Experimental Study on the Inversion of Coal Concentration in Mine Water by Visible-Near Infrared Spectroscopy

The coal concentration in mine water is the main indicator of mine water discharge. The accurate determination of coal concentration is of great significance for the purification and secondary utilization of mine water. To study the spectral inversion method of coal concentration in mine water, samples with different coal concentrations of 0mg/L-1000mg/L are prepared in this paper, and the ASD Field Spec 4 (350-2500nm) spectrometer is used for spectral collection. It is found that the maximum influence of different coal content on spectral reflectance is 0.9. Based on this, the CK-CNN (C-K-Convolutional Neural Networks) inversion model of coal content in mine water is proposed. This model uses the CARS (Competitive Adapative Reweighted Sampling) algorithm to extract sensitive wave bands and uses CNN (Convolutional Neural Networks) to establish spectral inversion model in sensitive wave bands, K-fold cross-validation is used to optimize the model, the model inversion accuracy is R2 = 0.9994, RMSE=6.1401, RPD=41.9692. In this study, CK-CNN was compared with five models: SPA+RF, CARS+RF, SPA+CNN, All Band +CNN, and CARS+CNN. The results show that the CK-CNN model has the best effect. In addition, the concentration of water coal in Jiaozuo Zhongma Coal Mine is 18.75mg/L, the actual concentration measured in the laboratory is 18.92mg/L, and the inversion error is 0.17mg/L. The inversion results meet the requirements of laboratory measurement in GB11901-1989. The research results show that the hyperspectral remote sensing in the visible-near-infrared band can quickly detect the coal concentration in the mine water. The CK-CNN model provides a new method for the determination of the coal content in the mine water. It has great significance to promote research on the influence of the coal concentration in the mine water on the Vis-NIR spectrum.