CN106970035A - The signal processing method of coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods - Google Patents
The signal processing method of coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods Download PDFInfo
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- 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
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Abstract
The present invention provides a kind of signal processing method that coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods.The method of the present invention includes：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 set up for the multinomial fitted；Multi-model EKF processing is carried out for signals and associated noises.The present invention can realize the filtering process of photomultiplier output signal for the situation for being difficult to determine based on system exact mechanism model in CVAFS method coal steam-electric plant smoke mercury concentration on-line monitorings field by the present invention, and method simply easily be implemented, good wave filtering effect.
Description
Technical field：
The present invention relates to a kind of signal processing method that coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods, belong to signal
Processing technology field.
Background technology：
Mercury is as a kind of hypertoxic trace heavy metals, and the potential lasting harmfulness shown in environmental pollution is more next
More paid attention to by both domestic and external.2011, the issue of Environmental Protection in China portion《Fossil-fuel power plant atmospheric pollutant emission standard》(GB13223-
2011) mercury is incorporated into control category, regulation coal-burning power plant's mercury and mercuric compounds maximum emission is 0.05mg/m3, 2015
This standard is brought up into 0.03mg/m again3.Although current China limits newly-built coal-burning power plant, it is still China generating master
Power, so the monitoring discharged to coal-burning power plant's mercury contaminants is particularly important with control.
The monitoring to gas mercury concentration mainly uses on-line continuous monitoring system Hg-CEM at present, and main within the system
Mercury in flue gas is determined by Cold vapour-atomic absorption spectrometry (CVAAS) and cold steam atomic fluorescence spectrometry (CVAFS)
Concentration.Wherein CVAFS is most common method, and it is that a kind of specific ground state atom is swashed because absorbing the radiation of specific wavelength
Hair, excited atom is launched the fluorescence of characteristic wavelength in the form of light radiation during deexcitation, determined by detector
The fluorescence intensity that atom is sent is so as to realize the trace analysis methods determined to concentration of element.Have relative to CVAAS, CVAFS
The advantages of sensitivity is higher, the range of linearity is wider, detection limit is lower, spectra1 interfer- is smaller, equipment is simpler.
Foreign countries' main flow mercury on-line monitoring instrument has Tekran and Thermo Scientific at present, and its equipment price is very
Costliness, correlation technique secrecy.It is not mature enough and domestic gas mercury on-line monitoring technique research is started late, reliability and accurate
Property all has much room for improvement.In order to produce, to adapt to China coal-burning power plant's operation characteristic, measurement accuracy high, cheap and with China
The mercury shape of independent intellectual property/concentration on-line monitoring instrument.The present invention starts with from the output voltage signal of mercury analyzer, pin
To carrying out software filtering processing containing noisy voltage signal, so as to ensure the mercury concentration value finally obtained accurately and reliably.
The general principle that mercury analyzer determines gas mercury concentration is that mercury cold steam is excited under the irradiation of light source, is swashed
Mercury atom after hair can send fluorescence when returning to ground state, and the fluorescence intensity is detected by photomultiplier, and then determine quilt
Survey mercury concentration.Thus, it is ensured that the concentration accuracy and accuracy that determine, it is necessary to photomultiplier and its interlock circuit
Characteristic and its noise are analyzed.
Guo Congliang etc. establishes photon noise, photocathode noise, two on the basis of analysis photomultiplier global noise
The mathematical modeling of secondary shot noise, dynode crosstalk and overall noise, is the based Denoising and relevant matches of photomultiplier
The design of circuit provides theoretical foundation.For above-mentioned noise, mainly take following steps to reduce the noise at present, advise first
Model photomultiplier manufactures and designs and condition of work, and a series of its secondary design circuits carry out the voltage exported to photomultiplier
Signal carries out hardware filtering, and the signal that its final process is obtained is ideal.By analyzing the above method, it is not directed to soft
Part is filtered, and relative complex cumbersome in the design of hardware circuit.The present invention should by multi-model EKF method
For in the nonlinear system, new method and new approaches to be provided for the signal transacting of mercury analyzer.Place is being filtered to signal
First have to obtain corresponding state space equation before reason, and empirical model conventional at present is Gaussian, by by this model
Contrasted with actual curve and find that the error of the model is larger, this will certainly influence final filter effect.
