Automated Mineralogy – A Valuable and Data-Rich Product to Advance the Green Energy Transition  

Abstract

Automated mineralogical analysis (quantitative scanning electron microscopy) is a powerful tool that has been used extensively to understand the occurrence and deportment of precious and base metals and critical minerals and is used to optimize the design of extractive metallurgy methodologies. However, this data-rich product can also be used in a predictive context and as the basis for data integration across the range of scales. Automated mineralogical data include quantitative mineral abundance and textural data that can form the basis for machine learning algorithms to improve statistical subsampling strategies, data integration, mineralogical upscaling, and to increase the value of x-ray fluorescence- and hyperspectral data.    The Mineral and Materials Characterization Facility in the Department of Geology and Geological Engineering at the Colorado School of Mines in Golden, USA, houses two scanning electron microscopy-based automated mineralogy systems. These systems are used to conduct research over a broad range of disciplines including all stages of the mine life cycle, energy and petroleum resources investigations, provenance and climate studies, and environmental and biological studies.    During this presentation, we will explore examples of how automated mineralogy can play a crucial role across the mine life cycle that spans from mineral exploration, mine planning and mining, extractive metallurgy, proactive waste rock and tailings management, to reclamation. The example use-inspired research projects, conducted through the Center to Advance the Science of Exploration to Reclamation in Mining (CASERM) using the Advanced Mineral Analysis and Characterization System (AMICS) from Bruker based on a field-emission scanning electron microscope from Hitachi, focus on the integration of diverse geoscience data types to accelerate and improve decision making across the mine life cycle.    Quantitative scanning electron microscopy provides important mineralogical and textural data that can inform statistical, thermodynamic, and kinetic models. These data improve not only our understanding of the subsurface in the context of hard-rock mining, but can inform other disciplines such as geothermal energy exploration and extraction and understanding the carbonation potential, helping move the world towards a greener future.