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.

Coal and gangue separation technology based on X-ray has broad development prospects due to its low energy consumption and pollution-free environment, which is also the key part in the green and intelligent mines. Aiming at the low recognition accuracy for coal and gangue with the particle size of 5–15 mm for the dual-energy X-ray coal and gangue separation technology, the coal and gangue recognition method based on the combination of X-ray transmission and diffraction principle was proposed. The dual-energy X-ray images based on the X-ray transmission principle were collected for coal and gangue with particle size larger than 15 mm, and Rc, Glc, Gl, Ra were extracted as the recognition features. EDXRD patterns based on the X-ray diffraction principle were collected for coal and gangue with particle size less than 15 mm, and the characteristic diffraction peaks were extracted as the recognition features. Then, the PSO-SVM model was established for coal and gangue recognition. The test results show that the proposed method can broaden the particle size range for dry coal preparation based on X-ray, and the recognition accuracy of coal and gangue with particle size less than 15 mm is 98%, which is 16.7% higher than that of the method based on the X-ray transmission principle alone. The comprehensive recognition accuracy of coal and gangue with particle size of 5–100 mm reached 97.5%. Consequently, this paper provides a new technical approach for coal and gangue identification.

To reduce the influence of material particle size on coal gangue identification, a particle size identification method, and an adaptive image enhancement method are proposed, which can accurately identify the particle size of poorly segmented and mutually blocked materials, effectively reduce the reflection and blur of the image surface and enhance the texture details. Through the research of coal gangue images with different particle sizes, it is found that the image quality and feature curve distribution of small particle size are different from those of large particle size, and the gradient features are worse. In this paper, the accurate identification of particle size is realized using the difference in image quality and texture, and the identification rate is 99.25%. Through the image enhancement method in this paper, 33.41% of the reflection on the image surface is removed, and the average gradient is improved by 74.01%, which effectively improves the image quality and the ability to express texture information. This algorithm has high environmental adaptability, and the identification rate can reach 99.16% in moderate illumination, 98.33% in dim illumination, and 96.33% in strong illumination. This research provides a valuable idea for image processing and identification technology based on machine vision.