CN104483714A - Circulating fluidized bed boiler break coal detection device and detection method - Google Patents
Circulating fluidized bed boiler break coal detection device and detection method Download PDFInfo
- Publication number
- CN104483714A CN104483714A CN201410604626.3A CN201410604626A CN104483714A CN 104483714 A CN104483714 A CN 104483714A CN 201410604626 A CN201410604626 A CN 201410604626A CN 104483714 A CN104483714 A CN 104483714A
- Authority
- CN
- China
- Prior art keywords
- coal
- controller
- lever
- fluidized bed
- circulating fluidized
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Landscapes
- Control Of Steam Boilers And Waste-Gas Boilers (AREA)
- Fluidized-Bed Combustion And Resonant Combustion (AREA)
Abstract
The invention discloses a circulating fluidized bed boiler break coal detection device and a detection method. The detection device comprises a controller electrically connected with each electric appliance in a boiler, m cameras respectively arranged at m coal bunker coal mouths of the boiler, a memory, a first alarm lamp, a second alarm lamp, a third alarm lamp, an alarm horn, a coal receiving board which is arranged in the lower portion of a coal bunker of the boiler and opens downwards in the horn-shaped mode, a lever of which one end penetrates through a strip hole of the side wall of the coal bunker and is hinged with the upper end of the coal receiving board, an elastic sealing sleeve used for sealing a gap between the lever and the strip hole, a vertical rod connected with the other end of the lever and a hammer arranged on the vertical rod. The detection device has advantages of high detection accuracy, effective error operation avoidance performance, good adjustability and good adaptability.
Description
Technical field
The present invention relates to CFB Circulating Fluidized Bed Boiler to break coal fault detection technique field, especially relate to a kind of break Circulating Fluidized Bed Boiler of coal fault of CFB Circulating Fluidized Bed Boiler that can fast, accurately detect and to break coal pick-up unit and detection method.
Background technology
Under the dual-pressure of energy and environment, the distinguishing features such as recirculating fluidized bed (CFB) boiler is strong with its fuel tolerance, burning efficiency is high, load adjustment ability good, pollutant emission is low, obtain in China and develop fast.Ended for the end of the year 2011, Circulating Fluidized Bed Boiler 3000 multiple stage of the existing different capabilities of China, puts into commercial operation more than 90000MW according to incompletely statistics.The 300MW level Circulating Fluidized Bed Boilers put into operation reaches 40 multiple stage, has put into operation then to reach 110 multiple stage with in the 300MW grade recycle fluidized-bed combustion boiler unit summation of building.In addition, Baima, 600MW supercritical circulating fluidized bed boiler unit Sichuan recirculating fluidized bed Demonstration Station project that single-machine capacity is maximum in the world has been tried out at full capacity by 168 hours smoothly in April, 2013 and has successfully been put into operation.Therefore, can predict, Circulating Fluidized Bed Boiler will obtain larger development in China.
Although CFB boiler just have employed low flow velocity, blast furnace hearth, middle Matter Transfer multiplying power, lays the measures such as long wall with refractory lining, refracto at the beginning of design, but be in operation and disconnected coal fault often occurs, the reason that disconnected coal fault occurs has: the moisture of coal is excessive, the uneven blocking of granularity, bridging etc.
When there is disconnected coal fault, if process will cause the fluctuation of boiler operatiopn state not in time, even cause the generation of blowing out accident.
Chinese patent mandate publication number: CN103439081A, authorize publication date on Dec 11st, 2013, disclose a kind of biomass recirculating fluidized bed boiler flow behavior measurement method, it is characterized in that, comprising the steps: to set up the Euler's two-fluid model for simulating biomass recirculating fluidized bed boiler flow characteristics; The full scale model of described biomass recirculating fluidized bed boiler is set up according to the structural parameters of biomass recirculating fluidized bed boiler; The grid model that stress and strain model sets up described biomass recirculating fluidized bed boiler is carried out to the full scale model of described biomass recirculating fluidized bed boiler; Obtain gas parameter corresponding to described biomass recirculating fluidized bed boiler, particle parameter, boundary condition parameter, initial wind speed; The flow process of the gas-particle two-phase in biomass recirculating fluidized bed boiler according to described Euler's two-fluid model, grid model, gas parameter, particle parameter, boundary condition parameter, initial wind speed simulation; By the flow process of described simulation, measure the velocity field regularity of distribution of gas-particle two-phase in burner hearth, obtain the flow characteristics of biomass recirculating fluidized bed boiler.The weak point of this invention is, function singleness, can not be used for detecting the coal that whether breaks.
Summary of the invention
Goal of the invention of the present invention is the deficiency easily occurring disconnected coal accident in order to overcome Circulating Fluidized Bed Boiler in prior art, provides a kind of break Circulating Fluidized Bed Boiler of coal fault of CFB Circulating Fluidized Bed Boiler that can fast, accurately detect and to break coal pick-up unit and detection method.
