Overview and prospects of modeling and optimal control for non-ferrous metallurgical processes and mineral processing

Abstract

The non-ferrous metallurgical industry holds an important strategic position for national construction and the achievement of a strong manufacturing country. Green, low-carbon, and efficient production in non-ferrous metallurgical enterprises relies on process modeling, optimization, and control. This article reviews the process modeling techniques and optimal control methods for non-ferrous metallurgical processes and mineral processing. Modeling and optimal control techniques encompass both traditional methods and artificial intelligence (AI) approaches, including deep learning, intelligent control, and reinforcement learning. The application of AI in non-ferrous metallurgical industries is receiving increasing attention. Therefore, we highlight the challenges and prospects of process modeling and optimal control, including the challenges of data scarcity, model interpretability, and integration complexity. Big data modeling, model incremental learning, cloud-edge collaborative control, and digital twin system-based optimization control will play an important role in future plants.

Keywords

Non-ferrous metallurgical / Non-ferrous metals recycling / Green extractive metallurgy / Intelligent mineral processing / Artificial intelligence / Process modeling / Optimal control / Digital twin system

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