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智能放煤理论与技术研究进展

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综放开采技术是厚及特厚煤层开采的有效方法,已成为我国在世界煤炭开采行业的标志性技术.综述了"四要素"放煤理论、顶煤采出率与含矸率关系、基于块度分布的采出率预测模型、煤流瞬时含矸率-累计含矸率关系等智能放煤理论研究进展.分析了智能放煤技术难点,指出含矸率是影响顶煤采出率和煤质的关键因素,放煤过程中含矸率的快速、准确计算是智能放煤技术突破的重点和关键.将智能放煤技术分为非图像识别智能放煤技术和图像识别智能放煤技术 2类,对不同技术的研究进展、优缺点及使用条件进行了详细分析.非图像识别智能放煤技术包括记忆放煤技术、声音振动信号识别技术、γ射线探测技术、探地雷达技术、微波照射+红外探测技术、激光扫描放煤量监测技术等,图像识别智能放煤技术包括井下照度环境精准控制、放煤图像去尘算法、含矸率计算精度保障策略、煤岩红外图像识别等.
Research progress on intelligent coal caving theory and technology
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.

longwall top-coal cavingintelligent coal caving"Four elements"coal caving theoryrock mixed ratioimage recognitionnon-image recognition

王家臣、杨胜利、李良晖、张锦旺、魏炜杰

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中国矿业大学(北京)能源与矿业学院,北京 100083

厚煤层绿色智能开采教育部工程研究中心,北京 100083

放顶煤开采煤炭行业工程研究中心,北京 100083

综放开采 智能放煤 "四要素"放煤理论 含矸率 图像识别 非图像识别

国家自然科学基金资助项目国家自然科学基金资助项目国家自然科学基金资助项目国家自然科学基金资助项目北京市自然基金资助项目

519340085237414852204163521210032232059

2024

10.13272/j.issn.1671-251x.18213

工矿自动化
中煤科工集团常州研究院有限公司

工矿自动化

CSTPCD北大核心
影响因子：0.867
ISSN：1671-251X
年,卷(期)：2024.50(9)