Quantitative Analysis of NO and NO2 from Vehicle Exhaust Emission Based on Fast ICA and ANN

Abstract

Abstract:

With the increasing number of vehicles, the harm from vehicle exhaust to the environment becomes more and more serious. So the monitoring of the concentration of vehicle exhaust emissions is very important to assess the emission levels. The NO and NO2 quantitative detection system based on nondispersion ultraviolet (NDUV) for vehicle exhaust emissions is built, and the original data of the mixed tail gas is obtained. And then, the identification and quantitative analysis of NO & NO2 gas is carried out with fast independent component analysis(Fast ICA) and artificial neural network(ANN) recognition algorithms. It can be drawn from the results that using the two algorithms, the NO concentration (under 600 ppm) and NO2 concentration (under 200 ppm) can be detected accurately and the maximum relative error is 1.54%, and the minimum is 0.25%.

Key words: vehicle exhaust emission, NO, NO2, quantitative analysis, fast independent component analysis, artificial neural network

CLC Number:

X734

ZHANG Kai, ZHANG Yu-Jun, HE Ying, YOU Kun, LIU Guo-Hua, CHEN Chen, GAO Yan-Wei, HE Chun-Gui, LU Yi-Bing, LIU Wen-Qing. Quantitative Analysis of NO and NO2 from Vehicle Exhaust Emission Based on Fast ICA and ANN[J]. Journal of Atmospheric and Environmental Optics, 2016, 11(6): 435-441.

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