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.

Currently, new sorting technology of coal and gangue not merely focuses on the identification of coal and gangue, but also emphasizes the fine identification of coal. Therefore, this paper takes the main coal types, including one-third coking coal, gas coal, and gangue in Huainan coalfield as the research object, and proposes a method to identify coal types and gangue. The ADXRD patterns and EDXRD patterns of the samples were obtained through X-ray diffractometer and EDXRD test platform, respectively. The differences between the phase composition and the internal connection between the ADXRD pattern and EDXRD pattern of the samples were discussed, and the conversion of ADXRD pattern to EDXRD pattern of coal and gangue was simulated, the PSO-SVM identification model of coal types and gangue was established. The experimental results show that this method provides a new and effective technical way for coal types and gangue identification, which has important reference significance for fine identification of coal and gangue.