End-to-end methane gas detection algorithm based on transformer and multi-layer perceptron
Chang Liu, Gang Wang, Chen Zhang, Pietro Patimisco, Ruyue Cui, Chaofan Feng, Angelo Sampaolo, Vincenzo Spagnolo, Lei Dong, Hongpeng Wu
Abstract
In this paper, an end-to-end methane gas detection algorithm based on transformer and multi-layer perceptron (MLP) for tunable diode laser absorption spectroscopy (TDLAS) is presented. It consists of a Transformer-based U-shaped Neural Network (TUNN) filtering algorithm and a concentration prediction network (CPN) based on MLP. This algorithm employs an end-to-end architectural design to extract information from noisy transmission spectra of methane and derive the CH 4 concentrations from denoised spectra, without intermediate steps. The results demonstrate the superiority of the proposed TUNN filtering algorithm over other typically employed digital filters. For concentration prediction, the determination coefficient (R 2 ) reached 99.7%. Even at low concentrations, R 2 remained notably high, reaching up to 89%. The proposed algorithm results in a more efficient, convenient, and accurate spectral data processing for TDLAS-based gas sensors.