Research on online correction algorithm with neural network multi-environment factors for CO detection of motor vehicle exhaust
Received:April 06, 2018  Revised:September 28, 2018  download
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Author NameAffiliationE-mail
LIU Guo-Hua Key Laboratory of Environment Optics and Technology of Chinese Academy of SciencesAnhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences ghliu@aiofm.ac.cn 
ZHANG Yu-Jun Anhui Institute of Optics and Fine Mechanics, CAS  
ZHANG Kai Anhui Institute of Optics and Fine Mechanics, CAS  
TANG Qi-Xing Anhui Institute of Optics and Fine Mechanics, CAS  
FAN Bo-Qiang Anhui Institute of Optics and Fine Mechanics, CAS  
LU Yi-Bing Anhui Institute of Optics and Fine Mechanics, CAS  
YOU Kun Anhui Institute of Optics and Fine Mechanics, CAS  
HE Ying Anhui Institute of Optics and Fine Mechanics, CAS  
YU Dong-Qi Anhui Institute of Optics and Fine Mechanics, CAS  
Abstract:The influence of temperature, humidity and pressure on the measurement of exhaust gas CO concentration after pretreatment is analyzed.An on-line correction algorithm with multi-environment factors of neural network for the vehicle exhaust CO detection has been proposed.First, the exhaust gas sample data has been trained offline to build the BP neural network model, and then the real-time measured temperature, humidity, pressure and decimal absorption value of the samples have been put into the model for its online correction.Then the corrected CO concentration has been achieved, so the measurement error of the NDIR sensor caused by environmental changes has been solved.Through the prototype experiment, the simulation experiment and the comparison with SEMTECH-EcoStar, the maximum relative deviation of the CO with the concentration from 0 to 0.2% is 4.8%when the temperature range is from 30 to 50℃, relative humidity is from 25 to 40%, the pressure is from 95 to 115 k Pa.The experiments have been carried out in the vehicle field to get the correction factor between 0.8 and 1, which verifies the necessity and reliability of the method and provided effective technical support for the detection of the CO concentration of the high-temperature exhaust gas from motor vehicles.
keywords:exhaust CO detection  infrared absorption  multiple environmental factors online correction  BP neural network
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Copyright:《Journal of Infrared And Millimeter Waves》