To achieve these goals, the present invention is by the following technical solutions:
A kind of Circulating Fluidized Bed Boiler is broken coal pick-up unit, comprise the controller be electrically connected with each electrical part in boiler, be located at m the camera at coal mouth place under m coal bunker of boiler respectively, storer, first alarm lamp, Secondary Report warning lamp, 3rd alarm lamp, alarming horn, to be located in the coal bunker bottom of boiler horn-like downwards open connect coal plate, one end through be located at coal bunker sidewall bar hole and with the lever connecing coal plate upper end thereof, for the elastic seal cartridge in the gap between closure lever and bar hole, the montant be connected with the lever other end and the weight be located on montant, lever is connected with coal bunker sidewall by articulated slab, and the coal bunker sidewall near bar hole coboundary is provided with the postive stop baffle with lever mated, and the coal bunker sidewall near bar hole lower limb is provided with the limit switch with lever mated, controller is electrically connected with storer, alarming horn, the first alarm lamp, Secondary Report warning lamp, the 3rd alarm lamp, limit switch and each camera respectively.
For evading the unreliable and limitation of single disconnected coal detection method, machinery, image and software detection combine by the present invention, and when the Indexes Abnormality of 2 kinds of methods any in 3 kinds of detection methods, controller then controls alarming horn and sends alerting signal; The flicker of each alarm lamp is used for providing disconnected coal to note prompting to operating personnel, reminds operating personnel to note observing; It is more accurate to detect, and effectively avoids maloperation.
Therefore, it is high that the present invention has detection accuracy, effectively avoids maloperation; Improve the feature of the safety and stability of boiler operatiopn.
As preferably, described lever comprises the adapter sleeve that one end is stretched into the fixed bar of coal bunker, expansion link and is located on the fixed bar other end; Adapter sleeve and expansion link are equipped with several length adjustment through holes, and adapter sleeve is connected with expansion link by bolt.。
The vibrational power flow of lever, make the adjustable length of lever, applicability is strong.
As preferably, described montant bottom is provided with screw rod, described screw rod is provided with successively upper cap nut, overhead gage, lower baffle plate and at least one lower nut from top to bottom; Described weight is sheathed on the screw rod between overhead gage, lower baffle plate, and weight comprises some pieces of counterweight discs; Screw diameter is greater than the diameter of montant.
Before using, repeatedly adjusting counterweight and expansion link collapsing length, guaranteeing when falling without coal, lever is due to the contact of the cause pressure limit switch of weight.
As preferably, described limit switch is provided with the L shaped plate that right-hand member upwards bends, and L shaped plate left part is hinged with the back up pad be located on limit switch, and L shaped plate left end is connected with limit switch by spring, and L shaped plate right-hand member matches with lever; The contact of limit switch matches with L shaped plate lower surface right part.
As preferably, the coal bunker sidewall below lever is provided with triangle bracket, and described limit switch is positioned on bracket.
Circulating Fluidized Bed Boiler is broken the detection method of coal pick-up unit, comprises the steps:
(1-1) the disconnected coal of machinery detects:
(1-1-1) safety time threshold value W is provided with in advance in controller; Controller obtains the electric signal that limit switch detects, and calculates the time T that lever continues the contact depressing limit switch; As T > W, controller is made and is currently in the judgement that first prejudges coaly state; As T≤W, controller is made current for there being the judgement of coaly state;
(1-1-2) carry out first under state of activation and prejudge coal control:
Make boiler when controller to prejudge coaly state by first and transfer the judgement having coaly state to, then pick-up unit enters state of activation;
In active state, as T > W, controller controls the first alarm lamp flicker;
In active state, as T≤W, controller controls the first alarm lamp and stops flicker;
(1-2) image break coal detect:
(1-2-1) m camera takes the image of lower coal mouth place coal flowing respectively, and each image to be stored in storer and to be handled as follows image by controller:
Controller utilizes impulse response function y
ij=med (x
i+r, j+s(r, s) ∈ A) filtering is carried out to picture signal f (x, y); Wherein, A is filtering window, and it is of a size of N=(2k+1) × (2k+1); { x
ijbe image manifold, med is median, and the gray-scale value of each pixel is set to the intermediate value of all pixel gray-scale values in this some neighborhood window by median; The value of any in digital picture or Serial No. is replaced with the Mesophyticum of each point value in a neighborhood of this point, allows the actual value that the pixel value of surrounding is close, thus eliminate isolated noise spot.(r, s) is coordinate, x
i+r, j+sfor the gray-scale value that pixel in filter window A is corresponding;
(1-2-2) motion surveyed area parameter is set:
A is set to 1-99 rectangle monitored area by controller, and the interframe that setting motion detects is divided into B;
(1-2-3) motor function detects:
Controller to set in each macro block in each surveyed area each picture element as (x, y), and difference threshold is Ta; The difference of described picture element T moment and T-n moment brightness Y is M
x, y(T)=| Y
x, y(T)-Y
x, y(T-n) |;
When || Y
x, y(T)-Y
x, y(T-n) ||>=Ta, then set L=0; Otherwise, setting L=1; Controller utilizes formula IM
sum=∑ L calculates and obtains macro block difference coefficient IM
