Study of raw coal identification method by dual-energy X-ray and dual-view visible light imaging
- 20 March 2022
- journal article
- research article
- Published by Taylor & Francis in International Journal of Coal Preparation and Utilization
- Vol. 43 (2) , 361-376
Abstract
In the process of pre-discharging gangue via the dual-energy X-ray coal and gangue sorting device, the pseudo-medium coal in the raw coal will be identified as gangue and then discarded. Clean coal can be recovered and resource waste can be reduced via the crushing and re-separation of pseudo-medium coal, which is the critical step in the treatment procedure of a coal preparation plant. By virtue of dual-view visible-light imaging, an image identification method was proposed to recover the pseudo-medium coal identified as gangue by dual-energy X-ray. The pseudo-medium coal was determined based on the differences in coal and gangue surface organization and reflective properties. Linear dispersion was used to evaluate the distinguishability of features, and the elements were sorted. The recognition effect of the classifier model on pseudo-medium coal and gangue under different feature combinations was experimentally observed. The results unraveled that the identification rate of mixed samples of pseudo-medium coal and gangue that were selected from Dayugou Coal Mine in Zhengzhou City, Henan Province, could reach 95% using the method herein. The invention can effectively recover pseudo-medium coal from the waste rock pre-discharged by the dual-energy X-ray sorting device, realizing the multi-classification of raw coal.Keywords
Funding Information
- National Natural Science Foundation of China (No.51904007, 51874004)
- Scientific research projects in Anhui universities (YJS20210373)
- Key Research and Development Program of Anhui Province (No.202004a07020043)
- Anhui Natural Science Foundation Project (No.1908085QE227)
- Research and Innovation Project Fund of Anhui University of Science and Technology (2020CX2040)
- Anhui Provincial University System Innovation Project (GXXT-2021-076)
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