CN105912872A - Measurement method of coal dust and in-furnace heat flow mixing effect on the basis of combustion image - Google Patents
Measurement method of coal dust and in-furnace heat flow mixing effect on the basis of combustion image Download PDFInfo
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Abstract
The invention discloses a measurement method of a coal dust and in-furnace heat flow mixing effect on the basis of a combustion image, and belongs to the technical field of thermotechnical measurement and image processing. The method comprises the following steps: firstly, carrying out edge detection on an unburned coal powder area in the combination image through an area first order difference algorithm with a fixed search direction, determining the mixing characteristic area of the coal dust and in-furnace heat flow, and carrying out block processing and gray level stage processing on the area. In the characteristic area, a gray parameter and a gray position distribution parameter which reflect a coal dust and in-furnace heat flow mixing situation are extracted, the gray parameter and the gray position distribution parameter construct a mixing coefficient which measurably evaluates the coal dust and in-furnace heat flow mixing effect, and therefore, the measurement of the coal dust and in-furnace heat flow mixing effect can be realized.
Description
Technical field
The invention belongs to thermal measurement and technical field of image processing, be specifically related to a kind of coal dust based on burning image with
Hot-fluid mixed effect measure in stove.
Background technology
Coal dust mixes the mixing referring to as-fired coal powder with burner hearth hot-fluid medium with hot-fluid in stove, and coal dust mixes with hot-fluid in stove
Effect to combustion position important in stove, the bad harm that will produce following several respects of mixed effect: cannot create
Good catch fire, surely fire condition, affect coal dust firing, increase coal dust firing time；Coal dust is possibly inside burner hearth cannot be complete
After-flame, affects unit economy；When boiler furnace subregion air is very few and during hot-fluid mixed effect difference in coal dust and stove, will
Affect coal dust normal combustion, cause the coal dust cannot be fully oxidized, easily cause Boiler Furnace slagging.Thus can to boiler safety, stable,
Effec-tive Function impacts.
Along with the development of image technique, China's major part firepower unit boiler is assembled with image flame detecting device, for
Furnace flame detection provides fired state visual information, for boiler combustion status based on image information detection and optimizing research
Technology platform is provided.The emphasis of current domestic most image flame study on monitoring and application is sentenced in combustion diagnosis, combustion stability
Not and the aspect such as boiler temperature field measurement.Such as document 1: Sheng Yang, Liu He. furnace flame based on support vector machine fire extinguishing differentiation side
Method research. modern electric, 2007 (2): 66-69. introductions, extract feature according to image information, and combine neutral net and prop up
Holding the methods such as vector machine and can realize the differentiation of the fire extinguishing to boiler combustion, the method has used pattern recognition and artificial intelligence technology,
Have the highest accuracy.Such as document 2: Wu Yiquan, Song Yu, Zhou Huaichun. burning figure based on gray level entropy multi-threshold segmentation and SVM
As state recognition. Proceedings of the CSEE, 2013 (20): 66-73. introductions, utilize gray level entropy multi-threshold segmentation and support
Vector machine method realizes combustion stability and differentiates.Such as document 3: Dai Weibao, Zouping China. rebuild burner hearth based on improved PSO
Cross-section temperature field. Proceedings of the CSEE, 2007 (14): 13-17. introductions, set up nonlinear optimization by burning image
Model, utilizes particle swarm optimization can obtain temperature field.Also just like document 4: Li Xinli, Li Ling, Lu Gang, etc. based on flame freely
The combustion process NO_x emitted smoke of base imaging and support vector machine. Proceedings of the CSEE, 2015 (06): 1413-1419.
Introduce, by flame radical image procossing and flame temperature study on monitoring NOxDischarge, combination supporting vector machine method is carried out
On-line prediction.
Achievement in research for boiler combustion based on burning image monitoring is a lot of at present, but for based on burning image
Coal dust is the most few with the research of hot-fluid troubled water.
Summary of the invention
The invention discloses a kind of coal dust based on burning image and hot-fluid mixed effect measure in stove, the skill of employing
Art scheme is as follows:
By extracting the coal dust firing characteristics of image of flame image sensor acquisition, calculate mixed coefficint, it is achieved mixing effect
Fruit is evaluated, and concretely comprises the following steps:
Step one, divides the unburned district in burning image and combustion zone, is characterized region with combustion zone, and carries out piecemeal
And gray proces；
Step 2, extracts the grey parameter in characteristic area；
Step 3, extracts the grayscale position distributed constant in characteristic area；
Step 4, calculates mixed coefficint according to grey parameter and grayscale position distributed constant.
In step one, determine that the method for characteristic area is: determine that breeze airflow is at burning figure according to flame image sensor
As f (x, y) in the direction of motion and position of center line, and then determine rim detection effective coverage；To edges all in effective coverage
The straight line of the breeze airflow direction of motion searches shade of gray maximum point,Maximum point coordinate (x is organized by thisij,yij) and breeze airflow
The direction of motion, determines coal dust and the characteristic area of hot-fluid mixed effect in stove；Wherein, x, y are image coordinate, f (x, y) be (x,
Y) gray value at place；The determination method of shade of gray maximum point is: Δ f (xk,yk)=f (xk+1,yk+1)-f(xk,yk) be f (x,
Y) single order forward difference, by finding the forward difference of maximum, the shade of gray being in burning image in prescribed direction is
A little louder；
The method that characteristic area carries out piecemeal process is: is divided into by characteristic area according to burning image positional information and having
T × t the continuously arranged subregion of pixel；
The method that characteristic area carries out Gray Classification process is: G (x, y) be (x, y) place's gray value, if
Pixel belongs to the i-th gray level set Gi, wherein i=1,2, L, n.
In step 2, the method extracting the grey parameter in characteristic area is: in characteristic area, selects to belong to the i-th ash
The pixel number m of degree level setiThe ratio a of pixel number m total with regioni=mi/ m is as n dimensional feature vector the i-th component, thus
Build n dimensional feature vector A=(a1,L,ai,L,an)。
Coal dust is poor with hot-fluid mixed effect, and coal dust burns the most in time, then the pixel that gray value is little is many；Coal dust mixes with hot-fluid
Effective, coal dust fully burns, and the pixel that gray value is big is many.Thus, corresponding in n level gray level image, when mixed effect is worst,
During in characteristic area, pixel is distributed in relatively low gray level set；When mixed effect is optimal, in region, pixel concentrates on higher
In gray level set；And when most preferably mixing, the point in region should belong to the n-th gray level set, corresponding n ties up best features
Vector is Ab=(0, L, 0, L, 0,1), A and AbDistance is the nearest, then mixed effect is the best.
But different grey-scale set is different on the impact of mixed effect evaluation.The gray level set that gray value is less
Pixel is the most, and mixed effect is the poorest；The gray level set pixel that gray value is bigger is the most, and mixed effect is the best.In order to
Metrics process embodies this point, needs when tolerance plus weight.Weight is the biggest, and its corresponding grey scale level set is adjusted the distance
Affect the biggest；Distance is the biggest, and mixed effect is the poorest, so the weight of gray level set should be successively decreased successively.Meanwhile, in order to embody ash
The equidistant relation of gray value between degree level set, definitionAs weight vectors.Therefore grey parameter isWherein, pi,ai,abiRepresent vector P, A, A respectivelybIn i-th component, grey parameter is the least, coal dust
The best with hot-fluid mixed effect.
According to the general characteristic of coal dust firing, the nearest away from unburned district, burn the most insufficient, the most remote away from unburned district, then burn
The most violent.If the situation insufficient away from the burning of position farther out, unburned district occurs, i.e. occur in burning image from cut-off rule relatively far field
The situation that territory gray scale is relatively low, illustrates that coal dust is poor with hot-fluid mixed effect, and burning is not in time.The gray scale of characteristic area is extracted for this
Coal dust is described by position distribution feature with hot-fluid mixed effect.
