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The interference between overlapping gas absorption lines often occurs in the measurement of multi-component gas mixture with using tunable diode laser absorption spectroscopy (TDLAS). This is also the main problem of the technology in some applications. For instance, in the early application of multi-component gas mixture measurement in coal mines, we found that the absorption lines of carbon monoxide (CO) and methane (CH4) seriously overlapped. The absorption signal of trace CO gas was annihilated and could not be effectively demodulated, especially in the presence of high concentration of CH4. This problem could not be solved just by accurately selecting the spectral lines due to the band absorption of CH4. Therefore, in this paper, we introduce the support vector regression (SVR) model to deal with the interference between CO and CH4 absorption lines. The spectral signals of 14 groups of mixed gases with different concentrations of CO and CH4 are used as the training sets, and the five-fold cross-validation is adopted to prevent the model from overfitting. After 15 iterations in 30 seconds, the optimal regression model of CO and CH4 can be obtained respectively. Furthermore, it is worth noting that based on the experimental data, the linear kernel function is selected to construct the two gas SVR models, and the parameters of the SVR models are optimized by the sequential minimal optimization(SMO) algorithm. With the assistance of the SVR models, the absorption spectra of the two gases can be demodulated effectively, and finally the accurate measurement results are obtained. The measurement results show that the absolute error of trace CO and CH4 concentration(volume fraction of gas) are less than 2 × 10–6 and 0.2 × 10–2 respectively. Meanwhile, the correlation coefficient between the measured values and the actual values of CO and CH4 are 0.998 and 0.9995, respectively. In addition, the dynamic stability for each of the two regression models is fully verified by the experiment of the inflation process. Consequently, this method can eliminate the interference between the overlapping spectra, and can fully meet the requirements for accurately measuring the gas mixture. We hope that the SVR model can provide an effective solution for the real-time monitoring of multi-component gas mixture, and thus greatly improving the adaptability of TDLAS technology in the future.
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Keywords:
- tunable diode laser absorption spectroscopy (TDLAS) /
- gas mixture /
- overlapping spectral lines /
- support vector regression
[1] Zhang Z R, Pang T, Yang Y, Xia H, Cui X J, Sun P S, Wu B, Wang Y, Sigrist M W, Dong F Z 2016 Opt. Express 24 A943
Google Scholar
[2] Wang F P, Chang J, Wang Q, Wei W, Qin Z G 2017 Sens. Actuators A 259 152
Google Scholar
[3] Avetisov V, Bjoroey O, Wang J, Geiser P, Paulsen K G 2019 Sensors 19 5313
Google Scholar
[4] Nwaboh J A, Werhahn O, Ortwein P, Schiel D, Ebert V 2013 Meas. Sci. Technol. 24 015202
Google Scholar
[5] Vallon R, Soutade J, Verant J L, Meyers J, Paris S, Mohamed A 2010 Sensors 10 6081
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[6] 夏滑, 吴边, 张志荣, 庞涛, 董凤忠, 王煜 2013 物理学报 62 214208
Google Scholar
Xia H, Wu B, Zhang Z R, Pang T, Dong F Z, Wang Y 2013 Acta Phys. Sin. 62 214208
Google Scholar
[7] Zhang L W, Zhang Z R, Sun P S, Pang T, Xia H, Cui X J, Guo Q, Sigrist M W, Shu C M, Shu Z F 2020 Spectrochim. Acta, Part A 239 118495
Google Scholar
