[口头报告]Research on High-Precision Laser Methane Detection Method Based on Adaptive Filtering Algorithm
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[口头报告]Research on High-Precision Laser Methane Detection Method Based on Adaptive Filtering Algorithm

Research on High-Precision Laser Methane Detection Method Based on Adaptive Filtering Algorithm
编号:195 稿件编号:430 访问权限:仅限参会人 更新:2024-05-15 17:37:32 浏览:554次 口头报告

报告开始:2024年05月31日 15:50 (Asia/Shanghai)

报告时间:10min

所在会议:[S2] Safety Engineering and Occupational Health » [S2-3B] Afternoon of May 31st B

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摘要
Tunable Diode Laser Absorption Spectroscopy (TDLAS) technology has been widely used in the field of trace gas concentration detection. Within this domain, Wavelength Modulation Spectroscopy (WMS) techniques are effective in reducing the impact of low-frequency noise (1/f noise) during detection. However, noise from optical components and electronic devices can still interfere with the extracted second harmonic (2f) signal, affecting the system's accuracy in detecting trace gas concentrations. Considering that the system's noise can be approximated as a set of non-stationary signals, a study has explored an adaptive filtering algorithm based on the Least Mean Square (LMS) error to suppress the noise. This method involves extracting the reference noise signal through cross-correlation analysis and then adaptively optimizing the noisy second harmonic signal. Experimental calibration was conducted on methane of different volume concentrations and their corresponding 2f signal amplitudes using a laboratory-built TDLAS detection system. The results indicate that the proposed method can enhance the system's accuracy and stability. This research outcome could provide valuable insights for achieving high-precision detection of trace methane gas in areas such as energy extraction and environmental monitoring.
 
关键字
tunable diode laser absorption spectroscopy,wavelength modulation spectroscopy,cross-correlation analysis,adaptive filtering,second harmonic
报告人
Boran Li
China University of Mining and Technology

稿件作者
Hao Shao China University of Mining and Technology
Boran Li China University of Mining and Technology
Kai Wang China University of Mining and Technology
Bei Zhang China University of Mining and Technology
Chaojie Liu China University of Mining and Technology
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