sum; Controller calculates each macro block difference coefficient IM in each surveyed area
sumsum, obtains the difference coefficient value of each surveyed area, and calculates the difference coefficient A of filtering window A further
sum;
(1-2-4)
Controller utilizes step (1-1-3) to detect present image at interval of B frame; As the A of the image of any one camera
sum≤ threshold value, then controller controls the flicker of Secondary Report warning lamp; As the A of the image of all cameras
sum> threshold value, then controller controls Secondary Report warning lamp stopping flicker;
(1-3) software break coal detect:
(1-3-1) be provided with in storer there are 11 input node X=[X1, ..., X11], single hidden layer is 9 node Y=[Y1, ..., Y9], the BP neural network model of 1 output node Z, wherein, in storer, store q bar learning sample, network performance objective error SSE≤0.00001, train epochs is at least d step;
(1-3-2) initialization BP neural network model:
(1-3-2-1) weight and threshold values initialization: controller utilizes Gauss random function to produce to meet normal distribution, average is 0, variance is 1 and the random number initializes weights W of span in interval [0,1]
ij, threshold values θ and θ
j, i=1 ..., 11; J=1 .., 9; Setting network performance error is ε;
(1-3-2-2) variable normalization: be provided with in controller and 11 input node X1 ..., 11 groups of X corresponding respectively in X11
maxand X
min, controller utilizes formula X '=(X-X
min)/(X
max-X
min) calculate the X1 of q bar learning sample respectively ..., the normalized value X ' of X11
1, X '
2..., X '
11;
The Z corresponding with output contact Z is provided with in controller
maxand Z
min, controller utilizes formula Z '=(Z-Z
min)/(Z
max-Z
min) calculate the normalized value Z ' of the Z of q bar learning sample; Obtain through normalized s group learning sample;
(1-3-3) BP neural network model is trained:
(1-3-3-1) controller inputs s group learning sample in BP neural network model, and s initial value is 1, and the target output value setting s group sample is Z ';
Utilize formula
calculating hidden layer neuron exports;
Utilize formula
calculate output layer neuron actual output Z
r; Wherein, function
Utilize formula
calculate single sample bias E
s;
(1-3-3-2) from output layer, successively weight and threshold values is oppositely adjusted:
Controller makes the weights W of output layer
jincrease by 0.6 × δ × Y
j+ 0.45 × Δ W
j, Δ W
jfor the weight that previous adjustment increases, Δ W
o=0; Wherein, δ=(Z '-Z
r) × Z
r× (1-Z
r),
Controller makes the weight of hidden layer increase by 0.6 × δ
j× X
i+ 0.45 × Δ W
ij;
Wherein, δ
j=Y
j× (1-Yj) × (δ
j× W
j);
(1-3-3-3) as s < q, make s value increase by 1, return step (1-3-3-1); Otherwise proceed to step (1-3-3-4);
(1-3-3-4) formula is utilized
calculate total error E
t, wherein p is sample sequence number;
Work as E
t≤ ε or study step number are less than d, and training terminates, and obtains the BP neural network model trained; Otherwise proceed to step (1-3-3-1);
(1-3-4) controller Real-time Collection main steam flow, feedwater flow, coal-supplying amount, bed temperature, combustion chamber draft, primary air pressure, primary air fan electric current, secondary wind pressure, overfire air fan electric current, induced draft fan electric current and exhaust gas temperature 11 technological parameters, step (1-2-2-2) is utilized to be normalized 11 technological parameters, and each technological parameter through normalized is sent in 11 input nodes of the BP neural network model trained, obtain model output valve Z
r, recycling formula V=Z
r× (Z
max-Z
min)+Z
minrenormalization obtains real-time oxygen content V, and controller calculates
wherein V
1for the targeted oxygen content relevant to current main steam flow set in storer;
(1-3-5) when
duration is more than T
1second, then controller controls the 3rd alarm lamp flicker;
When
duration is more than T
2second, then controller controls the 3rd alarm lamp stopping flicker;
(1-4) when first, second, third alarm lamp all glimmers or any two flickers, controller is made disconnected coal and is judged, and controls alarming horn warning.
As preferably, when main steam flow is 29% to 31.5%, V
1be 8.2% to 8.9%; When main steam flow is 37.5% to 41%, V
1be 7.5% to 8.1%; When main steam flow is 47.5% to 53%, V
1for 6.7.5% to 7.1%; When main steam flow is 57.5% to 61%, V
1for 5.7.5% to 6.1%; When main steam flow is 67.5% to 73%, V
1be 4.65% to 5.2%; When main steam flow is 78.5% to 83%, V
1be 4.0% to 4.4%; When main steam flow is 87% to 92%, V
1be 3.5% to 3.9%; When main steam flow is 92% to 100%, V
1be 3.3% to 3.5%.
As preferably, q is 500 to 560.
As preferably, c is 1.18 to 1.32; D is 10000 to 11200.
As preferably, T
1be 12 to 17; T
2be 2 to 3.
Therefore, the present invention has following beneficial effect:
(1) detection accuracy is high, effectively avoids maloperation;
(2) improve the safety and stability of boiler operatiopn;
(3) adjustability is good, and applicability is good.
Accompanying drawing explanation
Fig. 1 is a kind of structural representation of the present invention;
Fig. 2 is a kind of theory diagram of the present invention;
Fig. 3 is a kind of process flow diagram of embodiments of the invention;
Fig. 4 is the structural representation of a kind of amplification at A place in Fig. 1.
In figure: controller 1, alarming horn 2, connect coal plate 3, bar hole 4, lever 5, elastic seal cartridge 6, be located at the weight 7 on montant, articulated slab 8, postive stop baffle 9, limit switch 10, fixed bar 11, expansion link 12, adapter sleeve 13, length adjustment through hole 14, upper cap nut 15, overhead gage 16, lower baffle plate 18, lower nut 19, counterweight disc 17, L shaped plate 20, back up pad 21, spring 22, contact 23, bracket 24, , coal bunker 25, montant 26, first alarm lamp 27, Secondary Report warning lamp 28, storer 29, camera 30, 3rd alarm lamp 31.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention will be further described.
