Volume 45 Issue 6
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Guo Tiantai, Hong Bo, Pan Zengrong, Kong Ming. Application of improved SVM in quantitative analysis of mine gas concentration[J]. Infrared and Laser Engineering, 2016, 45(6): 617011-0617011(8). doi: 10.3788/IRLA201645.0617011
Citation: Guo Tiantai, Hong Bo, Pan Zengrong, Kong Ming. Application of improved SVM in quantitative analysis of mine gas concentration[J]. Infrared and Laser Engineering, 2016, 45(6): 617011-0617011(8). doi: 10.3788/IRLA201645.0617011
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Application of improved SVM in quantitative analysis of mine gas concentration

doi: 10.3788/IRLA201645.0617011
  • 1.

    Department of College of Metrological Technology and Engineering,China Jiliang University,Hangzhou 310018,China;

  • 2.

    Entry-exit Inspection and Quarantine Bureau of Fujian Province,Fuzhou 350000,China

  • Received Date: 2015-10-10
  • Rev Recd Date: 2015-11-18
  • Publish Date: 2016-06-25
  • A quantitative analysis model of mine gas concentration based on improved support vector machine (SVM) was adopted. Five mine gases were used for experiment, which included methane, ethane, propane, n-butane and carbon dioxide. Mid-infrared spectral data of these five gases and mixed gases were collected with Fourier infrared spectrometer. 236 groups of these mixed gases were divided into 186 groups for calibration set and 50 groups for validation set. Principal component analysis (PCA) was used to reduce the dimensionality of the infrared spectral data, and 3 eigenvalues were extracted as input, which helped to improve convergence speed and reduce calculation time. Particle swarm optimization (PSO) and genetic algorithm (GA) were used to optimize parameters of support vector machine (SVM) method respectively, and PSO was adopted for its better optimization effect over GA. The mixed gases were detected through this algorithm, and experiment results show that the average errors of concentration predictions of five gases are all less than 1.78%, and the maximum errors of concentration predictions of five gases are all less than 4.98%. The time cost for concentration prediction is all less than 103 s for the 50 groups. This suggested that the improved SVM method based on PSO can be used to predict the gas concentration accurately, and can meet the requirement of real-time detection of mine gases, which has great value in the study of concentration prediction of mine gases.
  • [1] Zhang Liqing, Qin Yujin, Jiang Wenzhong, et al. State-of-the-art of the prediction method and prospect of mine gas emission in China[J]. Coal Mine Safety, 2007(8): 58-60. (in Chinese)章立清, 秦玉金, 姜文忠, 等. 我国矿井瓦斯涌出量预测方法研究现状及展望[J]. 煤矿安全, 2007(8): 58-60.
    [2] Li Jingyu, Zhang Yingwei, Li Wenzhe, et al. Study on biogas content by gas chromatography[J]. Study on Agricultural Mechanization, 2015(6): 255-257. (in Chinese)李晶宇, 张影微, 李文哲, 等. 气相色谱法测定沼气气体成分及含量的研究[J]. 农机化研究, 2015(6): 255-257.
    [3] Wang Xiaozeng, Yang Jiuhong, Zeng Hui. Study of CH4 electrochemical sensor non-linear self-correction in extended range[J]. Coal Mine Safety, 2010(9): 18-20. (in Chinese)王小增, 杨久红, 曾辉. 瓦斯气体电化学传感器超量程段的非线性自校正研究[J]. 煤矿安全, 2010(9): 18-20.
    [4] Chen Yuanyuan, Wang Zhibin, Wang Zhaoba, et al. Research on concentration retrieval of gas FTIR spectra by interval extreme learning machine and genetic algorithm[J]. Spectroscopy and Spectral Analysis, 2014, 34(5): 1244-1248. (in Chinese)陈媛媛, 王志斌, 王召巴, 等. 区间极限学习机结合遗传算法用于红外光谱气体浓度反演的研究[J]. 光谱学与光谱分析, 2014, 34(5): 1244-1248.
    [5] Zheng Longjiang, Li Peng, Qin Ruifeng, et al. Research situation and developing tendency for optical measurement technology of gas density[J]. Laser Optoelectronics Progress, 2008, 45(8): 24-32. (in Chinese)郑龙江, 李鹏, 秦瑞峰, 等. 气体浓度检测光学技术的研究现状和发展趋势[J]. 激光与光电子学进展, 2008, 45(8): 24-32.
    [6] Richter D, Fried A. Development of a tunable mid-IR difference frequency laser source for highly sensitive airborne trace gas detection[J]. Appl phys B, 2002, (75): 281-288.
    [7] Liang Yuntao, Tang Xiaojun, Luo Haizhu, et al. Quantitative spectrum analysis of characteristic gases of spontaneous combustion coal[J]. Spectroscopy and Spectral Analysis 2011(9): 2480-2484. (in Chinese)梁运涛, 汤晓君, 罗海珠, 等. 煤层自然发火特征气体的光谱定量分析[J]. 光谱学与光谱分析, 2011(9): 2480-2484.
    [8] Li Li, Wang Yiding, Li Shuwei. Application of infrared gas detection technology to safe production and transportation in natural gas industry[J]. Natural Gas Industry, 2011, 31(1): 1-5. (in Chinese)李黎, 王一丁, 李树维. 红外气体检测技术在天然气安全生产中的应用[J]. 天然气工业, 2011, 31(1): 1-5.
    [9] Zhang Lin, Shao Shengyu, Yang Liu, et al. Progress of research on infrared spectrometry in gas quantitative analysis[J]. Analytical Instruments, 2009(2): 6-9. (in Chinese)张琳, 邵晟宇, 杨柳,等. 红外光谱法气体定量分析研究进展[J]. 分析仪器, 2009(2): 6-9.
    [10] Zhao Jianhua, Gao Mingliang, Wu Xiujuan. Study on the quantitative analysis of common fire toxic gases by PLS-BP model[J]. Fire Safety Science, 2010(3): 158-164. (in Chinese)赵建华, 高明亮, 武秀娟. PLS-BP法定量分析火灾中的常见有毒有害气体[J]. 火灾科学, 2010(3): 158-164.
    [11] Tang Xiaojun, Hao Huimin, Li Yujun, et al. Analysis of mixed alkane gas based on tikhonov regularization spectra selection and optimal neural network parameters selection[J]. Spectroscopy and Spectral Analysis, 2011(6): 1673-1677. (in Chinese)汤晓君, 郝惠敏, 李玉军, 等. 基于Tikhonov正则化特征光谱选择与最优网络参数选择的轻烷烃气体分析[J]. 光谱学与光谱分析, 2011(6): 1673-1677.
    [12] Bai Peng, Xie Wenjun, Liu Junhua. New method of mixed gas infrared spectrum analysis based on SVM[J]. Spectroscopy and Spectral Analysis, 2007, 27(7): 1323-1327. (in Chinese)白鹏, 谢文俊, 刘君华. 混合气体红外光谱支持向量机分析的新方法[J]. 光谱学与光谱分析, 2007, 27(7): 1323-1327.
    [13] Wang Yuanbin. Infrared spectrum analysis of the gas in coal mine based on SVM[J]. Intelligent Computing and Intelligent System, 2009(1): 608-611.
    [14] Jin Ye,Yang Kai, Wu Yongjiang, et al. Application of particle swarm optimization based least square support vector machine in quantitative analysis of extraction solution of safflower using near-infrared spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2012, 40(6): 925-931. (in Chinese)金叶, 杨凯, 吴永江, 等. 基于粒子群算法的最小二乘支持向量机在红花提取液近红外定量分析中的应用[J]. 分析化学, 2012, 40(6): 925-931.
    [15] Gu Yanping, Zhao Wenjie, Wu Zhansong. Least squares support vector machine algorithm[J]. J Tsinghua Univ(Sci Tech), 2010, 50(7): 1063-1066. (in Chinese)顾燕萍, 赵文杰, 吴占松. 最小二乘支持向量机的算法研究[J]. 清华大学学报(自然科学版), 2010, 50(7): 1063-1066.
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Application of improved SVM in quantitative analysis of mine gas concentration

doi: 10.3788/IRLA201645.0617011
  • 1. Department of College of Metrological Technology and Engineering,China Jiliang University,Hangzhou 310018,China;
  • 2. Entry-exit Inspection and Quarantine Bureau of Fujian Province,Fuzhou 350000,China
  • Received Date: 2015-10-10
  • Rev Recd Date: 2015-11-18
  • Publish Date: 2016-06-25

Abstract: A quantitative analysis model of mine gas concentration based on improved support vector machine (SVM) was adopted. Five mine gases were used for experiment, which included methane, ethane, propane, n-butane and carbon dioxide. Mid-infrared spectral data of these five gases and mixed gases were collected with Fourier infrared spectrometer. 236 groups of these mixed gases were divided into 186 groups for calibration set and 50 groups for validation set. Principal component analysis (PCA) was used to reduce the dimensionality of the infrared spectral data, and 3 eigenvalues were extracted as input, which helped to improve convergence speed and reduce calculation time. Particle swarm optimization (PSO) and genetic algorithm (GA) were used to optimize parameters of support vector machine (SVM) method respectively, and PSO was adopted for its better optimization effect over GA. The mixed gases were detected through this algorithm, and experiment results show that the average errors of concentration predictions of five gases are all less than 1.78%, and the maximum errors of concentration predictions of five gases are all less than 4.98%. The time cost for concentration prediction is all less than 103 s for the 50 groups. This suggested that the improved SVM method based on PSO can be used to predict the gas concentration accurately, and can meet the requirement of real-time detection of mine gases, which has great value in the study of concentration prediction of mine gases.

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