机动车尾气对环境的危害日益加重,机动车尾气排放浓度的检测对大气污染治理具有重要意义。设计了基于非分散紫外的机动车 尾气NO、NO2浓度检测系统,搭建了实验装置,获得NO、NO2混合气体的吸收光强后,利用快速不动点(Fast ICA)算法和人工神经 网络模式识别算法对机动车尾气排放NO、NO2组分进行定量分析。实验结果表明,利用所设计的算法对600 ppm以内的NO气 体和200 ppm以内的NO2气体浓度进行测量,其相对误差最大为1.54%,最小为0.25%。

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 ultra- violet (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 and 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%.