Coal and Gangue Classification Based on Laser-Induced Breakdown Spectroscopy and Deep Learning
Mengyuan Xu, Yachun Mao, Zelin Yan, Mengqi Zhang, Dong Xiao · 2023
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Description
Mengyuan Xu and co-authors sort coal from its rocky waste with laser sparks and a neural network. Gangue, the low-carbon stone mixed into mined coal, makes up a sizable share of output and drags down fuel quality. Laser-induced breakdown spectroscopy flashes each lump into a spark whose light lists its elements, but reading those spectra by eye is slow.
The team first bent each one-dimensional spectrum into a two-dimensional image using a Gramian angular field. Their network, sharpened by an attention step and residual links across feature levels, then judged the images. It beat rival machine and deep learning models on every metric, with accuracy near ninety-eight percent and perfect precision. Mines could use such sorting to lift coal quality before burning.
Catalog record
| CREATOR | Mengyuan Xu, Yachun Mao, Zelin Yan, Mengqi Zhang, Dong Xiao |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 28 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |