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Artificial intelligence in copper metallurgy: A review of collaborative cross-process optimization
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DOI：10.1016/j.mineng.2026.110665.png)
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Abstract
En 中文
Copper metallurgy includes mineral processing, pyrometallurgy, and hydrometallurgy, and is a key component of global metal supply chains. As copper production systems have become increasingly complex, their operation exhibits strong nonlinearity, dynamic variation, and multi-stage coupling. These characteristics generate large volumes of heterogeneous process data. Conventional analytical methods often have limited ability to describe such complex processes and to make full use of industrial data. Under these conditions, artificial intelligence (AI) has been increasingly applied to process modeling, state identification, decision support, and system optimization. Existing reviews of AI in metallurgy and mineral processing have mainly focused on specific algorithms, individual process units, or broader nonferrous metallurgical systems. However, a systematic review of AI across the full copper production chain remains lacking. From a full-chain perspective, attention is directed to cross-process collaborative optimization, data interoperability, and intelligent system integration in copper metallurgy. A four-layer architecture consisting of perception, execution, control, and management is proposed as a general framework for the intelligent transformation of copper metallurgy. On this basis, relevant studies are examined across four major domains: mineral processing, pyrometallurgical processes, hydrometallurgical processes, and cross-process integrated technologies. Key enabling approaches are also discussed, including dynamic sensing, standardized data interoperability protocols, and multi-level collaborative optimization. Flash smelting is used as a representative case to illustrate the implementation logic of the framework and its potential industrial application. The review provides a basis for further research and development in intelligent copper metallurgical systems.
Keywords:
Copper metallurgy
Artificial intelligence
Intelligent system
System integration
Intelligent manufacturing
Intelligent transformation
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