In step 3, the method extracting the grayscale position distributed constant in characteristic area is: characteristic area is divided into N number of son
Region, sets up grayscale position distributed constantWhereinFor subregion gray-level features,For jth
Subregion the i-th gray level set pixel number, piFor the i-th component in P, siIt is that the i-th subregion central point is to segmentation straight line
Distance.
In step 4, grey parameter and grayscale position distributed constant are normalized, according to grey parameter and grayscale position
Distributed constant determines mixed coefficintWherein α is weight coefficient, and span is (0,1),For Normalized Grey Level parameter,Join for Normalized Grey Level position distribution
Number.
Coal dust in burning image can be evaluated by the size according to mixed coefficint with hot-fluid mixed effect in stove, mixing
Coefficient is the biggest, and mixed effect is the best, and mixed coefficint is the least, and mixed effect is the poorest, thus realizes coal dust and mix effect with hot-fluid in stove
The tolerance of fruit.
The present invention, according to coal dust firing image, constructs tolerance coal dust and the mixed coefficint of hot-fluid mixed effect in stove, real
Coal dust and hot-fluid mixing evaluation method in stove are showed；The method calculates quick and precisely, it is possible to realize coal dust mixing during boiler combustion
The on-line monitoring of situation.
Accompanying drawing explanation
Fig. 1 is broad flow diagram of the present invention；
Fig. 2 is the burning image of the embodiment of the present invention；
Fig. 3 is the burning image region segmentation search effective coverage schematic diagram of the embodiment of the present invention；
Fig. 4 is the burning image shade of gray maximum point schematic diagram of the embodiment of the present invention；
Fig. 5 is the burning image region segmentation schematic diagram of the embodiment of the present invention；
Fig. 6 is that the burning image piecemeal of the embodiment of the present invention processes schematic diagram；
Fig. 7 is that the burning image Gray Classification of the embodiment of the present invention processes schematic diagram.
Detailed description of the invention
The present invention is described further with specific embodiment below in conjunction with the accompanying drawings.
In boiler combustion process, coal dust and hot-fluid mixed effect in stove directly affect ignition time and the burning effect of coal dust
Rate, can be evaluated mixed effect by the inventive method, and its key step is as shown in Figure 1.
By flame image sensor acquisition burning image, the combustion that the embodiment of the present invention uses size to be 320 × 240
Burn image, see Fig. 2.
According to known flame image sensor mounting location information, determine breeze airflow centrage position in burning image
Putting and the direction of motion, the direction of position of center line and motion has many possible, but centrage must be parallel with the direction of motion.According to
Heart line position and the direction of motion determine the effective coverage of the region first-order difference algorithm in fixing search direction, see Fig. 3.Search successively
The l bar straight line to be measured shade of gray maximum point parallel with it that in rope effective coverage, centrage both sides are neighbouring, detailed process is: meter
Calculate straight line to be measured in the breeze airflow direction of motion all adjacent before and after the first-order difference Δ f (x of 2k,yk)=f (xk+1,
yk+1)-f(xk,yk), search the maximum first-order difference Δ f (x on this straight linei,yi)=max (Δ f (x1,y1),Δf(x2,y2),L,
Δf(xk,yk),L,Δf(xq,yq)), then (xi,yi) it is the shade of gray maximum point of this straight line, and it is to be measured to continue search for next
Straight line, straight line selecting sequence is followed from left to right or principle from top to bottom, finally gives l shade of gray maximum point, gray scale ladder
Degree maximum point position is as shown in Figure 4.With one cut-off rule ax+ vertical with known direction of this l shade of gray maximum point matching
By+c=0, segmentation unburned district and combustion zone, see Fig. 5, on the right side of Fig. 5, be characteristic area.In the embodiment of the present invention, l takes
31。
Characteristic area is carried out piecemeal process, segmentation of feature regions is become the adjacent subarea territory of several sizes t × t, phase
Space is not had, as shown in Figure 6 between adjacent subregion.
After piecemeal processes, successively every sub regions is carried out Gray Classification process, as shown in Figure 7.Subregion selecting sequence
For from left to right or from top to bottom, its concrete processing procedure is: pixel in traversal subregion (x, y), it is judged that pixel ash
Angle value G (x, y) size, ifThen (x y) belongs to the i-th gray level set G to pixeli, wherein
I=1,2, L, n.Every sub regions can be obtained by this process and belong to each gray level set pixel number,For jZi district
Territory belongs to the i-th gray level set pixel number.In the embodiment of the present invention, t takes 10, and n takes 8.
Belong to each gray level set pixel number according to all subregion, obtain in characteristic area, belong to each gray level collection
The pixel number closed.It is the pixel number of the i-th gray level set,For the total pixel number in region.In order to
Understand burning image characteristic area gray scale situation, need to build a n dimensional feature vector A=(a1,L,ai,L,an), wherein with than
Value ai=mi/ m is as the i-th component of n dimensional feature vector.Ideally, in coal dust and stove hot-fluid mixed effect preferably time, combustion
Burning best results, burning image characteristic area brightness is the highest, so with Ab=(0, L, 0, L, 0,1) as n dimension best features to
Amount.Simultaneously withAs weight vectors, calculate grey parameterWherein, pi,ai,
abiRepresent vector P, A, A respectivelybIn i-th component, N is subregion number.
Breeze airflow moves in combustion zone, more gos deep in stove, burns the most abundant.If deep place is burnt not in there is stove
The most i.e. from the situation that cut-off rule area grayscale farther out is relatively low in burning image, illustrate that coal dust is with hot-fluid mixed effect in stove relatively
Difference, burning is not in time.For this grayscale position distribution characteristics that need to extract characteristic area, coal dust is entered with hot-fluid mixed effect in stove
Line description.Processed by piecemeal and Gray Classification processes, the gray level set pixel number of every sub regions can be obtainedRoot
According to the gray level aggregate information of every sub regions, each subregion gray-level features can be calculatedFor knowing clearly
Solve the positional information of each subregion, using cut-off rule as reference, calculate every sub regions center distance to cut-off ruleSubregion gray scale is the least, and the mixing of this region is the most insufficient, and distance is the most remote, and this sub-window position is the most important,
Half-tone information and positional information by every sub regions can calculate grayscale position distributed constant
Differentiate that coal dust needs to consider grey parameter and grayscale position distributed constant, first with hot-fluid troubled water in stove
Above-mentioned parameter need to be normalized.
The normalization formula of grey parameter isGrayscale position distributed constant normalization formula
For
Mixed coefficint is calculated according to grey parameter and grayscale position distributed constantWherein α is
Weight coefficient, span is (0,1).Monochrome information during grey parameter reflection burning and violent situation of burning, grayscale position
Coal dust position distribution situation when distributed constant embodies burning, two parameter suffers from important function to evaluating troubled water, all should
Mixed coefficint finds full expression, so two parameter weight should not have big difference.But grey parameter can be more directly
Solving the troubled water of coal dust and hot-fluid, the weight of grey parameter should be higher than that grayscale position distributed constant, so taking α=0.6.
Claims (5)
1. a coal dust based on burning image and hot-fluid mixed effect measure in stove, it is characterised in that by extracting fire
The coal dust firing characteristics of image that flame imageing sensor gathers, calculates mixed coefficint, it is achieved mixed effect evaluation, concretely comprises the following steps:
Step one, divides the unburned district in burning image and combustion zone, is characterized region with combustion zone, and carries out piecemeal and ash
Degree processes；
Step 2, extracts the grey parameter in characteristic area；
Step 3, extracts the grayscale position distributed constant in characteristic area；
Step 4, calculates mixed coefficint according to grey parameter and grayscale position distributed constant.
