CN104331736B - Ultra-supercritical boiler NOx emission dynamic prediction method based on RBF neural - Google Patents
Ultra-supercritical boiler NOx emission dynamic prediction method based on RBF neural Download PDFInfo
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- 230000001537 neural effect Effects 0.000 title claims abstract description 21
- 238000000034 method Methods 0.000 title claims abstract description 20
- 230000003068 static effect Effects 0.000 claims abstract description 16
- 238000012549 training Methods 0.000 claims abstract description 14
- 238000013528 artificial neural network Methods 0.000 claims abstract description 10
- 239000003245 coal Substances 0.000 claims description 17
- 239000000446 fuel Substances 0.000 claims description 17
- 230000006870 function Effects 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 10
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 6
- 239000001301 oxygen Substances 0.000 claims description 6
- 229910052760 oxygen Inorganic materials 0.000 claims description 6
- 238000009423 ventilation Methods 0.000 claims description 5
- 210000005036 nerve Anatomy 0.000 abstract description 4
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 28
- 239000000523 sample Substances 0.000 description 13
- 230000008569 process Effects 0.000 description 6
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- 238000012360 testing method Methods 0.000 description 4
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- 230000000052 comparative effect Effects 0.000 description 2
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- 238000004519 manufacturing process Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000010926 purge Methods 0.000 description 2
- GQPLMRYTRLFLPF-UHFFFAOYSA-N Nitrous Oxide Chemical class [O-][N+]#N GQPLMRYTRLFLPF-UHFFFAOYSA-N 0.000 description 1
- 206010037660 Pyrexia Diseases 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
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- 238000000738 capillary electrophoresis-mass spectrometry Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
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Abstract
The invention discloses a kind of ultra-supercritical boiler NOx emission dynamic prediction methods based on RBF neural, belong to environment protection emission parameter measuring technical field.The present invention adopts the following technical scheme that：SS1 chooses static auxiliary variable and dynamic auxiliary variable；SS2 carries out RBF neural network structure fitting to the static auxiliary variable and the dynamic auxiliary variable, obtains the training emission of NOx of boiler dynamic prediction model based on RBF neural network structure；RBF neural parameter is adjusted, obtains the NOx emission dynamic prediction model of the ultra-supercritical boiler based on RBF networks.Under the conditions of identical training error etc. is set, the intrinsic nerve member number of dynamic model of the present invention is considerably less than static models, and model structure is simpler, and the training time is shorter, and generalization ability is stronger.
Description
Technical field
The present invention relates to the ultra-supercritical boiler NOx emission predictive method of current main-stream more particularly to based on RBF nerves
The ultra-supercritical boiler NOx emission dynamic prediction method of network, belongs to environment protection emission parameter measuring technical field.
Background technology
With the development of ultra supercritical thermoelectricity technology and the increasingly complexity of power production process, it is ensured that process units stabilization,
Efficiently, green operation, needs a pair significant process variable closely related with the safety in production, economical operation, environment protection emission of system
Carry out monitoring and optimal control in real time.However, some important parameters are often in the ring of high temperature, high pressure, dusty, highly corrosive
Border, it is difficult to directly measure.By taking coal-burning power plant's CEMS instrument as an example, when only having short-term by the data that sampling analysis device measures
Inevitably there is measuring point no signal or measurement error beyond allowed band, need artificial regular in the characteristics of effect property, longtime running
Inspection and maintenance (relevant industries standard regulation is calibrated daily with standard sample gas), probe timing purges, instrument periodic check etc., consumption
A large amount of manpower and material resources.
