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VOLUME 126 NO. 10 OCOTBER 2026

Contents Journal Comment: Geometallurgy as a foundation for operational excellence by G.T. Nwaila . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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The Journal of the Southern African Institute of Mining and Metallurgy

GEOMETALLURGY THEMED EDITION

Nonlinear data normalisation for reliable spatial inference with variational Gaussian processes by I.G. Gonçalves , G.T. Nwaila..................................................................................................................................... The variational Gaussian process model for grade estimation possesses all the beneficial characteristics of the kriging estimator. However, its use requires a transformation of the data to a Gaussian distribution. This work explores some transformations that were specifically adapted for this model. Results show that the best transformation provides results similar to the ones used for geostatistical simulation. Geometallurgical domain characterisation and process performance prediction for ferromanganese ores from the Paling Pan MnFe deposit, Postmasburg Manganese Field, South Africa by M. Buthelezi, G.T. Nwaila, D. Bussin, A.J.B. Smith C. Vorster, G.M. Bybee, M.S.D. Manzi............................ This study develops a geometallurgical framework for the efficient processing of manganese ore in the Paling Pan Fe-Mn deposit. Four principal ore textures were characterised, each influencing beneficiation differently. This integrated, data-driven approach enhances resource efficiency, economic viability, and sustainability in Mn ore exploitation. The role of mineralogy in predictive geometallurgy by M. Becker.................................................................................................................................................................... A review of the geometallurgical literature indicates that mineralogy represents the geological blueprint of an orebody, encapsulating the primary properties that govern metallurgical response. Examples are presented to illustrate the application of the mineral composition triad for mineralogical data validation, elemental distributions, and mineral grades. Data-driven and machine-learning approaches offer considerable opportunities for modelling complex, non-linear relationships. Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis: A multi-method comparative study at an open-pit gold mine by I.N. Aslam, M.S. Perdana, H. Harisman, F. Aliva, N.A. Putri1, R. Ayuni........................................................... This study presents a multi-method evaluation of Meta’s Segment Anything Model for automated segmentation and particle size distribution analysis of post-blast rock fragments at an open-pit gold mine in Sumatra, Indonesia. Four complementary investigations were conducted. Processing time was reduced from 30 minutes to 2 – 5 minutes per image. The findings support the potential of the Segment Anything Model as an open-source tool for rock fragmentation analysis. Data-driven delineation of geometallurgical domains in porphyry copper systems by T. Mapupu, G.T. Nwaila............................................................................................................................................. This study presents a machine-learning workflow applied to a porphyry copper dataset for partitioning an orebody into spatially coherent domains. The resulting domains show reduced internal variability, while remaining geologically plausible and broadly consistent with known alteration patterns. This reproducible workflow links orebody heterogeneity to processing behaviour and provides a practical, data-driven framework for domaining in complex deposits. Integrating diamond breakage into existing kimberlite geometallurgical frameworks: A perspective by S. Burness, S. Bremner, M. Becker, G.H. Howarth, A.N. Mainza........................................................................ Reducing diamond breakage during processing has become essential for sustaining value in modern kimberlite operations. This paper provides a perspective on the current understanding of diamond breakage with consideration of the diamond characteristics that may influence mechanical response and the approaches available for investigating them. Greater consideration of diamond-specific characteristics is proposed when assessing breakage and value loss during kimberlite processing. In situ coarse-particle gold leaching from Witwatersrand ores using cyanide and glycine by J.J. Mahumana, D. Groot, C. Adendorff, V. Notole G.T. Nwaila.......................................................................... This study evaluates in situ coarse-particle leaching gold extraction from coarse Carbon Leader Middelvlei reef blends using cyanide and glycine under ambient bottle-roll conditions. Differences in matrix mineralogy, gold-grain compactness, and connected porosity provide plausible explanations for blend-dependent behaviour. The 48 hour endpoint was predefined for comparative screening. Follow-up work will include at least one longer-duration experiment continued to a stable recovery plateau.

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very mine plan makes a promise about how an orebody will perform. That promise extends beyond tonnes and grade to the metal recovered, the energy and water consumed, the quality of the product sold, and the waste left behind. Its credibility depends on how well we understand the material that will enter the plant. Geometallurgy should therefore be a nonnegotiable part of responsible project development and operational management. This special themed edition of the SAIMM Journal, focused on Smart Sensors and Data Intelligence for Risk Management and Operational Excellence Through Geometallurgy, addresses that responsibility. It brings attention to a fundamental challenge, that is, translating measurements of heterogeneous rock into reliable decisions about mining and processing. The significance of this theme lies in the connection between orebody knowledge and the consequences of acting on it. Grade alone cannot describe processing behaviour. Two parcels with similar metal content can differ substantially in hardness, mineral associations, liberation characteristics, and metallurgical response. Those differences can alter throughput, recovery, reagent demand, and concentrate quality. When they are absent from the planning model, their consequences emerge during production, when the options for responding may be costly or constrained. Geometallurgy makes these relationships explicit and allows their implications to inform decisions before the material reaches the plant. A central difficulty is the imbalance between abundant geological and geochemical observations and comparatively sparse metallurgical testwork. Laboratory tests remain indispensable, but extending their results across an orebody requires evidence that the relationships remain valid beyond the tested samples. Sample representativity, geological domains, and changes in scale all matter. Blending and operating conditions can further modify the response. A credible geometallurgical model must preserve the connection between measurements and predictions, making its assumptions and uncertainty visible wherever its outputs influence a decision. Smart sensors can strengthen this connection by expanding the coverage and frequency of observations, from core characterisation to material movement and plant performance. Their usefulness depends on calibration, quality assurance and quality control, and an understanding of what each measurement represents. Measurements collected at different locations, times, and scales must be reconciled before they can support a common interpretation. More data become valuable when they resolve uncertainty that matters to an operational choice. The same discipline applies to machine learning and digital twins. Predictions must remain grounded in geology, mineralogy, and process behaviour, with validation appropriate to the decisions they support. A model that performs well on familiar samples may be unreliable in an untested ore domain. Quantified uncertainty helps identify where additional testwork is justified, where a blend needs caution, and where a production forecast requires contingency. As an operation advances, reconciliation with actual performance should inform model updates and expose assumptions that no longer hold. Operational excellence requires this knowledge to change practice. Geometallurgical predictions should inform mine scheduling, blending, and process settings, with clear responsibility for acting on them. Performance should be assessed through outcomes such as more dependable throughput and recovery, improved product quality, and better management of energy, water, and waste. These outcomes depend on geologists, metallurgists, mining engineers, and data specialists working from a shared understanding of the orebody and its uncertainty. Developing that capability deserves sustained investment alongside the technology. The case for geometallurgy is ultimately a case for accountable decisions. Understanding variability does not eliminate risk; it allows us to recognise its sources, assess its consequences, and choose how to respond. The scope of a geometallurgical programme should befit[HS1.1] the deposit and the decision at hand. The obligation to understand material variability and its effect on performance should be non-negotiable. I invite readers to approach this edition with a practical question: what must change in our own operations to make that understanding part of every consequential decision? G.T. Nwaila Director of the African Research Centre for Ore Systems Science (CORES) and Deputy Head of School School of Geosciences | Faculty of Science

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Nonlinear data normalisation for reliable spatial inference with variational Gaussian processes by I.G. Gonçalves1, G.T. Nwaila²

Affiliation: 1 Geological Modeling Laboratory, Federal University of Pampa, Brazil 2 African Research Centre for Ore Systems Science (CORES), School of Geosciences, University of the Witwatersrand, South Africa Correspondence to: I.G Gonçalves Email: italogoncalves@unipampa.edu.br

Dates:

Received: 12 Jan. 2026 Revised: 28 Sep. 2026 Accepted: 02 Oct. 2026 Published: Oct. 2026 How to cite: Gonçalves I.G., Nwaila G.T. 2026. Nonlinear data normalisation for reliable spatial inference with variational Gaussian processes. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 10, pp. 585 – 596 DOI ID: https://doi.org/10.17159/24119717/3880/2026 ORCiD: I.G. Gonçalves https://orcid.org/0000-0002-5546-6102 G.T. Nwaila https://orcid.org/0000-0002-3974-9890

Abstract

Machine learning methods for grade estimation have been rising in popularity in recent years. Among these, the variational Gaussian process model possesses all the beneficial characteristics of the kriging estimator: conditioning on data, declustering properties, and an uncertainty measure that increases with distance from the data points. However, its use requires a transformation of the data to a Gaussian distribution, especially if the data is compositional, as is the case with drillhole assays. This work explores some transformations that were specifically adapted for this model, making use of principal components analysis, independent components analysis, monotonic splines, and flow methods. Results show that the best transformation is case-dependent, and that they provide similar results to the ones used for geostatistical simulation. An open-source implementation of the methods is available to the community.

Keywords

Variational Gaussian process; normal score transform; mineral deposit modelling.

Introduction Spatial prediction has become an indispensable component of modern earth and mineral systems analysis, underpinning decision-making across exploration targeting, orebody characterisation, resource estimation, and geometallurgical modelling. However, spatial datasets used in geological, geochemical, and geometallurgical investigations are characterised by marked deviations from Gaussian distribution. Heavy-tailed grade distributions, multi-modal responses associated with lithological and geochemical heterogeneity, compositional constraints, and complex spatial dependence structures introduce asymmetry and nonlinearity that challenge widely used modelling frameworks. Gaussian process (GP) models (Rasmussen, Williams, 2006), and particularly their variational formulations (Hensman et al., 2015), have become increasingly attractive for spatial inference because they can capture nonlinear relationships, propagate uncertainty rigorously, and incorporate anisotropic kernels that reflect geological architecture. However, a core assumption embedded in the latent structure of Gaussian processes is that the transformed data should approximate a multivariate Gaussian distribution. When this assumption is violated, kernel hyperparameters may be poorly estimated, predictive means may exhibit structural bias, and uncertainty intervals may display under- or over-confidence. These issues can propagate into downstream technical and economic evaluations of mineral deposits, including recoverable resource estimates, metallurgical process response predictions, and mine planning decisions that depend on stable representations of spatial uncertainty. Normalisation is therefore not a peripheral preprocessing decision, but a central element of the modelling workflow. The way spatial data are transformed prior to variational Gaussian process (VGP) modelling (Gonçalves et al., 2022) determines the geometry of the latent space, the smoothness and anisotropy recovered by the kernel, and the calibration of predictive probabilistic outputs. Despite its importance, normalisation is often treated as a routine operation, implemented through simple Z-score scaling or logarithmic transforms without consideration of spatial effects, compositional structure, or multivariate dependencies. In practice, these choices determine whether the resulting VGP captures the true spatial behaviour of the underlying process or produces misleadingly precise predictions that obscure geological variability.

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Nonlinear data normalisation for reliable spatial inference with variational Gaussian processes The challenge is amplified in natural resource applications where datasets are typically sparse, irregularly sampled, and affected by both geological and analytical noise. Geochemical variables may possess strong positive skewness, reflecting enrichment processes, while mineralogical and geometallurgical attributes can exhibit non-linear cross-correlations arising from mineral associations, alteration fronts, or process domains. Compositional datasets, such as oxide proportions, reside in the simplex and obey closure constraints that invalidate many conventional transformations. Spatial cross-validation further reveals that naïve normalisation can result in models that perform well in random splits but degrade when honouring spatial autocorrelation structures. Recent methodological advances have begun to address aspects of these challenges. Spline-based univariate Gaussianisation has been demonstrated as an effective means of handling skewness while preserving rank structure (Gonçalves et al., 2020). More flexible nonlinear transformations, including projection pursuit multivariate transformation (PPMT) (Barnett et al., 2014) and normalising flows (Grathwohl et al., 2019), have gained traction in the geostatistical and machine learning literature, respectively, although their data demands and interpretability constraints limit routine application in natural resource settings. Yet, there has been no systematic analysis of how these various normalisation strategies compare when applied to spatial datasets under a unified VGP framework, nor how they influence predictive accuracy, uncertainty calibration, and spatial continuity. This study addresses that gap by examining a suite of univariate, multivariate, and nonlinear normalisation methods applied to representative geometallurgical datasets. The focus is on understanding how each transformation reshapes the latent Gaussian geometry and how these differences manifest in spatial predictions and associated uncertainty measures. Variational Gaussian processes are used as the modelling platform because of their suitability for large datasets and their explicit reliance on latent Gaussian structure. Spatial cross-validation is employed to ensure that model comparisons reflect realistic generalisation behaviour rather than artefacts of random partitioning.

Theory Variational Gaussian process modelling The variational Gaussian process is a generalisation of the standard Gaussian process (Rasmussen, Williams, 2006), which in turn is a Bayesian derivation of kriging. It is based on the concept of latent variables, i.e., random Gaussian fields that are functions of the input coordinates. A vector-valued field f(x) is generated from a number M of inducing points t distributed over the same region as the data points x, with corresponding latent variables u(t). Aggregating all the N data points in D dimensions in an N×D matrix X, and the corresponding P latent variables in an N×P matrix F, the corresponding probability distribution is given by (Hensman et al., 2015; Gonçalves et al., 2022, Gonçalves, Wellmann, 2025): [1] [2] [3] 586

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[4] where Kij is a covariance matrix computed for all pairs of inputs with any of the usual functions (Gaussian, spherical, exponential, etc.). For the sake of simplicity, they are assumed to be the same for all p latent variables, but this is not a requirement. Furthermore, mp and Sp are the posterior mean and covariance matrix of the up latent variables, which are trainable parameters in the model. In other words, a field is generated by kriging the latent variables from the inducing points to the data points. If M<N, a computational gain is obtained at the potential cost of a loss in spatial detail. The latent variables are linked to the measurements y through a likelihood function p(y|f). If the likelihood is Gaussian, the model will seek to minimise the squared error of predictions similarly to kriging, but other likelihood functions can be used (Hensman et al., 2015; Gonçalves et al., 2022), even allowing the model to deal with categorical data (Gonçalves et al., 2023). The model is trained by maximising the evidence lower bound (ELBO), an approximation of the true data likelihood p(Y): [5] where Y is the matrix aggregating all measurements, p(up)~N(0,Kuu) is the prior distribution of the inducing points’ latent variables, q(up)~N(mp,Sp) is the posterior, and KL[q(up)||p(up)] is the Kullback-Leibler divergence between them. The expectation Eq(fn) is computed by Monte Carlo, with samples generated from q(up) that are interpolated to the data points. The same approach can be used to generate samples at new spatial locations after the model is trained.

Normalisation methods The formulation in the aforementioned assumes that the measurements y are Gaussian-distributed, which is often not the case in the geosciences, as data distributions can be skewed and/or constrained. In the GP literature, data normalisation is denominated as warping (Snelson et al., 2004, Gonçalves et al., 2020). Warping consists of a bijective function y=φ(z) that maps the measurements z to normalised values. It is essentially similar to the Gaussian anamorphosis employed in geostatistics, but it can have trainable parameters. To incorporate warping in VGP training, Equation 5 is extended with a change of variables formula: [6] where is the Jacobian of the transformation. Specific warping functions are described in the following, according to their uses.

Scaling By default, the VGP implementation presented here assumes that the data has zero mean and unit variance. The Z score warping is simply given by: [7] where μp and σp are the trainable mean and variance. They can be initialised with the empirical mean and variance of the data. The Journal of the Southern African Institute of Mining and Metallurgy

Nonlinear data normalisation for reliable spatial inference with variational Gaussian processes Constraints Quantities such as chemical assays have non-negativity constraints. In such cases the log (φ(zp)=logzp) or softplus (φ-1) (yp)=log(1+e(yp)) functions can be applied. Chemical assays are often treated as compositional (Pawlowsky-Glahn et al., 2015), encompassing a positive and constant sum constraint. This can be dealt with by applying the centred log-ratio transformation (clr): [8] Note that, due to the subtraction of the mean, the resulting vector is linearly dependent. This can be avoided by using a vector basis in a (P-1)-dimensional space. The isometric log-ratio transformation (ilr) derives such a basis from sequential partitions of the original composition, but it is not explored in this work.

