CN102607009A - Fouling monitoring system for convection heating surface of boiler - Google Patents
Fouling monitoring system for convection heating surface of boiler Download PDFInfo
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- CN102607009A CN102607009A CN2012100401582A CN201210040158A CN102607009A CN 102607009 A CN102607009 A CN 102607009A CN 2012100401582 A CN2012100401582 A CN 2012100401582A CN 201210040158 A CN201210040158 A CN 201210040158A CN 102607009 A CN102607009 A CN 102607009A
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- heating surface
- boiler
- convection heating
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Abstract
The invention discloses a fouling monitoring system for a convection heating surface of a boiler, which belongs to the technical field of boiler combustion monitoring, and comprises a plurality of sound generators, sound receivers, a signal conditioner, a junction box, a power amplifier and an input-output device. The sound generators are arranged on one or a plurality of planes around a water cooling wall of the boiler. The sound receivers are installed on sound guide tubes of the sound generators. The sound generators are connected with the power amplifier. The sound receivers are connected with the signal conditioner. The power amplifier is connected with the signal conditioner through the junction box and the input-output device. The fouling monitoring system is capable of accurately measuring a smoke-side temperature field on the convection heating surface of the boiler so as to monitor alerts upon fouling of the convection heating surface of the boiler by the aid of data in a power plant DCS (distributed control system) database.
Description
Technical field
The invention belongs to the boiler combustion monitoring technical field, relate in particular to the grey fouling monitoring of a kind of boiler convection heating surface system.
Background technology
The thermal power generation unit boiler of liquid fuel that uses solid fuel in (like coal) or contain ash can produce slagging scorification at furnace wall or heating surface because the sticky particle of fusing that high-temperature flue gas is carried secretly or partial melting collides when normal operation.Deposit on heating surface and temperature is lower than the soot particle of ash fusion point, then can cause dust stratification.In case boiler heating surface forms dust stratification or slagging scorification; The consequence that can cause the heat-transfer capability of heating surface to reduce, the working medium caloric receptivity reduces, the fume side temperature raises and influences the economy of boiler; And then reduce power plant efficient, and seriously then cause unexpected blowing out, directly jeopardize the security of operation of boiler.
For solving the above-mentioned problem of being brought by the dirt of boiler convection heating surface ash, each power plant all is furnished with soot blower.The grey dirty of boiler convection heating surface cleans through the grey mode of blowing at regular time and quantity in power plant.But this method usually makes the top blast ash that is heated too frequent or to blow grey dynamics not enough.It is too frequent to blow ash, and not only grey employed working medium is blown in waste, and causes heating surface mechanical fatigue and heat fatigue aggravation, and the life-span of heating surface reduces; It is not enough to blow grey dynamics, not only can cause the dirty situation aggravation of heating surface ash, and cause heating surface formation to be difficult to dispose slag blanket easily.
Summary of the invention
The objective of the invention is to, to the dirty problem of boiler convection heating surface ash, propose the grey fouling monitoring of a kind of boiler convection heating surface system, it is dirty to be used for on-line real time monitoring boiler convection heating surface ash, and then instructs the dirty removing work of boiler convection heating surface ash.
For realizing above-mentioned purpose; Technical scheme provided by the invention is; A kind of boiler convection heating surface ash fouling monitoring system is characterized in that said system comprises: be installed in a plurality of sonic generators on one or more plane around the boiler water wall, be installed in acoustic receiver, signal conditioner, terminal box, power amplifier and input/output unit on the acoustic waveguide tube of sonic generator;
Said sonic generator links to each other with power amplifier;
Said acoustic receiver links to each other with signal conditioner;
Said power amplifier links to each other with input/output unit through terminal box with signal conditioner.
Saidly be installed in that the sonic generator on the same plane is an even number around the boiler water wall, and each sonic generator the position on the boiler water wall all with same plane on another sonic generator symmetry.
Said acoustic receiver is an even number.
The present invention can accurately measure boiler convection heating surface fume side temperature field, in conjunction with the data in the Power Plant DCS database, realizes the dirty monitoring of reporting to the police of boiler convection heating surface ash.
