High-precision coal classification using laser-induced breakdown spectroscopy (LIBS) coupled with the CST-PCA-based ISSA-KELM

As one of the main energy sources in human production and life, the accurate and rapid classification of coal is of great significance to industrial production and the control of pollution emissions. However, the complex composition and highly similar elemental composition of coal with different physical properties and chemical composition lead to a high degree of similarity in coal spectral data measured by laser-induced breakdown spectroscopy (LIBS), which poses a great challenge to accurate classification and identification work. In this paper, based on LIBS technology, we integrate the chi-square test (CST) and principal component analysis (PCA) to construct a quadratic dimensionality reduction network (CST-PCA), and for the first time, we propose a new improved sparrow search algorithm (ISSA) by introducing spatial pyramid matching (SPM) chaotic mapping, adaptive inertia weights (w) and Gaussian mutation, and combine it with kernel based extreme learning machine (KELM) to construct an ISSA-KELM data classification model to classify and identify seven types of coal samples. Firstly, 2520 12248-dimensional coal spectral data were preprocessed using a combination of the chi-square test (CST) and principal component analysis (PCA). The KELM was hyper-parameter optimised using ISSA. By comparing with the unoptimized model, the accuracy of coal classification reaches 99.773%. The experimental results show that the CST-PCA-based ISSA-KELM algorithm effectively optimizes the parameters, improves the classification accuracy of coal, and provides a new data processing scheme for accurate qualitative analysis of coal.