Analysis of Coal Outlet Parameters by Simulation Modeling of Longwall Top Coal Caving

Klishin, V. I.; Starodubov, A. N.; Kramarenko, V. A.; Kadochigova, A. N.; Kaplun, A. V.

doi:10.1134/s1062739123040051

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…These prominent coal seams are pivotal in ensuring a stable coal supply. Longwall top coal caving (LTCC) is a primary technique employed for mining thick coal seams, wherein the seam is vertically divided into two sections: bottom and top coal [4][5][6]. A conventional working face is then established at the location of the bottom coal, where mine pressure leads to top coal fragmentation.…”

Section: Introductionmentioning

confidence: 99%

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Huo,

Zhao,

Zhu

et al. 2024

Sustainability

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China has made notable advancements in the intelligent construction of coal mines. However, for longwall top coal caving (LTCC) mining faces, a key obstacle impeding the intelligent transition of the coal-cutting process is automated control. This paper focuses on the aforementioned issue and comprehensively considers the pre-, intra-, and post-coal-caving stages. In this work, diverse detection and monitoring technologies are integrated at various stages through a computer platform, facilitating the construction of an automated coal caving control system with self-perception, self-learning, self-decision-making, and self-execution capabilities. Key technologies include ground-penetrating radar-based top coal thickness detection, inertial navigation-based shearer positioning, tail beam vibration-based identification of coal and gangue, and magnetostrictive sensor-based monitoring of the tail beam and insert plate attitude. In this study, the 12309 working face of the Wangjialing Coal Mine was experimentally validated, and the efficacy of the aforementioned key technologies was assessed. The results demonstrated that the control requirements for automated coal caving are satisfied by the maximum errors. Automatic regulation of coal caving was realized through the implementation of this system, thereby facilitating initiation and cessation and yielding promising experimental outcomes. Overall, this system offers practical insights for intelligent construction in current LTCC mining faces and the sustainable development of coal resources.

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Section: Introductionmentioning

confidence: 99%

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Huo,

Zhao,

Zhu

et al. 2024

Sustainability

View full text Add to dashboard Cite

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Optimization of drawing sequence in longwall top coal caving mining through an FDM‐DEM model

Huo,

Wang,

Zhu

et al. 2024

Energy Science & Engineering

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The Longwall top coal caving (LTCC) technology is regarded as one of the most crucial approaches for exploiting thick coal seams. A crucial and effective approach for improving the recovery rate of top coal and reducing coal resource losses in LTCC faces is to reasonably select process parameters based on actual mining and geological conditions of different mines. The main focus of this paper is the engineering background of the 12,309 LTCC face in Wangjialing Coal Mine. A numerical model is developed using FALC3D and PFC3D software, employing a finite difference method and discrete element method. This model takes into account predetermined cutting and caving ratios, as well as drawing intervals. To examine the caving process and roof particles, three different drawing sequences were examined: sequential drawing, segmented sequential drawing, and intermittent drawing. The findings suggest that, in terms of the reset shape of the drawing body before individual and entire caving, the segmented sequential drawing method exhibits noticeable drawbacks compared to the other two methods. From the perspective of the drawing weight, following “closing drawing opening when seeing gangue”, the sequential drawing, segmented sequential drawing, and intermittent drawing methods can yield 32.42 t, 26.87 t, and 35.78 t of top coal, with corresponding recovery rates of 73.39%, 60.81%, and 82.97%. Therefore, it can be concluded that intermittent drawing is suitable for implementation on LTCC working face 12,309.

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Improved mesh-free SPH approach for loose top coal caving modeling

Dong,

Zhang,

Liu

et al. 2024

Particuology

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Analysis of Coal Outlet Parameters by Simulation Modeling of Longwall Top Coal Caving

Cited by 3 publications

References 6 publications

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Application of an Automated Top Coal Caving Control System: The Case of Wangjialing Coal Mine

Optimization of drawing sequence in longwall top coal caving mining through an FDM‐DEM model

Improved mesh-free SPH approach for loose top coal caving modeling

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