Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis.

Coal plays an indispensable role in the world's energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal.