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Collaborative Mining Sequence Optimization for Multiple Stopes under Intensive Mining
Author(s) -
Long Zhang,
Jianhua Hu,
Xinzhong Wang,
Xiuwei Chai,
Lei Zhao
Publication year - 2021
Publication title -
advances in civil engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.379
H-Index - 25
eISSN - 1687-8094
pISSN - 1687-8086
DOI - 10.1155/2021/6683157
Subject(s) - sequence (biology) , mining engineering , computer science , stability (learning theory) , entropy (arrow of time) , geology , mathematical optimization , algorithm , data mining , mathematics , machine learning , physics , quantum mechanics , genetics , biology
The optimization of a mining sequence not only reduces stress concentration in surrounding rock but also prevents underground debris flows, significantly improving safety. Firstly, the 870–898 m level of the eastern mining area in the Tiaoshuihe phosphate mine was divided into 25 ore blocks, and six different mining sequences were designed for this area. Then, it was calculated that five ore blocks must be processed simultaneously to reach the annual production output. The distances between the five simultaneously mined ore blocks will inevitably affect the efficiency of the equipment for any scheme. So, a collaborative model considering both the area stability and production capacity was established by combining the distance between the centers of the five ore blocks as an index. Differences in stability, deformation, and plastic zone size between the schemes are compared. The calculation results show that a mining scheme with a convex stepped shape produces the best results. These results provide a general method for entropy-based mining sequence optimization and an optimal solution for the Tiaoshuihe phosphate mine.

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