Decorrelation The P latent variables are assumed to be independent for the sake of computational efficiency. If the measurements are not decorrelated, the final results may be sub-optimal. The simplest decorrelation method is principal components analysis (PCA) (Jolliffe, 2002). PCA finds the directions of maximum variance in the data space and returns an orthonormal matrix Q. The transformation is given by φ(z)=Qz. If the variables have strong correlations, it is possible to retain only the first few principal components that explain the majority of the variability in the data. Datasets often contain extreme values and/or outliers that can influence the PCA results. In this case a robust method can be used, such as the minimum covariance determinant (MCD) after Rousseeuw and Van Driessen (1999). It consists of computing the data covariance matrix using only a fraction of the data points that are closer to the mean in the Mahalanobis sense.

traversing the graph, different normalisation strategies can be tested, also considering the characteristics of the data (non-negative, compositional, etc.).

Methodology The evaluation presented in this study is based on a comparative analysis of univariate and multivariate normalisation strategies applied to representative spatial datasets drawn from geometallurgical domains. The purpose of this section is to describe the datasets, justify their selection, outline the transformation procedures applied, and detail the variational Gaussian process modelling and cross-validation framework used to assess predictive accuracy, uncertainty calibration, and spatial structure recovery.

Data description Four datasets were selected to capture a spectrum of distributional and dependence structures commonly encountered in mineralsystems analysis: ➤ Jura: the Jura dataset (Goovaerts, 1997) contains 7 variables that represent heavy metal contamination. Each variable has a different degree of asymmetry and they present non-linear relationships. ➤ Synthetic: this dataset contains 6 variables with highly non-linear relationships, generated explicitly to test a geostatistical simulation algorithm (De Figueiredo et al., 2021). ➤ Copper: a Cu deposit in Brazil, where many different elements were analysed (Hoffimann et al., 2022). ➤ Assen: a Fe deposit in South Africa, presenting multiple lithological domains and multi-modal distributions.

Gaussianisation Data distributions may present asymmetry and/or heavy tails. A simple way to correct this is to employ monotonic splines (Gonçalves et al., 2020), defining a set of (y,z) knots, which are interpolated to obtain φ(zp) and φ-1(yp). These splines are defined separately for each of the P directions, so if the data have a complex distribution this method may not be sufficient for a complete Gaussianisation. The data can be rotated prior to the application of splines to deal with non-Gaussianity in oblique directions. The method known as independent components analysis (ICA) (Hyvärinen, Oja, 2000) can be used to find the directions of maximum non-Gaussianity. Flow methods generate a time-variant vector field that is used to move the data points closer to a Gaussian distribution (Grathwohl et al., 2019). This vector field is integrated for a number of time steps, and over time they may aggregate in a region of space, spread apart in others, make curves, etc. The flow can be reversed by integrating the vector field backwards in time. In this work, the vector field is defined by the mean of a GP generated from inducing points spread over z space.

Warping chains A single warping function may not be sufficient to obtain good results. In this case, multiple warping functions can be chained together, each one working with the output of the previous one. Such chains can be built to mimic the behaviour of the known approaches in geostatistics. Figure 1 demonstrates the chains employed in this work. By choosing different paths and partially The Journal of the Southern African Institute of Mining and Metallurgy

Figure 1—Warping chains employed in this work. Blue: univariate path. Red: multivariate path VOLUME 126

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Nonlinear data normalisation for reliable spatial inference with variational Gaussian processes The characteristics of each dataset and the methodologies employed are described in Table 1. To explore a range of practical situations, a different validation strategy was applied to each one. The last two datasets are compositional, consisting of normalised oxide proportions from a set of drillhole or bench-scale samples. Because compositional data lie in the simplex and obey a closure constraint (Pawlowsky-Glahn et al., 2015), they provide an essential test of normalisation strategies that must respect this geometry. These datasets therefore provide a realistic basis for evaluating warping methods. Across all datasets, the spatial configuration reflects irregular sampling common in exploration and operational settings. Sampling density varies across structural domains, and drilling patterns reflect operational priorities, leading to non-isotropic spatial support. These characteristics ensure that the evaluation framework reflects conditions typical of real-world geometallurgical modelling, where data sparsity and anisotropy interact strongly with the normalisation and GP modelling choices.

Figure 2 displays the performance of different warping chains on the Jura dataset, using a set of 100 validation points as reference. The results vary for each element but overall, the ICA path performed best in terms of the interval score and, thus, has the best calibrated uncertainty. Figure 3 displays the obtained normal scores, while Figure 4 allows a comparison of the distributions of the original data and samples computed at the data points. A close correspondence can be observed between the two. Figure 4—Distribution of the Jura data (lower triangle, bars) and samples computed on the data locations (upper triangle, black lines)

Validation metrics

Copper

To assess the efficiency of the proposed approach over a wide range of situations, different validation methods were applied to each dataset (Table 1). Different validation metrics were also tested. The following metrics are available in the scikit-learn library (Pedregosa et al., 2011): Root mean square error (RMSE), mean absolute error (MAE), and median absolute error (MDAE). In the figures below, a subscript p indicates that the metric was computed from the prediction, averaging all predictive samples, while the subscript s indicates the aggregation of the errors of the individual samples. The samples were also used to compute the squared bias (average deviation from the true values) and variance, treating the samples as the predictions from individual models. The interval score (IS) (Gneiting, Raftery, 2007) was also computed with a significance of 0.05. IS assesses the accuracy of confidence intervals, which in this case were derived empirically from the samples. For compositional data the Aitchison distance is also computed (Pawlowsky-Glahn et al., 2015). For all metrics a lower value is better, and 25 samples were found to be sufficient to obtain stable results.

Due to the sheer number of variables, the distribution of the Copper dataset is best visualised through a PCA (Figure 8). It can be seen that the point cloud is not perfectly aligned with the principal components axes and there are some extreme points in the lower right corner of the plot. Due to this the robust PCA version was used in the warping chain for this dataset. The validation strategy here was to divide the data into 5 folds, using the methodology by Linnenbrink et al. (2024). Four folds were used for training, and one was held out for validation. Figures 9 to 10 display the results on the validation set. In this case the ICA path displayed better results overall.

Results Jura

Synthetic data This dataset was designed specifically to challenge the standard normalisation methods. In this case the flow path performed better overall. Figures 5 to 7 display the results.

Assen The Assen dataset displays a strong multimodality induced by differences in lithology (Figure 11). To take this feature into account, this deposit was modelled with the method described in Gonçalves and Wellmann (2025), where extra latent variables are assigned to the lithological classes and reused to influence the latent variables responsible for the assays. Due to the relatively small sample size concentrated in a small number of drillholes, a simple random train/test split was made, keeping 80% of the data for training.

Table 1

Description of the datasets Dataset

Dimensionality

Data points

Variables

Characteristics

Validation method

Jura

259

Variable marginal distributions; non-linear relationships; nonnegative.

Held-out data in a regular grid.

Synthetic

200

Non-linear and non-monotonic relationships.

Held-out data in a regular grid.

Copper

2000

Compositional.

Spatial k-fold (Linnenbrink et al., 2024).

Assen

807

Compositional; multimodal.

Random train/test split.

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Figure 2—Validation results for the Jura dataset, relative to a simple linear rescaling

Figure 3—Normal scores obtained for the Jura dataset The Journal of the Southern African Institute of Mining and Metallurgy

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Figure 5—Validation results for the Synthetic dataset, relative to a simple linear rescaling

Figure 6—Normal scores obtained for the Synthetic dataset 590

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Figure 7—Distribution of the Synthetic data (lower triangle, bars) and samples computed on the data locations (upper triangle, black lines)

Figure 8—The first two principal components of the clr-transformed Copper dataset The Journal of the Southern African Institute of Mining and Metallurgy

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Figure 9—Validation results for the Copper dataset, relative to a simple linear rescaling of the clr-transformed values

Figure 10—Validation results for the Copper dataset, relative to a simple linear rescaling of the clr-transformed values (continued) 592

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Figure 11—The first two robust principal components of the Assen dataset

Figure 12—Validation results for the Assen dataset, relative to a simple linear rescaling of the clr-transformed values The Journal of the Southern African Institute of Mining and Metallurgy

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Figure 13—Normal scores obtained for the Assen dataset

Figure 14—Distribution of the clr-transformed Assen data (lower triangle, bars) and samples computed on the data locations (upper triangle, black lines)

The validation results are displayed in Figure 12. Some elements were insensitive to the choice of method, while for others the best results were obtained with simpler warping paths. The flow path seems to be slightly better overall. Due to the influence of lithology, the model normalised the data only within each category (Figure 13). The two clusters visible in Figure 11 were not merged by the flow warping as would be 594

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expected. Nevertheless, the relationships between the variables were recovered (Figure 14).

Discussion The results did not reveal a clear winner among the methods studied, although the ICA and flow paths performed better than the simpler methods overall. All methods performed better than The Journal of the Southern African Institute of Mining and Metallurgy

Nonlinear data normalisation for reliable spatial inference with variational Gaussian processes simple rescaling, demonstrating that asymmetries and correlations do impact modelling results and should not be ignored. Apart from this, the choice of method is a matter of geological interpretation and an accuracy/complexity trade-off. For the Assen data normalisation was not complete, but that does not necessarily mean that the model was sub-optimal. The corresponding procedure in traditional geostatistics would be to make separate models for each geological domain, in which it would be possible to fully normalise the data. VGP achieves a similar result without the need for separate models. From the results is seems that approximate Gaussianity is sufficient to obtain good results, without the need for multiple passes as in PPMT. In machine learning it is always beneficial to avoid unnecessarily complex models due to the risk of overfitting and sub-optimal training convergence.

Conclusions This study demonstrates that spatial data normalisation is a decisive factor in the reliability of variational Gaussian process inference for geological, geochemical, and geometallurgical datasets. Transformations that merely rescale or symmetrise the data are insufficient when confronted with the heavy-tailed distributions, non-linear dependencies and compositional constraints that characterise natural-resource systems. Effective normalisation must regularise both marginal and joint behaviour while preserving spatial continuity and geological structure. The comparative evaluation across the four datasets demonstrates that the choice of normalisation strategy exerts some influence on predictive accuracy, uncertainty calibration, and spatial structure. All transformations improved accuracy relative to modelling on raw variables, but the choice of method still depends on the interpretation and experience of the professional.

Acknowledgements The authors acknowledge the Brazilian National Council for Scientific and Technological Development (CNPq) for financial support (process 407752/2022-6).

CRediT author statement IG: Conceptualisation, methodology, software, investigation, writing; GN: Conceptualisation, methodology, investigation, writing.

References

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Gonçalves, Í.G., Guadagnin, F., Cordova, D.P. 2022. Learning spatial patterns with variational Gaussian processes: Regression. Computers & Geosciences, 161, 105056. https://doi.org/10.1016/j.cageo.2022.105056 Gonçalves, Í.G., Guadagnin, F., Cordova, D.P. 2023. Variational Gaussian processes for implicit geological modeling. Computers & Geosciences, vol. 174. https://doi.org/10.1016/j.cageo.2023.105323 Gonçalves, Í. G., & Wellmann, F. (2025). Uncertainty propagation in deep Gaussian process networks. Mathematical Geosciences, vol. 57, no. 6, pp. 1115–1133. https://doi.org/10.1007/s11004-025-10187-4 Goovaerts, P. 1997. Geoestatistics for Natural Resources Evaluation. Oxford University Press. Grathwohl, W., Chen, R.T.Q., Bettencourt, J., Sutskever, I., Duvenaud, D. 2019. Ffjord: Free-form continuous dynamics for scalable reversible generative models. 7th International Conference on Learning Representations, ICLR 2019, pp. 1–13. Hensman, J., Matthews, A.G., Ghahramani, Z. 2015. Scalable variational Gaussian process classification. Journal of Machine Learning Research, vol. 38, pp. 351–360. Hoffimann, J., Augusto, J., Resende, L., Mathias, M., Mazzinghy, D., Bianchetti, M., Mendes, M., Souza, T., Andrade, V., Domingues, T., Silva, W., Silva, R., Couto, D., Fonseca, E., Gonçalves, K. 2022. Modeling Geospatial Uncertainty of Geometallurgical Variables with Bayesian Models and Hilbert–Kriging. Mathematical Geosciences, vol. 54, no. 7, pp. 1227–1253. https://doi.org/10.1007/s11004-022-10013-1 Hyvärinen, A., Oja, E. 2000. Independent component analysis: algorithms and applications. Neural Networks, vol. 13, pp. 411–430. www.elsevier.com/locate/neunet Jolliffe, I.T. 2002. Principal Component Analysis (2nd ed.). Springer. Linnenbrink, J., Milà, C., Ludwig, M., Meyer, H. 2024. KNNDM CV: K-fold nearest-neighbour distance matching cross-validation for map accuracy estimation. Geoscientific Model Development, vol. 17, no. 15, pp. 5897–5912. https://doi.org/10.5194/gmd-17-5897-2024 Pawlowsky-Glahn, V., Egozcue, J.J., Tolosana-Delgado, R. 2015. Modeling and Analysis of Compositional Data. John Wiley & Sons. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, vol. 12, pp. 2825–2830. http://scikit-learn.sourceforge.net. Rasmussen, C.E., Williams, C.K.I. 2006. Gaussian processes for machine learning. MIT Press. https://doi.org/10.1142/S0129065704001899 Rousseeuw, P., Van Driessen, K. 1999. A fast algorithm for the minimum covariance determinant estimator. Technometrics, vol 41, no. 3. http://amstat.tandfonline.com/doi/abs/10.1080/00401706.1999 .10485670 Snelson, E., Rasmussen, C.E., Ghahramani, Z. 2004. Warped Gaussian processes. Adv. Neural Inf. Proc. Syst., vol. 16, pp. 337–344. u VOLUME 126

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ENGINEERING HEALTH & SAFETY FOR SUSTAINABLE 2026 FUTURE-READY Nonlinear data normalisation for reliable spatial inference with variational Gaussian processes MINES HYBRID CONFERENCE

11-12 NOVEMBER 2026 — CONFERENCE 13 NOVEMBER 2026 — INDUSTRY AWARDS DAY GALLAGHER CONVENTION CENTRE, JOHANNESBURG, SOUTH AFRICA

MMMA

Abstract submission – 31 July 2026 I Extended Abstract/Papers submission- 31 August 2026

BACKGROUND We are excited to announce our upcoming industry Conference and Industry Awards Day, dedicated to advancing mining safety and health, innovation, and sustainability and recognising the excellent work in creating ZERO HARM future ready mines. This year’s theme, “Engineering Health, Safety for Sustainable Future-Ready Mines”, underscores our collective commitment to achieving zero harm while embracing modernisation, technology, and collaboration as the foundation for safer, future-ready mining operations. This conference will serve as a vital platform for knowledge-sharing and idea exchange among key stakeholders, including mining companies, the Department of Mineral Resources and Energy (DMRE), the Minerals Council South Africa, labour unions, and health and safety practitioners at all levels in the minerals industry.

CONFERENCE THEMES 1. Driving Zero Harm Forward • Reinforcing the vision of eliminating fatalities. • Sharing progress, lessons learned, and renewed commitments. 2. Technology in Action • Case studies of mines implementing breakthrough technologies. • Practical examples of problems solved through innovation. 3. Future-Ready Mine Design • Strategies for long-term sustainability and resilience. • Integrating safety and health into planning and operational frameworks. 4. Engineering Safety Excellence • Advances in engineering solutions that enhance safety. • Best practices in design, maintenance, and risk management. 5. Engineering in the Digital Age • Modernisation and digital transformation in mining operations. • Leveraging data, automation, and AI for safer outcomes. 6. Technology Advancements and Innovation • Exploring robotics, wearables, sensors, and predictive analytics. • Emerging technologies shaping the future of mining safety. 7. Health and Wellbeing in Mining • Protecting workers’ physical and mental health. • Occupational health strategies, wellness programs, and medical innovations. 8. Sustainability and Responsible Mining • Environmental stewardship and climate-conscious practices. • Linking safety with sustainability for long-term industry resilience.

Why Attend? • Gain insights from global case studies and pioneering projects. • Network with industry leaders, engineers, and technology innovators. • Explore practical solutions for building safer, sustainable mines. • Be part of shaping the future of mining through collaboration and innovation. Join us as we work together towards Safe Mines, Healthy Lives, and Sustainable Futures!

PARTNERSHIP OPPORTUNITIES Sponsorship opportunities are available. Companies wishing To partner on this event should contact the Conference Coordinator.

WHO SHOULD ATTEND

The conference should be of value to: • Safety practitioners • Mine management • Mine health and safety officials • Engineering managers • Underground production supervisors • Surface production supervisors • Environmental scientists • Minimizing of waste • Operations manager • Processing manager • Contractors (mining) • Including mining consultants, suppliers and manufacturers • Education and training • Energy solving projects • Water solving projects • Unions • Academics and students • DMRE • Occupational health practitioners • Leadership and ManagementTechnical and Engineers

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SUBMIT AN ABSTRACT

Call for papers on the topics of safety, health and environment Prospective authors are invited to submit titles and abstracts of their presentations in English and not longer than 500 words. Abstracts should be submitted to: Include Abstract Submission Deadline: 31 July 2026 Extended Abstract/Paper Submission: 31 August 2026

For further information contact: Gugu Charlie, Conference Co-ordinators E-mail:gugu@saimm.co.za | Tel: +27 11 538-0238 | Web: www.saimm.co.za 596

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Affiliation: 1 African Research Centre for Ore Systems Science (CORES), School of Geosciences,University of the Witwatersrand, South Africa 2 Paleoproterozoic Mineralisation (PPM) Research Group, Department of Geology, University of Johannesburg, South Africa 3 Department of Science and Innovation-National Research Foundation Centre of Excellence in Mineral and Energy Resource Analysis, Department of Geology, University of Johannesburg, South Africa Correspondence to: M. Buthelezi Email: musawenkosi.buthelezi@students.wits. ac.za

Dates:

Received: 30 Jan. 2026 Revised: 25 Aug. 2026 Accepted: 02 Oct. 2026 Published: Oct. 2026 How to cite: Buthelezi M., Nwaila G.T., Bussin D. Smith A.J.B., Vorster C., Bybee G.M., Manzi M.S.D. 2026. Geometallurgical domain characterisation and process performance prediction for ferromanganese ores from the Paling Pan MnFe deposit, Postmasburg Manganese Field, South Africa .Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 10, pp. 597 – 606 DOI ID: https://doi.org/10.17159/2411-9717/3899/2026 ORCiD: M. Buthelezi https://orcid.org/0000-0003-2704-9317 G.T. Nwaila https://orcid.org/0000-0002-3974-9890 D. Bussin https://orcid.org/0000-0002-4747-6765 C. Vorster https://orcid.org/0000-0003-0514-2991 G.M. Bybee https://orcid.org/0000-0002-3156-551X M.S.D. Manzi https://orcid.org/0000-0002-1654-5211

Geometallurgical domain characterisation and process performance prediction for ferromanganese ores from the Paling Pan MnFe deposit, Postmasburg Manganese Field, South Africa by M. Buthelezi1, G.T. Nwaila1, D. Bussin²,³, A.J.B. Smith²,³, C. Vorster²,³, G.M. Bybee1, M.S.D. Manzi1 Abstract

Manganese is a critical metal for the global steel industry, with South Africa as the leading producer of Mn ore. Efficient processing of its complex ores, particularly those in the Postmasburg Manganese Field, requires site-specific understanding of their variability. This study develops a geometallurgical framework for the Paling Pan Fe-Mn deposit, integrating mineralogy, geochemistry, and quantified ore texture to define domains that predict processing performance. Four principal ore textures, those being, massive, vuggy, layered, and conglomeritic, were characterised, each influencing beneficiation differently. A dataset of 256 samples was used to train predictive machine learning (XGBoost) models for magnetic separation and dense media separation. Incorporating texture-differentiated domains improved prediction accuracy, particularly for dense media separation yield (2.6% increase in chemical oxygen demand). Magnetic separation yields ranged from 72.3% to 92.3%, with massive ores performing best. SHAP analysis identified manganese grade, Fe grade, and ore texture as critical predictors for magnetic separation, while specific gravity and gangue content dominated dense media separation performance. This integrated, data-driven approach provides a robust methodology for optimising plant design, ore blending, and processing strategies, enhancing resource efficiency, economic viability, and sustainability in manganese ore exploitation.

Keywords geometallurgy, manganese ores, magnetic separation, dense media separation, XGBoost, process optimisation, Postmasburg Manganese Field

Introduction Manganese (Mn) is critical for the global industry, primarily in steel manufacturing but also in aluminium alloys and battery components (Habashi, 2008; Sun et al., 2020; Song et al., 2023). South Africa is the major global producer of Mn ore, underscoring the importance of understanding its deposits for optimal extraction (Von der Heyden et al., 2024). Current research in geometallurgy examines the interplay between ore grade, mineralogy, and processing efficiency, with ore texture being a crucial but often qualitative variable. Despite its recognised importance, ore texture is rarely quantified or operationalised within geometallurgical predictive models for Mn deposits. The Paling Pan Fe-Mn deposit is located in the western belt of the Postmasburg Manganese Field (PMF) (Figure 1). The deposit is characterised by significant heterogeneity in ore characteristics. Previous studies have documented a variety of ore textures, including massive, vuggy, layered, and conglomeritic types, in the western belt of the PMF and at Paling Pan (Gutzmer, Beukes, 1996; Bussin, 2022). This textural variability presents a substantial challenge for achieving consistent processing performance and accurate grade prediction. While the mineralogy, dominated by braunite and bixbyite with gangue minerals such as kaolinite, diaspore, and hematite, is well-understood, the quantitative association between ore texture and processing response remains inadequately explored. This study bridges this gap by integrating detailed geological logging, geochemical analysis, and machine learning (ML) to objectively classify ore textures and predict key processing performance indicators. We adopt a narrow, process-facing definition of geometallurgy, where the primary value of geological attributes

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Geometallurgical domain characterisation and process performance prediction lies in their ability to predict plant responses such as recovery, product quality, and energy consumption (Lishchuk et al., 2015). Consequently, we define ore texture as a key geological driver and quantify its impact, integrating it with chemistry and ML into a deployable domaining protocol for the Paling Pan deposit. This study is a companion study to Buthelezi et al. (2026), who examined ML-based classification of ore textures and MnO grade prediction at the same deposit. While the previous study focused on geological characterisation, the present work addresses a distinct metallurgical objective: the definition of geometallurgical domains and the prediction of processing performance for magnetic separation and dense media separation.

Geological setting Regional context The Paling Pan Fe-Mn deposit is situated within the PMF, with the latter located in the centre of the Maremane dome anticline between the towns of Postmasburg and Sishen in the Northern Cape Province of South Africa (Figure 1) (Smith, Beukes, 2016). The PMF extends along a north-south strike of approximately 65 km. The mineralisation within the PMF occurs at the contact between the approximately 2.5 Ga Campbellrand Subgroup carbonates of the Griqualand West region of the Transvaal Supergroup and the approximately 2.0 Ga pre-Gamagara erosional unconformity (Gutzmer, Beukes, 1996; Beukes et al., 2016; Smith, Beukes, 2016).

Paling Pan Fe-Mn ore deposit In this contribution, detailed stratigraphic and mineralogical descriptions of the Paling Pan Fe–Mn deposit are not reproduced in full; instead, the reader is referred to the Supplementary Data files archived in the South African Journal of Geology repository (Buthelezi et al., 2026). To avoid duplicating this material and to prevent plagiarism, only a summary is provided here. The supplementary files document the drill-core-defined succession from the Campbellrand dolostone footwall through the lower wad

Figure 1—Simplified geological map showing (a) the location of the Transvaal Supergroup in South Africa and (b) the approximate position of the Paling Pan deposit within the Griqualand West Basin (modified after Warke, 2016; Buthelezi et al., 2026) 598

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unit and principal mineralised zone to the Doornfontein Member conglomerate and overlying Doornfontein and Sishen shales (Figure 2), as well as the ore textures, thickness ranges, and genetic interpretation of Gutzmer and Beukes (1996) and Bussin (2022).

Data and analytical methods Datasets The dataset for this study was sourced from Bussin (2022) and comprises 256 lithogeochemical samples (ore zones) collected from 36 drill cores in the Paling Pan Fe-Mn deposit. Textures were defined through systematic megascopic logging of drill cores. For each of the 256 analysed samples, clast size and support, visible porosity, and density/consolidation were recorded semiquantitatively, and representative samples were verified using a hand lens and, where necessary, petrographic examination. The samples fall into four textural classes: conglomeratic (n = 100, 39.1%), layered (n = 68, 26.6%), massive (n = 67, 26.2%), and vuggy (n = 21, 8.2%). Sampling was not initially uniform, as secondary targets were selected based on field observations. This selective sampling approach introduces a degree of human bias and imposes certain limitations on the dataset, particularly in the representation of textural transitions and lower-grade zones. Therefore, the findings should be interpreted as characteristic of mineralised zones rather than averages for the entire deposit. Lithogeochemical analyses were conducted using X-ray fluorescence (XRF) spectrometry on borate fusion discs at SGS Laboratories in Johannesburg, employing a Malvern Analytic WD-XRF instrument. The major oxide components analysed included Al2O3, CaO, Fe2O, K2O, Na2O, P2O5, SiO2, TiO2, MnO, V2O5, and BaO, with results expressed in weight percent (wt.%). The machine learning (ML) workflow requires complete training features but not prediction targets. Therefore, missing compositions, which are only in NaO (n = 2; 0.008 % of the dataset), are imputed using the k-nearest neighbours’ algorithm (Hron et al., 2010). The analytical error for most elements is less than 0.01%, except for P2O5, which has an error of 0.002%, as indicated in an unpublished SGS laboratory certificate and documented by Bussin (2022). Evaluation of the beneficiation response of the Paling ferromanganese deposit was conducted via multi-stage Permroll

Figure 2—Regional map of the PMF showing the distribution of Gamagara (Mapedi formation) shales associated with Fe-Mn ore (Western Belt) and the location of the Paling Pan Exploration area in relation to Postmasburg and Sishen; (b) schematic stratigraphic section at the Paling Pan deposit (modified after Gutzmer, Beukes,1996; Bussin, 2022; Buthelezi et al., 2026) The Journal of the Southern African Institute of Mining and Metallurgy

Geometallurgical domain characterisation and process performance prediction magnetic separation on six 2.5 kg composite samples compiled from 42 representative reverse circulation (RC) drill chip intervals across seven boreholes12. To standardise test feed kinetics, samples were homogenised using a two-tier riffle splitter and screened to an intermediate > 1 mm to < 5.6 mm size fraction prior to sequential pass-through over barium ferrite (BaFe; 2,000 Gauss), samarium cobalt (SmCo; 4,000 Gauss), and neodymium (Nd; 7,000 Gauss) permanent magnetic rollers at MINTEK. Products comprising rougher, scavenger 1, scavenger 2 concentrates, and final tailings were pulverised at the University of Johannesburg for majorelement borate-fusion XRF spectrometry at SGS Randfontein and quantitative X-ray diffraction (XRD) phase analysis at SPECTRUM. Pre-screening of the test feed removed the < 1 mm fines, which selectively discarded soft kaolinite gangue and artificially elevated measured feed grades relative to field estimates. Furthermore, testing on fine particle sizes (> 1 mm to < 5.6 mm) introduces significant particle-size liberation bias when extrapolated to standard lump manganese market specifications (+42 mm). Dataset fidelity was further compromised by the exclusion of nonore lithologies inevitably incorporated during open-pit mining cuts (e.g., soft shale partings, wad, and dolostone), analytical errors in XRD quantification arising from preferred orientation of platy clay minerals and solid-solution stoichiometry in the (Fe, Mn)2O3 series, and the necessary rejection of preliminary geochemical data produced by unaccredited analytical facilities (Bussin, 2022).

Methodology for geometallurgical domain definition and modelling The core of this study involved a multi-stage methodology to define geometallurgical domains and build predictive models. Thresholds for domain definition were selected based on plant operational specifications and historical grade control criteria. First, initial domains were established using chemical thresholds relevant to processing performance. These chemical domains were then enhanced by integrating the four primary ore textures (massive, vuggy, layered, and conglomeritic), creating composite domains such as high-Mn massive and mixed-chemistry conglomeritic.

Subsequently, a comprehensive feature engineering step was undertaken to prepare the data for ML, which included the creation of distinct predictor variables. Ore texture and the engineered composite geometallurgical domains (e.g., ‘high Mn massive’) were treated as nominal categorical features. Both were one‑hot encoded. This avoids imposing an arbitrary order on the texture categories and is appropriate because the composite domains have low cardinality (twenty categories). No integer or target encoding was used. All encoding, imputation, and scaling were performed inside each cross‑validation fold using a scikit‑learn pipeline, so no information from the held‑out fold influenced the training data. This eliminates the target leakage that would occur if domain‑level averages were computed across the full dataset before cross‑validation. Additionally, key geochemical features were derived, including converting MnO and Fe2O3 to elemental Mn and Fe grades, deriving phosphorus content from P2O5, and calculating a total gangue index as SiO2 + Al2O3. For model development, the XGBoost Regressor algorithm was selected due to its proven performance with tabular data and ability to handle complex, non-linear relationships (Sun et al., 2023). The modelling approach compared three scenarios to quantify the value of domain information: (1) a baseline model with no domain information, (2) a model with simple chemical domains, and (3) a model with the enhanced texture-differentiated domains. Hyperparameter tuning was performed using a randomised search with 5‑fold grouped cross‑validation by drill hole. All samples from a given drill hole were assigned to the same fold, so no drill hole appeared in both training and validation sets. This prevents spatial similarity between samples from the same drill hole from inflating performance estimates. Random 5‑fold cross‑validation was also run for comparison; it produced higher R² values (e.g., X vs. Y), confirming that the grouped approach gives a more conservative and realistic assessment of performance on unseen parts of the deposit (Figure 3).