As Fig. 1, embodiment shown in Fig. 2 is that a kind of Circulating Fluidized Bed Boiler is broken coal pick-up unit, comprise the controller 1 be electrically connected with each electrical part in boiler, be located at 8 cameras 30 at coal mouth place under 8 coal bunkers of boiler respectively, storer 29, first alarm lamp 27, Secondary Report warning lamp 28, 3rd alarm lamp 31, alarming horn 2, to be located in coal bunker 25 bottom of boiler horn-like downwards open connect coal plate 3, one end through be located at coal bunker sidewall bar hole 4 and with the lever 5 connecing coal plate upper end thereof, for the elastic seal cartridge 6 in the gap between closure lever and bar hole, the montant 26 be connected with the lever other end and the weight 7 be located on montant, lever is connected with coal bunker sidewall by articulated slab 8, and the coal bunker sidewall near bar hole coboundary is provided with the postive stop baffle 9 with lever mated, and the coal bunker sidewall near bar hole lower limb is provided with the limit switch 10 with lever mated, as shown in Figure 2, controller is electrically connected with storer, alarming horn, the first alarm lamp, Secondary Report warning lamp, the 3rd alarm lamp, limit switch and each camera respectively.
As shown in Figure 1, lever comprises the adapter sleeve 13 that one end is stretched into the fixed bar 11 of coal bunker, expansion link 12 and is located on the fixed bar other end; Adapter sleeve and expansion link are equipped with length adjustment through hole 14, and adapter sleeve is connected with expansion link by bolt.
Montant bottom is provided with screw rod, described screw rod is provided with successively upper cap nut (15), overhead gage 16, lower baffle plate 18 and at least one lower nut 19 from top to bottom; Weight is sheathed on the screw rod between overhead gage, lower baffle plate, and weight comprises 5 pieces of counterweight discs 17; Screw diameter is greater than the diameter of montant.
As shown in Figure 4, limit switch is provided with the L shaped plate 20 that right-hand member upwards bends, and L shaped plate left part is hinged with the back up pad 21 be located on limit switch, and L shaped plate left end is connected with limit switch by spring 22, and L shaped plate right-hand member matches with lever; The contact 23 of limit switch matches with L shaped plate lower surface right part.
Coal bunker sidewall below lever is provided with triangle bracket 24, and described limit switch is positioned on bracket.Postive stop baffle extends horizontally and the bending tabular of bending downwards, the circular in cross-section of the lower limb of postive stop baffle.First alarm lamp and Secondary Report warning lamp all adopt stroboscopic lamp.
As shown in Figure 3, detection method comprises the steps:
Step 100, the disconnected coal of machinery detects:
Step 110, is provided with safety time threshold value W in controller in advance; Controller obtains the electric signal that limit switch detects, and calculates the time T that lever continues the contact depressing limit switch; As T > W, controller is made and is currently in the judgement that first prejudges coaly state; As T≤W, controller is made current for there being the judgement of coaly state; W is 12 seconds;
Step 120, carry out first under state of activation and prejudge coal control:
Make boiler when controller to prejudge coaly state by first and transfer the judgement having coaly state to, then pick-up unit enters state of activation;
In active state, as T > W, controller controls the first alarm lamp flicker;
In active state, as T≤W, controller controls the first alarm lamp and stops flicker;
Step 200, image break coal detect:
Step 210,8 cameras take the image of lower coal mouth place coal flowing respectively, and each image to be stored in storer and to be handled as follows image by controller:
Controller utilizes impulse response function y
ij=med (x
i+r, j+s(r, s) ∈ A) filtering is carried out to picture signal f (x, y); Wherein, A is filtering window, and it is of a size of N=(2k+1) × (2k+1); { x
ijbe image manifold, med is median, and (r, s) is coordinate, x
i+r, j+sfor the gray-scale value that pixel in filter window A is corresponding;
Step 220, arranges motion surveyed area parameter:
A is set to 1-99 rectangle monitored area by controller, and the interframe that setting motion detects is divided into B;
Step 230, motor function detects:
Controller to set in each macro block in each surveyed area each picture element as (x, y), and difference threshold is Ta; The difference of described picture element T moment and T-n moment brightness Y is M
x, y(T)=| Y
x, y(T)-Y
x, y(T-n) |;
When || Y
x, y(T)-Y
x, y(T-n) ||>=Ta, then set L=0; Otherwise, setting L=1; Controller utilizes formula IM
sum=∑ L calculates and obtains macro block difference coefficient IM
sum; Controller calculates each macro block difference coefficient IM in each surveyed area
sumsum, obtains the difference coefficient value of each surveyed area, and calculates the difference coefficient A of filtering window A further
sum;
Step 240,
Controller utilizes step 230 pair present image to detect at interval of B frame; As the A of the image of any one camera
sum≤ threshold value, then controller controls the flicker of Secondary Report warning lamp;
As the A of the image of all cameras
sum> threshold value, then controller controls Secondary Report warning lamp stopping flicker;
Step 300, software break coal detect:
Step 310, be provided with in storer and there are 11 input node X=[X1, ..., X11], single hidden layer be 9 node Y=[Y1 ..., Y9], the BP neural network model of 1 output node Z, wherein, store q=500 bar learning sample in storer, network performance objective error SSE≤0.00001, train epochs is at least d=10000 step;
