The monitoring of flue gas of the thermal power plants is of great significance in energy conservation and environmental protection.Spectral technique has been widely used in the gas monitoring system for predicting t...The monitoring of flue gas of the thermal power plants is of great significance in energy conservation and environmental protection.Spectral technique has been widely used in the gas monitoring system for predicting the concentrations of specific gas components.This paper proposes flue gas monitoring system with empirically-trained dictionary(ETD)to deal with the complexity and biases brought by the uninformative spectral data.Firstly,ETD is extracted from the raw spectral data by an alternative optimization between the sparse coding stage and the dictionary update stage to minimize the error of sparse representation.D1,D2 and D3 are three types of ETD obtained by different methods.Then,the predictive model of component concentration is constructed on the ETD.In the experiments,two real flue gas spectral datasets are collected and the proposed method combined with the partial least squares,the background propagation neural network and the support vector machines are performed.Moreover,the optimal parameters are chosen according to the 10-fold root-mean-square error of cross validation.The experimental results demonstrate that the proposed method can be used for quantitative analysis effectively and ETD can be applied to the gas monitoring systems.展开更多
The in-situ instrumentation technique for measuring mercury and itsspeciation downstream a utility 100 MW pulverized coal (PC) fired boiler system was developed andconducted by the use of the Ontario hydro method (OHM...The in-situ instrumentation technique for measuring mercury and itsspeciation downstream a utility 100 MW pulverized coal (PC) fired boiler system was developed andconducted by the use of the Ontario hydro method (OHM) consistent with American standard test methodtogether with the semi-continuous emissions monitoring (SCEM) system as well as a mobile laboratoryfor mercury monitoring. The mercury and its speciation concentrations including participate mercuryat three locations of before air preheater, before electrostatic precipitator (ESP) and after ESPwere measured using the OHM and SCEM methods under normal operation conditions of the boiler systemas a result of firing a bituminous coal. The vapor-phase total mercury Hg(VT) concentration declinedwith the decrease of flue gas temperature because of mercury species transformation from oxidizedmercury to particulate mercury as the flue gas moved downstream from the air preheater to the ESPand after the ESP. A good agreement for Hg°, Hg^(2+) and Hg( VT) was obtained between the twomethods in the ash-free area. But in the dense particle-laden flue gas area, there appeared to be abig bias for mercury speciation owing to dust cake formed in the filter of OHM sampling probe. Theparticulateaffinity to the flue gas mercury and the impacts of sampling condition to accuracy ofmeasure were discussed.展开更多
基金supported by the National Natural Science Foundation of China(61375055)the Program for New Century Excellent Talents in University(NCET-12-0447)+2 种基金the Natural Science Foundation of Shaanxi Province of China(2014JQ8365)the State Key Laboratory of Electrical Insulation and Power Equipment(EIPE16313)the Fundamental Research Funds for the Central University
文摘The monitoring of flue gas of the thermal power plants is of great significance in energy conservation and environmental protection.Spectral technique has been widely used in the gas monitoring system for predicting the concentrations of specific gas components.This paper proposes flue gas monitoring system with empirically-trained dictionary(ETD)to deal with the complexity and biases brought by the uninformative spectral data.Firstly,ETD is extracted from the raw spectral data by an alternative optimization between the sparse coding stage and the dictionary update stage to minimize the error of sparse representation.D1,D2 and D3 are three types of ETD obtained by different methods.Then,the predictive model of component concentration is constructed on the ETD.In the experiments,two real flue gas spectral datasets are collected and the proposed method combined with the partial least squares,the background propagation neural network and the support vector machines are performed.Moreover,the optimal parameters are chosen according to the 10-fold root-mean-square error of cross validation.The experimental results demonstrate that the proposed method can be used for quantitative analysis effectively and ETD can be applied to the gas monitoring systems.
文摘The in-situ instrumentation technique for measuring mercury and itsspeciation downstream a utility 100 MW pulverized coal (PC) fired boiler system was developed andconducted by the use of the Ontario hydro method (OHM) consistent with American standard test methodtogether with the semi-continuous emissions monitoring (SCEM) system as well as a mobile laboratoryfor mercury monitoring. The mercury and its speciation concentrations including participate mercuryat three locations of before air preheater, before electrostatic precipitator (ESP) and after ESPwere measured using the OHM and SCEM methods under normal operation conditions of the boiler systemas a result of firing a bituminous coal. The vapor-phase total mercury Hg(VT) concentration declinedwith the decrease of flue gas temperature because of mercury species transformation from oxidizedmercury to particulate mercury as the flue gas moved downstream from the air preheater to the ESPand after the ESP. A good agreement for Hg°, Hg^(2+) and Hg( VT) was obtained between the twomethods in the ash-free area. But in the dense particle-laden flue gas area, there appeared to be abig bias for mercury speciation owing to dust cake formed in the filter of OHM sampling probe. Theparticulateaffinity to the flue gas mercury and the impacts of sampling condition to accuracy ofmeasure were discussed.
