Zhang Feng, Zhu Yuchen, Li Lin, Zhao Suping, Zhang Xiaoyan, Chen Chaobo
Electronic Information Engineering, Xi'an Technological University, Xi'an 710021, China.
Molecules. 2025 Jul 20;30(14):3040. doi: 10.3390/molecules30143040.
Accurate and reliable detection of coal mine gases is the key to ensuring the safe service of coal mine production. Fourier Transform Infrared (FTIR) spectroscopy, due to its high sensitivity, non-destructive nature, and potential for online monitoring, has emerged as a key technique in gas detection. However, the complex underground environment often causes baseline drift in IR spectra. Furthermore, the variety of gas species and uneven distribution of concentrations make it difficult to achieve precise and reliable online analysis using existing quantitative methods. This paper aims to perform a quantitative analysis of coal mine gases by FTIR. It utilized the adaptive smoothness parameter penalized least squares method to correct the drifted spectra. Subsequently, based on the infrared spectral distribution characteristics of coal mine gases, they could be classified into gases with mutually distinct absorption peaks and gases with overlapping absorption peaks. For gases with distinct absorption peaks, three spectral lines, including the absorption peak and its adjacent troughs, were selected for quantitative analysis. Spline fitting, polynomial fitting, and other curve fitting methods are used to establish a functional relationship between characteristic parameters and gas concentration. For gases with overlapping absorption peaks, a wavelength selection method bassed on the impact values of variables and population analysis was applied to select variables from the spectral data. The selected variables were then used as input features for building a model with a backpropagation (BP) neural network. Finally, the proposed method was validated using standard gases. Experimental results show detection limits of 0.5 ppm for CH, 1 ppm for CH, 0.5 ppm for CH, 0.5 ppm for n-CH, 0.5 ppm for i-CH, 0.5 ppm for CH, 0.2 ppm for CH, 0.5 ppm for CH, 1 ppm for CO, 0.5 ppm for CO, and 0.1 ppm for SF, with quantification limits below 10 ppm for all gases. Experimental results show that the absolute error is less than 0.3% of the full scale (F.S.) and the relative error is within 10%. These results demonstrate that the proposed infrared spectral quantitative analysis method can effectively analyze mine gases and achieve good predictive performance.
准确可靠地检测煤矿气体是确保煤矿安全生产的关键。傅里叶变换红外（FTIR）光谱技术因其高灵敏度、无损特性以及在线监测潜力，已成为气体检测的关键技术。然而，复杂的地下环境常常导致红外光谱出现基线漂移。此外，气体种类繁多且浓度分布不均，使得利用现有定量方法难以实现精确可靠的在线分析。本文旨在利用FTIR对煤矿气体进行定量分析。采用自适应平滑参数惩罚最小二乘法校正漂移光谱。随后，根据煤矿气体的红外光谱分布特征，将其分为具有相互不同吸收峰的气体和吸收峰重叠的气体。对于具有明显吸收峰的气体，选择包括吸收峰及其相邻波谷在内的三条谱线进行定量分析。采用样条拟合、多项式拟合等曲线拟合方法建立特征参数与气体浓度之间的函数关系。对于吸收峰重叠的气体，应用基于变量影响值和总体分析的波长选择方法从光谱数据中选择变量。然后将所选变量用作构建反向传播（BP）神经网络模型的输入特征。最后，使用标准气体对所提方法进行验证。实验结果表明，CH的检测限为0.5 ppm，CH的检测限为1 ppm，CH的检测限为0.5 ppm，正己烷的检测限为0.5 ppm，异己烷的检测限为0.5 ppm，CH的检测限为0.5 ppm，CH的检测限为0.2 ppm，CH的检测限为0.5 ppm，CO的检测限为1 ppm，CO的检测限为0.5 ppm，SF的检测限为0.1 ppm，所有气体的定量限均低于10 ppm。实验结果表明，绝对误差小于满量程（F.S.）的0.3%，相对误差在10%以内。这些结果表明，所提红外光谱定量分析方法能够有效分析矿井气体并实现良好的预测性能。
