Comminution performance prediction from ore mineralogy and texture
DOI:
https://doi.org/10.17159/Keywords:
geometallurgy, mineralogy, comminution, machine learning, Bond work index, platinum group elementsAbstract
Geometallurgy integrates geological, mineralogical and metallurgical data to optimise the mining value chain. Within this framework, understanding how ore mineralogy and texture influence comminution behaviour is critical for improving energy efficiency, liberation efficiency and downstream metallurgical performance. This study investigates two low-grade Merensky Reef platinum group element (PGE) ores, Ore A and Ore B, with contrasting mineralogical and textural characteristics. Automated mineralogy using the TESCAN Integrated Mineral Analyzer (TIMA), hyperspectral image-based texture analysis and machine-learning methods were used to quantify modal mineralogy, grain-size distributions, intergrowth patterns and liberation characteristics. Ore A is dominated by coarse, interlocking silicate minerals, with limited sulphide and alteration content, and therefore exhibits higher mechanical competence. Ore B contains finer sulphides and a higher abundance of alteration minerals, including serpentine and talc, which introduce mechanically weak interfaces and enhance crack propagation. Laboratory comminution tests and work-index-based assessments, supported by Monte Carlo simulations, indicate that Ore B requires less specific energy to achieve the target grind size. Its finer grain size and weaker mineral phases promote intergranular fracture and more efficient liberation. In contrast, Ore A shows greater resistance to breakage because of its coarse silicate framework and therefore requires higher energy input. Among the evaluated predictive models, linear regression yielded the best performance (R² = 0.885), indicating that comminution performance can be estimated with reasonable accuracy from mineralogical and textural descriptors, although the dataset remains limited. These results show that integrating quantitative mineralogy, texture metrics and predictive modelling can support block-scale mine-to-mill optimisation. Ore-type-specific blending, selective mining and circuit design may reduce energy consumption and limit unnecessary ultra-fine tailings generation. Overall, the study presents a data-driven framework for texture-informed geometallurgical forecasting in complex PGE orebodies.
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Copyright (c) 2026 Viwe Notole, Glen Nwaila, Sehliselo Ndlovu, Mehdi Safari
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