Detection of Methane Concentration Based on TDLAS Technology and Wavelet Transform Denoising Algorithm
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摘要: 为进一步提高甲烷浓度检测精度，搭建了基于TDLAS（tunable diode laser absorption spectroscopy）技术的甲烷浓度检测实验系统，利用甲烷在波长1653.72 nm处吸收强度很高且可以最大限度消除其他气体干扰的特性，通过提取二次谐波信号实现甲烷浓度检测。然后分别采用heursure硬阈值算法、heursure软阈值算法和sqtwolog固定阈值算法作为小波变换阈值算法，通过分析未去噪及小波变换去噪处理后得到的甲烷吸收信号谱图、甲烷二次谐波信号谱图、甲烷吸收信号的信噪比和均方根误差，优选sqtwolog固定阈值算法作为小波变换阈值算法。不同浓度的甲烷标气线性拟合实验及特定浓度的甲烷标气重复性实验结果表明：通过小波变换（采用sqtwolog固定阈值算法）能有效降低噪声干扰，去噪处理后提取的二次谐波信号与甲烷真实浓度拟合优度R2为0.984，拟合效果更佳。采用TDLAS技术结合小波变换去噪算法，实现甲烷浓度检测的同时也能提高甲烷浓度检测精度。Abstract: To improve the detection accuracy of methane concentration, an experimental system based on tunable diode laser absorption spectroscopy(TDLAS) technology was built. Taking advantage of the high absorption intensity of methane at a wavelength of 1653.72 nm and its ability to eliminate the interference of other gases to the greatest extent, methane concentration was detected by extracting the second harmonic signal. The heursure hard threshold algorithm, heursure soft threshold algorithm, and sqtwolog fixed threshold algorithm are used as the wavelet transform threshold algorithms, respectively; the sqtwolog fixed threshold algorithm is preferred as the wavelet transform threshold algorithm by analyzing the methane absorption signal spectrum, the methane second harmonic signal spectrum, the signal-to-noise ratio and root mean square error of the methane absorption signal obtained without denoising and after denoising. The results of the linear fitting experiment of methane standard gas with different concentrations and the repeatability experiment of methane standard gas of a specific concentration show that the noise interference can be effectively reduced by the wavelet transform using the sqtwolog fixed threshold algorithm. The goodness of fit R2 between the second-harmonic signal extracted after denoising and the real methane concentration was 0.984, indicating that the fitting effect was better. TDLAS technology combined with the wavelet transform denoising algorithm can realize the detection of methane concentration and improve the detection accuracy of methane concentration. TDLAS technology combined with the wavelet transform denoising algorithm can realize the detection of methane concentration and improve its detection accuracy.
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图 1 甲烷浓度检测实验系统示意图
Figure 1. Schematic diagram of methane concentration detection experimental system
图 2 甲烷、二氧化碳和水蒸气的吸收截面（温度为296 K、波数为6030~6060 cm−1）
Figure 2. Absorption cross-sections of methane, carbon dioxide and water vapor (temperature 296 K, wave number 6030~6060 cm−1)
图 3 甲烷、二氧化碳和水蒸气的模拟吸光度
Figure 3. Simulated absorbance of methane, carbon dioxide and water vapor
图 4 未去噪及小波变换（采用不同阈值算法）去噪处理后得到的甲烷吸收信号谱图
Figure 4. Absorption signal spectra of methane obtained without denoising and after denoising by wavelet transform with different threshold algorithm
图 5 未去噪及小波变换（采用不同阈值算法）去噪处理后得到的甲烷二次谐波信号谱图
Figure 5. Second harmonic signal spectra of methane obtained without denoising and after denoising by wavelet transform with different threshold algorithm
图 6 未去噪及小波变换（采用sqtwolog固定阈值算法）去噪处理后得到的不同浓度甲烷标气二次谐波信号谱图
Figure 6. Second harmonic signal spectra of methane standard gas with different concentrations obtained without denoising and after denoising by wavelet transform with sqtwolog fixed threshold algorithm
图 7 二次谐波信号与甲烷真实浓度拟合曲线
Figure 7. Fitting curves of second harmonic signal and real concentration of methane
图 8 甲烷浓度真实值与检测值对比
Figure 8. Comparison of actual values and measured values of methane concentration
表 1 小波变换（采用不同阈值算法）去噪效果对比
Table 1. Comparison of denoising effects of wavelet transform using different threshold algorithm
Different threshold algorithm Signal-to-noise ratio Root mean square error Without denoising 14.4094 12.9441 Heursure hard threshold algorithm 14.4093 12.9441 Heursure soft threshold algorithm 14.4391 12.8999 Sqtwolog fixed threshold algorithm 15.2204 11.7901 