Description of drawings
Fig. 1 is a boiler convection heating surface ash fouling monitoring system architecture sketch map;
Fig. 2 is a single path acoustic thermometry sketch map;
Fig. 3 is the neutral net sketch map.
The specific embodiment
Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that following explanation only is exemplary, rather than in order to limit scope of the present invention and application thereof.
Fig. 1 is a boiler convection heating surface ash fouling monitoring system architecture sketch map, and among Fig. 1, boiler convection heating surface ash fouling monitoring system comprises sonic generator 1, acoustic receiver 2, signal conditioner, terminal box, power amplifier and input/output unit.
Wherein, a plurality of sonic generators 1 are installed on one or more plane around the boiler water wall, and acoustic receiver 2 is installed on the acoustic waveguide tube 3 of sonic generator 1.Sonic generator 1 links to each other with power amplifier, and acoustic receiver 2 links to each other with signal conditioner, and power amplifier links to each other with input/output unit through terminal box with signal conditioner.
Input/output unit generally links to each other with processing terminal, and processing terminal can adopt desktop computer, notebook or work station.The program of the program of installation and control sonic generator and reception acoustic signals on the processing terminal.Input/output unit is used under the control of processing terminal, sends the sound wave generation signals through terminal box to power amplifier.Simultaneously, input/output unit is used for also being used for the sound wave reception signal that receiving signal reason device sends through terminal box.
Power amplifier is used to receive the sound wave generation signals and the guide sound wave producer sends sound wave.
Sonic generator is used to send the sound wave of CF.
Acoustic receiver is used to receive the sound wave that sonic generator sends, and the sound wave that receives is sent to signal conditioner.
Signal conditioner is used for converting the sound wave that acoustic receiver receives to sound wave and receives signal and send to input/output unit through terminal box.
Fig. 2 is a single path acoustic thermometry sketch map.As shown in Figure 2; The preferred laying mode of sonic generator of the present invention and acoustic receiver is; Be installed in that the sonic generator on the same plane is an even number around the boiler water wall, and each sonic generator the position on the boiler water wall all with same plane on another sonic generator symmetry.Because sonic generator is corresponding each other with acoustic receiver, so acoustic receiver also is an even number.
This structure is a kind of single path acoustic construction, and is following based on the thermometric process of this structure:
The sound wave generation signals is sent by the sonic generator of burner hearth left side measuring point; Acoustic receiver by the left and right sides measures; Through the fly over measurement of time of sound wave; Can be used for confirming the average speed of sound wave on propagation path, following according to the wave equation of the equation of motion of plane wave, plane wave and principle equation that the equation of gas state is derived the sound wave thermometric:
In the formula, τ is the time of flying over, and L is the measuring point distance, and c is the spread speed of sound wave in the medium, and R is the perfect gas universal constant, and t is a gas temperature, and γ is the adiabatic exponent (ratio of specific heat at constant pressure and specific heat at constant volume) of gas, and m is a molecular weight gas.
When system brings into operation; The sonic generator that is installed in the boiler convection heating surface receives main program and produces the sound wave generation signals; Send sound wave through the power amplifier sonic generator and propagate into boiler furnace inside via acoustic waveguide tube; Acoustic receiver receives acoustic signals and passes back in main frame (processing terminal) main program via signal conditioner and input/output unit, main program calculate sound wave fly over the time with calculate boiler smoke side temperature.Result of calculation is deposited in the database, and database timer access Power Plant DCS data system is preserved desired data, so that neural network prediction boiler smoke side temperature.
Artificial neural network obtains the relation between input quantity and the output quantity through training.Artificial neural network comprises interconnected one by one neuron, is connected through linearity or nonlinear transfer function between the neuron.This structure makes artificial neural network can be applied to a nonlinear system.This paper sets up this title of BP network that model uses and derives from its learning algorithm: error back propagation (Error Back Propagation) learning algorithm, be called for short the BP algorithm, and the BP network is the network that adopts the BP learning algorithm.The classic BP network is the full feedforward network that connects, and can be divided into three parts: input layer, hidden layer and output layer.Input layer is accepted extraneous information, and hidden layer is the characteristic present layer, is used to store the characteristic information of learning object, and output layer is exported the network calculations result.
