert the mono-channel image into a binary image.
The size of the threshold determines the central area of the flame, and the larger the threshold, the smaller the central area of the flame, and the smaller the threshold, the larger the central area of the flame.
The threshold is a multilayer fixed threshold T, and the extracted central area of the flame is the central area of the multilayer flame.
The flame temperature at multiple locations can be qualitatively reflected by the multilayer flame center area.
Because the camera and the acquisition card may be different, the selection of the multilayer fixed threshold value T selects the appropriate threshold value of each layer by a repeated test mode. For convenience of explanation, two fixed thresholds, 0.8 × 255 and 0.9 × 255, are taken as examples, and fig. 3 shows the difference of the central flame area when different thresholds are taken.
S106: calculating the central area ratio of the flame through the binary image;
and if the threshold is a multilayer fixed threshold T, the ratio of the central area of the flame is the ratio of the central area of the multilayer flame.
Calculating flame center area ratio R by black and white pixels1…Rn. A larger ratio of the area of the flame kernel indicates a better combustion effect.
S107: calculating the eccentricity of the flame center through the binary image;
the eccentricity is the distance H between the flame center and the hearth center.
Smaller eccentricity indicates better air distribution, whereas air distribution may be less optimal. Further, the larger the eccentricity is, the lower the cooling wall efficiency and the shorter the useful life is.
When the position of the flame center is calculated, the center of the minimum circumscribed rectangle of the innermost flame profile is adopted, and the innermost flame profile refers to the profile of the innermost flame in the multilayer flame obtained when the multilayer fixed threshold is adopted. Because the innermost flame pixel performs most obviously, the judgment is stable.
As shown in FIG. 4, point A is the center of the minimum circumscribed rectangle of the innermost flame profile, point B is the center of the furnace, and the distance from A to B is the eccentricity H.
S108: combining the area ratio of the flame center and the eccentricity of the flame center into a characteristic value vector;
s2: modeling is carried out aiming at the characteristic values extracted from the training chart to obtain a flame combustion image recognition model;
the flame combustion image recognition model is built according to a manual recognition result, the manual recognition result is a result of manually marking the collected flame combustion image through subjective judgment of an experienced engineer, and an expert judgment system is formed.
The manual identification result comprises optimization and optimization-free, and a conclusion whether the optimization is started or not is given according to the standard.
The flame burning image for manual identification is representative and has a certain number of scales.
An SVM algorithm is adopted for modeling, and the flame combustion image recognition model is mainly used for carrying out classification on flame combustion images which need optimization and do not need optimization.
S3: identifying the characteristic values extracted from the test chart by adopting a flame combustion image identification model to obtain a model identification result;
the model identification result comprises optimization required and optimization not required.
S4: and optimizing and adjusting through a combustion optimization model according to the model identification result.
S401: extracting input data and output data of the combustion optimization model, and preprocessing the input data and the output data;
the input data comprises load, oxygen quantity at a hearth outlet, primary air speed, wind direction pressure difference, secondary air door opening, over-fire air, coal quality and coal feeding quantity of a coal mill;
the output data comprises the carbon content of fly ash, the carbon content of large slag, a hearth outlet Nox, a hearth outlet SO2 and the smoke dust content of the hearth outlet;
if the input data and the output data are missing, performing median filling on the missing data. This initializes the parameters of the combustion optimization model.
Input data and output data are extracted from the DCS system.
S402: establishing a combustion optimization model according to input data and output data;
the modeling is performed based on a machine learning algorithm,
the machine learning algorithm includes one or more of SVM, neural network, gaussian process.
And optimizing the hyper-parameters of the combustion optimization model through an evolutionary computation framework to obtain a better model.
S403: and optimizing the input parameters through the combustion optimization model according to the model identification result.
And optimizing the adjustable input parameters of the DCS based on the evolutionary computation framework.
The fly ash predicted by the combustion optimization model is optimized with the aim of containing carbon or nitride to obtain an optimized combustion scheme.
The intelligent video technology and the combustion parameter optimization technology are combined, the problems existing in the traditional optimization and the manual observation are solved, whether optimization is needed or not is judged through image recognition, the influence of subjective factors and experience is reduced, and the instantaneity and the reliability of boiler combustion optimization are improved.
The above-mentioned embodiments only express the specific embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (10)
1. An online boiler combustion optimization method based on flame combustion image judgment is characterized by comprising the following steps: the method comprises the following steps:
s1: acquiring a flame combustion image and extracting a characteristic value through the flame combustion image, wherein the characteristic value comprises the area ratio of the flame center and the eccentricity of the flame center, and the flame combustion image comprises a training image and a testing image;
s2: modeling is carried out aiming at the characteristic values extracted from the training chart to obtain a flame combustion image recognition model;
s3: identifying the characteristic values extracted from the test chart by adopting a flame combustion image identification model to obtain a model identification result;
s4: and optimizing and adjusting through a combustion optimization model according to the model identification result.
2. The on-line boiler combustion optimization method based on flame combustion image judgment as claimed in claim 1, wherein: the step S1 includes the following sub-steps:
s101: collecting a flame combustion image;
s102: extracting a characteristic value of the flame combustion image;
s103: preprocessing the flame combustion image;
s104: carrying out channel separation on the preprocessed flame combustion image and extracting to obtain a single channel image;
s105: converting the single channel image into a binary image;
s106: calculating the central area ratio of the flame through the binary image;
s107: calculating the eccentricity of the flame center through the binary image;
s108: and combining the area ratio of the flame center and the eccentricity of the flame center into a characteristic value vector.
3. The on-line boiler combustion optimization method based on flame combustion image judgment as claimed in claim 2, wherein: the method of channel separation in step S104 is one of RGB channel separation, HSV channel separation, YcbCr channel separation, and L a b channel separation.
4. The on-line boiler combustion optimization method based on flame combustion image judgment as claimed in claim 3, wherein: the channel separation method in step S104 is RGB channel separation, and the single channel image is a B-channel grayscale image.
5. The on-line boiler combustion optimization method based on flame combustion image judgment as claimed in claim 2, wherein: in step S105, a threshold is set to convert the mono-channel image into a binary image.
6. The on-line boiler combustion optimization method based on flame combustion image judgment as claimed in claim 5, wherein: the threshold is a multilayer fixed threshold, and the extracted central area of the flame is the central area of the multilayer flame; the central area ratio of the flame is the central area ratio of the multilayer flame.
7. The on-line boiler combustion optimization method based on flame combustion image judgment as claimed in claim 2, wherein: in the step S103, the flame combustion image is subjected to pre-processing of denoising and suppressing an over-bright halo background.
8. The on-line boiler combustion optimization method based on flame combustion image judgment as claimed in claim 2, wherein: the eccentricity in step S107 is the distance between the flame center and the furnace center.
9. The on-line boiler combustion optimization method based on flame combustion image judgment according to claim 1 or 2, characterized in that: the step S4 includes the following sub-steps:
s401: extracting input data and output data of the combustion optimization model, and preprocessing the input data and the output data;
s402: establishing a combustion optimization model according to input data and output data;
s403: and optimizing the input parameters through the combustion optimization model according to the model identification result.
10. The on-line boiler combustion optimization method based on flame combustion image judgment as claimed in claim 9, wherein: the input data comprises load, furnace outlet oxygen quantity, primary air speed, wind direction pressure difference, secondary air door opening, over-fire air, coal quality and coal feeding quantity of a coal mill;
the output data comprises the carbon content of fly ash, the carbon content of slag, a furnace outlet Nox, a furnace outlet SO2 and the smoke dust content of the furnace outlet.
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