[8] He Q X, Dang P P, Liu Z W, Zheng C T, Wang Y D 2017 Opt. Quantum Electron. 49 115
Google Scholar
[9] Kluczynski P, Gustafsson J, Lindberg A M, Axner O 2001 Spectrochim. Acta, Part B 56 1277
Google Scholar
[10] Wang F, Wu Q, Huang Q X, Zhang H D, Yan J H, Cen K F 2015 Opt. Commun. 346 53
Google Scholar
[11] Lin X, Yu X L, Li F, Zhang S H, Xin J G, Chang X Y 2013 Appl. Phys. B: Lasers Opt. 110 401
Google Scholar
[12] Köhring M, Huang S, Jahjah M, Jiang W, Ren W, Willer U, Caneba C, Yang L, Nagrath D, Schade W, Tittel F K 2014 Appl. Phys. B 117 445
Google Scholar
[13] Zhang T, Kang J, Meng D, Wang H, Mu Z, Zhou M, Zhang X, Chen C 2018 Sensors 18 4295
Google Scholar
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Google Scholar
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Google Scholar
[15] Gabrysch M, Corsi C, Pavone F S, Inguscio M 1997 Appl. Phys. B 65 75
Google Scholar
[16] Cai T D, Gao G Z, Wang M R 2016 Opt. Express 24 859
Google Scholar
[17] Malegori C, Marques E J N, Freitas S T d, Pimentel M F, Pasquini C, Casiraghi E 2017 Talanta 165 112
Google Scholar
[18] 张志荣, 夏滑, 董凤忠, 庞涛, 吴边 2013 光学精密工程 21 2771
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Google Scholar
[19] Shao L G, Fang B, Zheng F, Qiu X B, He Q S, Wei J L, Li C L, Zhao W X 2019 Spectrochim. Acta, Part A 222 117118
Google Scholar
[20] Qu J, Chen H Y, Liu W Z, Zhang B, Li Z B 2015 Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering Zhengzhou, PRC, April 11−13, 2015 p2111
[21] Laref R, Losson E, Sava A, Adjallah K, Siadat M 2018 2018 Ieee International Conference on Industrial Technology (Icit) Lyon, France, February 19−22, 2018 p1335
[22] Reid J, Labrie D 1981 Appl. Phys. B 26 203
[23] Smola A J, Scholkopf B 2004 Stat. Comput. 14 199
Google Scholar
[24] Gordon I E, Rothman L S, Hill C, et al. 2017 J. Quant. Spectrosc. Radiat. Transfer 203 3
Google Scholar
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图 1 DFB激光器电流-波长调谐性能曲线
Figure 1. Current-wavelength tuning curve of DFB laser.
图 2 实验系统原理图
Figure 2. Schematic diagram of the experimental system.
图 3 CO (20 × 10–6, 400 × 10–6)和CH4 (1 × 10–2)的模拟吸收光谱图
Figure 3. Simulated absorption spectra of CO (20 × 10–6, 400 × 10–6) and CH4 (1 × 10–2).
图 4 三组气体(Group 1、Group 4、Group 10)的2f/1f信号
Figure 4. 2f/1f signals of three groups of gases (Group 1, Group 4, Group 10).
图 5 CO和CH4浓度设置值和测试值对比 (a) CO浓度值; (b) CH4浓度值
Figure 5. Comparisons between set and test values: (a) CO concentration; (b) CH4 concentration.
图 6 设置值和测量值之间相关性及测量误差 (a) CO相关性; (b) CH4相关性; (c) CO测量误差; (d) CH4测量误差
Figure 6. Correlation and test errors between set and average values: (a) CO correlation; (b) CH4 correlation; (c) CO errors; (d) CH4 errors.
图 7 不同浓度CH4对CO测量结果的干扰
Figure 7. Interference on CO measurement results by different concentrations of CH4.
图 8 第一组充气过程中CO和CH4浓度变化
Figure 8. Concentration measured of CO and CH4 during the first set of inflation.
图 9 第二组充气过程中CO和CH4浓度变化
Figure 9. Concentration measured of CO and CH4 during the second set of inflation.
表 1 训练数据集
Table 1. Training data set.
Group Standard gas
category and
concentrationRatio Gas category and
concentration in
multi-pass cellCO/10–6 CH4/10–2 CO/10–6 CH4/10–2 1 19.3 0 — 19.3 0 2 54.0 0 — 54.0 0 3 102.0 0 — 102.0 0 4 0 0.50 — 0 0.50 5 0 1.04 — 0 1.04 6 0 2.02 — 0 2.02 7 0 5.02 — 0 5.02 8 54.0 1.04 1∶1 27.0 0.52 9 102.0 1.04 1∶1 51.0 0.52 10 19.3 1.04 1∶1 9.6 0.52 11 19.3 0.50 1∶1 9.6 0.25 12 19.3 5.02 1∶1 9.6 2.51 13 19.3 2.02 1∶1 9.6 1.01 14 19.3 8.50 1∶1 9.6 4.25 
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表 2 SVR模型主要参数
Table 2. Optimal parameters of SVR model.