Coal dust the most according to claim 1 and hot-fluid mixed effect measure in stove, it is characterised in that in step one,
The method determining characteristic area is: according to flame image sensor determine breeze airflow burning image f (x, y) in motion side
To and position of center line, and then determine rim detection effective coverage；To all along the breeze airflow direction of motion in effective coverage
Straight line searches shade of gray maximum point, organizes maximum point coordinate (x by thisij,yij) and the breeze airflow direction of motion, determine coal dust
With the characteristic area of hot-fluid mixed effect in stove；Wherein, x, y are image coordinate, and (x y) is (x, y) gray value at place to f；Gray scale
The determination method of gradient maximum point is: Δ f (xk,yk)=f (xk+1,yk+1)-f(xk,yk) be f (x, single order forward difference y),
By finding the forward difference of maximum, it is the shade of gray maximum point in prescribed direction in burning image；
The method that characteristic area carries out piecemeal process is: is divided into by characteristic area according to burning image positional information and has t × t
The continuously arranged subregion of individual pixel；
The method that characteristic area carries out Gray Classification process is: G (x, y) be (x, y) place's gray value, if
Pixel belongs to the i-th gray level set Gi, wherein i=1,2, L, n.
Coal dust the most according to claim 1 and hot-fluid mixed effect measure in stove, it is characterised in that in step 2,
Image slices vegetarian refreshments is re-assigned to n gray level set according to gray value, sets up grey parameterWherein ai
=mi/ m, miBeing the pixel number of the i-th gray level set, m is characterized the total pixel number in region, and it constitutes n dimensional feature vector A=
(a1,L,ai,L,an), Ab=(0, L, 0, L, 0,1) is n dimension best features vector, abiFor AbI-th component,For weight vectors, piRepresent the i-th component in P.
Coal dust the most according to claim 1 and hot-fluid mixed effect measure in stove, it is characterised in that in step 3,
Characteristic area is divided into N number of subregion, sets up grayscale position distributed constantWhereinFor subregion
Gray-level features,For jth subregion the i-th gray level set pixel number, piFor the i-th component in P, siIt it is the i-th sub-district
Territory central point is to the distance of segmentation straight line.
Coal dust the most according to claim 1 and hot-fluid mixed effect measure in stove, it is characterised in that in step 4,
Grey parameter and grayscale position distributed constant are normalized, determine mixing according to grey parameter and grayscale position distributed constant
CoefficientWherein α is weight coefficient, and span is (0,1),
For Normalized Grey Level parameter,For Normalized Grey Level position distribution parameter.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106778816A (en) * | 2016-11-23 | 2017-05-31 | 华北电力大学 | Combustion stability method of discrimination based on burning mixed coefficint and fuzzy diagnosis |
| CN107254336A (en) * | 2017-07-18 | 2017-10-17 | 府谷县新亚新能源科技发展有限责任公司 | A kind of device and method of moulded coal large-scale production |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4907281A (en) * | 1986-10-16 | 1990-03-06 | Imatran Voima Oy | Method of image analysis in pulverized fuel combustion |
| CN104008385A (en) * | 2014-03-19 | 2014-08-27 | 合肥工业大学 | Coal-fired power plant furnace chamber flame judging method based on double images |
| CN105183935A (en) * | 2015-07-20 | 2015-12-23 | 昆明理工大学 | Evaluation method for flame burning condition and stability |
-
2016
- 2016-04-27 CN CN201610269775.8A patent/CN105912872B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4907281A (en) * | 1986-10-16 | 1990-03-06 | Imatran Voima Oy | Method of image analysis in pulverized fuel combustion |
| CN104008385A (en) * | 2014-03-19 | 2014-08-27 | 合肥工业大学 | Coal-fired power plant furnace chamber flame judging method based on double images |
| CN105183935A (en) * | 2015-07-20 | 2015-12-23 | 昆明理工大学 | Evaluation method for flame burning condition and stability |
Non-Patent Citations (1)
| Title |
|---|
| 刘禾等: "基于火焰序列图像的煤粉燃烧稳定性判别", 《动力工程》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106778816A (en) * | 2016-11-23 | 2017-05-31 | 华北电力大学 | Combustion stability method of discrimination based on burning mixed coefficint and fuzzy diagnosis |
| CN107254336A (en) * | 2017-07-18 | 2017-10-17 | 府谷县新亚新能源科技发展有限责任公司 | A kind of device and method of moulded coal large-scale production |
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