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| Identification and Detection of Multi-Component Trace Gases Based on Near-Infrared TDLAS Technology Based on SVM |
| 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
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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 C2H2, 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 C2H2 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.
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Received: 2023-06-21
Accepted: 2023-12-14
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Corresponding Authors:
LIU Xiang
E-mail: xjlx1906@126.com
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[1] LI Jin-yi, YANG Xue, ZHANG Chen-ge, et al(李金义, 杨 雪, 张宸阁, 等). Acta Optica Sinica(光学学报), 2022, 42(18): 1830001.
[2] Agarwal S, Seifert L, Zhu D, et al. Applied Sciences，2023，13(3): 1370.
[3] Navas M J, Jiménez A M, Asuero A G. Clinica Chimica Acta, 2012, 413(15-16): 1171.
[4] Zhao C L, Huang D F, Liu Z Y, et al. Acta Photonica Sinica, 2022, 51(2): 0230001.
[5] Timothy L M, Charles T C, Robert M H, et al. Environmental Science & Technology, 1994, 28(5): 224A.
[6] Christopher S G, Spearrin R M, Jeffries J B, et al, Progress in Energy and Combustion Science, 2017, 60: 132.
[7] Elpelt-Wessel I, Reiser M, Morrison D, et al. Atmosphere，2022, 13(1): 53.
[8] XIAO Shi-jie, WANG Qiao-hua, LI Chun-fang, et al(肖仕杰, 王巧华, 李春芳, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2022, 42(4): 1243.
[9] Jha R K. IEEE Sensors Journal, 2021, 22(1): 6.
[10] SUN Peng-shuai, ZHANG Zhi-rong, XIA Hua, et al(孙鹏帅, 张志荣, 夏 滑, 等). Acta Optica Sinica(光学学报), 2015, 35(2): 0230001.
[11] WANG Yan, ZHANG Rui(王 燕, 张 锐). Acta Optica Sinica(光学学报), 2016, 36(2): 0230002.
[12] GAO Yan-wei, ZHANG Yu-jun, CHEN Dong, et al(高彦伟, 张玉钧, 陈 东, 等). Acta Optica Sinica(光学学报), 2016, 36(3): 0330001.
[13] ZHANG Rui-lin, TU Xing-hua(张瑞林, 涂兴华). Acta Optica Sinica(光学学报), 2022, 42(2): 0210001.
[14] Raza M, Xu K, Lu Z, et al. Optics & Laser Technology, 2022, 154: 108285.
[15] YANG Shu-han, QIAO Shun-da, LIN Dian-yang, et al(杨舒涵, 乔顺达, 林殿阳, 等). Chinese Optics(中国光学), 2023, 16(1): 151.
[16] WANG Shu-tao, ZHAN Shu-jie, LIU Shi-yu, et al(王书涛, 展书杰, 刘诗瑜, 等). Acta Optica Sinica(光学学报), 2021, 41(10): 1030004. |
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