CN106970035B - Signal processing method based on CVAFS method measurement coal steam-electric plant smoke mercury concentration - Google Patents
Signal processing method based on CVAFS method measurement coal steam-electric plant smoke mercury concentration Download PDFInfo
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
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- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
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- G01N2021/3107—Cold vapor, e.g. determination of Hg
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Abstract
The present invention provides a kind of signal processing method based on CVAFS method measurement coal steam-electric plant smoke mercury concentration.The method comprise the steps that the acquisition of mercury analyzer output voltage signal；The pretreatment of voltage signal data；Voltage curve carries out piecewise polynomial fitting；Corresponding state space equation is established for the multinomial fitted；The processing of multi-model Extended Kalman filter is carried out for signals and associated noises.The present invention is used to be difficult to determining situation based on system exact mechanism model in CVAFS method coal steam-electric plant smoke mercury concentration on-line monitoring field, can realize the filtering processing of photomultiplier tube output signal through the invention, method is simply easily implemented, good wave filtering effect.
Description
Technical field:
The present invention relates to a kind of signal processing methods based on CVAFS method measurement coal steam-electric plant smoke mercury concentration, belong to signal
Processing technology field.
Background technique:
A kind of trace heavy metals of the mercury as severe toxicity, the potential lasting harmfulness shown in environmental pollution are more next
More by attention both domestic and external.2011, " fossil-fuel power plant atmospheric pollutant emission standard " (GB13223- of Environmental Protection in China portion publication
2011) mercury is incorporated into control scope, it is specified that coal-burning power plant's mercury and mercuric compounds maximum emission is 0.05mg/m3, 2015
This standard is increased to 0.03mg/m again3.Although current China limits newly-built coal-burning power plant, it is still China's power generation master
Power, so being particularly important to the monitoring of coal-burning power plant's mercury contaminants discharge with control.
On-line continuous monitoring system Hg-CEM is mainly used to the monitoring of gas mercury concentration at present, and it is main within the system
Mercury in flue gas is measured by Cold vapour-atomic absorption spectrometry (CVAAS) and cold steam atomic fluorescence spectrometry (CVAFS)
Concentration.Wherein CVAFS is most common method, it is that a kind of specific ground state atom is swashed because absorbing the radiation of specific wavelength
Hair, excited atom are launched the fluorescence of characteristic wavelength in the form of light radiation during deexcitation, are measured by detector
The fluorescence intensity that atom issues is to realize the trace analysis methods measured to concentration of element.Have relative to CVAAS, CVAFS
The advantages that sensitivity is higher, the range of linearity is wider, detection limit is lower, spectra1 interfer- is smaller, equipment is simpler.
Foreign countries' mainstream mercury online monitoring instruments have Tekran and Thermo Scientific at present, and equipment price is very
Valuableness, the relevant technologies secrecy.And domestic gas mercury on-line monitoring technique research is started late, not mature enough, reliability and accurate
Property is all to be improved.In order to produce, to adapt to coal-burning power plant, China operation characteristic, measurement accuracy high, cheap and with China
The mercury shape of independent intellectual property/concentration online monitoring instruments.The present invention starts with from the output voltage signal of mercury analyzer, needle
Software filtering processing is carried out to noise-containing voltage signal, to guarantee that the mercury concentration value finally obtained is accurate and reliable.
The basic principle that mercury analyzer measures gas mercury concentration is that mercury cold steam is excited under the irradiation of light source, is swashed
Mercury atom after hair can issue fluorescence when returning to ground state, which is detected by photomultiplier tube, and then determine quilt
Survey mercury concentration.Thus, it is ensured that the concentration accuracy and accuracy of measurement, it is necessary to photomultiplier tube and its interlock circuit
Characteristic and its noise are analyzed.
Guo Congliang etc. establishes photon noise, photocathode noise, two on the basis of analyzing photomultiplier tube global noise
The mathematical model of secondary shot noise, dynode crosstalk and overall noise is the based Denoising and relevant matches of photomultiplier tube
The design of circuit provides theoretical basis.For above-mentioned noise, mainly takes following steps to reduce the noise at present, advise first
The design and manufacture and operating condition of model photomultiplier tube, a series of circuits of secondary design carry out the voltage exported to photomultiplier tube
Signal carries out hardware filtering, and the signal that final process obtains is ideal.By analyzing the above method, it is not directed to soft
Part filtering, and it is relative complex cumbersome in the design of hardware circuit.The present invention answers multi-model Extended Kalman filter method
For providing new method and new approaches for the signal processing of mercury analyzer in the nonlinear system.Place is being filtered to signal
First have to obtain corresponding state space equation before reason, and currently used empirical model is Gaussian, by by this model
It is compared with actual curve and finds that the error of the model is larger, this certainly will will affect final filter effect.
