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Volume 45 Issue 2
Feb.  2023
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LIANG Chengquan, LYU Deshen, ZHU Haoliang, LU Xiao. Detection of Methane Concentration Based on TDLAS Technology and Wavelet Transform Denoising Algorithm[J]. Infrared Technology , 2023, 45(2): 209-216.
Citation: LIANG Chengquan, LYU Deshen, ZHU Haoliang, LU Xiao. Detection of Methane Concentration Based on TDLAS Technology and Wavelet Transform Denoising Algorithm[J]. Infrared Technology , 2023, 45(2): 209-216.
shu

Detection of Methane Concentration Based on TDLAS Technology and Wavelet Transform Denoising Algorithm

  • 1.

    College of Mechanical and Electrical Engineering, Nanning University, Nanning 530200, China

  • 2.

    School of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China

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  • Received Date: May 10, 2022
  • Revised Date: August 24, 2022
    Graphical Abstract
    • Abstract
  • To improve the detection accuracy of methane concentration, an experimental system based on tunable diode laser absorption spectroscopy(TDLAS) technology was built. Taking advantage of the high absorption intensity of methane at a wavelength of 1653.72 nm and its ability to eliminate the interference of other gases to the greatest extent, methane concentration was detected by extracting the second harmonic signal. The heursure hard threshold algorithm, heursure soft threshold algorithm, and sqtwolog fixed threshold algorithm are used as the wavelet transform threshold algorithms, respectively; the sqtwolog fixed threshold algorithm is preferred as the wavelet transform threshold algorithm by analyzing the methane absorption signal spectrum, the methane second harmonic signal spectrum, the signal-to-noise ratio and root mean square error of the methane absorption signal obtained without denoising and after denoising. The results of the linear fitting experiment of methane standard gas with different concentrations and the repeatability experiment of methane standard gas of a specific concentration show that the noise interference can be effectively reduced by the wavelet transform using the sqtwolog fixed threshold algorithm. The goodness of fit R2 between the second-harmonic signal extracted after denoising and the real methane concentration was 0.984, indicating that the fitting effect was better. TDLAS technology combined with the wavelet transform denoising algorithm can realize the detection of methane concentration and improve the detection accuracy of methane concentration. TDLAS technology combined with the wavelet transform denoising algorithm can realize the detection of methane concentration and improve its detection accuracy.
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    • References (15)
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