光谱学与光谱分析2024,Vol.44Issue(10) :2909-2915.DOI:10.3964/j.issn.1000-0593(2024)10-2909-07

基于SVM与近红外TDLAS技术的多组分痕量气体识别与检测

Identification and Detection of Multi-Component Trace Gases Based on Near-Infrared TDLAS Technology Based on SVM

房孝猛 王华来 徐晖 黄孟强 刘向
光谱学与光谱分析2024,Vol.44Issue(10) :2909-2915.DOI:10.3964/j.issn.1000-0593(2024)10-2909-07

基于SVM与近红外TDLAS技术的多组分痕量气体识别与检测

Identification and Detection of Multi-Component Trace Gases Based on Near-Infrared TDLAS Technology Based on SVM

房孝猛 1王华来 1徐晖 1黄孟强 1刘向1
扫码查看

作者信息

  • 1. 南京信息工程大学电子与信息工程学院,江苏南京 210044
  • 折叠

摘要

基于可调谐半导体激光吸收光谱技术(TDLAS),采用频分多路复用(FDM)方法,研究了一种基于支持向量机(SVM)分类的近红外多组分痕量气体识别与检测系统.激光光谱技术表征气体吸收谱线时,气体在近红外波段比远红外吸收能力低,单一波段激光光谱检测气体存在吸收信号弱,各气体组分相互干扰大.为提升探测精度,精准识别气体组分并同时进行多成分检测,基于可调谐半导体激光吸收光谱技术,采用频分复用的近红外TDLAS技术,搭配SVM分类算法进行混合气体的实时检测,有效避免了各气体的交叉干扰,实现了一氧化氮NO、硫化氢H2S、氨气NH3、二氧化氮NO2、乙炔C2H2、二氧化碳CO2、甲烷CH4、氯化氢HC1八种气体标志物的痕量检测.当8个激光器同时工作时,系统控制带通滤波器进行分时滤波,并将差分锁相后的二次谐波数据依次传输至上位机实时显示.识别率超过96.3%,含量平均预测准确率均高于99.6%,取得了 CH4最低检测下限为0.01 μL·L-1的高精度检测效果,NO2为0.05 μL·L-1、C2H2为0.03 μL·L-1,其余气体检测下限均小于5 μL·L-1.对系统多通道检测进行抗干扰和检测下限分析,验证系统稳定工作时实现混合气体的高精度浓度检测.采用分布反馈激光器驱动和锁相放大器与数据处理的SVM算法模型结合,实现近红外TDLAS技术的多组分痕量气体识别与检测,可满足微量气体痕量级检测,对将来进行超低浓度混合气体探测有着非常重要的意义.

Abstract

Based on tunable semiconductor laser absorption spectroscopy(TDLAS)and frequency division multiplexing(FDM)method,a near-infrared multi-component trace gas identification and detection system based on support vector machine(SVM)classification was studied.When laser spectroscopy technology characterizes gas absorption spectral lines,the absorption capacity of gas in the near-infrared band is lower than that in the far-infrared band.The absorption signal of gas detected by single-band laser spectrum is weak,and each gas component interferes with each other greatly.To improve detection accuracy,accurately identify gas components and perform multi-component detection at the same time,based on tunable semiconductor laser absorption spectroscopy technology,the frequency division multiplexing near-infrared TDLAS technology method is used,and the SVM classification algorithm is used to perform the real-time detection process of mixed gases.It effectively avoids cross-interference of various gases and realizes trace detection of eight gas markers:nitric oxide NO,hydrogen sulfide H2S,ammonia NH3,nitrogen dioxide NO2,acetylene C2 H2,carbon dioxide CO2,methane CH4,and hydrogen chloride HCl.When eight lasers work simultaneously,the system controls the band-pass filter to perform time-sharing filtering.It sequentially transmits the second harmonic data after differential phase locking to the host computer for real-time display.The recognition rate is over 96.3%,and the average content prediction accuracy is higher than 99.6%.It has achieved high-precision detection results with the lowest detection limit of CH4 being 0.01 μL·L-1,NO2 being 0.05 μL·L-1,and C2 H2 being 0.03 μL·L-1,and the detection limits of other gases are below 5 μL·L-1.Conduct anti-interference analysis and detection lower limit analysis on the multi-channel detection of the system to verify that the system can achieve high-precision concentration detection of mixed gases when the system is operating stably.This system uses a distributed feedback laser drive and lock-in amplifier combined with the SVM algorithm model of data processing to realize multi-component trace gas identification and detection of near-infrared TDLAS technology,which can meet the trace level detection of trace gases and provide ultra-low performance for the future.The detection of concentration mixed gases is of very important significance.

关键词

可调谐半导体激光吸收光谱/频分多路复用/支持向量机/混合气体探测

Key words

TDLAS/Frequency divisionmultiplexing/Support vector machines/Mixed gas detection

引用本文复制引用

基金项目

国家自然科学基金项目(61905116)

苏州市姑苏创新进取人才项目(ZXL2021303)

江苏科技智库计划(青年)项目(JSKX24019)

出版年

2024
光谱学与光谱分析
中国光学学会

光谱学与光谱分析

CSTPCDCSCD北大核心
影响因子:0.897
ISSN:1000-0593
参考文献量16
段落导航相关论文