The content of the invention
The purpose of the present invention is based on CVAFS methods measurement coal steam-electric plant smoke mercury for above-mentioned problem there is provided one kind
The signal processing method of concentration, processing is filtered using multi-model Kalman filtering algorithm, so as to ensure what is finally obtained
Mercury concentration value is accurately and reliably.
Above-mentioned purpose is achieved through the following technical solutions：
The signal processing method of coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods, this method includes：Mercury analyzer is exported
The acquisition of voltage signal；The pretreatment of voltage signal data；Voltage curve carries out piecewise polynomial fitting；It is many for what is fitted
Item formula sets up corresponding state space equation；Multi-model EKF processing is carried out for signals and associated noises.
Further, the acquisition of the mercury analyzer output voltage signal comes from glimmering produced by Trace Hg for detecting
Output voltage signal after the hardware filtering processing of the photomultiplier of luminous intensity.
Further, the voltage signal data pretreatment comprises the following steps：
S11, from several crests of acquisition, a peak is looked at random, on the premise of Shannon's sampling theorem is met, obtain electricity
Press sample value；
S12, according to the voltage example values of acquisition draw out corresponding voltage curve.
Further, the voltage curve piecewise polynomial fitting comprises the following steps：
S21, the characteristic according to voltage curve, segment processing is carried out to voltage curve, and separation is that at peak value, peak is divided into
Rise peak and lower decresting；
S22, for rising peak and lower decresting fitting of a polynomial is carried out respectively, the exponent number of fitting is from 1 to 6, by fitting
Both the complexity and fitting precision of curvilinear function consider, it is determined that appropriate exponent number, wherein polynomial fitting method pass through
Polyfit functions are realized in MATLAB, examine the overall fitting to imitate with root-mean-square error RMSE and mean absolute error MAE
Really, RMSE and MAE expression formula is as follows：
Wherein, N is number of samples, and yi is the magnitude of voltage after i-th of fitting,For i-th of sample voltage actual value.
Further, the system state space equation of setting up comprises the following steps：
S31, the multinomial according to fitting, determine quantity of state, state matrix, observed quantity and the observation in state space equation
Matrix；
S32, by analyzing mercury analyzer, the characteristic of noise in voltage signal is determined, so that in voltage signal
Add this noise like.
Further, the multi-model EKF is particularly directed to two state space equations set up,
Using two principle identical expanded Kalman filtration algorithms, examined by root-mean-square error RMSE and mean absolute error MAE
Test overall filter effect, RMSE and MAE expression formula are as follows：
Wherein, N is number of samples, and Zi is the magnitude of voltage after i-th of filtering process.
Beneficial effect：
Compared with prior art, the invention has the advantages that：
1. the system for being difficult to determine suitable for mechanism model；
2. the effect of piecewise polynomial fitting is not only above overall fitting of a polynomial, but also higher than existing Empirical Mode
Type；
3. piecewise polynomial fitting is not only simple, and any order derivative can be asked, the fitness with EKF
Height, does not appear in its Jacobian matrix non-existent problem when asking for observing matrix；
4. compared to digital filter, multi-model spreading kalman is applied in mercury analyzer signal transacting, it filters effect
Fruit is more preferably.
5. relative to conventional hardware filtering method, under conditions of certain filtering requirements are met, it is soft that the present invention is designed
Part filtering method is simpler, is more convenient, it is easier to implement.
