改进型PSO-SVM算法对井下多组分气体定量分析的研究

doi:10.3964/j.issn.1000-0593(2019)09-2883-06

Abstract
References
Related Citation (15)

Download: PDF (1404 KB)
Export: BibTeX | EndNote (RIS)

Abstract For quantitative analysis of multi-component gas mixtures, there are incomparable advantages for the quantitative analysis technology of characteristic spectrum. However, the efficiency and accuracy of quantitative detection depends on the capabilities of the spectral data processing algorithms. Optimizing the parameters of spectral analysis algorithms and improving the processing of spectral data are important means to improve the speed and accuracy of quantitative analysis. According to the problem in selecting parameter of support vector machine(SVM) when detecting quantitatively the concentration of multi-component gas in underground mine, an improved Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm was proposed. The algorithm is mainly used to research the problem that the multi-component gas mixed spectrum data is large and the spectral feature information overlaps. The algorithm constrains the convergence path of the PSO algorithm through particle variation, and it improves model optimization efficiency through particle information sharing, and it uses the setting of dynamic insensitive areas to improve model accuracy. A rapid quantitative detection system for multi-component gas was designed in underground mine. The infrared light source was drived by signal modulation module controlled by PC, and the signal light was irradiated on the detector by the air chamber with dust and steam filter. On the basis of the pressure and temperature sensor compensation, the detected optical signals were transmitted to the CPU by signal processing module. Finally, quantitative analysis of the gas concentrations for the various components was achieved by the improved PSO-SVM algorithm. On the basis of the actual sample gas collection and pretreatment in the underground, five kinds of gas components of CH₄, C₂H₆, C₃H₈, SO₂ and CO₂ were tested. The concentration range of CH₄ was 0～10%, and the concentration range of other gases was 0～10%. Infrared spectral data of these five gases were collected with Fourier infrared spectrometer. 800 groups of these gases were divided into 400 groups for calibration set and 400 groups for validation set. The quantitative analysis model of multi-component gas was established by SVM. The parameters of SVM were optimized by improved PSO, and the quantitative parameters were reconstructed by the obtained optimal parameters. The infrared spectral data collected by the algorithm and the traditional BP network algorithm were used to invert the gas concentration of each component. The experimental results show that the convergence range of the optimal value is reduced due to the constraint of the mutated particle, which improves the convergence speed. The modeling time of the algorithm is only 1/10 of that of the traditional method; Since the insensitive area is given by the spectral characteristics of the gas, the cross-sensitivity effect of the characteristic spectrum is reduced, which improves the prediction accuracy of the model. It improves the accuracy of model predictions, with an average error of about 1/5 of traditional methods. It is feasible to use improved PSO combined with SVM for quantitative analysis of multi-component gas in underground. The improved PSO-SVM algorithm has good applicability for the separation of multi-component gas mixed infrared spectral data, and it has certain practical application value.

Key words：Multicomponent gas quantitative analysis; Particle swarm optimization(PSO); Support vector machine (SVM); Infrared spectroscopy

Received: 2018-08-22 Accepted: 2018-12-27

ZTFLH:

O657.3

Corresponding Authors: WANG Ming-quan E-mail: wangmq@nuc.edu.cn

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)09-2883-06 OR https://www.gpxygpfx.com/EN/Y2019/V39/I09/2883

[1] Wu Hongpeng, Dong Lei, Zheng Huadan, et al. Nature Communication, 2017, 8: 15331.
[2] LI Jing-yu, ZHANG Ying-wei, LI Wen-zhe, et al(李晶宇, 张影微, 李文哲, 等). Agricultural Mechanization(农机化研究), 2015, 6: 255.
[3] SUN Ming-guo, MA Hong-liang, LIU Qiang, et al(孙明国, 马宏亮, 刘强, 等). Acta Optica Sinica(光学学报), 2018, 38(5): 0530003-1.
[4] PENG Peng, DI Chang-an, ZHANG Yong-jian(彭澎, 狄长安, 张永建, 等). Acta Armamentarii(兵工学报). 2018, 39(7): 1397.
[5] Cao Y, Sanchez N, Griffin R J, et al. Optics Express, 2015, 23: 2121.
[6] LI Nan, ZHU Xiu-fang, PAN Yao-zhong, et al(李楠, 朱秀芳, 潘耀忠, 等). Journal of Remote Sensing(遥感学报), 2018, 22(4): 560.
[7] ZHA Shen-long, LIU Kun, TAN Tu, et al(查申龙, 刘锟, 谈图, 等). Acta Photonica Sinica(光子学报). 2017, 46(6): 0612002-1.
[8] PANG Tao, WANG Yu, XIA Hua, et al(庞涛, 王煜, 夏滑, 等). Acta Photonica Sinica(光子学报), 2016, 45(9): 0912003.
[9] Yin Xukun, Dong lei, Wu Hongpeng, et al. Sensors and Actuators B, 2017, 247: 329.
[10] LIU Quan-jin, ZHAO Zhi-min, LI Ying-xin, et al(刘全金, 赵志敏, 李颖新). Acta Electronica Sinica(电子学报), 2016, 44(4): 995.
[11] WANG Zhi-wen, ZHANG Ji-long, WANG Zhi-bin, et al(王智文, 张记龙, 王志斌, 等). Transducer and Microsystem Technologies(传感器与微系统), 2011, 30(3): 67.
[12] QU Jian, CHEN Hong-yan, LIU Wen-zhen, et al(曲健, 陈红岩, 刘文贞, 等). Chinese Journal of Sensors and Actuators(传感技术学报), 2015, 28(5): 774.
[13] ZENG Yi-shuai, YANG You-liang, MA Cui-hong(曾怡帅, 杨友良, 马翠红). Chinese Journal of Luminescence(发光学报), 2016, 37(7): 859.
[14] GUO Tian-tai, HONG Bo, PAN Zeng-rong, et al(郭天太, 洪博, 潘增荣, 等). Infrared and Laser Engineering(红外与激光工程), 2016, 45(6): 0617011-1.
[15] Li Duan, Zhang Hongxin, Muhammad, et al. The Journal of China Universities of Posts and Telecommunications, 2017, 24(3): 83.