Laser Linewidth Analysis and Filtering/Fitting Algorithms for Improved TDLAS-Based Optical Gas Sensor
Chen Tong,
Chaotan Sima,
Muqi Chen,
Xiaohang Zhang,
Tailin Li,
Yan Ai,
Ping Lu
Affiliations
Chen Tong
Next Generation Internet Access National Engineering Research Center, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
Chaotan Sima
Next Generation Internet Access National Engineering Research Center, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
Muqi Chen
Next Generation Internet Access National Engineering Research Center, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
Xiaohang Zhang
Next Generation Internet Access National Engineering Research Center, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
Tailin Li
Next Generation Internet Access National Engineering Research Center, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
Yan Ai
Next Generation Internet Access National Engineering Research Center, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
Ping Lu
Next Generation Internet Access National Engineering Research Center, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China
Tunable Diode Laser Absorption Spectroscopy (TDLAS) has been widely applied in in situ and real-time monitoring of trace gas concentrations. In this paper, an advanced TDLAS-based optical gas sensing system with laser linewidth analysis and filtering/fitting algorithms is proposed and experimentally demonstrated. The linewidth of the laser pulse spectrum is innovatively considered and analyzed in the harmonic detection of the TDLAS model. The adaptive Variational Mode Decomposition-Savitzky Golay (VMD-SG) filtering algorithm is developed to process the raw data and could significantly eliminate the background noise variance by about 31% and signal jitters by about 12.5%. Furthermore, the Radial Basis Function (RBF) neural network is also incorporated and applied to improve the fitting accuracy of the gas sensor. Compared with traditional linear fitting or least squares method (LSM), the RBF neural network brings along the enhanced fitting accuracy within a large dynamic range, achieving an absolute error of below 50 ppmv (about 0.6%) for the maximum 8000 ppmv methane. The proposed technique in this paper is universal and compatible with TDLAS-based gas sensors without hardware modification, allowing direct improvement and optimization for current optical gas sensors.