Step 320, initialization BP neural network model:
Step 321, weight and threshold values initialization: controller utilizes Gauss random function to produce to meet normal distribution, average is 0, variance is 1 and the random number initializes weights W of span in interval [0,1]
ij, threshold values θ and θ
j, i=1 ..., 11; J=1 .., 9; Setting network performance error is ε;
Step 322, variable normalization: be provided with in controller and 11 input node X1 ..., 11 groups of X corresponding respectively in X11
maxand X
min, controller utilizes formula X '=(X-X
min)/(X
max-X
min) calculate the X1 of q bar learning sample respectively ..., the normalized value X ' of X11
1, X '
2..., X '
11;
The Z corresponding with output contact Z is provided with in controller
maxand Z
min, controller utilizes formula Z '=(Z-Z
min)/(Z
max-Z
min) calculate the normalized value Z ' of the Z of q bar learning sample; Obtain through normalized s group learning sample;
Step 330, training BP neural network model:
Step 331, controller inputs s group learning sample in BP neural network model, and s initial value is 1, and the target output value setting s group sample is Z ';
Utilize formula
calculating hidden layer neuron exports;
Utilize formula
calculate output layer neuron actual output Z
r; Wherein, function
Utilize formula
calculate single sample bias E
s;
Step 332, successively oppositely adjusts weight and threshold values from output layer:
Controller makes the weights W of output layer
jincrease by 0.6 × δ × Y
j+ 0.45 × Δ W
j, Δ W
jfor the weight that previous adjustment increases, Δ W
o=0; Wherein, δ=(Z '-Z
r) × Z
r× (1-Z
r),
Controller makes the weight of hidden layer increase by 0.6 × δ
j× X
i+ 0.45 × Δ W
ij;
Wherein, δ
j=Y
j× (1-Yj) × (δ
j× W
j);
Step 333, as s < q, makes s value increase by 1, returns step 331; Otherwise proceed to step 334;
Step 334, utilizes formula
calculate total error E
t, wherein p is sample sequence number;
Work as E
t≤ ε or study step number are less than d, and training terminates, and obtains the BP neural network model trained; Otherwise proceed to step 331;
Step 340, controller Real-time Collection main steam flow, feedwater flow, coal-supplying amount, bed temperature, combustion chamber draft, primary air pressure, primary air fan electric current, secondary wind pressure, overfire air fan electric current, induced draft fan electric current and exhaust gas temperature 11 technological parameters, step (1-2-2-2) is utilized to be normalized 11 technological parameters, and each technological parameter through normalized is sent in 11 input nodes of the BP neural network model trained, obtain model output valve Z
r, recycling formula V=Z
r× (Z
max-Z
min)+Z
minrenormalization obtains real-time oxygen content V, and controller calculates
wherein V
1for the targeted oxygen content relevant to current main steam flow set in storer;
Step 350, when
duration is more than T
1second, then controller is made and is currently in the judgement that second prejudges coaly state, and controls the flicker of Secondary Report warning lamp;
When
duration is more than T
2second, then controller controls Secondary Report warning lamp stopping flicker;
Step 400, disconnected coal judges and reports to the police:
When first, second alarm lamp all glimmers, controller is made disconnected coal and is judged, and controls alarming horn warning.
Wherein, when main steam flow is 29% to 31.5%, V
1be 8.2% to 8.9%; When main steam flow is 37.5% to 41%, V
1be 7.5% to 8.1%; When main steam flow is 47.5% to 53%, V
1for 6.7.5% to 7.1%; When main steam flow is 57.5% to 61%, V
1for 5.7.5% to 6.1%; When main steam flow is 67.5% to 73%, V
1be 4.65% to 5.2%; When main steam flow is 78.5% to 83%, V
1be 4.0% to 4.4%; When main steam flow is 87% to 92%, V
1be 3.5% to 3.9%; When main steam flow is 92% to 100%, V
1be 3.3% to 3.5%; C is 1.2, T
1be 11, T
2be 3.
Should be understood that the present embodiment is only not used in for illustration of the present invention to limit the scope of the invention.In addition should be understood that those skilled in the art can make various changes or modifications the present invention, and these equivalent form of values fall within the application's appended claims limited range equally after the content of having read the present invention's instruction.
Claims (10)
1. a Circulating Fluidized Bed Boiler is broken coal pick-up unit, it is characterized in that, comprise the controller (1) be electrically connected with each electrical part in boiler, be located at m the camera (30) at coal mouth place under m coal bunker of boiler respectively, storer (29), first alarm lamp (27), Secondary Report warning lamp (28), 3rd alarm lamp (31), alarming horn (2), to be located in coal bunker (25) bottom of boiler horn-like downwards open connect coal plate (3), one end through be located at coal bunker sidewall bar hole (4) and with the lever (5) connecing coal plate upper end thereof, for the elastic seal cartridge (6) in the gap between closure lever and bar hole, the montant (26) be connected with the lever other end and the weight (7) be located on montant, lever is connected with coal bunker sidewall by articulated slab (8), coal bunker sidewall near bar hole coboundary is provided with and the postive stop baffle of lever mated (9), and the coal bunker sidewall near bar hole lower limb is provided with and the limit switch of lever mated (10), controller is electrically connected with storer, alarming horn, the first alarm lamp, Secondary Report warning lamp, the 3rd alarm lamp, limit switch and each camera respectively.