Brief description of the drawings
Fig. 1 comes from TekranMercury Data in Tekran 2600Mercury Analysis System
Crest figure after the processing produced in certain period monitoring coal-fired plant flue gas provided in System during mercury content；
Fig. 2 is simulation curve of the voltage crest of the present invention in MATLAB；
Fig. 3 is piecewise polynomial of the invention and overall multinomial, the fitting effect comparison diagram of empirical model；
Fig. 4 is the flow chart of the multi-model Kalman filtering of the present invention；
Fig. 5 is the filter effect figure of multi-model EKF of the invention to noisy voltage signal；
Fig. 6 is the filter effect figure of digital filter of the invention to noisy voltage signal.
Embodiment
With reference to embodiment, the present invention is furture elucidated, it should be understood that following embodiments are only used for
The bright present invention rather than limitation the scope of the present invention.
Below by the present invention is further illustrated with Figure of description in conjunction with the embodiments.
A kind of signal processing method that coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods, 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 sets up corresponding state space equation；Multi-model EKF processing is carried out for signals and associated noises.
Embodiment 1：
The mercury analyzer voltage signal comes from Tekran 2600Mercury Analysis System
Mercury analyzer is produced during mercury content in certain period monitoring coal-fired plant flue gas provided in TekranMercury Data System
Hardware filtering processing after crest, as shown in Figure 1.
Further, the voltage signal data pretreatment comprises the following steps：
S11, from several crests of acquisition, a peak is looked at random, the present invention takes second peak, Shannon sampling is fixed meeting
On the premise of reason, 23 groups of magnitudes of voltage are obtained；
S12,23 groups of magnitudes of voltage of acquisition are imported in MATLAB, draw out Voltage Peak curve as shown in Figure 3.
Further, the voltage curve piecewise polynomial fitting comprises the following steps：
S21, the characteristic according to voltage curve, segment processing is carried out to voltage curve, and separation is at peak value, so by peak
It is divided into rising peak and lower decresting；
S22, for rising peak and lower decresting fitting of a polynomial is carried out respectively, the exponent number of fitting is from 1 to 6, by fitting
Both the complexity and fitting precision of curvilinear function consider, it is determined that appropriate exponent number.Wherein polynomial fitting method passes through
Polyfit functions are realized in MATLAB.Present invention determine that rising peak and the exponent number of polynomial fitting of lower decresting be 4
Rank, it is respectively ya and yb that note, which rises peak and lower decresting polynomial fitting, and expression is：
Ya=a1x4+a2x3+a3x2+a4x+a5
Yb=b1x4+b2x3+b3x2+b4x+b5
Piecewise polynomial fitting design sketch is as shown in figure 4, piecewise polynomial and overall multinomial, the fitting of empirical model
Root-mean-square error RMSE and mean absolute error MAE are as shown in table 1.
The fitting effect error of table 1 is contrasted
| Fitting effect criterion | 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 setting up comprises the following steps：
S31, the multinomial according to fitting, determine quantity of state, state matrix, observed quantity and the 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；Φ points of state matrix
It is not：
Observed quantity is respectively Za=a1x4+a2x3+a3x2+ a4x+a5, Zb=b1x4+b2x3+b3x2+b4x+b5；Observing matrix
Respectively：
S32, by analyzing mercury analyzer, determine the characteristic of noise in voltage signal, determine system noise and sight
Noise is surveyed, so as to add this noise like in voltage signal.For observation noise, according to the photomultiplier of the foundation such as Guo Congliang
The mathematical modeling 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.And because once fluorescence is detected, its state is just
It is determined that, and theoretical model will not change over time, so system noise is 0 herein.
By above-mentioned two step, two state space equations finally obtained are：
(1) rising peak system equation is：
Observational equation is：
Za(k)=a1 (k) x4+a2(k)x3+a3(k)x2+ a4 (k) x+a5 (k)+v (k),
(2) decresting system equation is under：
Observational equation is：
Zb(k)=b1 (k) x4+b2(k)x3+b3(k)x2+ b4 (k) x+b5 (k)+v (k),
Wherein w is that 0, v is that average is 0, and variance is 5 white Gaussian noise.