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[1] 陈国华, 董浩宇, 张强, 等. 狭长受限空间甲烷-空气爆炸事故研究评述[J]. 安全与环境学报, 2020, 20(3): 946-959.CHEN Guohua, DONG Haoyu, ZHANG Qiang, et al. Review on the methane-air explosion accidents in the narrow confined space[J]. Journal of Safety and Environment, 2020, 20(3): 946-959. [2] 张旭, 郭腾霄, 杨柳, 等. 基于近红外TDLAS检测技术的甲烷浓度场重建研究[J]. 红外技术, 2018, 40(6): 603-611. http://hwjs.nvir.cn/article/id/hwjs201806014ZHANG Xu, GUO Tengxiao, YANG Liu, et al. Research of methane concentration field reconstruction based on near infrared TDLAS detection technology[J]. Infrared Technology, 2018, 40(6): 603-611. http://hwjs.nvir.cn/article/id/hwjs201806014 [3] 李传, 杨炳雄, 范凌, 等. 基于近红外光谱差分吸收法的甲烷激光式检测系统研究[J]. 煤炭技术, 2015, 34(10): 251-253. https://www.cnki.com.cn/Article/CJFDTOTAL-MTJS201510095.htmLI Chuan, YANG Bingxiong, FAN Ling, et al. Research of methane's laser detection system based on near-infrared differential absorption spectroscopy technique[J]. Coal Technology, 2015, 34(10): 251-253. https://www.cnki.com.cn/Article/CJFDTOTAL-MTJS201510095.htm [4] 李志永, 谭荣清, 黄伟, 等. 傅里叶变换红外光谱技术测量甲烷气压的实验研究[J]. 中国激光, 2017, 44(3): 49-54. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201703007.htmLI Zhiyong, TAN Rongqing, HUANG Wei, et al. Methane pressure detection based on Fourier transform infrared spectroscopy[J]. Chinese Journal of Lasers, 2017, 44(3): 49-54. https://www.cnki.com.cn/Article/CJFDTOTAL-JJZZ201703007.htm [5] 樊保龙. 大尺度条件下甲烷-空气和煤尘-空气混合及爆炸特性研究[D]. 北京: 北京理工大学, 2015.FAN Baolong. Study on Mixing and Explosion Characteristics of Methane-Air and Coal-dust-air at Large Scale[D]. Beijing: Beijing Institute of Technology, 2015. [6] DENG Jun, CHEN Weile, WANG Weifeng, et al. Study on online detection method of methane gas in coal mine based on TDLAS technology[C]//Proceedings of the 11th International Mine Ventilation Congress, 2018(4): 318-332. [7] WANG Zhimin, WANG Han, YU Yingchun, et al. Simulation and analysis of CH4 concentration measurement based on QCL-TDLAS[J]. IOP Conference Series Earth and Environmental Science, 2020, 568(1): 012013. doi: 10.1088/1755-1315/568/1/012013 [8] GAO Zongli, YE Weilin, ZHENG Chuantao, et al. Wavelet-denoising technique in near-infrared methane detection based on tunable diode laser absorption spectroscopy[J]. Optoelectronics Letters, 2014(10): 299-303. [9] 张义, 康信龙, 李长吾, 等. 基于TDLAS技术的空间网格化甲烷检测方法[J]. 大连工业大学学报, 2015, 34(2): 136-140. https://www.cnki.com.cn/Article/CJFDTOTAL-DLQG201502016.htmZHANG Yi, KANG Xinlong, LI Changwu, et al. Space grid methane detection method based on TDLAS technology[J]. Journal of Dalian Polytechnic University, 2015, 34(2): 136-140. https://www.cnki.com.cn/Article/CJFDTOTAL-DLQG201502016.htm [10] 叶年年, 冯若尘, 田思雨, 等. 基于TDLAS的甲烷气体检测技术综述[J]. 内蒙古煤炭经济, 2019(12): 43-44. https://www.cnki.com.cn/Article/CJFDTOTAL-LMMT201912020.htmYE Niannian, FENG Ruochen, TIAN Siyu, et al. Overview of methane gas detection technology based on TDLAS[J]. Inner Mongolia Coal Economy, 2019(12): 43-44. https://www.cnki.com.cn/Article/CJFDTOTAL-LMMT201912020.htm [11] 毕诚. 基于TDLAS的空间对射型飞机货舱火警探测硬件系统研究[D]. 天津: 中国民航大学, 2019.BI Cheng. Research on hardware system on spatial anti-radiation fire detection of aircrafts cargo based on TDLAS[D]. Tianjin: Civil Aviation University of China, 2019. [12] 越方禹, 毛峰, 王涵, 等. 高功率半导体激光器红外缺陷发射与热效应[J]. 激光与光电子学进展, 2019, 56(11): 1-9. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201911001.htmYUE Fangyu, MAO Feng, WANG Han, et al. Infrared defect emission and thermal effect in high power diode lasers[J]. Laser & Optoelectronics Progress, 2019, 56(11): 1-9. https://www.cnki.com.cn/Article/CJFDTOTAL-JGDJ201911001.htm [13] 彭琛. 基于光声气体检测的半导体激光器耦合技术研究[D]. 绵阳: 西南科技大学, 2012.PENG Chen. Semiconductor Laser Coupling Technology Based on Photoacoustic Gas Detection[D]. Mianyang: Southwest University of Science and Technology, 2012. [14] 张莹, 王立洪. 基于残差的非线性自回归模型的拟合优度检验[J]. 南京大学学报(数学半年刊), 2012, 29(1): 93-104. https://www.cnki.com.cn/Article/CJFDTOTAL-SXXT201201013.htmZHANG Ying, WANG Lihong. Goodness-of-fit test using residuals in infinite-order nonlinear autoregressive models[J]. Journal of Nanjing University Mathematical Biquarterly, 2012, 29(1): 93-104. https://www.cnki.com.cn/Article/CJFDTOTAL-SXXT201201013.htm [15] 桂文林, 伍超标. 标准差和平均差的内在关系[J]. 统计与决策, 2004(4): 122-123. https://www.cnki.com.cn/Article/CJFDTOTAL-TJJC200404070.htmGUI Wenlin, WU Chaobiao. Intrinsic relationship between standard deviation and mean difference[J]. Statistics and Decision, 2004(4): 122-123. https://www.cnki.com.cn/Article/CJFDTOTAL-TJJC200404070.htm -
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