Sequential rules and identification method of coal-gangue-rock caving flow in fully mechanized top-coal-caving workface of extra thick coal seam

The intelligent coal-gangue identification is the bottleneck of top-coal-caving mining operation. After more than ten years of continuous exploration and research, based on the deposition environment and lithological characteristics of coal seams, the authors have proposed a high-precision and real time detection method based onlow level natural ray radiation in coal and rock seams and the automatic identification principle of coal and gangue. It lays the foundation for the intelligent identification and control of coal and gangue in top-coal-caving mining working face. According to the complex structural characteristics of extra thick coal seam containing multi-layer gangue, in order to improve the reliability of coal gangue identification, the characteristics of occurrence and natural radiation of coal and gangue in extra thick coal seam are analyzed. The research of coal-gangue(parting)rock (immediate roof rock) timing law in the caving flow is carried out, and it is concluded that the layer position, spacing and number of gangue layer in top coal affect the cavingflow sequential rules, which can present some different timing states according to which the release flow timing zoning is carried out. Accordingly, the mixed gangue rate will present different characteristics. That is, when the partingis drawn outwith no timing order, the mixing rate fluctuates within a fixed range, and when the coal and gangue are caved gradually, the mixing rate shows the characteristics of rising-stable-continuous step increase, and the mixing of immediate roof rock makes the mixing rate increase linearly. According to the timing characteristics of coal-gangue-rock fall flow, the radiation change characteristics of natural rays under different timing conditions are studied, and the relationship between the step change of natural ray radiation intensity and the storage parameters of partingis determined. Based on the relationship above the main parameters of coal-gangue-rock automatic identification and the identification methods of two different window closing methods are proposed. Finally, the field test has been carried out in the Tashan Coal Mine of Tongmei Group, China, and the test and research results have been well verified. The expected results have been achieved, which lays a foundation for the field application of intelligent top caving.