K. Rose, C.G. Creason, S. Montross, N. Cordero Rodriguez, Z. Jackson, S. O’Barr, R. Hess, C. Atkins, G. Hazle, S. Skipwith, A. Nawacki, F. Taglia, J. Hird
National Energy Technology Laboratory,
United States
Keywords: REE, critical minerals, materials, characterization, field, feedstock, resource, AI, ML, artificial intelligence, machine learning
Summary:
The increasing global demand for rare-earth elements (REEs) and other critical minerals (CMs) necessitates new technological solutions to access new feedstocks and secure domestic supply chains. This project presents an NETL-developed technology for rapidly characterizing REE/CM occurrences in unconventional sources, including in situ sedimentary systems, mine refuse, and waste impoundments. Our innovation is a machine-learning (ML) informed, multi-system modeling software system integrates with commercially available portable X-ray Fluorescence (p-XRF) systems to rapidly evaluates the potential for REE/CMs occurrences and their mode. This field-deployable innovation provides near real-time detection of unconventional REE/CM resources. Beyond simple detection, it characterizes the chemical form and spatial distribution of these elements within the host material. This data is critical for optimizing extraction strategies and techniques, enhancing resource recovery and minimizing operating costs and environmental impacts. Our core objective is to rapidly deploy this validated system to empower the commercial mining and beneficiation sectors to identify, quantify, and characterize REE/CMs from untapped domestic feedstocks. We achieve this by integrating robust, peer-reviewed ML-informed tools and methods, and datasets from REE/CM research with new field and laboratory data. The result is a user-friendly, integrated system—a comprehensive database coupled with intuitive analytical software—designed for widespread adoption. This tool represents the first commercial solution for rapid, low-cost identification of unconventional REE/CM feedstocks. It will improve deposit identification, enable accurate reserve quantification, and provide insights into host-media characteristics and REE/CM forms, guiding technically and economically prudent extraction strategies. This innovation is not just a technological advancement; it's a strategic imperative set to bolster domestic critical mineral supply chains, fuel economic growth, and fortify national security.