From Mineral Liberation to Metallurgical Recovery: The Role of Automated Mineralogy in Sulfide Ore Processing

Sulfide ores are the primary source of nickel, copper, cobalt, platinum-group metals (PGMs), and several strategically important critical minerals. Yet their beneficiation remains challenging because of complex mineralogical associations, heterogeneous textures, variable liberation characteristics, and intricate mineral deportment patterns that strongly influence flotation performance and metallurgical recovery. Automated mineralogy has emerged as a transformative tool for quantitative ore characterization, providing detailed information on mineral composition, grain associations, particle texture, liberation, and exposure relationships. This review critically examines the role of automated mineralogy in sulfide ore processing and its contribution to linking mineral characterization with metallurgical performance prediction. Particular attention is given to the development and application of the Mineral Liberation Analyzer (MLA), QEMSCAN, SEM–EDS platforms, optical microscopy-based systems, X-ray micro-computed tomography (µCT), and hyperspectral imaging technologies. The review discusses how these techniques support quantitative liberation analysis, mineral deportment studies, flotation diagnostics, geometallurgical characterization, and recovery forecasting across a wide range of sulfide ore types. Recent advances in particle-based modeling are examined, highlighting the transition from conventional liberation metrics toward predictive frameworks that relate particle attributes directly to flotation behavior and metallurgical response. The integration of machine learning and artificial intelligence with automated mineralogical datasets is also evaluated, emphasizing emerging opportunities for recovery prediction, digital geometallurgy, process optimization, and decision support. Current limitations associated with spatial resolution, ultra-fine particle characterization, data uncertainty, model transferability, and industrial implementation are critically assessed. Future developments are expected to focus on real-time mineralogical monitoring, online process control, digital twins, AI-assisted recovery prediction, and autonomous concentrators. Automated mineralogy is increasingly evolving from a descriptive analytical technique into a predictive framework that supports next-generation mineral processing, geometallurgical modeling, and sustainable resource utilization.