Research progress on intelligent coal caving theory and technology - WANG Jiachen; YANG Shengli; LI Lianghui; ZHANG Jinwang; WEI Weijie (Gong-kuang zidonghua)
The longwall top-coal caving technology is an effective method for extracting thick and ultra-thick coal seams, and it has become a hallmark technology in China's coal mining industry. This paper reviews the research progress on the "Four elements" coal caving theory, the relationship between the top coal recovery rate and the rock mixed ratio, a recovery rate prediction model based on block distribution, and the relationship between instantaneous rock mixed ratio and cumulative rock mixed ratio. The challenges of intelligent coal caving technology are analyzed, emphasizing that the rock mixed ratio is a key factor affecting the top coal recovery rate and coal quality. Rapid and accurate calculation of the rock mixed ratio during the coal caving process is crucial for breakthroughs in intelligent coal caving technology. This technology is categorized into two types: non-image recognition and image recognition. The research progress, advantages, disadvantages, and usage conditions of different technologies are discussed in detail. Non-image recognition intelligent coal caving technology includes memory coal caving technology, sound and vibration signal detection technology, γ-ray detection technology, ground penetrating radar technology, microwave irradiation combined with infrared detection technology, and laser scanning coal caving monitoring technology. Image-based intelligent coal caving technology encompasses precise control of underground illumination environment, dust removal algorithms for coal caving images, accuracy assurance strategies for rock mixed ratio calculations, and infrared image recognition of coal and rock.