Stability Improvement of the TDLAS-Based CO Monitoring Module in a Coal Mine by Using a Spectral Denoising Algorithm Based on SVR
Abstract
:1. Introduction
2. Set-Up of the CO Monitoring Module and the Spectroscopic Principle
3. Principle of TDLAS Spectral Background Extraction for CO Gas
4. Analysis
4.1. TDLAS Spectral Background Extraction
4.2. Quantitative Analysis
5. Conclusions and Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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By Using the SVR-Based Algorithm | By Using Least Square Polynomial Fit | |
---|---|---|
Correlation coefficients of the extracted spectral backgrounds for N2 and 25 ppm CO | N2: 0.9996 25 ppm CO: 0.9997 | N2: 0.9971 25 ppm CO: 0.9962 |
Peak-to-peak values of N2 absorbance spectra | 0.022 | 0.045 |
Signal-to-noise ratios of 25 ppm CO absorbance spectra | 13.35 | 6.95 |
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Wang, Y.; Li, L.; Li, H.; Hu, F.; Qian, P. Stability Improvement of the TDLAS-Based CO Monitoring Module in a Coal Mine by Using a Spectral Denoising Algorithm Based on SVR. Photonics 2024, 11, 11. https://doi.org/10.3390/photonics11010011
Wang Y, Li L, Li H, Hu F, Qian P. Stability Improvement of the TDLAS-Based CO Monitoring Module in a Coal Mine by Using a Spectral Denoising Algorithm Based on SVR. Photonics. 2024; 11(1):11. https://doi.org/10.3390/photonics11010011
Chicago/Turabian StyleWang, Yin, Lianqing Li, Haoran Li, Feng Hu, and Pengbo Qian. 2024. "Stability Improvement of the TDLAS-Based CO Monitoring Module in a Coal Mine by Using a Spectral Denoising Algorithm Based on SVR" Photonics 11, no. 1: 11. https://doi.org/10.3390/photonics11010011