For input information; Want to propagate into forward on the node of hidden layer earlier; After the characteristic of each unit of process is activation primitive (being called again with function, transfer function or the mapping function etc.) computing of Sigmoid type, pass to output node to the output information of implicit node, provide the output result at last.The learning process of network is made up of forward and backpropagation two parts.In the forward-propagating process, the neuronic state of each layer only influences one deck neuroid down.If output layer can not obtain desired output, be exactly between real output value and the desired value error to be arranged, change back-propagation process so over to; Error signal is returned along original connecting path; Through revising the neuronic weights of each layer, propagate and calculate to input layer one by one, pass through the forward-propagating process again; Through the utilization repeatedly of these two processes, make error signal minimum.The neutral net sketch map is as attaching shown in 3.
After heating surface received grey dirt, its heat conductive efficiency can variation, for the boiler convection heating surface, just show in the minimizing of its heat, thus can be from this respect analysis, thus grey dirty characteristic coefficient extracted.
In certain slag blanket physics, chemical characteristic scope, ash deposition thickness δ is arranged
dCan try to achieve by following formula:
δ
d＝D
0·(q
notolean/q
clean) (0-1)
In the formula,
D
0-constant;
q
NotoleanThe actual caloric receptivity of-heating surface;
q
CleanThe potential caloric receptivity of-cleaning heating surface
The potential caloric receptivity of cleaning heating surface be exactly monitored heating surface under actual operating mode, the potential caloric receptivity in heating surface when cleaning.Therefore, can define grey dirty characteristic parameter is:
T＝1-Q
sj/Q
qj (0-2)
In the formula:
Q
Sj: the actual caloric receptivity of heating surface
Q
Qj: cleaning heating surface caloric receptivity
When heating surface cleaned, T was a minimum of a value, promptly goes to zero; When dirt was very thick, tube wall temperature approached flue-gas temperature, and the heating surface caloric receptivity goes to zero, and it is maximum that T reaches, and promptly is tending towards 1.Can see the grey dirty situation of the T fine reaction of ability from top analysis, therefore, select the parameter of grey dirty characteristic parameter T as the grey dirty degree of monitoring.
For monitored heating surface, the actual caloric receptivity Q of heating surface
SjCan calculate through recording temperature, pressure and the flow of importing and exporting working medium; And these parameters of temperature, pressure and flow can obtain through power plant existing data collecting system (DAS) very accurately, obtain the actual caloric receptivity Q of heating surface then by following formula
Sj:
Q
sj＝D(h
2-h
1) (0-3)
Wherein:
D: the working medium flow that heating surface is corresponding;
h
2: the sender property outlet enthalpy;
h
1: working medium import enthalpy;
Can calculate the actual caloric receptivity of each heating surface so accurately through the existing data collecting system of power plant (DAS).
Potential caloric receptivity under each heating surface clean condition is the variable that receives many factor affecting, demonstrates very strong non-linear characteristics, and problem solves through setting up neural network model hereto.
In the practical applications process, the great majority of running into all are nonlinear problems, can not its piece-wise linearization understood and handle.And the mathematical tool of analyzing nonlinear system is considerably less.For the non-linear system of essence; Common least square method has been difficult to use; Because the model that this type systematic is corresponding is difficult to change into the least square form; Promptly about the linear shape model of parameter space, and, often need about being identified the various prioris and the hypothesis such as version of system for the identification problem of non-nonlinear system.Therefore, they carry out to some special non-nonlinear system basically.
The process of setting up of artificial neural network is comparatively simple, need not understand in depth research object, and it is to simulate research object in the subordinate act, and and lets pass the inherent mechanism of research object.This point can solve the problem that is difficult for setting up Mathematical Modeling because research object is complicated especially.In addition, because artificial neural network only has from being input to the mapping ability of output, so except output valve, it no longer provides other information, neutral net has been arranged just, the identification of nonlinear system just becomes possibility.It is input, the output data through the Direct Learning system that neutral net is discerned system; The destination of study is to make desired error function reach minimum; Thereby summarize the relation that lies in system's input, the output data, this relation lies in neutral net inside.
For a specific heating surface, through analyzing, can use the intraductal working medium flow, intraductal working medium pressure, the intraductal working medium inlet temperature, intake, Coal-fired capacity, burner this Several Parameters of mode that puts into operation characterizes operating condition.
Cleaning heating surface caloric receptivity q
Qj=f (furnace outlet gas temperature, intraductal working medium flow, intraductal working medium pressure, intraductal working medium inlet temperature, intake, Coal-fired capacity, the burner mode that puts into operation).
Cleaning heating surface caloric receptivity, furnace outlet gas temperature, intraductal working medium flow, intraductal working medium pressure, intraductal working medium inlet temperature, intake, Coal-fired capacity, burner this Several Factors of mode that puts into operation has constituted a continuous nonlinear dynamic system of time like this, and the cleaning heating surface caloric receptivity amount of required identification just.Through the heating surface caloric receptivity of different operating modes under the DAS of the power plant system acquisition heating surface clean conditions, as sample network is trained, stain identification and the prediction of back to be implemented in heating surface to cleaning heating surface caloric receptivity.
Realize that whole flow process of the present invention controlled by main program, main program is based on Labview software, generation signal, the reception of acoustic signals, the sound wave that comprises sound wave fly over time measurement and boiler smoke side temperature computation.During running software, send instruction hardware is operated successively, data acquisition; Calculate boiler convection heating surface fume side temperature field, the result is saved in the database SQL system, database SQL system visit Power Plant DCS database; Related data input BP neutral net input layer; The potential caloric receptivity of heating surface during BP neural network prediction cleaning, calculation procedure calculate heating surface caloric receptivity actual time, calculate the dirty coefficient of boiler convection heating surface ash; Deposit the database SQL system in, so that software provides flue-gas temperature field, the dirty coefficient of each convection heating surface ash, related data variation tendency and grey dirty alarm signal.
The above; Be merely the preferable specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, any technical staff who is familiar with the present technique field is in the technical scope that the present invention discloses; The variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of claim.
Claims (3)
1. boiler convection heating surface ash fouling monitoring system is characterized in that said system comprises: be installed in a plurality of sonic generators on one or more plane around the boiler water wall, be installed in acoustic receiver, signal conditioner, terminal box, power amplifier and input/output unit on the acoustic waveguide tube of sonic generator;
Said sonic generator links to each other with power amplifier;
Said acoustic receiver links to each other with signal conditioner;
Said power amplifier links to each other with input/output unit through terminal box with signal conditioner.
2. the grey fouling monitoring of a kind of boiler convection heating surface according to claim 1 system; It is characterized in that saidly being installed in that the sonic generator on the same plane is an even number around the boiler water wall, and each sonic generator the position on the boiler water wall all with same plane on another sonic generator symmetry.
3. the grey fouling monitoring of a kind of boiler convection heating surface according to claim 2 system is characterized in that said acoustic receiver is an even number.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109934417A (en) * | 2019-03-26 | 2019-06-25 | 国电民权发电有限公司 | Boiler coke method for early warning based on convolutional neural networks |
| CN111242279A (en) * | 2020-01-03 | 2020-06-05 | 浙江浙能技术研究院有限公司 | System and method for predicting slagging of hearth of ultra-supercritical pulverized coal boiler |
| CN116720446A (en) * | 2023-07-16 | 2023-09-08 | 天津大学 | Method for monitoring thickness of slag layer of water-cooled wall of boiler in real time |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111242279B (en) * | 2020-01-03 | 2023-09-08 | 浙江浙能技术研究院有限公司 | Slag bonding prediction system and method for ultra-supercritical pulverized coal boiler furnace |
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| CN116720446B (en) * | 2023-07-16 | 2023-11-21 | 天津大学 | Method for monitoring thickness of slag layer of water-cooled wall of boiler in real time |
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Application publication date: 20120725 |