Multi-parameter methane measurement using near-infrared tunable diode laser absorption spectroscopy based on back propagation neural network

• DOI: 10.1016/j.infrared.2022.104275
• Publication Date: 2022-06-24T04:11:38Z
• Authors: Yafei Li; Shuo Yang; Yang Lu; Zhuo Ma; Fang Song; Kaiyuan Zheng; Xiuying Li; Yiding Wang; Frank K. Tittel; Chuantao Zheng
• Keywords: Backpropagation; SIGNAL (programming language)

In order to achieve high-precision and continuous measurement of methane (CH4) isotope abundance (δ13CH4), a near-infrared CH4 sensor system was developed based on back propagation (BP) neural network using tunable diode laser absorption spectroscopy (TDLAS). A pair of CH4 isotope absorption lines with similar low-state energy levels near 1658 nm were selected for minimizing temperature dependence. A LabVIEW-based signal processing platform was realized for real-time signal processing. Wavelet denoising (WD) algorithm was employed to pre-process the absorption signal for increasing signal-to-noise ratio (SNR), and the BP neural network algorithm was adopted to invert δ13CH4. The BP neural network algorithm exhibits potential for multi-parameter analysis, including gas temperature and pressure. Related experiments were carried out to prove the high-precision and reliability of the neural network inversion method. Through experimental comparison, the measurement precision of δ13CH4 is enhanced by ∼ 11 times using the neural network inversion method compared to the maximum absorbance extracting method. © 2022 Elsevier B.V.