基于TDLAS技术的CH<sub>4</sub>气体检测与温度补偿方法

doi:10.3964/j.issn.1000-0593(2021)11-3632-07

Abstract
References
Related Citation (15)

Download: PDF (3139 KB)
Export: BibTeX | EndNote (RIS)

Abstract Accurate detection of CH₄ is essential to prevent gas explosion and ensure safe production. However, the gas detection technology based on tunable diode laser absorption spectroscopy (TDLAS) has a large error due to temperature change. This paper explored the CH₄ detection based on TDLAS technology and the temperature compensation method, analyzed the impact of temperature on CH₄ absorption line, and finally eliminated the impact of environmental temperature on the CH₄ detection through algorithm compensation model. This study used TDLAS technology’s principle and theory to design the transmitter unit, absorption cell, signal receiver unit and data processing unit. A CH₄ detection system based on TDLAS technology was established, the concentration of CH₄ at different ambient temperatures (10~50 ℃) was measured, and the effect of temperature change on the intensity and half width at half-maximum of CH₄ absorption line at 1.653 μm was analyzed. In order to eliminate the influence of temperature on CH₄ detection and improve the compensation effect, the particle swarm optimization (PSO) was employed to optimize the optimal weight and the threshold of back propagation neural network (BPNN). The PSO-BP temperature compensation model of CH₄ was established, which overcame the characteristics of slow convergence rate and easy to fall into local optimum of the BPNN The result indicated that: (1) Based on TDLAS technology, the CH₄ detection concentration dropped with the increasement of ambient temperature, the relative error range within the whole experimental temperature was 4.25%~12.13%. The relationship between CH₄ detection concentration and temperature under different ambient temperatures can be expressed as a cubic polynomial; (2) The absorption intensity and half width at half-maximum of CH₄ gas decrease with the increase of temperature relationship between it and temperature was a monotonous decreasing function. The relative change rate of temperature on the absorption line intensity of CH₄ gas was greater than the half width. The absorption line intensity of CH₄ gas was more susceptible to the temperature change; (3) The absolute mean error (MAE) of the BPNN and PSO-BP model test samples were 12.88% and 1.81%, the mean absolute percentage error (MAPE) were 2.3% and 0.3%, the root mean square (RMSE) were 15.96% and 2.69%, and the correlation coefficient R² were 0.980 6 and 0.999 6, respectively. By establishing the PSO-BP temperature compensation model, the compensation effect was mostly distributed within the error range of ±1.0%, and MAE, MAPE, RMSE, R² and another evaluation indexes were greatly improved. It has a certain reference significance to improve the accurate detection of CH₄ in the mine with TDLAS technology.

Key words：Tunable diode laser absorption spectroscopy; CH₄ gas concentration; Accurate detection; Neural networks; Particle swarm optimization; Temperature compensation

Received: 2020-09-15 Accepted: 2021-01-27

ZTFLH:

TN249

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)11-3632-07 OR https://www.gpxygpfx.com/EN/Y2021/V41/I11/3632

[1] CHEN Xian-feng, ZHANG Yin, XU Xiao-jiang, et al（陈先锋, 张银, 许小江, 等）. Journal of Mining & Safety Engineering（采矿与安全工程学报）, 2012, 29(3): 429.
[2] JIANG Fu-xing, YIN Yong-ming, ZHU Quan-jie, et al（姜福兴, 尹永明, 朱权洁，等）. Chinese Journal of Rock Mechanics and Engineering（岩石力学与工程学报），2014, 33(S2): 3581.
[3] ZHAO Yong-yi, CHANG Jian-hua, SHEN Wan, et al（赵勇毅, 常建华, 沈婉，等）. Infrared Technology（红外技术）, 2019, 41(8): 778.
[4] DENG Jun, XIAO Yang, CHEN Xiao-kun, et al（邓军, 肖旸, 陈晓坤，等）. Journal of Mining & Safety Engineering（采矿与安全工程学报）, 2011, 28(4): 638.
[5] ZHOU Pei-li, TAN Wen, PENG Zhi-min（周佩丽, 谭文, 彭志敏）. Chinese Journal of Scientific Instrument（仪器仪表学报）, 2019, 40(11): 233.
[6] LI Ao-qi, WANG Biao, XU Yue, et al（李奥奇, 王彪, 许玥，等）. Laser Journal（激光杂志）, 2019, 40(12): 6.
[7] ZHANG Zhi-rong, PANG Tao, SUN Peng-shuai, et al（张志荣, 庞涛, 孙鹏帅, 等）Chinese Journal of Lasers（中国激光）, 2019, 46(12): 265.
[8] LI Ming-xing, LIU Jian-guo, KAN Rui-feng, et al（李明星, 刘建国, 阚瑞峰，等）. Acta Optica Sinica（光学学报）, 2015, 35(4): 399.
[9] LI Zhe, ZHANG Zhi-rong, SUN Peng-shuai, et al（李哲, 张志荣, 孙鹏帅, 等）. Infrared and Laser Engineering（红外与激光工程）, 2017, 46(9): 243.
[10] LI Bin, ZHENG Chuan-tao, LIU Hui-fang, et al. Sensors and Actuators B: Chemical. 2016, 225: 188.
[11] YUAN Song, KAN Rui-feng, HE Ya-bo, et al（袁松, 阚瑞峰, 何亚柏, 等）. Chinese Journal of Lasers（中国激光）, 2013, 40(5): 247.
[12] NIE Wei, KAN Rui-feng, XU Zhen-yu, et al（聂伟, 阚瑞峰, 许振宇, 等）. Acta Physica Sinica（物理学报）, 2017, 66(20): 97.
[13] ZHANG Xu, GUO Teng-xiao, YANG Liu, et al（张旭, 郭腾霄, 杨柳, 等）. Infrared Technology（红外技术）, 2018, 40(6): 603.
[14] ZHANG Zeng-fu, ZOU De-bao, CHEN Wen-liang，et al（张增福, 邹得宝, 陈文亮, 等）. Opto-Electronic Engineering（光电工程）, 2014, 41(6): 32.
[15] TONG Xiu-xiu, KANG Zhi-hong（佟秀秀, 康志宏）. Science Technology and Engineering（科学技术与工程）, 2007, 33(4): 500.
[16] XIA Hui, LIU Wen-qing, ZHANG Yu-jun, et al（夏慧, 刘文清, 张玉钧, 等）. Optical Technique（光学技术）, 2008，(2): 298.
[17] LI Meng-shan, LIAN Su-yun, WANG Fan, et al. Chemical Engineering Research and Design, 2020, 155：98.
[18] DENG Jun, LEI Chang-kui, CAO Kai, et al（邓军, 雷昌奎, 曹凯, 等）. Journal of China Coal Society（煤炭学报）, 2018, 43(10): 2800.
[19] TIAN Zhen, JING Shuang-xi, ZHAO Li-juan, et al（田震, 荆双喜, 赵丽娟, 等）. Journal of Henan Polytechnic University·Natural Science（河南理工大学学报·自然科学版), 2020, 39(1): 68.
[20] LI Hui-min, LI Zhen-lei, HE Rong-jun, et al（李慧民, 李振雷, 何荣军，等）. Journal of Mining & Safety Engineering（采矿与安全工程学报）, 2014, 31(2): 203.
[21] WANG Ping, HUANG Zhen-yi, ZHANG Ming-ya, et al. Journal of Iron and Steel Research(International), 2008，15(3): 87.