Further, the multi-model EKF is particularly directed to two state space equations set up,
Processing is filtered using two principle identical expanded Kalman filtration algorithms, specific filtering flow 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 sampled point number takes 200, wherein it is 50 to rise peak, under
Decresting is 150.The cycle-index for rising peak EKF is 6000, the circulation time of lower decresting EKF
Number is 5000.Multi-model EKF design sketch is as shown in Figure 6.In order to verify the superiority of inventive algorithm, to containing
The voltage signal for having same noise carries out digital filter processing, and digital filter coefficient is 0.5, and its filter effect figure is as shown in Figure 6.
Multi-model EKF and root-mean-square error RMSE and mean absolute error MAE after digital filter are as shown in table 2.
The filter effect of table 2 is contrasted
| Filter effect criterion | Multi-model EKF | Digital filter |
| RMSE | 0.7502 | 1.8308 |
| MAE | 0.6056 | 1.4633 |
As can be seen from Table 2：RMSE and MAE after multi-model EKF based on piecewise polynomial model is equal
Less than digital filter, illustrate that the multi-model EKF effect based on piecewise polynomial model is more preferable.
Claims (6)
1. a kind of signal processing method that coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods, it is characterised in that：This method bag
Include：The acquisition of mercury analyzer output voltage signal；The pretreatment of voltage signal data；Voltage curve carries out piecewise polynomial plan
Close；Corresponding state space equation is set up for the multinomial fitted；Multi-model spreading kalman is carried out for signals and associated noises
Filtering process.
2. the signal processing method according to claim 1 that coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods, its feature
It is：The acquisition of the mercury analyzer output voltage signal comes from the photoelectricity for detecting the fluorescence intensity produced by Trace Hg
Output voltage signal after the hardware filtering processing of multiplier tube.
3. the signal processing method according to claim 1 that coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods, its feature
It is：The voltage signal data pretreatment comprises the following steps：
S11, from several crests of acquisition, a peak is looked at random, on the premise of Shannon's sampling theorem is met, obtain voltage sample
This value；
S12, according to the voltage example values of acquisition draw out corresponding voltage curve.
4. the signal processing method according to claim 1 that coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods, its feature
It is：The voltage curve piecewise polynomial fitting comprises the following steps：
S21, the characteristic according to voltage curve, segment processing is carried out to voltage curve, and separation is that at peak value, peak is divided into rising
Peak and lower decresting；
S22, for rising peak and lower decresting fitting of a polynomial is carried out respectively, the exponent number of fitting is from 1 to 6, by matched curve
Both the complexity and fitting precision of function consider, it is determined that appropriate exponent number, wherein polynomial fitting method pass through
Polyfit functions are realized in MATLAB, examine the overall fitting to imitate with root-mean-square error RMSE and mean absolute error MAE
Really, RMSE and MAE expression formula is as follows：
Wherein, N is number of samples, and yi is the magnitude of voltage after i-th of fitting,For i-th of sample voltage actual value.
5. the signal processing method according to claim 1 that coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods, its feature
It is：The system state space equation of setting up comprises the following steps：
S31, the multinomial according to fitting, determine quantity of state, state matrix, observed quantity and the observation square in state space equation
Battle array；
S32, by analyzing mercury analyzer, determine the characteristic of noise in voltage signal, thus in voltage signal add
This noise like.
6. the signal processing method according to claim 1 that coal steam-electric plant smoke mercury concentration is measured based on CVAFS methods, its feature
It is：The multi-model EKF is particularly directed to two state space equations set up, using two principles
Identical expanded Kalman filtration algorithm, overall filtering is examined by root-mean-square error RMSE and mean absolute error MAE
Effect, RMSE and MAE expression formula are as follows：
Wherein, N is number of samples, and Zi is the magnitude of voltage after i-th of filtering process.
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