2. Circulating Fluidized Bed Boiler according to claim 1 is broken coal pick-up unit, it is characterized in that, described lever comprises the adapter sleeve (13) that one end is stretched into the fixed bar (11) of coal bunker, expansion link (12) and is located on the fixed bar other end; Adapter sleeve and expansion link are equipped with several length adjustment through holes (14), and adapter sleeve is connected with expansion link by bolt.
3. Circulating Fluidized Bed Boiler according to claim 1 is broken coal pick-up unit, it is characterized in that, described montant bottom is provided with screw rod, described screw rod is provided with successively upper cap nut (15), overhead gage (16), lower baffle plate (18) and at least one lower nut (19) from top to bottom; Described weight is sheathed on the screw rod between overhead gage, lower baffle plate, and weight comprises some pieces of counterweight discs (17); Screw diameter is greater than the diameter of montant.
4. Circulating Fluidized Bed Boiler according to claim 1 is broken coal pick-up unit, it is characterized in that, described limit switch is provided with the L shaped plate (20) that right-hand member upwards bends, L shaped plate left part is hinged with the back up pad (21) be located on limit switch, L shaped plate left end is connected with limit switch by spring (22), and L shaped plate right-hand member matches with lever; The contact (23) of limit switch matches with L shaped plate lower surface right part.
5. the Circulating Fluidized Bed Boiler according to claim 1 or 2 or 3 or 4 is broken coal pick-up unit, and it is characterized in that, the coal bunker sidewall below lever is provided with triangle bracket (24), and described limit switch is positioned on bracket.
6. be applicable to Circulating Fluidized Bed Boiler according to claim 1 to break the detection method of coal pick-up unit, it is characterized in that, comprise the steps:
(1-1) the disconnected coal of machinery detects:
(1-1-1) safety time threshold value W is provided with in advance in controller; Controller obtains the electric signal that limit switch detects, and calculates the time T that lever continues the contact depressing limit switch; As T > W, controller is made and is currently in the judgement that first prejudges coaly state; As T≤W, controller is made current for there being the judgement of coaly state;
(1-1-2) carry out first under state of activation and prejudge coal control:
Make boiler when controller to prejudge coaly state by first and transfer the judgement having coaly state to, then pick-up unit enters state of activation;
In active state, as T > W, controller controls the first alarm lamp flicker;
In active state, as T≤W, controller controls the first alarm lamp and stops flicker;
(1-2) image break coal detect:
(1-2-1) m camera takes the image of lower coal mouth place coal flowing respectively, and each image to be stored in storer and to be handled as follows image by controller:
Controller utilizes impulse response function y
ij=med (x
i+r, j+s(r, s) ∈ A) filtering is carried out to picture signal f (x, y); Wherein, A is filtering window, and it is of a size of N=(2k+1) × (2k+1); { x
ijbe image manifold, med is median, and (r, s) is coordinate, x
i+r, j+sfor the gray-scale value that pixel in filter window A is corresponding;
(1-2-2) motion surveyed area parameter is set:
A is set to 1-99 rectangle monitored area by controller, and the interframe that setting motion detects is divided into B;
(1-2-3) motor function detects:
Controller to set in each macro block in each surveyed area each picture element as (x, y), and difference threshold is Ta; The difference of described picture element T moment and T-n moment brightness Y is M
x, y(T)=| Y
x, y(T)-Y
x, y(T-n) |;
When || Y
x, y(T)-Y
x, y(T-n) ||>=Ta, then set L=0; Otherwise, setting L=1; Controller utilizes formula IM
sum=∑ L calculates and obtains macro block difference coefficient IM
sum; Controller calculates each macro block difference coefficient IM in each surveyed area
sumsum, obtains the difference coefficient value of each surveyed area, and calculates the difference coefficient A of filtering window A further
sum;
(1-2-4)
Controller utilizes step (1-1-3) to detect present image at interval of B frame; As the A of the image of any one camera
sum≤ threshold value, then controller controls the flicker of Secondary Report warning lamp; As the A of the image of all cameras
sum> threshold value, then controller controls Secondary Report warning lamp stopping flicker;
(1-3) software break coal detect:
(1-3-1) be provided with in storer there are 11 input node X=[X1, ..., X11], single hidden layer is 9 node Y=[Y1, ..., Y9], the BP neural network model of 1 output node Z, wherein, in storer, store q bar learning sample, network performance objective error SSE≤0.00001, train epochs is at least d step;
(1-3-2) initialization BP neural network model:
(1-3-2-1) weight and threshold values initialization: controller utilizes Gauss random function to produce to meet normal distribution, average is 0, variance is 1 and the random number initializes weights W of span in interval [0,1]
ij, threshold values θ and θ
j, i=1 ..., 11; J=1 .., 9; Setting network performance error is ε;
(1-3-2-2) variable normalization: be provided with in controller and 11 input node X1 ..., 11 groups of X corresponding respectively in X11
maxand X
