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Coal content in gangue is an important production index, and a commercial method to detect it is yet to be devised. The prediction of coal and gangue particle volumes is crucial. The shape clustering method is adopted to automatically classify coal or gangue particles based on their shapes to build volume models that can adapt to different shapes. Subsequently, volume models for coal and gangue particles of different shapes are established. Without shape clustering, the average relative errors of the volume model for gangue are 13.41%, 12.87%, 11.42%, and 9.12% for particles sizes of 25–13 mm, 50–25 mm, 100–50 mm, and &gt;100 mm, respectively, whereas they are 12.54%, 11.82%, 10.36%, and 7.69%, respectively, after shape clustering. Without shape clustering, the average relative errors of the volume model for coal are 9.97%, 11.10%, 12.44%, and 11.06%, respectively, whereas they are 9.08%, 8.98%, 11.53%, and 8.27% after shape clustering. The reduction in error indicates the effectiveness of the proposed volume prediction.

Modeling of coal and gangue volume based on shape clustering and image analysis