Nitrogen oxides (NOx) pollution that fire coal generates is the primary goal that China's nitrogen oxides is administered.The denitration of coal dust is made
In terms of with the same phase denitration including fugitive constituent and the out-phase denitration of coal tar two, mechanism is extremely complex.Meanwhile influence denitration efficiency
Factor is also very much, including fire coal kind, temperature, residence time, reburning fuel ratio, oxygen concentration, fineness of pulverized coal, Secondary Air air distribution again
Deng.Forecasting Methodology of the prior art chooses total fuel quantity, secondary air register aperture, CCOFA burnout degrees aperture, economizer exit oxygen
(O2), coal characteristic parameter (Car, Har, Qar, Nar, Q, Var), coal pulverizer ventilation quantity, coal pulverizer coal-supplying amount are measured as auxiliary
Variable.Qualified coal dust is worn into mill since fuel quantity instruction changes to feeder rotation speed change, raw coal, tube cell conveying, is arrived again
Burner injection is one big inertia, Large Time Delay Process in the fever of stove chamber inner combustion, especially in the dynamic process of lifting load,
The injection of burner moment has larger deviation in the coal dust in burner hearth and instantaneous coal-supplying amount.Instantaneous coal-supplying amount can not accurate characterization
Into the instantaneous coal dust amount of burner hearth, choose instantaneous coal-supplying amount (or feeder aperture) and be unsatisfactory for the spirit that modeling auxiliary variable is chosen
Quick property, accuracy requirement.
Neural network have powerful None-linear approximation performance, how by Radial Basis BP Neural Network (RBF) into
The method of Mobile state modeling becomes the task of top priority among being applied to the prediction of discharged nitrous oxides.It is badly in need of a kind of according to model being estimated
Meter or soft instrument solve the above problems.
Invention content
The present invention adopts the following technical scheme that：Ultra-supercritical boiler NOx emission dynamic prediction side based on RBF neural
Method, which is characterized in that include the following steps：
SS1 chooses static auxiliary variable and dynamic auxiliary variable；
SS2 carries out RBF neural network structure fitting to the static auxiliary variable and the dynamic auxiliary variable, obtains base
In the training emission of NOx of boiler dynamic prediction model of RBF neural network structure；The RBF neural includes input layer, implies
Layer, output layer, the input layer include n sampled point, and the hidden layer includes N number of node, and the output layer includes 1 RBF
Neural network exports, for arbitrary n sampled point { (xi,ti)|xi∈Rn,ti∈ R }, i=1,2 ..., n, structure are n-N-1's
RBF neural exports
Wherein wj(j=1,2 ..., N) it is weights of j-th of hidden layer node to output node layer；φj(x) (j=1,
2 ..., N) gaussian kernel function for j-th hidden layer node, i.e.,
Wherein cjFor the data center of kernel function, σjIt is described implicit for all samples for the extension constant of the kernel function
Node layer output matrix is
RBF neural output matrix form be
WhereinTo connect the weight matrix of hidden layer and output layer,Reality output matrix for RBF neural；
SS3 is adjusted RBF neural parameter, and the NOx emission for obtaining the ultra-supercritical boiler based on RBF networks is moved
State prediction model, by RBF neural output valve Y and actual value T=(t1 t2 ... tn) between error sum of squares conduct
The training objective error function of RBF neural, i.e.,
Make network output valve Y and actual value T=(t to find optimal output weights W1 t2 ... tn) between error put down
Side and and output weights W Norm minimums, RBF neural parameter is optimized in two steps：Pass through the wide of matrix φ first
The inverse optimal value for acquiring W of justice,
W=φ+T
Then using the error sum of squares of Y and T as object function, hidden node data center c is optimized by gradient descent algorithmj
And extension constant σj, object function is to cjAnd σjGradient be respectively：
Data center cjAnd extension constant σjMore new formula be：
η is learning rate, k=1,2 ..., n.
Preferably, static auxiliary variable goes out including total fuel quantity, secondary air register aperture, CCOFA burnout degrees aperture, economizer
Mouth oxygen amount, coal characteristic parameter, coal pulverizer ventilation quantity, dynamic auxiliary variable include total fuel quantity time delay elements one, total fuel quantity
Time delay elements two, total fuel quantity time delay elements three.
The advantageous effect that the present invention is reached：(1) optimization algorithm that provides of the present invention, effectively merged static modelling and
The advantages of dynamic modeling, chooses the time delay elements of important auxiliary variable (total fuel quantity) as dynamic auxiliary variable, passes through nerve
Network is fitted, and has obtained the model for fully including ultra-supercritical boiler pulverized coal preparation system dynamic characteristic；(2) identical training is being set
Under the conditions of error etc., the intrinsic nerve member number of dynamic model is considerably less than static models, and model structure is simpler, the training time
Shorter, generalization ability is stronger.
Description of the drawings
Fig. 1 is the operational flow diagram of the present invention.
Fig. 2 is NOx content estimated value and assay value Comparative result curve graph under large sample.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention
Technical solution, and be not intended to limit the protection scope of the present invention and limit the scope of the invention.
Subjects of the present invention become for Guohua Xuzhou Power Generation Co., Ltd. SG3099/27.46-M545 types ultra supercritical parameter
Pressure operation spirally-wound tubes Once-through Boiler, rises to 1000MW, then the process of 500MW is down to by 1000MW in 24 hours by load 500MW
Middle SCR Reactor inlets NOx content (butt standard state, 6%O2 states under).
Before model training is carried out, the invalid data collection that probe purge stages are locked first is weeded out, and data set is pressed
Illuminated (12) is normalized in the range of [- 1,1], and renormalization is carried out according still further to formula (13) when model is exported and restored.
X'=(2x-xmax-xmin)/(xmax-xmin) (12)
X=[x'(xmax-xmin)+xmax+xmin]/2 (13)
Extensive mean square deviation εMSEIt calculates as follows
N in formulatestFor test sample number, t'iBe normalization after i-th of sample actual value, x'iIt is i-th after normalizing
The predicted value of a sample.
Following scheme can be used in static state modeling method：Static scheme input includes：Total fuel quantity, secondary air register aperture (6
It is a), CCOFA burnout degrees aperture (4), oxygen content at economizer outlet, coal characteristic parameter (Car, Har, Qar, Nar, Q, Var),
The instantaneous coal-supplying amount (6) of coal pulverizer ventilation quantity (6), coal pulverizer, the altogether input of 30 auxiliary variables.
The modeling method of the present invention uses：Total fuel quantity, secondary air register aperture (6), CCOFA burnout degrees aperture (4),
Oxygen content at economizer outlet, coal characteristic parameter (Car, Har, Qar, Nar, Q, Var), coal pulverizer ventilation quantity (6), total fuel quantity
Time delay elements one, total fuel quantity time delay elements two, total fuel quantity time delay elements three, the altogether input of 27 auxiliary variables.
Selecting identical 300 operating modes, identical 100 operating modes are as test sample, training as training sample
Bias target is uniformly set as 0.3%.Shown in table one is dynamic model compared with the performance of two kinds of static models.
Table one
By table one it can be seen that the network structure that obtains of dynamic modelling method simplify, generalization ability it is strong.It is more preferable in order to obtain
Extensive effect, increase training sample number to 1000, separately take 1000 as test sample, training bias target is still set as
0.3%.Shown in table two be under large sample dynamic model compared with the performance of static models.
Table two
Shown in Fig. 2 is that dynamic model estimation output valve (dotted line) and analysis meter actual measurement import NOx contain under large sample
The Comparative result curve graph of (solid line) is measured, all experiments are completed under same PC, and allocation of computer is Core I5-
3230M, 4G RAM.Simulation software uses MATLAB R2014a, and operating system is WINDOWS 8.
NOx emission dynamic prediction can be implanted into industrial control process computer program by soft instrument mode.
The Major Difficulties of dynamic modeling are the selection of dynamic variable scheme and determining for dynamic characteristic time, can be with
Time domain width and dynamic auxiliary variable distribution density are determined by gridding method optimizing.Pass through the emulation ratio with static state modeling method
Compared with statistical data shows that dynamic modeling obtains preferable generalization ability with most succinct model structure, has higher engineering should
With value.
The above is only the preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, without departing from the technical principles of the invention, several improvement and deformation can also be made, these are improved and deformation
Also it should be regarded as protection scope of the present invention.
Claims (1)
1. the ultra-supercritical boiler NOx emission dynamic prediction method based on RBF neural, which is characterized in that including walking as follows
Suddenly：
SS1 chooses static auxiliary variable and dynamic auxiliary variable；The static state auxiliary variable includes total fuel quantity, secondary air register is opened
Degree, CCOFA burnout degrees aperture, oxygen content at economizer outlet, coal characteristic parameter, coal pulverizer ventilation quantity, the dynamic auxiliary variable
Including total fuel quantity time delay elements one, total fuel quantity time delay elements two, total fuel quantity time delay elements three；
SS2 carries out RBF neural network structure fitting to the static auxiliary variable and the dynamic auxiliary variable, is based on
The training emission of NOx of boiler dynamic prediction model of RBF neural network structure；The RBF neural includes input layer, implies
Layer, output layer, the input layer include n sampled point, and the hidden layer includes N number of node, and the output layer includes 1 RBF
Neural network exports, for arbitrary n sampled point { (xi,ti)|xi∈Rn,ti∈ R }, i=1,2 ..., n, structure are n-N-1's
RBF neural exports
Wherein wj(j=1,2 ..., N) it is weights of j-th of hidden layer node to output node layer；φj(x) (j=1,2 ...,
N) the gaussian kernel function for j-th of hidden layer node, i.e.,
Wherein cjFor the data center of kernel function, σjFor the extension constant of the kernel function, for all samples, the hidden layer section
Putting output matrix is
RBF neural output matrix form be
WhereinTo connect the weight matrix of hidden layer and output layer,Reality output matrix for RBF neural；
SS3 is adjusted RBF neural parameter, and the NOx emission dynamic for obtaining the ultra-supercritical boiler based on RBF networks is pre-
Model is surveyed, by RBF neural output valve Y and actual value T=(t1 t2 ... tn) between error sum of squares as RBF god
Training objective error function through network, i.e.,
Make network output valve Y and actual value T=(t to find optimal output weights W1 t2 ... tn) between error sum of squares
And output weights W Norm minimums, RBF neural parameter is optimized in two steps：Pass through the generalized inverse of matrix φ first
The optimal value of W is acquired,
W=φ+T
Then using the error sum of squares of Y and T as object function, hidden node data center c is optimized by gradient descent algorithmjAnd
Extend constant σj, object function is to cjAnd σjGradient be respectively：
Data center cjAnd extension constant σjMore new formula be：
η is learning rate, k=1,2 ..., n.
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| CN104763999A (en) * | 2015-03-04 | 2015-07-08 | 内蒙古瑞特优化科技股份有限公司 | Power plant pulverized coal boiler combustion performance online optimizing method and system |
| CN105629738B (en) * | 2016-03-24 | 2018-06-29 | 内蒙古瑞特优化科技股份有限公司 | SCR flue gas denitrification systems control method and equipment |
| CN106021916B (en) * | 2016-05-18 | 2018-08-21 | 厦门大学 | One kind being suitable for ultra-supercritical boiler NOxThe computational methods of discharge capacity analysis |
| CN108038561A (en) * | 2017-09-21 | 2018-05-15 | 南京航空航天大学 | A kind of Multipurpose Optimal Method of SCR denitration preformed catalyst |
| CN108956876B (en) * | 2018-07-12 | 2020-12-29 | 浙江大学 | Measurement delay correction method for smoke on-line continuous monitoring system |
| CN109187914B (en) * | 2018-09-18 | 2020-12-25 | 哈尔滨锅炉厂有限责任公司 | Coal quality characteristic-based method for predicting NOx generation amount of coal-fired power plant |
| CN110096785B (en) * | 2019-04-25 | 2020-09-01 | 华北电力大学 | Stack self-encoder modeling method applied to ultra-supercritical unit |
| CN112325329B (en) * | 2020-10-13 | 2021-11-02 | 华中科技大学 | High-temperature corrosion prevention boiler air door opening control method and system |
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| CN103115356A (en) * | 2013-02-07 | 2013-05-22 | 华北电力大学 | Monitoring method and optimal control method for supercritical circulating fluidized bed (CFB) boiler combustion signals |
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