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

作　　者：房孝猛 王华来 徐晖 黄孟强 刘向 FANG Xiao-meng;WANG Hua-lai;XU Hui;HUANG Meng-qiang;LIU Xiang(School of Electronic and Information Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学电子与信息工程学院,江苏南京210044

出　　处：《光谱学与光谱分析》2024年第10期2909-2915,共7页Spectroscopy and Spectral Analysis

基　　金：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)资助。

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H_(2)S、氨气NH_(3)、二氧化氮NO_(2)、乙炔C_(2)H_(2)、二氧化碳CO_(2)、甲烷CH_(4)、氯化氢HCl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CH_(4)最低检测下限为0.01μL·L^(-1)的高精度检测效果,NO_(2)为0.05μL·L^(-1)、C_(2)H_(2)为0.03μL·L^(-1),其余气体检测下限均小于5μL·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。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 H_(2)S,ammonia NH_(3),nitrogen dioxide NO_(2),acetylene C_(2)H_(2),carbon dioxide CO_(2),methane CH_(4),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 CH_(4) being 0.01μL·L^(-1),NO_(2) being 0.05μL·L^(-1),and C_(2)H_(2) being 0.03μL·L^(-1),and the detection limits of other gases are below 5μL·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 s

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 

分 类 号：TH744[机械工程—光学工程]