Performance evaluation metrics We evaluate the performance of our models using standard metrics suitable for regression tasks. Model performance was rigorously evaluated using 5-fold cross-validation, and we employed the coefficient of determination, CoD, the mean absolute error (MAE), and the root mean squared error (RMSE). The CoD value is computed as [1] where Yi are the observed values, Ŷi are the predicted values, and Ȳ is the mean of the observed values. A CoD value of 1 indicates a perfect prediction, while negative values suggest that the model performs worse than a simple mean prediction (Nagelkerke, 1991; Draper, Smith, 1998). The mean absolute error (MAE) is given by [2]

Figure 3—Flowchart illustrating the data integration and ML workflow used in the study The Journal of the Southern African Institute of Mining and Metallurgy

which measures the average absolute difference between the observed and predicted values. These metrics enable us to assess the overall fit of the regression model, as well as the average magnitude of the prediction errors, thereby providing a clear picture of model performance (Willmott, Matsuura, 2005). The RMSE is computed as [3]

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Geometallurgical domain characterisation and process performance prediction where Yi are the observed values, Ŷi are the predicted values, and N is the total number of observations. The RMSE represents the standard deviation of the prediction errors, with a lower value indicating better model accuracy as it signifies smaller average deviations between predicted and observed values (Chai, Draxler, 2014).

SHapley Additive exPlanations (SHAP) The application of SHapley Additive exPlanations (SHAP) provides a robust framework for quantifying individual predictor contributions in our ML models, offering both global and local interpretability (Lundberg, Lee, 2017). SHAP values provide a theoretically consistent way to attribute model predictions to individual features. The method is based on cooperative game theory, where each feature acts as a player, and its contribution is its average marginal effect across all possible combinations of features. This gives a fair distribution of credit for the prediction. SHAP satisfies three key properties: local accuracy, meaning the sum of feature contributions equals the prediction minus the average prediction; missingness, meaning features with no effect receive zero credit; and consistency, meaning if a feature becomes more important in a new model, its SHAP value does not decrease. These properties make SHAP attributions reliable and comparable across models. The SHAP explanation model takes the form: [4] Where Ǿi represents the Shapley value for feature I, M denotes the total number of features, and z′∈{0,1}M indicates the presence of simplified input features. The exact computation of Ǿi is given by: [5] This formulation ensures that each feature's contribution reflects its marginal impact across all possible feature permutations (Lundberg, Lee, 2017). This enables us to move beyond a "black box" model and understand which geological and chemical features drive the predictions for magnetic separation and dense media separation (DMS) performance.

Results and discussions Geometallurgical domain characterisation Application of the enhanced domaining protocol at the Paling Pan Fe–Mn deposit delineated twenty geometallurgical domains, capturing the heterogeneity of the orebody (Figure 4). The most prevalent domain is mixed chemistry conglomeritic, representing 28.5% of samples (n = 73), followed by high Mn massive (13.3%, n = 34) and high Mn layered (9.4%, n = 24). Additional domains include mixed chemistry layered, mixed chemistry massive, high Mn vuggy, medium Mn (30 wt.%– 35 wt.%) subtypes across layered, massive, conglomeritic, and vuggy textures, low Mn (28 wt.% – 30 wt.%) layered, massive, and conglomeritic domains, as well as high Mn conglomeritic, mixed chemistry vuggy, and a distinct high Fe conglomeritic domain. This distribution mirrors field observations, where conglomeritic and layered textures are widespread and associated with complex mineralogy typical of karst‑hosted manganese deposits (Gutzmer, Beukes, 1996; Tsikos et al., 2003). Classification was based on Mn thresholds: high Mn (> 40 wt.%), medium Mn (25 wt.% –3 5 wt.%), and low grade (< 25 wt.%). The chemical signatures of each domain, including systematic variations in Fe, Al2O3, and SiO2 are illustrated in Figures 4a and b, 600

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highlighting distinct compositional trends across the deposit.

Processing performance by domain The magnetic separation test work conducted using a multi-stage permanent roll (Permroll) apparatus at intensities of 2000, 4000, and 7000 Gauss revealed clear domain-specific performance trends. The results from the simple geochemistry-based domains (Figure 5a) show a strong correlation between MnO grade and mass recovery. The cumulative mass recovery in the magnetic concentrates varied significantly, with the highest yield of 92.3% achieved for the highgrade Mn (> 32%) domain, followed by 90.3% for the Mn (> 40%) domain. In contrast, the lower-grade Mn (> 28%) domain showed a substantially lower recovery of 72.3%. This performance hierarchy is consistent with the defined geometallurgical domains in the reference values, confirming that domains exceeding the Mn > 32% threshold deliver optimal magnetic separation yields. This trend is further refined in the ore texture-based domains (Figure 5b), which integrates geochemistry with mineralogical texture for the top eight domains. The enhanced predictive capability of this model explains the mineralogical observation that the strong magnetic response of bixbyite, effectively concentrated in the rougher and scavenger 1 stages, drives the high recoveries in Mn-rich domains. Although hematite's weaker magnetic susceptibility typically causes it to report to the non-magnetic fraction at standard intensities (Elliott, Barati, 2020), it becomes magnetically susceptible at high field strengths. This explains the significant increase of hematite in the magnetic concentrate (scavenger 2) and the corresponding decrease in Mn grade observed in this study. The integration of texture in Figure 5b, thus provides a more robust framework for predicting processing performance, moving beyond simple chemical composition to account for the textural associations that ultimately control separation efficiency during both magnetic separation and DMS processes. The ore texture proved to be a critical factor influencing processing efficiency. Massive ores, with their dense and homogeneous nature, consistently delivered superior outcomes, averaging a magnetic yield of 80.4%, a DMS yield of 85.1%, and a high-density profile (~3.6 g/cm³ × 20; Figure 5b). In contrast, vuggy ores, characterised by porosity and heterogeneous mineral distribution, exhibited lower and more variable performance, with average magnetic and DMS yields of 73.0% and 72.8%, respectively, and reduced density (~3.2 g/cm³ × 20). This link between texture, mineral liberation, and density highlights ore texture as a primary source of processing variability (Parian et al., 2017).

Machine learning model performance The comparative analysis of the three ML feature‑set schemes (no_domain, simple_domain, enhanced_domain) demonstrates the tangible value of incorporating texture‑differentiated domains. Across both by‑drillhole and random validation schemes, performance differences were consistent but nuanced. For magnetic separation yield, the enhanced_domain achieved strong results (CoD) ≈ 0.811 by‑drillhole, 0.839 random), though the no_domain baseline remained competitive (CoD ≈ 0.817 and 0.840, respectively). Error metrics (MAE, RMSE) were lowest for no_ domain under random splits, but the enhanced domain maintained comparable precision, underscoring that textural information contributes explanatory power without sacrificing stability. For estimated density, improvements were marginal across all schemes. CoD values clustered tightly (0.752 – 0.760 by‑drillhole; 0.777 – The Journal of the Southern African Institute of Mining and Metallurgy

Geometallurgical domain characterisation and process performance prediction

Figure 4—Geometallurgical domain characterisation of the Paling Pan Fe-Mn deposit. (a) Range and mean values of Mn (wt.%) across geochemical domains. Horizontal bars represent the minimum–maximum range, while red markers indicate the mean concentration. (b) Average concentrations of Fe, Al2O, and SiO2 (wt.%) for each domain. The bar plot highlights compositional variations among ore textures, illustrating differences in oxide abundances across sampled domains The Journal of the Southern African Institute of Mining and Metallurgy

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Figure 5—Predicting Fe-Mn processing performance from geometallurgical properties (magnetic separation, density, DMS) at the Paling Pan Fe-Mn ore deposit. (a) Simple domains based solely on geochemistry. (b) Ore texture-based domains, including geochemistry for the top 8 domains

Figure 6—Performance of machine‑learning models across three domain feature sets (enhanced domain, no domain, simple domain) under two validation schemes (by‑drillhole and random). Each panel presents mean values with standard deviation error bars for three predictive targets, DMS yield, estimated density, and magnetic separation yield, evaluated using the CoD, MAE, and RMSE. The plots highlight how domain incorporation influences predictive accuracy and precision, with texture‑differentiated domains yielding notable improvements in recovery predictions 602

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Figure 7—Comparative feature importance scores from SHAP analysis for XGBoost models predicting three key metallurgical performance indicators: (a) magnetic separation yield, (b) estimated density, and (c) dense media separation yield

0.780 random), with error metrics nearly identical (MAE ≈ 0.087 – 0.089, RMSE ≈ 0.106 – 0.108). This confirms that density prediction is primarily controlled by chemical composition, and domain integration does not materially alter variance explained. The most significant gains were observed for DMS yield. Under by‑drillhole splits, simple_domain achieved the best balance (CoD = 0.701, MAE = 2.667, RMSE = 3.369), while under random splits, no_domain slightly edged CoD (0.699) but with higher errors. The enhanced_domain consistently reduced RMSE relative to baseline (e.g., 3.406 vs. 3.373 by‑drillhole; 3.466 vs. 3.428 random), highlighting that textural features improve precision in gravity‑based separation predictions. Although CoD improvements are numerically modest, the concurrent reduction in MAE and RMSE is operationally significant. These reductions represent models that are not only more accurate on average but also more precise and reliable (Koch et al., 2019; Tusa et al., 2025). In industrial contexts, even marginal gains in prediction fidelity translate directly into economic value through optimised recovery, reduced energy consumption, and consistent product quality (Figure 6).

Feature importance analysis The SHAP analysis of feature importance within the XGBoost models provided clear, quantifiable insights into the dominant controls on processing performance. For magnetic separation yield, Mn wt.% was the most influential feature (mean |SHAP| = 2.153), followed by ore texture (1.140) and the composite geometallurgical domain (0.306) (Figure 7a). This confirms that while mineral chemistry exerts primary control, textural attributes and spatial domain context also contribute meaningfully, likely through their influence on mineral liberation during comminution (Lamberg, 2011; Parian et al., 2017). For estimated density, Mn grade % again dominated (0.102), with ore texture (0.048) and SiO2–Al2O3 wt.% (0.023) providing secondary importance (Figure 7b). Despite measurable textural contributions, domain integration did not improve density prediction performance, indicating that chemical composition remains the stronger determinant of variance. In contrast, for DMS The Journal of the Southern African Institute of Mining and Metallurgy

yield, ore texture was overwhelmingly dominant (2.661), followed by SG (2.036) and Al2O3 (0.946) (Figure 7c). This underscores that for gravity‑based separation, the ore’s textural properties and liberation characteristics are critical, as efficiency is highly sensitive to particle size, shape, and porosity (Napier-Munn, 2015; Wills, Finch, 2015).

Geometallurgical Implications The geometallurgical domain classification and predictive models developed in this study provide a critical, data-driven framework for optimising the entire mining value chain of ferromanganese ores within the Paling Pan Fe-Mn deposit. The transition from a traditional, chemically focused resource model to an advanced mineralogical approach enables a spatially-based predictive model essential for modern production management (Lamberg, 2011). This framework has direct and actionable implications for mine planning, process optimisation, resource efficiency, and exploration strategy, which are of significant relevance to the Southern African mining context. The primary implication lies in enabling predictive mine planning and intelligent ore blending. By integrating the geometallurgical domains, defined by both modal geochemistry and ore texture, into the resource block model, mine planning evolves from a geometric exercise to a metallurgically-informed strategy. This allows for strategic sequencing and blending to ensure a consistent plant feed, directly mitigating the operational disruptions and recovery losses caused by unforeseen variations in ore hardness and mineral liberation characteristics (Lamberg, 2011; Lund et al., 2013; Lishchuk et al., 2015). The study also demonstrates potential for pre‑emptive process design and dynamic optimisation. Predictive ML models using texture and geochemistry to forecast comminution energy and liberation yield enable a shift from reactive to pre‑emptive processing. This facilitates domain‑specific routing within the plant (Hunt et al., 2013; Parian, 2017; Dominy et al., 2018). For example, high‑yield massive ores can be directed to simple magnetic circuits, while conglomeritic ores, where liberation is more challenging, are managed with flexible, multi‑stage routes designed for higher VOLUME 126

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Geometallurgical domain characterisation and process performance prediction recovery. This aligns with deeper levels of geometallurgical implementation, where process changes are planned based on forecasted feed quality (Lamberg et al., 2011; Lindberg, 2013; Lishchuk et al., 2015). Another advantage of the geochemistry-aware approach relates to the oxidation state of Mn in the final ore product delivered to the Mn smelters that produce ferromanganese and silicomanganese for steelmaking. The Mn minerals in the ore needs to be reduced by coke in this process, with the energy required to do this varying between the different Mn ore minerals because of different Mn2+, Mn3+ and Mn4+ contents, with energy requirement increasing as the oxidation state increases (Cairncross, Beukes, 2013; Von der Heyden et al., 2024). Better modelling of the relative abundances of Mn minerals in the different domains can therefore inform Mn smelters of the energy requirements of their processes depending on variations in the ore feed. A significant implication for sustainable resource development is enhanced resource efficiency and reduced environmental liability. While this study did not demonstrate reductions in ore‑waste misclassification, other work has shown that predictive geometallurgical models can reduce misclassification and improve resource utilisation (Abdolmaleki et al., 2025). Extending this framework with more mineralogical data could form future work to achieve similar outcomes. By accurately forecasting in situ processing yield, such models can maximise resource utilisation, extend mine life, and reduce environmental footprint by lowering waste rock and tailings volumes (Lishchuk, 2016). This supports industry goals of improved environmental, social, and governance (ESG) performance. Finally, the methodology provides a foundation for value‑driven, data‑led exploration. Applying this texture‑aware, geochemistry‑based framework early in the project lifecycle allows exploration to be targeted not only towards geological tonnage but towards economic tonnage with favourable processing characteristics. As demonstrated at the Kemi chromite mine, systematic grain size measurement was used for recovery forecasts (Lishchuk et al., 2015). Similarly, future integration of mineralogical datasets with textural and geochemical predictors could de‑risk exploration projects and prioritise domains with superior potential returns, a critical strategic consideration for long‑term viability in the Southern African mining context.

Conclusion This study demonstrates that integrating ore texture into geometallurgical domain classification significantly improves the prediction of processing performance for ferromanganese ores at the Paling Pan deposit. While chemistry remains the primary control on density, textural attributes strongly influence recovery in magnetic and gravity-based separation, with massive ores consistently outperforming vuggy and conglomeritic types. The machine learning models, supported by SHAP analysis, highlight Mn wt.%, Fe wt.%, and ore texture as key drivers of beneficiation outcomes, confirming the operational importance of texture in recovery variability. These findings have direct implications for predictive mine planning, intelligent ore blending, and process optimisation, enabling more consistent plant performance and reduced operational risk. Beyond immediate processing benefits, the framework also informs smelter energy requirements by modelling Mn oxidation states, thereby contributing to sustainable resource efficiency. Overall, the integration of geochemistry, ore texture, and 604

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machine learning establishes a deployable geometallurgical protocol that enhances economic viability, supports environmentally responsible mining, and provides a strategic advantage for the longterm exploitation of manganese resources in South Africa.

Acknowledgements The authors acknowledge the support provided by 4Arrows Mining and Exploration and PMG Mining (Pty) Ltd for access to the processing data and samples used in this study. Special thanks to SGS Laboratories and MINTEK for analytical support. The Department of Science and Technology–National Research Foundation Centre of Excellence in Mineral and Energy Resource Analysis (DSTI-NRF CIMERA) is acknowledged for its financial support.

Credit authorship contribution statement M. Buthelezi: Conceptualisation, investigation, writing - original draft. G.T. Nwaila: Software, methodology, writing - review and editing. D. Bussin: Data curation, investigation. A.J.B. Smith: Writing - review and editing. G.M. Bybee: Validation, writing - review and editing. M.S.D. Manzi: Supervision, project administration, writing review and editing.

Ethics declarations Competing financial interests The author declares that there are no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Sun, X., Hao, H., Liu, Z., Zhao, F. 2020. Insights into the global flow pattern of manganese. Resources Policy, vol. 65, pp. 101578. https://doi.org/10.1016/j.resourpol.2019.101578 Sun, J., Dang, W., Wang, F., Nie, H., Wei, X., Li, P., Zhang, S., Feng, Y., Li, F. 2023. Prediction of TOC content in organic-rich shale using machine learning algorithms: Comparative study of random forest, support vector machine, and XGBoost. Energies, vol. 16, no. 10, pp. 4159. https://doi.org/10.3390/en16104159 Tsikos, H., Beukes, N.J., Moore, J.M., Harris, C. 2003. Deposition, diagenesis, and secondary enrichment of metals in the Paleoproterozoic Hotazel Iron Formation, Kalahari manganese field, South Africa. Economic Geology, vol. 98, no. 7, pp. 14491462. https://doi.org/10.2113/gsecongeo.98.7.1449. Tusa, L., Frenzel, M., Pereira, L., Thiele, S., Tolosana-Delgado, R., and Gutzmer, J. (2025). Geometallurgy: Future Directions. SEG Discovery, no. 140, pp. 27–39. https://doi.org/10.5382/Geo-and-Mining-26. Von der Heyden, B.P., Smith, A.J.B., Tsikos, H., Tadie, M., Mhlanga, X., Van Eeden, L., Backeberg, N., Schultz, T.G., Djeutchou, C. 2024. Manganese in South Africa: its mineral economics, geology and geometallurgy. South African Journal of Geology, vol. 127, no. 4, pp. 765–796. https://doi.org/10.25131/sajg.127.0039 Warke, M.R. 2016. Stratigraphic and geochemical framework of the Palaeoproterozoic rise in atmospheric oxygen: Transvaal supergroup (South Africa). PhD thesis, The University of Manchester. Wills, B.A., Finch, J. 2015. Wills' mineral processing technology: an introduction to the practical aspects of ore treatment and mineral recovery. Butterworth-heinemann. Willmott, C.J., Matsuura, K. 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, vol. 30, no. 1, pp. 79–82. https://doi.org/10.3354/cr030079 ➤

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The role of mineralogy in predictive geometallurgy by M. Becker

Affiliation: 1 Centre for Minerals Research, Department of Chemical Engineering, University of Cape Town, Rondebosch Correspondence to: M. Becker Email: megan.becker@uct.ac.za

Dates:

Received: 02 Feb. 2026 Revised: 26 Feb. 2026 Accepted: 02 Oct. 2026 Published: Oct. 2026 How to cite: Becker M. 2026. The role of mineralogy in predictive geometallurgy. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 10, pp. 607 – 614 DOI ID: https://doi.org/10.17159/24119717/3900/2026 ORCiD: M. Becker https://orcid.org/0000-0002-7025-137X

Abstract

Since geometallurgy emerged as a discipline aimed at managing ore variability and reducing risk across the mining value chain, mineralogy has remained a fundamental descriptor. A review of the geometallurgical literature indicates that predictive geometallurgical modelling can be broadly classified into three approaches: chemistry-based models, proxy-based models, and mineralogical models, with a hybrid machine learning approach emerging. To appreciate these models, one must understand the relationship between mineral chemistry, mineral grade, and elemental grade – which is expressed as the mineral composition triad, together with mineral texture. Examples are presented to illustrate the application of the mineral composition triad for mineralogical data validation, calculation of quantitative elemental distributions, and estimation of mineral grades from elemental assays. These different modelling approaches are evaluated against the premise that mineralogy represents the geological blueprint of an orebody, preserving the signatures of ore-forming and subsequent alteration processes and, therefore, encapsulating the primary properties that govern metallurgical response. Consequently, predictive geometallurgical models should, either directly or indirectly, reflect the relationship between mineralogy and the response variable. Where such relationships cannot be demonstrated, the quality and representativity of both the primary and response variable measurements require critical assessment. This highlights the need for greater emphasis on measurement confidence within geometallurgical practice and suggests that important knowledge gaps remain despite decades of mineral processing and metallurgical research. Data-driven and machine-learning approaches offer considerable opportunities for modelling complex, non-linear relationships; however, their widespread adoption will depend on maintaining sufficient transparency and interpretability to preserve geological and metallurgical understanding.

Keywords geometallurgy, mineralogy, chemistry, modelling, quality assurance and quality control

Introduction This concept paper reviews the central role of mineralogy in geometallurgy and its application in the development of predictive geometallurgical models. Key mineralogical descriptors and their application are outlined before exploring the premise that mineralogy is the blueprint that encapsulates all the primary ore properties governing metallurgical response. Important considerations that need to be interrogated where this relationship cannot be convincingly demonstrated are explored before a future outlook is proposed. The overall concept can be summarised, as in Figure 1.

The contribution of mineralogy to geometallurgy programmes Although the contribution of mineralogy to mining, mineral processing, and environmental management has been recognised for several decades (Becker, 2023), formal integration of these activities into the discipline known as geometallurgy only came about in the late 1990s and early 2000s, e.g., Beniscelli (2011). In their seminal paper, Williams and Richardson (2004) explicitly mention mineralogy as one of the key disciplines contributing to ore characterisation in geometallurgy (Figure 2). The overview by Walters (2008) and Keeney and Walters (2008) of the AMIRA P843 Geometallurgical Mapping and Mine Modelling (GEM) programme and associated publications, similarly highlighted mineralogy but also, importantly, introduced the concept of the geometallurgical matrix. The geometallurgical matrix is a useful construct for representing the interplay between sample size, number of samples, cost, simplicity of testwork, and scale-up (Figure 3). Because geometallurgy requires The Journal of the Southern African Institute of Mining and Metallurgy

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Figure 1—Overview of the structure of the paper illustrating the role of mineralogy in predictive geometallurgy

Figure 2—Disciplines contributing to geometallurgy (adapted from Williams and Richardson, 2004)

abundant data that reflect ore variability, the initial emphasis of the GEM project was on the development of proxy data and predictive geometallurgical tests. These tests were designed to be simple, inexpensive, quick, and easy to implement. Some examples of proxy data that can be readily measured using drill core are the Equotip Leeb rebound hardness, portable multi-element XRF, and hyperspectral imaging, whereas small-scale tests may represent point load or bottle roll cyanidation tests (Lechuti-Tlhalerwa et al., 2018; Cloete, Slabbert, 2018). The incorporation of mineralogy measurements both at the micro- and mesoscales, as well as using mineralogy data for ore textural classification, was an important component of the GEM programme’s research. More recently, a special issue of the Elements magazine devoted to geometallurgy emphasised the role of mineralogy in Geometallurgy (Butcher et al., 2023), whilst simultaneously highlighting the emerging techniques in the field. The Global Mining Guidelines Group (GMG) also recently published a geometallurgy white paper (GMG, 2025) that recognises mineralogy as an important contributor to geometallurgy. The white paper brings an industry perspective in highlighting how the current operating context has changed since earlier publications – now with a greater focus on data-driven technologies, ore characterisation 608

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Figure 3—The geometallurgical matrix construct (after Keeney and Walters, 2008; Cloete and Slabbert, 2018)

for future resources, waste management for closure planning, and reconciliation. Having established that mineralogy is widely recognised as an important component of geometallurgical programmes, one must ask where mineralogy fits into geometallurgical programmes. Lishchuk et al. (2015) proposed three different approaches: traditional, proxies, and mineralogical. The traditional approach focuses on predicting metallurgical response using feed chemical assay data, such as the prediction of Bond work index and acid consumption (King, Macdonald, 2016) or flotation recovery and concentrate grade (Mangwiro et al., 2025). The proxies approach predicts metallurgical response from small-scale ‘proxy’ tests and may use correction factors for scale-up. This may be used in combination with the feed chemical assay data. The mineralogical approach is based on mechanistic or empirical models using mineralogy, such as the prediction of the bulk oxidation state of Mn smelter feed based on the deportment of Mn to minimise energy consumption (Chetty, Gutzmer, 2016) or predicting the grindability of iron ore pellet feed as a function of its crystallinity (KwitkoRibeiro et al., 2016). All three of the above-mentioned approaches continue to be used, especially with the increasing worldwide focus on machine learning and data analytics, e.g., Bedregal Aguilar et al. (2025). A fourth approach that has emerged is the hybrid approach. Hybrid modelling combines empirical and fundamental models, essentially capturing the best of both (Von Stosch et al., 2014). Pereira et al. (2021) demonstrated the use of this hybrid approach to predict single-particle flotation kinetics. Here, quantitative mineralogy data describing the particle properties of the flotation feed, concentrate, and tails were initially used to train a recovery probability model. Thereafter, a first-order flotation kinetic model was fitted to every particle to ultimately predict its flotation kinetics. Although the approach of Pereira et al. (2021) has not yet routinely been incorporated into geometallurgical modelling, it is only a matter of time before this can be expected.

The mineralogical blueprint for ore response The application of any mineral-centric approach to predictive geometallurgical modelling is ultimately based on the following theory statement: Mineralogy is the blueprint that encapsulates all the properties of a rock (primary variables), carrying signatures of the ore-forming and subsequent alteration processes. These ultimately define the response to an applied process (response variables). The Journal of the Southern African Institute of Mining and Metallurgy

The role of mineralogy in predictive geometallurgy

Figure 4—Application of the mineral composition triad to (a) calculate the element distribution of Mg in five different platinum ores; and (b) compare Auto-SEMEDS (QEMSCAN) derived back-calculated elemental assays with measured elemental assays to increase confidence in mineralogy data

To apply our theory statement, we should first describe the mineralogy - this can be done using both the mineral composition triad and mineral texture. The mineral composition triad uses the interrelationship between bulk chemistry, mineral chemistry, and mineral grades (x) defined by Equation: 1 [1] Where: bi is the bulk chemistry of element i; Aij is the proportion of element i in mineral j; and xj is the grade of mineral j in the bulk. The mineral composition triad underpins three important outputs (Figure 4 and Figure 5). Firstly, it can be used to determine the quantitative element distribution for both valuable and deleterious elements. The example provided in Figure 4a shows how the distribution of magnesium varies across the flotation feeds in 5 different platinum ores. In this case, knowing whether the main host of magnesium is talc or serpentine would require different treatment strategies. Talc requires depressant addition because of its natural floatability, whereas serpentine may require dispersant addition because of its potential to slime coat valuable minerals (Dzingai et al., 2021; Molifie et al., 2023). Secondly, it can be used to back-calculate elemental assays from measured mineral grades. The parity chart given in Figure 4b illustrates how the backcalculated and measured elemental assays can be compared with one another, and in the example shown, it is evident that all the data fall within a 10% error margin of the y = x line, thus providing the required degree of confidence. Thirdly, it can be used as the basis for calculating mineral grades from chemical assay data, also known as element-to-mineral conversion (EMC; Whiten, 1998). EMC has been used in various geometallurgical studies, e.g., Mena Silva et al. (2020). Its key advantage is that it provides a methodology to turn abundant chemistry datasets into comprehensive mineralogy datasets. The widespread availability of certified reference materials for elemental assays in accredited analytical laboratories contributes to a greater level of confidence in these large datasets. Generally, the number of mineralogy datasets is several orders of magnitude lower due to the costly and time-consuming nature of acquiring this data. Machine learning algorithms using input data from the mineral composition triad, which are not necessarily restricted to linear relationships, are also increasingly being used in geometallurgy to predict mineral grades, e.g., Figure 5 as per Cupido et al. (2026). The mineral composition triad allows us to understand how the readily available chemical The Journal of the Southern African Institute of Mining and Metallurgy

assay datasets capture the core mineralogy information and why the traditional modelling approach has historically been, and remains, a popular approach to predictive modelling. Secondly, we can describe mineralogy through mineral texture that defines the interrelationship between mineral grains. For unbroken rocks and particles, one is interested in defining grain size distributions, grain shape distributions, porosity, and mineral association. Figure 6a provides an example of the grain size distribution of pyrite measured on polished 70 mm x 70 mm slabs of five different ores from a single polymetallic sulphide deposit. The data show the extent of the variability of pyrite, with grain size d50 measurements varying between 270 (ore 4) µm and 6700 (ore 1) µm. Mineral texture attributes as a consequence of an applied process, such as the liberation of a flotation feed after grinding, can also be described. After milling these same polymetallic samples, the liberation data show that ore 1 with the coarsest pyrite grain size distribution has the best liberation (Figure 6b). Measuring quantitative mineral texture information is generally the costliest analysis and is often more readily incorporated into process mineralogical studies (Becker et al., 2016). Inevitably, this raises the question as to whether a relationship exists between the mineral composition triad and mineral texture. For example, it has been an inherent heuristic in many flotation concentrators that higher-grade ores give better recovery. The example in Figure 7 supports this heuristic by demonstrating the correlation between metal recovery and flotation feed head grade of a polymetallic sulphide ore that achieved a Pearson correlation value of 0.59 (Figure 6). Given that higher-grade ores often have coarser grain size distributions of the valuable sulphide minerals, they will be more liberated at a given grind compared with the finer grain size distribution. Jameson (2012) showed that equivalentsized particles have a higher flotation rate constant when they are more liberated. In combination with the fact that flotation is a probabilistic process, i.e., the more valuable particles in the system, the greater the flotation rate (Sutherland, 1948; Polat, 2000), these particles with higher flotation rate constants are more likely to be recovered. Although Von der Heyden et al. (2024) presented a relationship between the mineral composition triad and mineral texture for manganese ores, the lack of studies on this topic suggests these relationships may be more readily applicable to high-grade and bulk commodities. These relationships are also likely to be more deposit-specific rather than broadly transferable. VOLUME 126

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Figure 5—Training (a) and testing (b) datasets showing the comparison of the predicted machine learning model (MLM) versus QEMSCAN measured mineral grades as trained with the non-parametric support vector regression machine learning algorithm. Reproduced with permission from Cupido et al. (2026). Confidence intervals are shown at the 90% confidence level

Figure 6—Pyrite (a) grain size distribution across five ore types and (b) cumulative liberation yield of three related flotation feeds after grinding. Measurements were determined using QEMSCAN. (Unpublished data, courtesy of Mulelu)

Application of the ‘mineralogical blueprint’ for ore response

Figure 7—Relationship between recovery and flotation feed head grade (Unpublished data, courtesy of Van der Westhuizen and Nienaber) 610

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To apply the theory statement, one must gather mineralogical measurements, but inevitably this also requires one to determine what types of geometallurgical samples will be analysed. In an ideal context, one would gather information on the mineral composition triad and mineral texture at every metre-interval of drill core, but this is not practical in terms of logistics, time, and cost. According to the guidelines presented by Scott (2025), geometallurgical samples can be divided into composite samples and variability samples. Composite samples dampen the geometallurgical variability, but because they are larger in mass, they allow metallurgical testwork at higher levels of the geometallurgical matrix and greater proximity to scale-up parameters (Figure 3). Variability samples preserve the variability of the ore, but due to the large number of samples that should be taken to capture this variability, only simple, inexpensive measurements can be acquired. A third type of sample could be proposed, namely, research samples in which comprehensive, detailed measurements are acquired on The Journal of the Southern African Institute of Mining and Metallurgy

The role of mineralogy in predictive geometallurgy a small number of samples, but these would be considered more academic in nature. Once the samples have been collected and characterised, predictive geometallurgical modelling is initiated to develop models that allow one to describe the relationship between the mineral composition triad and metallurgical response, or the relationship between mineral texture and metallurgical response, or a combination thereof. Recent studies applying machine learning algorithms have highlighted the non-additivity in modelling response variables (Campos et al., 2024), therefore, linear relationships should not always be expected, but are often a good starting point in modelling. Should one not be able to demonstrate a significant correlation between the mineral composition triad, mineral texture, and metallurgical response, the following questions should be interrogated to advance the geometallurgical programme:

Was the sampling representative? Firstly, one must consider whether the sampling is representative of the bulk material. The work by Scott (2025) is expected to be formally published as an accepted industry guideline by the Global Mining Guidelines Group, and such work provides instruction on how best to carry out representative sampling in data-scarce geometallurgy contexts (GMG, 2026a). Equally important is that, within the context of the material available, we follow subsampling best practice that promotes subsampling using spinning rifflers in conjunction with size and mass reduction (Lotter et al., 2018). Where this is not possible, for example, when the grain size distribution of unbroken particle textures needs to be mapped, then the uncertainty of the measurements must be communicated (Evans, Napier-Munn, 2013).

