Nuclear Engineering and Technology

  • 제56권9호
  • /
  • Pages.3750-3757
  • /
  • 2024
  • /
  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
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Improved FMM for well locations optimization in in-situ leaching areas of sandstone uranium mines

  • Mingtao Jia (School of Resources and Safety Engineering, Central South University) ;
  • Bosheng Luo (School of Resources and Safety Engineering, Central South University) ;
  • Fang Lu (Hunan Women's University) ;
  • YiHan Yang (Inner Mongolia Mining Co., Ltd.) ;
  • Meifang Chen (Beijing Research Institute of Chemical Engineering Metallurgy) ;
  • Chuanfei Zhang (Inner Mongolia Mining Co., Ltd.) ;
  • Qi Xu (Inner Mongolia Mining Co., Ltd.)
  • 투고 : 2023.11.05
  • 심사 : 2024.04.15
  • 발행 : 2024.09.25
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초록

Rapidly obtaining the coverage characteristics of leaching solution in In-situ Leaching Area of Sandstone Uranium Mines is a necessary condition for optimizing well locations reasonably. In the presented study, the improved algorithm of the Fast Marching Method (FMM) was studied for rapidly solving coverage characteristics to replace the groundwater numerical simulator. First, the effectiveness of the FMM was verified by simulating diffusion characteristics of the leaching solution in In-situ Leaching Area. Second, based on the radial flow pressure equation and the interaction mechanism of the front diffusion of production and injection well flow field, an improved FMM which is suitable for In-situ Leaching Mining, was developed to achieve the co-simulation of production and injection well. Finally, the improved algorithm was applied to engineering practice to guide the design and production. The results show that the improved algorithm can efficiently solve the coverage characteristics of leaching solution, which is consistent with those obtained from traditional numerical simulators. In engineering practice, the improved FMM can be used to rapidly analyze the leaching process, delineate Leaching Blind Spots, and evaluate the rationality of well pattern layout. Furthermore, it can help to achieve iterative optimization and rapid decision-making of production and injection well locations under largescale mining area models.

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