Deep learning-driven autonomous spodumene flotation: Integrating theoretical modeling and YOLOv11-M vision systems with semi-industrial trial validation

l Engineering, Northeastern University, 110819, Shenyang, China
2, State Key Laboratory of Mineral Processing, 110819, Shenyang, China
3, National-Local Joint Engineering Research Center of High-Efficient Exploitation Technology for Refractory Iron Ore Resources, 110819, Shenyang, China
a zhuyimin@mail.neu.edu.cn
History +
Received Accepted Published Online
9 Feb 2026 15 May 2026 24 Aug 2026
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Abstract
Driven by the global energy transition and industrial intelligence, the mining industry is evolving towards smarter and more efficient methods. In mineral processing, particularly flotation, t

原文链接:https://journal.hep.com.cn/ijmmm/EN/10.1007/s12613-026-3494-6