Are the mineralogical measurements reproducible and accurate? Unlike the field of analytical chemistry, where quality assurance and quality control programmes coupled with the use of certified reference materials are standard practice, analytical mineralogy is not as simple due to the lack of reference standards for mineral textural measurements, coupled with the very complex nature of mineral chemical variations. This does, however, not preclude individual laboratories from developing their own in-house standards and protocols. The study by Lastra and Paktunc (2016) reported the results of an interlaboratory study comparing the automated SEM-EDS mineralogy results of a copper concentrate provided to each of the laboratories as a milled sample aliquot. Results of the chalcopyrite mineral grades reported across the individual laboratories had a 7.5% relative standard deviation and just under 4% relative standard deviation in chalcopyrite liberation, but only once the outliers had been removed. In this case, the outliers could only be identified where multiple datasets existed that could approximate the true value. For any individual mineralogy laboratory, accompanying chemical assay datasets and/or quantitative X-ray diffraction (XRD) must be generated to allow comparison and validation of the mineralogy measurements (see application of the mineral composition triad, Figure 4b). Without such practice, the user has little confidence in whether the mineralogy measurements are accurate.

Were the appropriate mineralogical attributes measured and reported? Mineralogical instrumentation can provide a wealth of information, and close communication between the user and service provider The Journal of the Southern African Institute of Mining and Metallurgy

is required to ensure that the appropriate measurements are run to quantify relevant mineralogical parameters. For instance, the work of Dzingai et al. (2021) showed how the amount of naturally floating gangue in a hydrothermally altered magmatic Ni-Cu PGE sulphide ore could be more readily estimated based on understanding the talc grain size distribution as compared to the grade of talc in the flotation feed. Here, the end-user would specifically need to request a mineral textural measurement instead of the bulk mineralogy, which would require the instrument scientist to run a specific type of Auto-SEM-EDS measurement routine. Another example, although not directly linked to geometallurgy, showed that the crystallite size of pyrite, quartz, and kaolinite in coal mine dust, as determined by XRD, was a contributing factor to the pneumoconiotic potency of the coal (Kamanzi et al., 2024). Yet, crystallite size measurement is not a standard output when doing the Rietveld refinement needed for phase quantification and would be something that needs to be specifically requested. One could extend these further to question whether the measurements were obtained on the correct scale. Most instrumentation tends to focus on the meso and microscales, but consideration should also be given to how nanoscale properties may also affect ore response (Butcher, 2020; Månbro et al., 2025). A well-known example of this is the presence of solid-solution hosted and nano (colloidal) gold in pyrite, both of which require pretreatment prior to conventional cyanidation for gold extraction (Chryssoulis, McMullen, 2016).

Were the response variables measured correctly? If the answer to all of the above questions is affirmative, attention must then turn to whether the appropriate response variable was measured, whether the measurement scale was suitable, and whether the measurement was performed correctly. Any experimental measurement is subject to a range of errors. For example, in the case of the laboratory batch float, these errors would include sampling error, feed preparation error, error associated with air flow, froth level or froth recovery, weighing errors, and assay errors (Napier-Munn, 2012; Runge, 2010). The user needs confidence that replicate testing under best practice conditions that minimise experimental error has been carried out, and that confidence in the results is reported accordingly (Lotter, Fragomeni, 2010). Confidence that ore characterisation test results are comparable across different laboratories is also required, especially since the geometallurgical programme may have run over several years (Tafur, 2025). Here, ongoing and active long-term relationships with a single service provider are most likely to deliver this level of consistency. A current project by the Global Mining Guidelines Group focused on guidelines for common data reporting in geometallurgy is expected to provide more transparency in this area and encourage reporting that allows the uncertainties to be understood (GMG, 2026b). There is also the possibility that the response variable of interest is overshadowed by other, more prominent features, such as in gold flotation, where the response is determined by the carrier particle (e.g., pyrite) rather than the gold (Chryssoulis, McMullen, 2016).

Is the mechanism of the response fully understood? Should the questions raised in the aforementioned not provide insights that allow one to demonstrate the mineralogical blueprint for ore response, one is left with answering the difficult questions as to whether the response mechanism is fully understood. Nagaraj and Farinato (2016) focused a review on a century of research VOLUME 126

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The role of mineralogy in predictive geometallurgy and innovation in flotation chemistry, but still acknowledged that there were new and emerging challenges to solve. The review of Månbro et al. (2025) on the current status of research on mineral texture and non-random breakage also highlighted the need for further research, especially since the ability to exploit nonrandom breakage provides a key mechanism to reduce the energy consumption of breakage. A word of caution: although machine learning algorithms may allow the prediction of complex response features using state-of-the-art models on good quality datasets and may be advantageous due to their ability to cope with nonlinear relationships (Campos et al., 2024), these should always be grounded in the ability for these models to be explainable to those implementing them (Johnston, 2025). Otherwise, they run the risk of a lack of uptake, and the value of the work is not fully realised.

Concluding thoughts Mineralogy has historically been, and continues to be, recognised as an important component of predictive geometallurgical modelling. Mineralogy can be described in terms of the mineral composition triad, which articulates the relationship between mineral and elemental grades with mineral chemistry, as well as mineral texture. Although there are heuristics linking the mineral composition triad with mineral texture, further investigating and articulating this relationship across a range of commodities through different oreforming processes is an area of study expected to deliver valuable insights that can be further explored in geometallurgical modelling. The three established approaches to predictive geometallurgical modelling, those being, traditional, proxy-based, and mineralogical (after Lishchuk et al., 2015), are widely recognised and are often complementary, forming the basis of an emerging fourth hybrid approach that incorporates fundamental and empirical models. Mineralogy is recognised as the blueprint that encapsulates all the properties of a rock that define its response to an applied process – this is used as the ultimate basis for all predictive geometallurgical modelling. Should predictive geometallurgical modelling not be able to demonstrate the relationship between the mineralogy and the response variable, an assessment of the sampling and measurement of both primary and response variables is required. This calls for ensuring greater articulation of geometallurgical measurement confidence within the industry, but also highlights that, despite years of research in mineral processing and metallurgy, there is still more research to be done. The application of data-driven modelling coupled with machine learning algorithms presents a valuable opportunity in predictive geometallurgy, particularly when non-additive relationships occur. However, to ensure uptake within the industry, these models should still be explainable and communicable to industry practitioners, and their underlying mechanisms should be understood. This will allow mineralogy to realise its potential in predicting geometallurgical responses, enabling us to process the complex and sometimes unconventional ores needed to generate the metals our future society demands.

Acknowledgements Thank you to various colleagues in the Centre for Minerals Research for useful discussions leading to the formulation of the ideas presented here. Various delegates from the SAIMM Geometallurgy Conference in 2025, where this concept was first presented. The journal reviewers are also acknowledged and thanked for their constructive feedback. 612

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CRediT author statement MB: Conceptualisation and writing – reviewing and editing.

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Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis: A multi-method comparative study at an open-pit gold mine by I.N. Aslam1, M.S. Perdana1, H. Harisman1, F. Aliva1, N.A. Putri1, R. Ayuni1 Affiliation: 1 Mining Engineering Program, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia

Correspondence to: I.N. Aslam Email: izzanaslam@usk.ac.id

Dates:

Received: 23 Mar. 2026 Revised: 30 Aug. 2025 Accepted: 30 Sep. 2026 Published: Oct. 2026 How to cite: Aslam I.N., Perdana M.S., Harisman H. Aliva F., Putri N.A., Ayuni R. 2026. Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis: A multi-method comparative study at an open-pit gold mine. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 10, pp. 615 – 624 DOI ID: https://doi.org/10.17159/24119717/3941/2026 ORCiD: I.N. Aslam https://orcid.org/0000-0002-4163-6115 M.S. Perdana https://orcid.org/0009-0003-3818-1978 H. Harisman https://orcid.org/0009-0005-3240-5090 F. Aliva https://orcid.org/0009-0009-0853-4879 N.A. Putri https://orcid.org/0009-0003-5896-3926 R. Ayuni https://orcid.org/0009-0009-8646-9048

Abstract

Accurate assessment of post-blast rock fragmentation is critical for optimising the mining value chain. Conventional methods such as sieve analysis are time-consuming, while commercial image-analysis software requires costly licences and manual intervention. This study presents a multi-method evaluation of Meta’s Segment Anything Model for automated segmentation and particle size distribution analysis of post-blast rock fragments at an open-pit gold mine in Sumatra, Indonesia. Four complementary investigations were conducted: a laboratory-scale comparison against Split Desktop, a field-scale comparison against Strayos with excavator digging time correlation, a pixel-level evaluation against WipFrag using drone-captured imagery, and a statistical analysis of fragmentation metrics versus grizzly feeder clean-up time. The Segment Anything Model 2.1 achieved a mean intersection-over-union of 0.75 and Dice coefficient of 0.85 on high-quality patches, with particle size distribution errors reduced by 89% to 99% relative to WipFrag (up to 90 times lower). Linear regression revealed a significant correlation between maximum fragment diameter and grizzly feeder clean-up time (R² = 0.618). Analysis across 24 drone images identified over 23 000 fragments, confirming that more than 99% of blasted material passed the 750 mm feeder aperture. Processing time was reduced from 30 minutes to 2 – 5 minutes per image. These findings support the potential of the Segment Anything Model as an open-source tool for rock fragmentation analysis, contributing to operational assessment within the mine-to-mill optimisation framework.

Keywords rock fragmentation, deep learning segmentation, particle size distribution, mine-to-mill, grizzly feeder, open-pit mining

Introduction Post-blast rock fragmentation is a critical determinant of overall mining efficiency, directly influencing excavation productivity, haulage costs, and downstream mineral processing performance (Kinyua et al., 2022). The heterogeneous size distribution of blasted fragments, particularly the presence of oversized boulders, creates bottlenecks at multiple stages of the operational chain: boulders increase excavator digging time and energy consumption, cause blockages at grizzly feeders, and necessitate costly secondary breaking operations (Santos et al., 2020; Nikkhah et al., 2022). Seccatore (2019) emphasised that blasting constitutes the initial stage of comminution, underscoring the need for an integrated mineto-mill approach that optimises fragmentation from the point of detonation through to final processing (Nageshwaraniyer, 2018). Quantitative assessment of fragmentation quality relies on particle size distribution (PSD) analysis, traditionally performed through sieve analysis. While considered the gold standard for accuracy, sieve analysis is impractical for routine field application due to processing times of 4 – 6 hours per sample and susceptibility to human error. Image-based analysis has emerged as a practical alternative, with commercial software platforms such as WipFrag, Split Desktop, and Strayos offering automated or semi-automated PSD estimation from photographs of muck piles. However, these tools carry significant limitations: WipFrag and Split Desktop exhibit error rates of 32% – 60% on D₅₀ values compared to sieve analysis (Maerz et al., 2018; Girdner et al., 2018), require expensive licences, and demand substantial manual delineation effort that introduces operator subjectivity. Recent advances in deep learning, particularly the development of foundation models for computer vision, offer potential for rock fragmentation analysis as part of the broader adoption of data intelligence in mining. Meta’s Segment Anything Model (SAM), introduced by Kirillov et al. (2023), demonstrated strong zero-shot segmentation capabilities across diverse visual domains without task-specific training. Subsequent iterations, SAM 2.0 (Ravi et al., 2025) and SAM 2.1, have achieved significant improvements

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Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis in processing speed (6 × faster than SAM 1.0), segmentation accuracy, and robustness to complex visual conditions through enhanced streaming memory mechanisms and improved positional encoding. Zhao et al. (2024) pioneered the application of SAM for post-blast rock fragmentation identification, demonstrating its zero-shot segmentation capability using 83 images captured at 1 m above ground level. While promising, their study was limited by close-range sampling that provided insufficient spatial coverage for representative PSD assessment. Shan et al. (2024) further validated SAM’s potential for drilling rock image segmentation, confirming its analytical depth regarding fragment physical characteristics including size, shape, and distribution. Despite these encouraging results, several critical research gaps remain. To the authors’ knowledge, few studies have systematically compared SAM-based methods against multiple commercial software platforms within a unified analytical framework. The relationship between SAM-derived fragmentation metrics and operational performance indicators such as digging time and grizzly feeder clean-up time has received limited attention. The influence of image quality on SAM segmentation accuracy remains underexplored, and the direct quantitative link between SAMderived fragment size and clean-up time at the grizzly feeder has not been widely reported. This paper addresses these gaps through four complementary investigations conducted at an open-pit gold mine in Sumatra, Indonesia. Study 1 provides a controlled laboratory-scale comparison of SAM Rock Fragmentation against Split Desktop. Study 2 evaluates field-scale SAM against Strayos, the mine's commercial fragmentation analysis platform, with correlation analysis to excavator digging time. Study 3 presents a rigorous assessment of SAM 2.1 against WipFrag using drone-captured imagery with pixel-level ground truth. Study 4 investigates the direct relationship between SAM-derived fragmentation metrics and clean-up time at the grizzly feeder. The studies employed different SAM implementations reflecting the chronological development of the research program: Study 1 used SAM Rock Fragmentation, an open-source tool built on SAM 1.0, as this was the version available when the laboratory-scale investigation commenced. Studies 2, 3, and 4 used SAM 2.1 (the sam2_hiera_large checkpoint), which became available during the field-scale phase and offered improved segmentation accuracy and processing speed. Together, these investigations provide a multi-method evaluation of SAM's viability as an open-source tool for fragmentation analysis within the mineto-mill optimization framework.

Figure 1—Aerial drone image of a post-blast area captured at 28 m altitude 616

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Methodology Study area All investigations were conducted at an open-pit gold mine in Sumatra, Indonesia. The mine is situated along the Sumatra Fault Zone, a major geological structure associated with oblique subduction of the Indo-Australian plate. The deposit is classified as a high-sulfidation epithermal gold-silver system, with principal rock types including basalt andesite, volcanic breccia, porphyritic andesite, and phreatomagmatic diatreme breccia. The open-pit extraction employs conventional drill-and-blast methods with hydraulic excavators for loading. Detailed blast-design parameters (bench height, burden, spacing, hole diameter, explosive type, powder factor, and initiation practice) were not available for publication due to confidentiality requirements of the mine operator. The absence of these parameters precludes direct evaluation of blast-design effectiveness and constitutes a limitation of this study. A representative aerial view of a post-blast area at the study mine, captured at 28 m altitude, is presented in Figure 1. The mine utilises a grizzly feeder system with an aperture of 750 mm (mine operational specification, personal communication) to screen incoming material before primary crushing. Fragments exceeding this aperture are classified as boulders requiring secondary breaking, making the 750 mm threshold a critical operational benchmark for evaluating blast effectiveness.