CO-SVRmodel CH4-SVRmodel BoxConstraint(C ) 0.3443 0.0180 KernelScale 0.0617 0.9205 ε 0.1710 65.1810 Total function evaluations 15 15 Total elapsed time in seconds 26.2545 12.5730
DownLoad: CSV
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[1] Zhang Z R, Pang T, Yang Y, Xia H, Cui X J, Sun P S, Wu B, Wang Y, Sigrist M W, Dong F Z 2016 Opt. Express 24 A943
Google Scholar
[2] Wang F P, Chang J, Wang Q, Wei W, Qin Z G 2017 Sens. Actuators A 259 152
Google Scholar
[3] Avetisov V, Bjoroey O, Wang J, Geiser P, Paulsen K G 2019 Sensors 19 5313
Google Scholar
[4] Nwaboh J A, Werhahn O, Ortwein P, Schiel D, Ebert V 2013 Meas. Sci. Technol. 24 015202
Google Scholar
[5] Vallon R, Soutade J, Verant J L, Meyers J, Paris S, Mohamed A 2010 Sensors 10 6081
Google Scholar
[6] 夏滑, 吴边, 张志荣, 庞涛, 董凤忠, 王煜 2013 物理学报 62 214208
Google Scholar
Xia H, Wu B, Zhang Z R, Pang T, Dong F Z, Wang Y 2013 Acta Phys. Sin. 62 214208
Google Scholar
[7] Zhang L W, Zhang Z R, Sun P S, Pang T, Xia H, Cui X J, Guo Q, Sigrist M W, Shu C M, Shu Z F 2020 Spectrochim. Acta, Part A 239 118495
Google Scholar
[8] He Q X, Dang P P, Liu Z W, Zheng C T, Wang Y D 2017 Opt. Quantum Electron. 49 115
Google Scholar
[9] Kluczynski P, Gustafsson J, Lindberg A M, Axner O 2001 Spectrochim. Acta, Part B 56 1277
Google Scholar
[10] Wang F, Wu Q, Huang Q X, Zhang H D, Yan J H, Cen K F 2015 Opt. Commun. 346 53
Google Scholar
[11] Lin X, Yu X L, Li F, Zhang S H, Xin J G, Chang X Y 2013 Appl. Phys. B: Lasers Opt. 110 401
Google Scholar
[12] Köhring M, Huang S, Jahjah M, Jiang W, Ren W, Willer U, Caneba C, Yang L, Nagrath D, Schade W, Tittel F K 2014 Appl. Phys. B 117 445
Google Scholar
[13] Zhang T, Kang J, Meng D, Wang H, Mu Z, Zhou M, Zhang X, Chen C 2018 Sensors 18 4295
Google Scholar
[14] 张志荣, 吴边, 夏滑, 庞涛, 王高旋, 孙鹏帅, 董凤忠, 王煜 2013 物理学报 62 234204
Google Scholar
Zhang Z R, Wu B, Xia H, Pang T, Wang G X, Sun P S, Dong F Z, Wang Y 2013 Acta Phys. Sin. 62 234204
Google Scholar
[15] Gabrysch M, Corsi C, Pavone F S, Inguscio M 1997 Appl. Phys. B 65 75
Google Scholar
[16] Cai T D, Gao G Z, Wang M R 2016 Opt. Express 24 859
Google Scholar
[17] Malegori C, Marques E J N, Freitas S T d, Pimentel M F, Pasquini C, Casiraghi E 2017 Talanta 165 112
Google Scholar
[18] 张志荣, 夏滑, 董凤忠, 庞涛, 吴边 2013 光学精密工程 21 2771
Google Scholar
Zhang Z R, Xia H, Dong F Z, Pang T, Wu B 2013 Opt. Precis. Eng. 21 2771
Google Scholar
[19] Shao L G, Fang B, Zheng F, Qiu X B, He Q S, Wei J L, Li C L, Zhao W X 2019 Spectrochim. Acta, Part A 222 117118
Google Scholar
[20] Qu J, Chen H Y, Liu W Z, Zhang B, Li Z B 2015 Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering Zhengzhou, PRC, April 11−13, 2015 p2111
[21] Laref R, Losson E, Sava A, Adjallah K, Siadat M 2018 2018 Ieee International Conference on Industrial Technology (Icit) Lyon, France, February 19−22, 2018 p1335
[22] Reid J, Labrie D 1981 Appl. Phys. B 26 203
[23] Smola A J, Scholkopf B 2004 Stat. Comput. 14 199
Google Scholar
[24] Gordon I E, Rothman L S, Hill C, et al. 2017 J. Quant. Spectrosc. Radiat. Transfer 203 3
Google Scholar
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