Summary of the invention
It is a kind of based on CVAFS method measurement coal steam-electric plant smoke mercury the purpose of the present invention is in view of the above problems, providing
The signal processing method of concentration is filtered using multi-model Kalman filtering algorithm, thus what guarantee finally obtained
Mercury concentration value is accurate and reliable.
Above-mentioned purpose is achieved through the following technical solutions:
Based on the signal processing method of CVAFS method measurement coal steam-electric plant smoke mercury concentration, this method comprises: mercury analyzer exports
The acquisition of voltage signal；The pretreatment of voltage signal data；Voltage curve carries out piecewise polynomial fitting；It is more for what is fitted
Item formula establishes corresponding state space equation；The processing of multi-model Extended Kalman filter is carried out for signals and associated noises.
Further, the acquisition of the mercury analyzer output voltage signal is from glimmering caused by Trace Hg for detecting
The hardware filtering of the photomultiplier tube of luminous intensity treated output voltage signal.
Further, the voltage signal data pretreatment includes the following steps:
S11, from several wave crests of acquisition, look for a peak at random, under the premise of meeting Shannon's sampling theorem, obtain electricity
Press sample value；
S12, corresponding voltage curve is drawn out according to the voltage example values of acquisition.
Further, the voltage curve piecewise polynomial fitting includes the following steps:
S21, the characteristic according to voltage curve carry out segment processing to voltage curve, and separation is to be divided into peak at peak value
Rise peak and lower decresting；
S22, fitting of a polynomial is carried out respectively for rising peak and lower decresting, the order of fitting is from 1 to 6, by fitting
Both the complexity of curvilinear function and fitting precision comprehensively consider, and determine appropriate order, wherein polynomial fitting method passes through
Polyfit function is realized in MATLAB, examines the whole fitting to imitate with root-mean-square error RMSE and mean absolute error MAE
The expression formula of fruit, RMSE and MAE are as follows:
Wherein, N is number of samples, and yi is the voltage value after i-th of fitting,For i-th of sample voltage true value.
Further, the system state space equation of establishing includes the following steps:
S31, the multinomial according to fitting determine quantity of state, state matrix, observed quantity and observation in state space equation
Matrix；
S32, by analyzing mercury analyzer, the characteristic of noise in voltage signal is determined, thus in voltage signal
Add this noise like.
Further, the multi-model Extended Kalman filter is particularly directed to two state space equations established,
Using the identical expanded Kalman filtration algorithm of two principles, examined by root-mean-square error RMSE and mean absolute error MAE
Test whole filter effect, the expression formula of RMSE and MAE are as follows:
Wherein, N is number of samples, and Zi is the voltage value after i-th of filtering processing.
The utility model has the advantages that
Compared with prior art, the invention has the following advantages:
1. being suitable for mechanism model is difficult to determining system；
2. the effect of piecewise polynomial fitting is not only above whole fitting of a polynomial, but also is higher than existing Empirical Mode
Type；
3. piecewise polynomial fitting is not only simple, but also any order derivative can be asked, the fitness with Extended Kalman filter
Height does not appear in the problem of its Jacobian matrix is not present when seeking observing matrix；
4. multi-model spreading kalman is applied in mercury analyzer signal processing compared to digital filter, filtering effect
Fruit is more preferable.
5. under conditions of meeting certain filtering requirements, the present invention is designed soft relative to conventional hardware filtering method
Part filtering method is simpler, is more convenient, it is easier to implement.
Detailed description of the invention
Fig. 1 TekranMercury Data in Tekran 2600Mercury Analysis System
The wave crest figure that generates that treated when mercury content in certain period monitoring coal-fired plant flue gas provided in System；
Fig. 2 is simulation curve of the voltage wave crest of the present invention in MATLAB；
Fig. 3 is the fitting effect comparison diagram of piecewise polynomial and whole multinomial, empirical model of the invention；
Fig. 4 is the flow chart of multi-model Kalman filtering of the invention；
Fig. 5 is the filter effect figure of multi-model Extended Kalman filter of the invention to noisy voltage signal；
Fig. 6 is the filter effect figure of digital filter of the invention to noisy voltage signal.
Specific embodiment
With reference to embodiment, the present invention is furture elucidated, it should be understood that following specific embodiments are only used for
It is bright the present invention rather than limit the scope of the invention.
Below by the present invention is further illustrated with Figure of description in conjunction with the embodiments.
A kind of signal processing method based on CVAFS method measurement coal steam-electric plant smoke mercury concentration, including mercury analyzer output electricity
Press the acquisition of signal；The pretreatment of voltage signal data；Voltage curve carries out piecewise polynomial fitting；It is multinomial for what is fitted
Formula establishes corresponding state space equation；The processing of multi-model Extended Kalman filter is carried out for signals and associated noises.
