Research on Quantitative Analysis Method of Infrared Spectroscopy for Coal Mine Gases

Accurate and reliable detection of coal mine gases is the key to ensuring the safe service of coal mine production. However, the baseline of infrared absorption spectrum is easily disturbed by the complex underground environment, while 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 at the poor reliability and accuracy of infrared spectroscopy in the detection of coal mine gases. It utilizes the adaptive smoothness parameter penalized least squares method to correct the drifted spectra. Subsequently, based on the infrared spectral distribution characteristics of mine gases, they can 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 are 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 based on the impact value of variables and population analysis was applied to select variables from the spectral data. The selected variables are then used as input features for building a model with a BP neural network. Finally, the proposed method was validated using standard gases. The experimental results show that the reference error for 10 coal mine gases is less than 3‰F.S., and the relative error is less than 10%. These results demonstrate that the proposed infrared spectral quantitative analysis method can effectively analyze mine gases and achieve good predictive performance.