Geometallurgical Domain Characterisation and Process Performance Prediction for Ferromanganese Ores from the Paling Pan MnFe Deposit, Postmasburg Manganese Field, South Africa
DOI:
https://doi.org/10.17159/Abstract
Manganese (Mn) is a critical metal for global industry, with South Africa being the leading producer of Mn ore. The efficient processing of its complex ores, particularly those in the Postmasburg Manganese Field (PMF), requires a site-specific understanding of their inherent variability. This study presents a comprehensive geometallurgical framework for the Paling Pan Fe-Mn deposit, integrating mineralogy, geochemistry, and quantified ore texture to define domains that predict processing performance. We characterised four principal ore textures: massive, vuggy, layered, and conglomeritic, each with distinct mineralogical and textural attributes influencing beneficiation. A dataset of 256 samples was used to develop predictive machine learning (ML) models, specifically leveraging the XGBoost algorithm, for magnetic separation and dense media separation (DMS) performance. The results demonstrate that incorporating texture-differentiated geometallurgical domains enhances prediction accuracy for recovery-related performance indicators (magnetic separation and DMS), while density prediction remains largely chemistry-controlled. This approach yielded modest but consistent improvements in model performance, with the strongest gains observed for DMS yield prediction (a 2.6% increase in the coefficient of determination, CoD). Magnetic separation yields were predicted to range from 72.3% to 92.3% across different domains, with massive ores showing the most favourable performance. SHapley Additive exPlanations (SHAP) analysis identified Mn grade, Fe grade, and ore texture as the most critical features for predicting magnetic separation performance, while specific gravity and gangue content were key for DMS. This data-driven approach provides a robust methodology for optimising plant design and processing strategies, thereby improving resource efficiency and economic viability.
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Copyright (c) 2026 Musawenkosi Buthelezi, Glen, Daniel Bussin, Bertus Smith, Clarisa Vorster, Grant Bybee, Musa Manzi
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