Laser absorption spectroscopy based on dual-convolutional neural network algorithms for multiple trace gases analysis

Abstract

Methane (CH4) and nitrous oxide (N2O) as two typical greenhouse gases, have important effects on global climate change. In this paper, an external cavity quantum cascade laser (ECQCL) based gas sensor was developed for simultaneous CH4 and N2O detection by employing calibration-free direct absorption spectroscopy. In view of the important influence of spectral noise and background normalization process on gas concentration inversion, dual-convolutional neural networks (D-CNN) and baseline normalization algorithms were developed for spectral signal de-noising and concentration inversion, respectively. Compared to traditional methods, the results indicate that the proposed D-CNN de-noising algorithm can successfully improve the signal-to-noise ratio (SNR) by 2.37 times, and the correlation coefficients of the retrieved concentrations were improved from 0.9965 to 0.9991 for CH4 and 0.9983 to 0.9994 for N2O, respectively, which shows a great potential for analyzing unresolved mixture absorption spectra with high-precision and accuracy in simultaneous detection of multiple gas components.