Study 1: SAM Rock Fragmentation versus Split Desktop (laboratory scale) Ten photographs of rock pile fragmentation models were captured at standardised 500 × 500 pixel resolution from rock piles with known fragment sizes. Each image was analysed using three methods: manual measurement of individual fragment diameters using a digital calliper as ground truth (each visible fragment in the photograph was measured and recorded individually), Split Desktop with and without manual delineation, and SAM Rock Fragmentation, an open-source implementation based on SAM 1.0 (Morocho, 2023). For each method, P20, P50, and P80 values were computed and compared against ground truth using root mean square error (RMSE). Split Desktop analyses required approximately 30 minutes per image, including manual corrections, while SAM Rock Fragmentation operated fully automatically in approximately 5 minutes per image. The sample size of ten photographs was selected for an exploratory comparative study designed to evaluate method agreement rather than to characterise a population. As the primary objective was to compare the relative accuracy of three PSD estimation methods against the same ground truth, the ten images served as paired measurement cases rather than independent statistical samples. Fragment sizes were derived from the 2D segmented masks by computing the equivalent circular diameter for each fragment, defined as d = (4A/π)^0.5, where A is the pixel area of the segmented mask. This equivalent diameter represents the diameter of a circle with the same area as the irregular 2D projection of the fragment. The resulting size distributions are number-weighted (each fragment counted equally regardless of mass or volume) and represent projected 2D dimensions rather than true 3D particle sizes, which is a recognised limitation of all image-based fragmentation analysis methods. Pixel-based diameters were converted to real-world dimensions using the computed GSD. The Rosin-Rammler distribution model was then applied to characterise the cumulative PSD curves, expressed as Y = 1 - exp[- (x/xe)n], The Journal of the Southern African Institute of Mining and Metallurgy

Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis where Y is the cumulative fraction passing, xe is the characteristic particle size, and n is the uniformity index.

Study 2: SAM versus Strayos with digging time correlation (field scale) Drone imagery was captured at 28 m altitude above post-blast areas at the study mine using a DJI Mavic drone equipped with a camera having a focal length of 2.86 cm and sensor width of 0.132 cm. Each full-resolution image was cropped into nine sub-images for computational tractability. Ground sampling distance (GSD) was calculated for each sub-image using GSD = (flight height / focal length) × pixel size, yielding values ranging from 0.089 to 0.154 cm/ pixel. The GSD calculation assumes a nadir camera orientation and flat terrain; no corrections were applied for terrain relief, camera tilt, or lens distortion. This represents a limitation, as surface undulations in the muck pile can introduce localised scale errors. SAM segmentation was performed on each sub-image, and the resulting fragment masks were converted from pixel-based measurements to real-world dimensions using the computed GSD. PSD curves were generated following the Rosin-Rammler model and directly compared against PSD outputs from Strayos, the mine’s licensed cloud-based fragmentation analysis platform. Additionally, excavator digging time data from the corresponding loading points were collected to establish correlations between fragmentation characteristics and operational performance. The interquartile range (IQR) method was employed to identify and remove outliers from the equivalent circular diameter data. Fragments with diameters falling below Q1 − 1.5 × IQR or above Q3 + 1.5 × IQR were classified as outliers and excluded from the PSD analysis. The exact number of fragments removed per image was not recorded during the original analysis, which is acknowledged as a reporting limitation. The IQR method was selected because it is nonparametric and does not assume normality, making it appropriate for fragment size distributions that typically follow lognormal or Rosin-Rammler distributions rather than Gaussian distributions.

Study 3: SAM 2.1 versus WipFrag with pixel-level ground truth Twenty-four drone images (5472 × 3648 pixels) were captured at 28 m altitude across multiple post-blast areas. Each image was subdivided into overlapping square tiles (1216 × 1216 pixels, 20% overlap) for computational tractability, yielding approximately 25 tiles per image and 600 analysis patches in total. Prior to segmentation, all tiles were enhanced using contrast limited adaptive histogram equalisation (CLAHE) in the LAB colour space, with the clip limit optimised to maintain a peak signal-to-noise ratio above 30 dB. Four patches were selected for detailed evaluation based on visual quality classification (two high-quality and two low-quality). This purposive selection from 600 available patches introduces a risk of selection bias, as the chosen patches may not be representative of the full dataset. Future work should employ stratified random sampling or cross-validation across a larger subset of patches to provide more robust performance estimates: Patches 2 and 3 (high quality, with distinct texture, strong contrast, and dominant rock features) and Patches 0 and 16 (low quality, with blurred texture, low contrast, and minimal distinguishable rock features). For each selected patch, manual pixel-level segmentation was performed by a single annotator using LabelMe (MIT) to establish ground truth masks. The four patches were purposively selected from the quality extremes of the 600-patch dataset: The Journal of the Southern African Institute of Mining and Metallurgy

Patches 2 and 3 representing high-quality conditions and Patches 0 and 16 representing low-quality conditions. The delineation protocol required that only visually distinguishable fragments were labelled, overlapping fragments were delineated as separate objects without merging, and fines that could not be individually resolved were excluded. As only one annotator was involved, inter-rater agreement could not be assessed, and intra-rater repeatability was not tested. Ground truth uncertainty may therefore be present, particularly for fragments with ambiguous boundaries, and this should be considered when interpreting the reported accuracy metrics. SAM 2.1 was applied in fully automatic mode using the SAM2AutomaticMaskGenerator (checkpoint: sam2.1_hiera_large. pt; points_per_side = 128, points_per_batch = 256, pred_iou_ thresh = 0.9, stability_score_thresh = 0.95, crop_n_layers = 2, box_nms_thresh = 0.4, min_mask_region_area = 10.0, use_m2m = true). The model was executed on Google Colab with an NVIDIA A100 GPU. WipFrag 3.3 was used as the commercial benchmark. The scale was calibrated using the same ground sampling distance (GSD = 5.676 mm/pixel) applied to the SAM 2.1 analysis, ensuring dimensional consistency between the two methods. Edge detection was performed using the edge detection parameters (EDP) function with the best fit mode set to thorough. No additional manual edge correction was applied beyond the automated EDP output. After segmentation, overlapping masks from adjacent tiles were merged using a polygon union algorithm (Shapely library) with an overlap threshold of 0.2 (20% intersection over the smaller polygon area). Mask contours were simplified using the Ramer-Douglas-Peucker algorithm (epsilon = 0.002 × arc length). Additional post-processing included noise removal (fragments smaller than 5 pixels or larger than 50 000 pixels excluded) and circularity filtering (threshold = 0.5). Segmentation performance was quantified using five metrics: intersection over union (IoU), Dice coefficient, precision, recall, and F1-score. PSD prediction accuracy was assessed using mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE), with additional analysis of deviation at D₁₀, D₅₀, and D₈₀ percentiles. Beyond the patch-level evaluation, SAM 2.1 was deployed on all 24 drone images to analyse full-scale fragmentation characteristics across the mine site. The complete dataset of over 23 000 identified fragments was analysed to generate PSD curves, evaluate blast success against the 750 mm grizzly feeder threshold, and derive operational insights for mine planning.

Study 4: SAM 2.1 segmentation at the run-of-mine bin and clean-up time correlation A dedicated investigation was conducted to evaluate the relationship between SAM-derived fragmentation metrics and clean-up time at the grizzly feeder. The grizzly feeder at the study mine features a screen aperture of 75 cm, with steel bars of 35 cm width that serve as the calibration reference for pixel-to-real-world conversion. Clean-up time is defined as the duration required to clear material that becomes trapped between the grizzly bars, causing bottlenecks in the feeding process. A total of 45 clean-up time records were collected as primary data through direct observation at the run-ofmine bin. CCTV footage from the run-of-mine bin area was used to capture images of rock fragments on the grizzly feeder surface. From the 45 records, 23 images (51%) met the quality criteria for segmentation analysis. The remaining 22 images were excluded due to excessive motion blur, partial occlusion of the grizzly feeder surface by the excavator bucket, or insufficient lighting conditions VOLUME 126

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Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis that prevented reliable fragment boundary identification. The exclusion of nearly half the records may introduce selection bias towards clearer imaging conditions, and the results should be interpreted accordingly. SAM 2.1 (checkpoint: sam2_hiera_large. pt; points_per_side = 64, pred_iou_thresh = 0.75, stability_score_ thresh = 0.92, crop_n_layers = 1, min_mask_region_area = 15.0; NVIDIA T4 GPU) was deployed to segment rock fragments in each image, with the grizzly feeder steel bar (35 cm) used as the calibration object for converting pixel-based measurements to real-world dimensions. The calibration assumes the steel bar and rock fragments are at the same depth plane relative to the camera. In practice, CCTV perspective distortion, camera angle, and differences in depth between the bar surface and the rock fragments can introduce measurement uncertainty, which was not quantified in this study and represents a limitation of the clean-up time analysis. Statistical correlation analysis was performed using linear regression to evaluate two relationships: maximum fragment diameter versus clean-up time, and average diameter of fragments exceeding 75 cm versus clean-up time. The coefficient of determination (R²) was computed to quantify the explanatory power of each regression model.

Results and discussion Study 1: SAM Rock Fragmentation versus Split Desktop Table 1 presents the RMSE values for each of the ten fragmentation model photographs across three analytical methods. Split Desktop with manual delineation exhibited the highest variability and largest errors, with RMSE values ranging from 2.03 to 17.26, reflecting the inherent subjectivity of manual edge delineation. Several photographs (Photos 2, 3, 6, and 9) yielded RMSE values exceeding 10, indicating substantial inaccuracy. Split Desktop without manual delineation demonstrated markedly improved performance, with RMSE values ranging from 0.96 to 5.96, attributable to the consistency of its automated algorithmic approach. SAM Rock Fragmentation achieved RMSE values between 2.80 and 4.82, showing a narrower error range than either Split Desktop configuration. The overall PSD curves generated by SAM rRock fFragmentation more closely approximated the manual ground truth curves, particularly for fragments in the 0 mm – 100 mm range. A notable exception occurred with Photo 10, where Split Desktop with delineation outperformed both automated methods. This anomaly was attributed to a higher zoom level during image capture, suggesting that standardised imaging protocols are essential for consistent automated analysis. Overall, both SAM Rock

Figure 2—Comparison of particle size distribution curves from manual measurement (blue), Split Desktop with delineation (red), Split Desktop without delineation (orange), and SAM Rock Fragmentation (green) for a representative sample 618

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Table 1

RMSE comparison of Split Desktop and SAM Rock Fragmentation against manual ground truth Photo

Split Desktop

SAM Rock Fragmentation

6.39

0.96

3.18

17.26

1.18

3.51

13.16

1.76

3.86

8.14

4.49

4.37

8.12

2.69

3.36

11.99

2.28

4.02

8.51

2.64

2.80

6.62

2.53

2.97

11.64

4.81

4.72

2.03

5.96

4.82

Fragmentation and Split Desktop without delineation produced reliable results, with SAM offering the advantage of fully automated processing at approximately one-sixth of the processing time. A representative comparison of the particle size distribution curves from the three methods against manual ground truth is shown in Figure 2, illustrating the closer agreement of the SAM Rock Fragmentation curve with the actual measurements.

Study 2: SAM versus Strayos and digging time correlation The field-scale deployment of SAM on drone imagery from the study mine demonstrated the model’s capability to process largescale post-blast areas. After segmentation and outlier removal using the interquartile range method, the PSD analysis yielded

Figure 3—SAM segmentation output showing individual rock fragments identified by colour-coded masks The Journal of the Southern African Institute of Mining and Metallurgy

Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis representative fragment size distributions. For a representative area (Image 1), the analysis identified P10 = 1.46 cm, P50 = 3.55 cm, and P80 = 5.28 cm, indicating a fragmentation profile dominated by fine to medium fragments well-suited for the mine’s processing requirements. The SAM segmentation output for this area is presented in Figure 3, where individual rock fragments are distinguished by colour-coded masks. Comparison with Strayos outputs was limited to a qualitative assessment of PSD trends because the Strayos platform operates as a cloud-based service that does not permit export of raw fragmentlevel data for independent statistical analysis. Within this constraint, both methods identified broadly similar proportions of material in the fine, medium, and coarse fractions. However, SAM exhibited a tendency to segment fine particles more comprehensively than Strayos, particularly in areas with complex textures. The SAM model occasionally produced outliers at the P100 percentile, where very large apparent fragment sizes resulted from the model’s inability to distinguish fine, closely packed fragments from a single

Table 2

Average segment error metrics of SAM 2.1 by image quality category Patch quality

IoU

Dice

Precision

Recall

F1-score

High

0.7452

0.8535

0.7937

0.9247

0.8535

Low

0.4827

0.5649

0.3845

0.5521

0.4304

Δ (%)

−35.2

−33.8

−51.6

−40.3

−49.6

large surface. Analysis of the relationship between SAM-derived fragmentation metrics and excavator digging time indicated that areas with smaller P50 and P80 values generally exhibited shorter digging times, consistent with the established understanding that finer fragmentation reduces excavation resistance and improves

Figure 4—Segmentation results on high-quality patches: original image (left), manual ground truth (centre), and SAM 2.1 output (right) for Patch 2 (top) and Patch 3 (bottom)

Figure 5—Segmentation results on low-quality patches: original image (left), manual ground truth (centre), and SAM 2.1 output (right) for Patch 0 (top) and Patch 16 (bottom) The Journal of the Southern African Institute of Mining and Metallurgy

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Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis Table 3 Comparative PSD error metrics: SAM 2.1 versus WipFrag

Method

Quality

MAE

MSE

RMSE

SAM 2.1

High

1.78

7.60

2.71

SAM 2.1

Low

4.65

39.58

6.15

WipFrag

High

19.72

687.90

25.79

WipFrag

Low

18.49

546.44

23.37

bucket fill factors (Kinyua et al., 2022). However, this observation was not supported by formal regression analysis (regression coefficients, p-values, and confidence intervals were not computed), and should therefore be interpreted as a preliminary qualitative indication rather than a statistically validated relationship. The absence of quantitative digging-time correlation statistics constitutes a limitation of Study 2. Future work should collect paired digging-time and PSD data with sufficient sample size to enable formal regression analysis and hypothesis testing.

Study 3: SAM 2.1 segmentation accuracy versus WipFrag Segment error metrics

Figure 6—Particle size distribution curves of SAM 2.1 (green), WipFrag (red), and ground truth (blue) on high-quality patches

Table 2 summarises the pixel-level segmentation performance of SAM 2.1 across high- and low-quality patches. On high-quality patches, SAM 2.1 achieved an average IoU of 0.7452 and Dice coefficient of 0.8535, indicating that approximately 74.5% of segmented areas aligned with ground truth, with over 85% overlap in detected regions. Recall values averaging 0.9247 confirmed that the model captured nearly all relevant rock fragments, while precision of 0.7937 indicated limited false-positive classification of background as rock. The visual comparison between the original images, manual ground truth, and SAM 2.1 segmentation output on the high-quality patches, is presented in Figure 4, confirming the close correspondence reflected in the quantitative metrics. Performance on low-quality patches declined substantially, with average IoU dropping to 0.4827 (a 35.2% reduction) and precision falling to 0.3845 (a 51.6% reduction). This degradation reflects the fundamental challenge of segmenting rock fragments in images with blurred textures, low contrast, and minimal visual differentiation between fragments and background. Over-

Figure 7—Full-scale particle size distribution curve from SAM 2.1 analysis showing cumulative percentage passing versus screen size for a complete post-blast drone image (14 628 fragments identified) 620

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The Journal of the Southern African Institute of Mining and Metallurgy

Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis Table 4

Summary of blast evaluation results from SAM 2.1 analysis (selected images) Image

D₁₀ (mm)

D₅₀ (mm)

D₈₀ (mm)

Passing ≤ 750 mm (%)

Boulders > 750 mm

73.10

140.13

212.41

99.80

53.48

106.33

186.53

99.46

60.57

133.39

213.00

99.47

48.87

106.40

188.38

99.78

50.97

115.33

195.11

99.88

53.19

113.19

190.67

99.91

62.14

131.59

213.82

99.11

62.17

138.43

230.88

99.55

Figure 8—Linear regression of maximum fragment diameter versus grizzly feeder clean-up time (R² = 0.618, n = 23)

segmentation of background areas was observed, consistent with SAM 2.1’s architecture as a general-purpose segmentation model without domain-specific training. The over-segmentation behaviour on low-quality patches is visually evident in Figure 5, where the SAM 2.1 output exhibits excessive classification of background areas as rock fragments compared to the ground truth. These findings note that foundation models, despite training on diverse large-scale datasets, face difficulties in specialised domains with ambiguous visual characteristics. The 35% – 52% performance degradation across metrics underscores the need for either fine-tuning SAM on mining-specific datasets or implementing preprocessing pipelines to enhance image quality before segmentation.

