AI-driven Economic Optimization of Critical Minerals Recovery from Mine Tailings

The recovery of critical minerals and rare earth elements from mine tailings is becoming increasingly relevant in the context of resource security, circular economy, and the global energy transition. Mine tailings, traditionally considered environmental liabilities, may contain valuable residual metals and minerals that can support the supply of strategic raw materials while reducing the environmental footprint of mining activities. However, their recovery requires integrated approaches that consider mineralogical variability, processing efficiency, environmental risks, market conditions, and economic feasibility. In this context, artificial intelligence and data-driven methods offer new opportunities to optimize decision-making, improve resource estimation, assess recovery potential, and support sustainable valorization strategies for mine waste.This Research Topic aims to explore how artificial intelligence, economic modeling, and sustainability assessment can contribute to the optimization of critical minerals recovery from mine tailings. The main objective is to bring together interdisciplinary studies that address the technical, economic, environmental, and policy dimensions of tailings valorization. Contributions are expected to provide innovative methods, case studies, models, and decision-support frameworks that improve the feasibility of recovering critical minerals and rare earth elements from mining residues. By integrating AI-driven optimization, techno-economic analysis, circular economy principles, and environmental economics, this collection seeks to support more sustainable mining practices, reduce waste-related impacts, and strengthen the resilience of critical mineral supply chains.We welcome original research articles, reviews, perspectives, policy analyses, and case studies focused on the recovery of critical minerals and rare earth elements from mine tailings. Topics of interest include, but are not limited to, AI-driven optimization of mineral recovery, machine learning for tailings characterization, techno-economic assessment, circular economy strategies, environmental and social impacts, life cycle assessment, resource governance, supply chain resilience, and policy frameworks for sustainable mining. Studies integrating geochemistry, mineral processing, economic evaluation, environmental risk assessment, and data-driven decision-making are particularly encouraged. Contributions may address local, regional, or global case studies, with emphasis on practical approaches that support the sustainable revalorization of mine waste. Topic editors: Marco Antonio Cotrina Teatino, Jairo Jhonatan Marquina-Araujo, Jose Charango Munizaga-Rosas Source: https://www.frontiersin.org/research-topics/81224/ai-driven-economic-optimization-of-critical-minerals-recovery-from-mine-tailings