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CGTN’s Mike at Dexing: AI Sorting Turns Waste Ore to Gold
China
17:58, 21-Sep-2026
CGTN
CGTN’s Mike Walter visited the Dexing Copper Mine in Jiangxi and witnessed a transformation driven by AI-powered ore sorting. Independently developed in China, the intelligent optical-electronic sorting equipment can accurately separate ore from waste rock at a speed of 300 tons per hour, turning previously useless low-grade ores into economically viable resources. This innovation not only significantly improves the recovery rate of key materials, but also reduces mining costs and reliance on imports, there

原文链接：https://news.cgtn.com/news/2026-09-21/CGTN-s-Mike-at-Dexing-AI-Sorting-Turns-Waste-Ore-to-Gold-1QCtEVZLj56/p.html

，人工智能（AI）、大数据等新一代信息技术加速发展，全球矿业步入由新质生产力驱动的系统性变革期，勘探、采选、回收的闭环供应链加速构建。
在找矿勘查端，AI找矿技术快速迭代并迈入产业化应用，高精度、轻量化、综
化地球物理装备与“星-空-地-井”多源遥感技术多点突破，全空间立体探测体系加速成形，但数据、模型与复杂场景的适应性仍是主要瓶颈。
在开发运营端，智能化与绿色化双轮驱动。主要矿业国家密集出台智能化战略，矿山
本文作者：中国能源网

原文链接：https://c.m.163.com/news/a/L6IBQ26P05567I2C.html

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Innovation
Vale Launches First-Ever R&D&I Report and Details How Innovation Is Already Transforming the Mining Chain
Publication consolidates more than 350 projects and showcases applications of AI, automation, circularity, and decarbonization.
Vale has launched its first Research, Development & Innovation (R&D&I) Report, a publication that consolidates investments, governance, partnerships, and applied cases in the transformation of mining. More than just a compilation of technological initiatives, the report shows how R&D&I has become part of the company’s strategy to addr

原文链接：https://saladeimprensa.vale.com/ar/w/rd-report-mining-future/-/categories

金川集团矿山传统机械化装备智能破局
金川集团镍钴研究设计院联合二矿区开展的《二矿区充填皮带AI智慧管控系统项目》正式启动，将科技创新成果及时应用到具体产业上，推动产业高质量发展。
随着产业升级需要和企业深化改革，新技术逐步取代旧技术，在此背景下，金川集团镍钴研究设计院联合二矿区开展的《二矿区充填皮带AI智慧管控系统项目》正式启动，将科技创新成果及时应用到具体产业上，推动产业高质量发展。
在工业生产中，皮带

原文链接：https://www.greenmine.org.cn/shows/9/235727.html

于后续处理矿石筛分：将破碎后的矿石进行筛分，分离出有用矿物和废石人工智能在开采中的应用智能勘探：利用AI技术进行地质勘探，提高勘探效率和准确性智能开采：利用AI技术进行自动化开采，提高开采效率和安全性智能选
控：利用AI技术进行开采过程的实时监控，提高开采的安全性和环保性大数据在处理中的作用数据采集：实时监测镍钴矿的开采和加工过程，收集相关数据数据分析：利用大数据技术对采集到的数据进行分析，找出潜在的问题和

原文链接：https://m.renrendoc.com/paper/305348664.html

icial Intelligence on Nickel Exploration, Processing, and Supply Chain Resilience
Artificial intelligence is increasingly influencing the nickel value chain, from exploration and mine planning to process optimization, predictive maintenance, logistics, and sustainability monitoring. In exploration, AI-enabled geospatial analysis, remote sensing, and machine learning models can help identify prospective mineral zones by integrating geological, geochemical, geophysical, and satellite datasets. These tools support faster target generation and more efficient drilling programs, while still requirin

原文链接：https://www.giiresearch.com/report/ires2103512-nickel-market-global-forecast.html

US deploys AI agents to speed critical minerals recovery
Staff Writer | May 27, 2026 | 11:27 am Battery Metals Critical Minerals Education Suppliers & Equipment USA Rare Earth
Elias Nakouzi, materials scientist. (Photo: Pacific Northwest National Laboratory)
A US Department of Energy (DOE) research team has developed AI agents capable of designing and optimizing critical minerals recovery methods from industrial waste in days instead of the months or years typically required through manual laboratory work.
The team at the Pacific Northwest National Laboratory (PNNL), led by materials scienti

原文链接：https://www.mining.com/us-deploys-ai-agents-to-speed-critical-minerals-recovery/

-Solve技术”，利用微生物从精矿中选择性提取镁等杂质。近期，该公司已与美国Eagle镍矿合作，在矿区部署现场试验装置——实验室测试显示，该技术可将镍品位提升18%，并将镁杂质含量降低40%。
三、科技巨头与传统产业碰撞：AI赋能矿业革新
能够整合所有这些创新技术的核心，是人工智能（AI）。
目前，力拓、必和必拓（BHP）等矿业巨头已将AI应用于自动驾驶运输系统，并通过AI预测设备维护周期，而非在设备故障后被动应对。生成式人工智能（Gener

原文链接：https://caifuhao.eastmoney.com/news/20250902143714991628670

降低能耗和钢耗。02智能分选技术利用人工智能、机器学习等技术，实现矿石的智能分选，提高分选效率和精度。采选技术创新
123通过添加固化剂等措施，将尾矿转化为固态废弃物进行干堆存放，减少尾矿库的安全隐患和环境污
材料，如水泥、砖块等，既解决了尾矿的堆放问题，又创造了经济效益。尾矿建材化利用尾矿处理与资源化利用
05镍钴矿绿色加工技术创新
破碎与磨矿技术采用高效破碎和磨矿设备，降低能耗和钢耗，提高矿石破碎和磨矿效率。

原文链接：https://m.book118.com/html/2024/0503/5224112134011202.shtm

May 27, 2026 News Release
AI Speeds Selective and High-Yield Recovery of Critical Minerals from Industrial Waste
Researchers at PNNL developed an AI-guided workflow to optimize purification of critical minerals for industry partners using cheap commodity chemicals
Karyn Hede,
PNNL
Materials scientist Elias Nakouzi is creating an autonomous critical minerals recovery workflow that utilizes AI.
(Photo by Andrea Starr | Pacific Northwest National Laboratory)
RICHLAND, Wash.—A research team at the Department of Energy’s Pacific Northwest National Laboratory has deployed AI agents with the pote

原文链接：https://www.pnnl.gov/news-media/ai-speeds-selective-and-high-yield-recovery-critical-minerals-industrial-waste