Classification of different coals using laser induced breakdown spectroscopy (LIBS) combined with PCA-CNN

Abstract

Currently, China is still a major consumer of coal resources. Coal can be used in various fields such as industry and civil use, and can be used for power generation, heating, and building materials. There are many types of coal, each with its unique composition and properties. It has specific requirements for its use in various fields, which make the use of coal more reasonable and important for the sustainable development of the environment and resources. Therefore, the classification research of coal is of great significance. Due to the same component influence among various coals, there are certain challenges for coal classification. Therefore, a laser induced breakdown spectroscopy (LIBS) based on principal component analysis (PCA) combined with convolutional neural network (CNN) method was proposed to classify and recognize coal samples from six different regions. Through laser ablation of coal samples and collection of corresponding data, the data are dimensionalized and standardized, and then the spectral data are classified and trained through PCA-CNN optimization model. The final results indicate that the coal classification accuracy of the PCA-CNN deep learning network model can reach 98.15%. From this result class, it can be seen that laser induced breakdown spectroscopy technology combined with PCA-CNN can achieve rapid and accurate classification of coal samples from different regions, and provide a new coal quality detection data analysis and processing scheme.