Please note that this session was withdrawn and is no longer available in the respective programme. This withdrawal might have been the result of a merge with another session.
GI6.7 | Unlocking the power of automated mineralogy to de-risk and strengthen the raw material cycle from primary to secondary ore, and waste materials.
EDI PICO
Unlocking the power of automated mineralogy to de-risk and strengthen the raw material cycle from primary to secondary ore, and waste materials.
Convener: Nina Zaronikola | Co-conveners: Thomas Wallmach, Quentin Dehaine, Anna VanderbruggenECSECS, Eric PIRARD
Minerals represent the fundamental pillar to the science of geology revealing crucial information on ore forming processes, metal deportment, and mineral (enrichment) processing. During the last decades, there has been considerable development on automated mineralogical quantification systems from both mining industry and academia. The aim is to accurately comprehend minerals formation, quantify mineral abundances in a given sample, define materials behavior and applications in short- and long-term, and utilize this dataset within a geometallurgical framework. A wide range of materials can be analyzed by automated mineralogy technologies, such as bulk rock samples, crushed and ground ore, waste and tailings, industrial waste containing valuable metals like Li-ion batteries, and extractive metallurgy waste (e.g., dusts, slags, residues). Automated mineralogy approaches rely on different technologies and platforms (e.g., QEMSCAN, MLA, TESCAN-TIMA, SEM-AM, AMICS, INCAFeature, etc.). These SEM-based analysis and data exploration softwares are powerful tools, which provide robust and statistical information on the samples’ variability and textural quantifications that the traditional microscopy-based mineral analysis is still lacking. Automated mineralogy can unlock the identity, composition, size, and proportion of different mineral or metallic phases, and fully quantify mineralogy while investigating distribution and texture. In addition, particles can be analyzed for the size, shape, composition, texture, and mineral associations. Based on this exceptionally valuable information, it is possible to generate models on how materials can be further enriched and processed in the mining industry saving tremendous amount of time, money and predict the potential environmental impact of future waste rock and tailings (e.g., Acid Mine Drainage - AMD). The aim of this session is to focus on recent developments and applications to the raw material sector based on automated mineralogy and discuss ore genesis, critical metals deportment (Co, Ni, Li, Cu, Zn, In, Ge, REE, etc.), metals enrichment and recycling contributing to net-zero transition. This session further highlights the challenge for automated mineralogy to bring together geological, mineralogical, and metallurgical experience to advance our knowledge on ore formation, help to solve mineral processing problems and consider future economic opportunities arising from material recycling.