Embodiment 1:
The mercury analyzer voltage signal is in Tekran 2600Mercury Analysis System
Mercury analyzer generates when mercury content in certain period monitoring coal-fired plant flue gas provided in TekranMercury Data System
Hardware filtering treated wave crest, as shown in Figure 1.
Further, the voltage signal data pretreatment includes the following steps:
S11, from several wave crests of acquisition, look for a peak at random, the present invention takes second peak, and Shannon sampling is fixed meeting
Under the premise of reason, 23 groups of voltage values are obtained；
S12, will acquire 23 group voltage value import in MATLAB, draw out Voltage Peak curve as shown in Figure 3.
Further, the voltage curve piecewise polynomial fitting includes the following steps:
S21, the characteristic according to voltage curve carry out segment processing to voltage curve, and separation is at peak value, in this way by peak
It is divided into and rises peak and lower decresting；
S22, fitting of a polynomial is carried out respectively for rising peak and lower decresting, the order of fitting is from 1 to 6, by fitting
Both the complexity of curvilinear function and fitting precision comprehensively consider, and determine appropriate order.Wherein polynomial fitting method passes through
Polyfit function is realized in MATLAB.Present invention determine that rising peak and the order of polynomial fitting of lower decresting be 4
Rank, note rises peak and lower decresting polynomial fitting is respectively ya and yb, expression are as follows:
Ya=a1x4+a2x3+a3x2+a4x+a5
Yb=b1x4+b2x3+b3x2+b4x+b5
Piecewise polynomial fitting effect picture is as shown in figure 4, piecewise polynomial and the fitting of whole multinomial, empirical model
Root-mean-square error RMSE and mean absolute error MAE are as shown in table 1.
The comparison of 1 fitting effect error of table
| Fitting effect measurement standard | Piecewise fitting | Overall fit | Empirical model |
| RMSE | 0.7430 | 1.4347 | 8.2549 |
| MAE | 0.5447 | 1.0253 | 4.7585 |
As can be seen from Table 1: relative to overall fit and empirical model, the RMSE and MAE of piecewise fitting are minimum, that is, are fitted
Effect is best.
Further, the system state space equation of establishing includes the following steps:
S31, the multinomial according to fitting determine quantity of state, state matrix, observed quantity and observation in state space equation
Matrix.Wherein quantity of state is respectively Xa=[a1, a2, a3, a4, a5]T, Xb=[b1, b2, b3, b4, b5]T；State matrix Φ points
Not are as follows:
Observed quantity is respectively Za=a1x4+a2x3+a3x2+ a4x+a5, Zb=b1x4+b2x3+b3x2+b4x+b5；Observing matrix
It is respectively as follows:
S32, by analyzing mercury analyzer, determine the characteristic of noise in voltage signal, determine system noise and sight
Noise is surveyed, to add this noise like in voltage signal.For observation noise, according to the photomultiplier tube of the foundation such as Guo Congliang
The mathematical model of correlated noise can be approximately white Gaussian noise noise present in mercury analyzer voltage signal.The present invention
What is added in MATLAB emulation is the white Gaussian noise that variance is 5.Once state is just and since fluorescence is detected
It determines, and theoretical model will not change over time, so system noise is 0 herein.
By above-mentioned two step, two state space equations finally obtaining are as follows:
(1) rise peak system equation are as follows:
Observational equation are as follows:
Za(k)=a1 (k) x4+a2(k)x3+a3(k)x2+ a4 (k) x+a5 (k)+v (k),
(2) decresting system equation under are as follows:
Observational equation are as follows:
Zb(k)=b1 (k) x4+b2(k)x3+b3(k)x2+ b4 (k) x+b5 (k)+v (k),
It is 0 that wherein w, which is mean value for 0, v, the white Gaussian noise that variance is 5.
Further, the multi-model Extended Kalman filter is particularly directed to two state space equations established,
It is filtered using the identical expanded Kalman filtration algorithm of two principles, the specific process that filters is as shown in figure 4, in the figure
N is the number of sampled point, and in order to fully demonstrate filter effect, this number of sampling points takes 200, wherein rising peak is 50, under
Decresting is 150.The cycle-index for rising peak Extended Kalman filter is 6000, the circulation time of lower decresting Extended Kalman filter
Number is 5000.Multi-model Extended Kalman filter effect picture is as shown in Figure 6.In order to verify the superiority of inventive algorithm, to containing
There is the voltage signal of same noise to carry out digital filter processing, digital filter coefficient is 0.5, and filter effect figure is as shown in Figure 6.
Multi-model Extended Kalman filter with after digital filter root-mean-square error RMSE and mean absolute error MAE it is as shown in table 2.