min, controller utilizes formula X '=(X-X
min)/(X
max-X
min) calculate the X1 of q bar learning sample respectively ..., the normalized value X ' of X11
1, X '
2..., X '
11;
The Z corresponding with output contact Z is provided with in controller
maxand Z
min, controller utilizes formula Z '=(Z-Z
min)/(Z
max-Z
min) calculate the normalized value Z ' of the Z of q bar learning sample; Obtain through normalized s group learning sample;
(1-3-3) BP neural network model is trained:
(1-3-3-1) controller inputs s group learning sample in BP neural network model, and s initial value is 1, and the target output value setting s group sample is Z ';
Utilize formula
calculating hidden layer neuron exports;
Utilize formula
calculate output layer neuron actual output Z
r; Wherein, function
Utilize formula
calculate single sample bias E
s;
(1-3-3-2) from output layer, successively weight and threshold values is oppositely adjusted:
Controller makes the weights W of output layer
jincrease by 0.6 × δ × Y
j+ 0.45 × Δ W
j, Δ W
jfor the weight that previous adjustment increases, Δ W
o=0; Wherein, δ=(Z '-Z
r) × Z
r× (1-Z
r),
Controller makes the weight of hidden layer increase by 0.6 × δ
j× X
i+ 0.45 × Δ W
ij;
Wherein, δ
j=Y
j× (1-Yj) × (δ
j× W
j);
(1-3-3-3) as s < q, make s value increase by 1, return step (1-3-3-1); Otherwise proceed to step (1-3-3-4);
(1-3-3-4) formula is utilized
calculate total error E
t, wherein p is sample sequence number;
Work as E
t≤ ε or study step number are less than d, and training terminates, and obtains the BP neural network model trained; Otherwise proceed to step (1-3-3-1);
(1-3-4) controller Real-time Collection main steam flow, feedwater flow, coal-supplying amount, bed temperature, combustion chamber draft, primary air pressure, primary air fan electric current, secondary wind pressure, overfire air fan electric current, induced draft fan electric current and exhaust gas temperature 11 technological parameters, step (1-2-2-2) is utilized to be normalized 11 technological parameters, and each technological parameter through normalized is sent in 11 input nodes of the BP neural network model trained, obtain model output valve Z
r, recycling formula V=Z
r× (Z
max-Z
min)+ Z
minrenormalization obtains real-time oxygen content V, and controller calculates
wherein V
1for the targeted oxygen content relevant to current main steam flow set in storer;
(1-3-5) when
duration is more than T
1second, then controller controls the 3rd alarm lamp flicker;
When
duration is more than T
2second, then controller controls the 3rd alarm lamp stopping flicker;
(1-4) when first, second, third alarm lamp all glimmers or any two flickers, controller is made disconnected coal and is judged, and controls alarming horn warning.
7. Circulating Fluidized Bed Boiler according to claim 6 is broken the detection method of coal pick-up unit, it is characterized in that, when main steam flow is 29% to 31.5%, and V
1be 8.2% to 8.9%; When main steam flow is 37.5% to 41%, V
1be 7.5% to 8.1%; When main steam flow is 47.5% to 53%, V
1for 6.7.5% to 7.1%; When main steam flow is 57.5% to 61%, V
1for 5.7.5% to 6.1%; When main steam flow is 67.5% to 73%, V
1be 4.65% to 5.2%; When main steam flow is 78.5% to 83%, V
1be 4.0% to 4.4%; When main steam flow is 87% to 92%, V
1be 3.5% to 3.9%; When main steam flow is 92% to 100%, V
1be 3.3% to 3.5%.
8. Circulating Fluidized Bed Boiler according to claim 6 is broken the detection method of coal pick-up unit, and it is characterized in that, q is 500 to 560.
9. Circulating Fluidized Bed Boiler according to claim 1 is broken the detection method of coal pick-up unit, and it is characterized in that, c is 1.18 to 1.32; D is 10000 to 11200.
10. Circulating Fluidized Bed Boiler according to claim 1 is broken the detection method of coal pick-up unit, it is characterized in that, T
1be 12 to 17; T
2be 2 to 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410604626.3A CN104483714B (en) | 2014-10-31 | 2014-10-31 | Circulating fluidized bed boiler break coal detection device and detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410604626.3A CN104483714B (en) | 2014-10-31 | 2014-10-31 | Circulating fluidized bed boiler break coal detection device and detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104483714A true CN104483714A (en) | 2015-04-01 |
| CN104483714B CN104483714B (en) | 2017-02-22 |
Family
ID=52758279
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410604626.3A Active CN104483714B (en) | 2014-10-31 | 2014-10-31 | Circulating fluidized bed boiler break coal detection device and detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104483714B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105953221A (en) * | 2016-06-02 | 2016-09-21 | 陕西华电瑶池发电有限公司 | Coal blockage preventing device and method for bunker of circulating fluidized bed boiler |
| CN106202959A (en) * | 2016-07-20 | 2016-12-07 | 广东电网有限责任公司电力科学研究院 | CFBB pollutant generate method for numerical simulation and the system of process |
| CN106861897A (en) * | 2017-02-24 | 2017-06-20 | 中国恩菲工程技术有限公司 | The control device of separator |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2172861Y (en) * | 1993-08-02 | 1994-07-27 | 王国春 | Apparatus for weighing fine coal during flow |
| JPH10176805A (en) * | 1996-12-17 | 1998-06-30 | Hitachi Ltd | Pressurized fluidized bed combustion device for coal |
| CN2292982Y (en) * | 1997-03-06 | 1998-09-30 | 周俊浩 | Foreward grate boiler coal supply device |
