Nondestructive Identification of Coal and Gangue via Near-infrared Spectroscopy based on Improved Broad Learning

Abstract

Coal-gangue separation is an essential step in the coal preparation process. However, existing manual selection and mechanical separation methods require a large amount of labor, consume too much water, and involve health hazards. In this article, we solve this problem based on the near-infrared spectroscopy (NIRS) technique in tandem with improved broad learning. First, to remove outliers effectively, we propose an improved Mahalanobis distance-based method against masking and swamping effects when there is more than one outlier in the data set. Second, we employ least absolute shrinkage and selection operator (lasso) regularization to optimize the model structure and achieve the state-of-the-art accuracy of 99.01% ± 0.0113. Furthermore, the designed spectra acquisition device is able to automatically adjust the stand-off distance to the identified optimal value, and corresponding software for coal-gangue identification is released. The developed strategy can be applied to coal/gangue blocks, not limited to the powder samples, which is an attempt to progress toward a more realistic application. The experimental results demonstrate that the proposed strategy is of great potential for nondestructive identification of gangue from coal.