The comparison of 2 filter effect of table
| Filter effect measurement standard | Multi-model Extended Kalman filter | Digital filter |
| RMSE | 0.7502 | 1.8308 |
| MAE | 0.6056 | 1.4633 |
As can be seen from Table 2: the RMSE and MAE after the multi-model Extended Kalman filter based on piecewise polynomial model are equal
Less than digital filter, illustrate that the multi-model Extended Kalman filter effect based on piecewise polynomial model is more preferable.
Claims (1)
1. a kind of signal processing method based on CVAFS method measurement coal steam-electric plant smoke mercury concentration, it is characterised in that: this method packet
It includes: the acquisition of mercury analyzer output voltage signal；The pretreatment of voltage signal data；It is quasi- that voltage curve carries out piecewise polynomial
It closes；Corresponding state space equation is established for the multinomial fitted；Multi-model spreading kalman is carried out for signals and associated noises
Filtering processing；
The acquisition of the mercury analyzer output voltage signal is from the photoelectricity for detecting fluorescence intensity caused by Trace Hg
The hardware filtering of multiplier tube treated output voltage signal；
The voltage signal data pretreatment includes the following steps:
S11, from several wave crests of acquisition, look for a peak at random, under the premise of meeting Shannon's sampling theorem, obtain voltage sample
This value；
S12, corresponding voltage curve is drawn out according to the voltage example values of acquisition；
The voltage curve piecewise polynomial fitting includes the following steps:
S21, the characteristic according to voltage curve carry out segment processing to voltage curve, and separation is that peak is divided into rising at peak value
Peak and lower decresting；
S22, fitting of a polynomial is carried out respectively for rising peak and lower decresting, the order of fitting is from 1 to 6, by matched curve
Both the complexity of function and fitting precision comprehensively consider, and determine appropriate order, wherein polynomial fitting method passes through
Polyfit function is realized in MATLAB, examines the whole fitting to imitate with root-mean-square error RMSE and mean absolute error MAE
The expression formula of fruit, RMSE and MAE are as follows:
Wherein, N is number of samples, and yi is the voltage value after i-th of fitting,For i-th of sample voltage true value；
The system state space equation of establishing includes the following steps:
S31, the multinomial according to fitting determine quantity of state, state matrix, observed quantity and observation square in state space equation
Battle array；
S32, by analyzing mercury analyzer, the characteristic of noise in voltage signal is determined, to add in voltage signal
This noise like；
The multi-model Extended Kalman filter is particularly directed to two state space equations established, using two principle phases
Same expanded Kalman filtration algorithm, examines whole filtering to imitate by root-mean-square error RMSE and mean absolute error MAE
The expression formula of fruit, RMSE and MAE are as follows:
Wherein, N is number of samples, and Zi is the voltage value after i-th of filtering processing.
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| CN101226147A (en) * | 2008-01-16 | 2008-07-23 | 浙江大学 | Gas concentration quantitative analyzer |
| CN101753513A (en) * | 2010-01-21 | 2010-06-23 | 复旦大学 | Doppler frequency and phase estimation method based on polynomial forecasting model |
| CN102650527A (en) * | 2012-05-25 | 2012-08-29 | 北京航空航天大学 | Temperature compensation method for denoising fiber-optic gyroscope on basis of time series analysis |
| WO2013016438A2 (en) * | 2011-07-26 | 2013-01-31 | General Electric Company | Wastewater treatment plant online monitoring and control |
| CN105866504A (en) * | 2016-03-23 | 2016-08-17 | 东南大学 | Fiber current transformer temperature compensation method based on Kalman filtering |
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| CN102435549A (en) * | 2011-09-16 | 2012-05-02 | 北京雪迪龙科技股份有限公司 | Flue gas mercury analyzer |
| CN106324521B (en) * | 2016-09-05 | 2018-09-11 | 北京理工大学 | A kind of method of Combined estimator electrokinetic cell system parameter and state-of-charge |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101226147A (en) * | 2008-01-16 | 2008-07-23 | 浙江大学 | Gas concentration quantitative analyzer |
| CN101753513A (en) * | 2010-01-21 | 2010-06-23 | 复旦大学 | Doppler frequency and phase estimation method based on polynomial forecasting model |
| WO2013016438A2 (en) * | 2011-07-26 | 2013-01-31 | General Electric Company | Wastewater treatment plant online monitoring and control |
| CN102650527A (en) * | 2012-05-25 | 2012-08-29 | 北京航空航天大学 | Temperature compensation method for denoising fiber-optic gyroscope on basis of time series analysis |
| CN105866504A (en) * | 2016-03-23 | 2016-08-17 | 东南大学 | Fiber current transformer temperature compensation method based on Kalman filtering |
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