| CN2903712Y (en) * | 2006-04-21 | 2007-05-23 | 陈玉国 | Anti-flue reversing blocking-proof type coal supplier of circulation fluid-bed boiler |
| CN201052475Y (en) * | 2007-06-05 | 2008-04-30 | 永城煤电集团有限责任公司 | Coal bunker level automatic control peristalsis breaking preventing device |
| CN203319211U (en) * | 2013-05-04 | 2013-12-04 | 大唐淮南洛河发电厂 | Coal caking detection and protection device for redirection rollers of coal conveying belt |
| CN203428477U (en) * | 2013-08-14 | 2014-02-12 | 中国神华能源股份有限公司 | Material-blocking protection device for material conveying system |
| US20140299027A1 (en) * | 2011-02-01 | 2014-10-09 | Qinggang Lu | Large-scale circulating fluidized bed boiler |
-
2014
- 2014-10-31 CN CN201410604626.3A patent/CN104483714B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN2172861Y (en) * | 1993-08-02 | 1994-07-27 | 王国春 | Apparatus for weighing fine coal during flow |
| JPH10176805A (en) * | 1996-12-17 | 1998-06-30 | Hitachi Ltd | Pressurized fluidized bed combustion device for coal |
| CN2292982Y (en) * | 1997-03-06 | 1998-09-30 | 周俊浩 | Foreward grate boiler coal supply device |
| CN2903712Y (en) * | 2006-04-21 | 2007-05-23 | 陈玉国 | Anti-flue reversing blocking-proof type coal supplier of circulation fluid-bed boiler |
| CN201052475Y (en) * | 2007-06-05 | 2008-04-30 | 永城煤电集团有限责任公司 | Coal bunker level automatic control peristalsis breaking preventing device |
| US20140299027A1 (en) * | 2011-02-01 | 2014-10-09 | Qinggang Lu | Large-scale circulating fluidized bed boiler |
| CN203319211U (en) * | 2013-05-04 | 2013-12-04 | 大唐淮南洛河发电厂 | Coal caking detection and protection device for redirection rollers of coal conveying belt |
| CN203428477U (en) * | 2013-08-14 | 2014-02-12 | 中国神华能源股份有限公司 | Material-blocking protection device for material conveying system |
Non-Patent Citations (1)
| Title |
|---|
| 田秀山 等: "循环流化床锅炉断煤或落煤管堵塞的判断和处理", 《节能》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105953221A (en) * | 2016-06-02 | 2016-09-21 | 陕西华电瑶池发电有限公司 | Coal blockage preventing device and method for bunker of circulating fluidized bed boiler |
| CN105953221B (en) * | 2016-06-02 | 2018-03-27 | 陕西华电瑶池发电有限公司 | Coal for circulation fluid bed boiler storehouse coal-blocking-proof device of coal and method |
| CN106202959A (en) * | 2016-07-20 | 2016-12-07 | 广东电网有限责任公司电力科学研究院 | CFBB pollutant generate method for numerical simulation and the system of process |
| CN106202959B (en) * | 2016-07-20 | 2019-03-22 | 广东电网有限责任公司电力科学研究院 | The method for numerical simulation and system of circulating fluidized bed boiler pollutant generating process |
| CN106861897A (en) * | 2017-02-24 | 2017-06-20 | 中国恩菲工程技术有限公司 | The control device of separator |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104483714B (en) | 2017-02-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101839795B (en) | System and method for diagnosing leakage of pressure-bearing pipe of boiler | |
| US5181482A (en) | Sootblowing advisor and automation system | |
| CN104483714A (en) | Circulating fluidized bed boiler break coal detection device and detection method | |
| WO2015079766A1 (en) | Diagnostic device and diagnostic method for plant | |
| CN104534505B (en) | Energy-saving and safe burning control system and method | |
| CN105308191A (en) | Method for detecting abnormality in blast furnace, and method for operating blast furnace | |
| CN107543199A (en) | A kind of pulverized-coal fired boiler online detection of primary air and burning optimization management system | |
| CN103062781A (en) | Intelligent soot blowing method for heating surfaces of boilers on basis of principle of artificial neural network | |
| CN103925614B (en) | A kind of fired power generating unit fast cut-back fuel control method | |
| CN104300557B (en) | Power system active power oscillations type identification method based on support vector machine | |
| CN104459819B (en) | Boiler coal interruption detecting device and method | |
| CN104469284B (en) | The disconnected coal detection method of CFBB | |
| CN102608005B (en) | Piezoelectric sensor-based on-line measurement device and method for particle size distribution | |
| CN101476734B (en) | Intelligent ash blowing control system based on sound wave gas temperature field measuring technique | |
| CN108021743B (en) | Soft measurement method for coal entering amount of double-inlet and double-outlet coal mill | |
| CN101655700B (en) | Topographic control system of waste pyrolysis | |
| CN105698158A (en) | Method for detecting four-tube leak of circulating fluidized bed boiler | |
| CN104864382B (en) | Dry burning-resistant protecting device and method for gas/oil boiler | |
| CN110345463A (en) | A kind of boiler incipient fault recognition methods and device | |
| CN105806756A (en) | Method for correcting indoor and outdoor PM2.5 monitoring results by virtue of meteorological parameters | |
| Tan et al. | Monitoring near burner slag deposition with a hybrid neural network system | |
| CN104525377B (en) | A kind of online fault detect of electric cleaner, correction system | |
| CN110411397A (en) | A kind of on-Line Monitor Device and method of horizontal flue bottom dust stratification thickness | |
| CN204241702U (en) | Circulating Fluidized Bed Boiler is broken coal pick-up unit | |
| CN103742902A (en) | Heat signal monitoring system and method of circulating fluidized bed boiler |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |