基于支持向量机的煤矿极性气体光谱定量分析
Spectral Quantitative Analysis for Coal Mine Polar Gas Based on Support Vector Machine
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摘要: 目前煤矿井下极性气体定量分析和检测方法存在的问题，初步建立了基于支持向量机算法的极性气体定量分析校正模型；利用傅里叶变换红外光谱法对CH4、CO2、SF6等10种煤矿井下极性气体进行扫描，得出共310组检验样本数据，通过光谱数据预处理和SVM校正模型的训练与检验，实现了SVM校正模型，使模型预测精度基本达到煤矿气体的检测要求。经过模型验证，除C3H8、nC4H10正丁烷和CO2的准确度较低外，其他样本测试浓度和实际浓度偏差均不超过该组分满量程的10%，通过对理论值与测量值对比，分析结果满足现场实际需求，初步实现了利用红外光谱技术对煤矿井下极性气体的定量分析，在建模速度和模型预测精度等方面均取得了较好的效果，达到了快速检测分析井下灾害极性气体浓度的目的。Abstract: We discuss the problems of coal mine polar gas existing in the quantitative analysis and detection methods at present. Based on the support vector machine algorithm, we initially establish the polar gas quantitative analysis of calibration model by using fourier transform infrared spectroscopy to test 10 kinds of coal mine polar gas such as CH4, CO2, SF6, etc. We get 310 groups of test sample data through spectral data preprocessing and calibration model of SVM testing, and establish the nonlinear quantitative analysis model based on the SVM. By treating spectral data in advance, training and testing SVM calibration model, these make calibration model prediction accuracy meet the coal mine gas detection requirements. Model validation shows that all gas concentration of sample tests and the actual components concentration deviation, are no more than 10% of the full scale except C3H8，nC4H10 and CO2, by analyzing the theoretical value compared with measured value, which meets the coal mine actual requirements, and initially implements the coal mine polar mixed gas quantitative analysis with the infrared spectrum technology, obtains a certain effect on the aspect of modeling speed and prediction accuracy, which achieves a purpose of rapid detection and analysis on polar coal mine gas concentration.
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