Generative artificial intelligence in extractive metallurgy: Industrial applications, limitations and implementation pathways

Abstract

The extractive metallurgy sector is undergoing rapid digital transformation driven by Industry 4.0, advanced sensing, and artificial intelligence (AI). While machine learning has been widely adopted for predictive control and optimization, the role of generative artificial intelligence in metallurgical engineering remains inadequately characterized in the literature. This paper critically reviews the state of generative AI for extractive metallurgy, focusing on practical industrial applications rather than purely theoretical AI methods. We synthesize peer-reviewed research, industrial case studies, and emerging applications across comminution, flotation, hydrometallurgy, pyrometallurgy, ore sorting, and plant reliability. The review identifies five key generative AI models applicable to metallurgy: generative diffusion models, flow-based models, variational autoencoders, generative pre-trained transformers, and generative adversarial networks. Generative AI presents a transformative opportunity for extractive metallurgy, offering solutions for optimized process control, enhanced mineral recovery, predictive maintenance, and improved sustainability. While generative AI offers significant potential, its deployment requires rigorous validation, physics-informed modeling, and hybrid human AI workflows in metallurgical plants.

Keywords

generative AI / mineral processing digitalization / AI in metallurgy / data-driven process optimization / AI implementation

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