Particle size distribution error metrics and comparison with WipFrag Table 3 presents the PSD prediction error metrics for SAM 2.1 and WipFrag across both quality categories. On high-quality patches, SAM 2.1 achieved remarkably low error values (MAE = 1.78, MSE = 7.60, RMSE = 2.71), substantially outperforming WipFrag (MAE = 19.72, MSE = 687.90, RMSE = 25.79). SAM 2.1 reduced MAE by 91%, MSE by 99%, and RMSE by 90% compared to WipFrag on The Journal of the Southern African Institute of Mining and Metallurgy

Table 5

PSD evaluation at grizzly feeder (selected images) Image

P50 (cm)

P80 (cm)

Passing ≤ 75 cm (%)

19.91

31.28

97.87

24.22

40.00

92.97

25.64

51.90

92.54

17.94

28.27

97.93

17.77

25.35

98.88

17.89

26.60

99.44

these patches, corresponding to WipFrag errors being 11.1, 90.5, and 9.5 times larger than those of SAM 2.1, respectively. Even on low-quality patches, where SAM 2.1 performance degraded (MAE = 4.65, MSE = 39.58, RMSE = 6.15), it maintained substantial superiority over WipFrag (MAE = 18.49, MSE = 546.44, RMSE = 23.37), with error reductions of 75%, 93%, and 74% for MAE, MSE, and RMSE, respectively, corresponding to WipFrag errors being 4.0, 13.8, and 3.8 times larger. The superiority of SAM VOLUME 126

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Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis 2.1 over WipFrag is visually apparent in Figure 6, where the SAM 2.1 particle size distribution curve consistently approximates the ground truth more closely than the WipFrag curve across the full range of fragment sizes. Analysis of individual PSD percentiles revealed that SAM 2.1 excels at predicting median fragment sizes (D₅₀), with an average error of only 0.87 mm on high-quality patches. However, prediction accuracy was lower at the distribution extremes: D₁₀ error averaged 8.88 mm and D₈₀ error averaged 13.54 mm on high-quality patches. The processing time advantage of SAM 2.1 was equally compelling: an average of 2 minutes per patch compared to 30 minutes for WipFrag, representing a 15-fold efficiency improvement.

Full-scale blast evaluation and operational implications Deployment of SAM 2.1 across 24 drone images yielded comprehensive fragmentation data for operational assessment. Table 4 presents a representative subset of the blast evaluation results. Across all analysed blast areas, the proportion of material passing the 750 mm grizzly feeder aperture ranged from 99.11% to 99.91%, with boulder counts ranging from 12 to 86 per image. These results indicate highly effective blast designs, with minimal requirement for secondary breaking operations. Average fragment diameters ranged from 106.33 mm to 140.13 mm (D₅₀), with D₈₀ values between 186.53 mm and 216.28 mm. The relatively narrow distribution of D₅₀ values across different blast locations suggests consistent blast design application, while variations in D₁₀ (48.87 mm – 73.10 mm) reflect localised geological heterogeneity. Total fragment counts per image ranged from approximately 8 900 to 14 600, demonstrating SAM 2.1’s capacity to process complex scenes with thousands of individual objects. A representative full-scale particle size distribution curve generated from SAM 2.1 analysis of a complete drone image containing 14 628 identified fragments is presented in Figure 7. These fragmentation characteristics have direct implications for downstream operational efficiency. With P80 values consistently below 220 mm and over 99% of material passing the grizzly feeder, the current blast designs support uninterrupted material flow through the processing plant. The small fraction of boulders identified (typically 0.1% – 0.9% of total fragments) suggests that the analysed blasts produced limited oversize relative to the feeder aperture, while providing quantitative data for continuous improvement of blast designs. The integration of SAM-based PSD analysis into routine operational workflows enables proactive decision-making aligned with good mining practice principles and the broader paradigm of data intelligence for operational risk management in geometallurgy. By providing rapid, automated fragmentation assessment within minutes of drone image capture, mine planners can adjust excavation strategies, allocate secondary breaking resources, and communicate material characteristics to the processing plant in near real-time—a fundamental requirement of the mine-to-mill optimisation concept.

Clean-up time correlation at grizzly feeder SAM 2.1 segmentation of 23 CCTV images captured at the runof-mine bin grizzly feeder achieved an average stability score of 94.26%, demonstrating consistent performance in the challenging visual environment of the feeding station. The model successfully identified fragment boundaries in most images, though limitations were observed in cases involving visually complex structures, including occasional misclassification of the grizzly feeder steel bars 622

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as rock fragments and difficulty distinguishing individual fragments in densely packed fine material zones. Table 5 presents a subset of the PSD evaluation results for the grizzly feeder images. The analysis revealed P50 values ranging from 17.77 cm to 25.64 cm and P80 values from 25.35 cm to 51.90 cm across the 23 datasets. The cumulative percentage of material passing the 75 cm screen aperture ranged from 92.54% to 99.44%, indicating that the vast majority of blasted material could be processed by the grizzly feeder without intervention. However, the remaining 0.5% – 7.5% of material exceeding the screen aperture constituted the boulders responsible for operational delays requiring clean-up procedures. Linear regression analysis of maximum fragment diameter versus clean-up time yielded the equation y = 0.00004x - 0.0027 (where y is clean-up time in minutes and x is maximum fragment diameter in cm; slope = 0.00004 min/cm, intercept = −0.0027 min), with a coefficient of determination R² = 0.618. The authors acknowledge that R² alone does not establish statistical significance; p-values, confidence intervals, and residual diagnostics for the regression models were not computed in the original analysis. Access to the raw data was limited to the regression outputs reported in the aforementioned. Future work should include these inferential statistics to strengthen the basis of these findings. This indicates that the linear model explains 61.8% of the observed variability in clean-up time, indicating a moderately strong linear association between maximum fragment size and clean-up time at the grizzly feeder. The linear regression plot of maximum fragment diameter versus clean-up time is presented in Figure 8, illustrating the positive correlation and the scatter of the 23 data points around the regression line. The regression of average diameter of fragments exceeding 75 cm versus clean-up time produced the equation y = 0.00007x - 0.00531, with R² = 0.501. While moderately strong, the lower R² compared to the maximum diameter analysis suggests that the single largest fragment is a more reliable predictor of clean-up duration than the average size of oversized material. This finding has practical significance for operational risk management; preventing even a single extremely large boulder from reaching the grizzly feeder may be more effective for reducing clean-up time than achieving a general reduction in average boulder size. The combined findings from the PSD analysis and clean-up time correlation provide actionable intelligence for blast design optimisation as part of a data-driven risk management approach. Since more than 95% of material consistently passes the 75 cm screen, the current blast designs are broadly effective. However, the significant correlation between maximum fragment diameter and clean-up time demonstrates that targeted improvements in blast design to reduce the top-size of fragments could yield measurable reductions in grizzly feeder downtime and overall processing efficiency.

Conclusions This study presents the most comprehensive multi-method evaluation to date of the Segment Anything Model for post-blast rock fragmentation analysis in open-pit mining. Through four complementary investigations at an open-pit gold mine in Sumatra, Indonesia, the following principal conclusions are drawn. SAM-based methods consistently outperform commercial fragmentation analysis software across multiple accuracy metrics and operational scales. SAM 2.1 reduced PSD errors by 74% – 99% compared to WipFrag (with WipFrag errors being up to 90 times larger), while SAM Rock Fragmentation demonstrated comparable The Journal of the Southern African Institute of Mining and Metallurgy

Evaluation of the Segment Anything Model for post-blast rock fragmentation analysis or superior accuracy to Split Desktop, particularly when manual delineation subjectivity is considered. The influence of image quality on segmentation performance is substantial but predictable. SAM 2.1 segmentation metrics degraded by 35% – 52% between high- and low-quality image patches, with over-segmentation of background areas being the primary failure mode. This finding establishes clear guidelines for operational deployment; standardised drone flight parameters and consistent imaging conditions are prerequisites for reliable automated analysis. SAM-derived fragmentation metrics demonstrate operational validity through their correlation with excavator digging time and their alignment with Strayos outputs at field scale. The analysis of over 23 000 fragments across 24 drone images confirmed that more than 99% of blasted material met the 750 mm grizzly feeder specification, providing actionable intelligence for mine planning. The statistical analysis of SAM 2.1-derived fragmentation metrics at the grizzly feeder established a significant correlation between maximum fragment diameter and clean-up time (R² = 0.618), with a moderately strong relationship also observed for average boulder diameter above 75 cm (R² = 0.501). These findings quantitatively demonstrate that SAM-based fragmentation assessment may help anticipate downstream operational bottlenecks, enabling proactive blast design optimisation to minimise grizzly feeder downtime. The processing efficiency advantage of SAM (2 to 5 minutes per image versus 30 minutes for commercial software), combined with its open-source availability and zero licensing cost, positions it as a transformative data intelligence tool for routine fragmentation assessment. This efficiency enables the near-realtime feedback loop essential for mine-to-mill optimisation, operational risk management, and the integration of smart data into geometallurgical workflows. Future research should incorporate detailed blast-design parameters (burden, spacing, powder factor, rock-mass classification) to enable direct evaluation of the relationship between blast design and SAM-derived fragmentation metrics. Additional priorities include domain-specific fine-tuning of SAM using mining fragmentation datasets to improve performance on low-quality images, development of cloud-based processing pipelines to overcome local computational limitations, extension of the analysis to quantify the mathematical relationship between SAM-derived PSD and the full mineral processing chain efficiency, and longitudinal studies tracking the impact of SAM-informed blast design adjustments on overall mine productivity and cost reduction.

Acknowledgements The authors gratefully acknowledge the support of the mining contractor and the mine operator at the study site for providing access to operational data and field facilities. Special appreciation is extended to the drill and blast engineering team for their technical guidance. The authors also thank Mining Engineering Program, Faculty of Engineering, Universitas Syiah Kuala, for institutional support throughout this research.

Credit author statement I.N. Aslam: Overall project conceptualisation, project management, supervision, methodology, software, formal analysis, project funder. M.S. Perdana: Conceptualisation for project SAM 2.1 and WipFrag, deep learning analysis, and grizzly case, methodology, software, investigation, formal analysis, writing – original draft. H. Harisman: Supervision, writing – review and editing, formal analysis. The Journal of the Southern African Institute of Mining and Metallurgy

F. Aliva: Investigation for SplitDesktop, formal analysis, writing – original draft. N.A. Putri: Investigation for SAM 2.1, data curation, writing – original draft. R. Ayuni: Investigation for SAM 2.1, writing – review and editing.

References Data availability: The SAM 2.1 model and code are publicly available through Meta’s GitHub repository (https://github. com/facebookresearch/sam2). The SAM Rock Fragmentation tool is available at https://github.com/Yairama/SAMRock-Fragmentation. The drone imagery, CCTV images, segmentation masks, and clean-up time data used in this study are not publicly available due to confidentiality agreements with the mine operator. Requests for data access may be directed to the corresponding author. Girdner, K.K., Kemeny, J.M., Srikant, A., McGill, R. 2018. The split system for analyzing the size distribution of fragmented rock. In Measurement of Blast Fragmentation pp. 101-108. Routledge. Kinyua, E.M., Jianhua, Z., Kasomo, R.M., Mauti, D., Mwangangi, J. 2022. A review of the influence of blast fragmentation on downstream processing of metal ores. Minerals Engineering, vol. 186, pp. 107743. Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Dollár, P. 2023. October. Segment anything. In 2023 IEEE/CVF international conference on computer vision (ICCV) pp. 3992-4003. IEEE. Maerz, N.H., Palangio, T.C., Franklin, J.A. 2018. WipFrag image based granulometry system. Proceedings of the FRAGBLAST, 5, pp.91-99. Morocho, Y.C. 2023. Yairama/SAM-Rock-Fragmentation. GitHub Repository. https://github.com/Yairama/SAM-RockFragmentation [accessed 10 October 2024]. Nageshwaraniyer, S.S., Kim, K., Son, Y.J. 2018. A mine-to-mill economic analysis model and spectral imaging-based tracking system for a copper mine. Journal of the Southern African institute of mining and metallurgy, vol. 118, no. 1, pp.7–14. Nikkhah, A., Vakylabad, A.B., Hassanzadeh, A., Niedoba, T., Surowiak, A. 2022. An evaluation on the impact of ore fragmented by blasting on mining performance. Minerals, vol. 12, no. 2, pp.258. Ravi, N., Gabeur, V., Hu, Y.T., Hu, R., Ryali, C., Ma, T., Khedr, H., Rädle, R., Rolland, C., Gustafson, L., Mintun, E. 2025. May. Sam 2: Segment anything in images and videos. In International Conference on Learning Representations vol. 2025, pp. 28085–28128. Santos, D.R.S., Oliveira, M.R.P., Sampaio Filho, I.S., Paiva, R.S. 2020. Alimentação de rock boulders e não britáveis na britagem primária/rock boulders and non-crushable feed in primary crushing. Braz J Dev, vol. 6, no. 9, pp.73384–73399. Seccatore, J. 2019. A review of the benefits for comminution circuits offered by rock blasting. REM-International Engineering Journal, vol. 72, no. 1, pp.141–146. Shan, L., Liu, Y., Du, K., Paul, S., Zhang, X., Hei, X. 2024. Drilling rock image segmentation and analysis using segment anything model. Advances in Geo-Energy Research, 12(2), pp.89-101. Zhao, J., Li, D., Yu, Y. 2024. Identification of rock fragments after blasting by using deep learning-based segment anything model. Minerals, vol. 14, no. 7, pp.654. u VOLUME 126

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Data-driven delineation of geometallurgical domains in porphyry copper systems by T. Mapupu¹,², G.T. Nwaila¹,³

Affiliation: 1 School of Geosciences, University of the Witwatersrand, South Africa 2 Sibanye-Stillwater Digital Mining Laboratory (DigiMine), Wits Mining Institute (WMI), University of the Witwatersrand, South Africa 3 African Research Centre for Ore Systems Science (CORES), School of Geosciences, University of the Witwatersrand, South Africa Correspondence to: T. Mapupu Email: 1612442@students.wits.ac.za

Dates:

Received: 26 Mar. 2025 Revised: 29 Jun. 2025 Accepted: 30 Sep. 2026 Published: Oct. 2026 How to cite: Mapupu T., Nwaila G.T. 2026. Data-driven delineation of geometallurgical domains in porphyry copper systems. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 10, pp. 625 – 636 DOI ID: https://doi.org/10.17159/24119717/3943/2026 ORCiD: T. Mapupu https://orcid.org/0000-0001-5903-2056 G.T. Nwaila https://orcid.org/0000-0002-3974-9890

Abstract

Partitioning an orebody into spatially coherent domains supports stationarity and improves the reliability of resource estimates and metallurgical forecasts. Conventional domaining commonly relies on lithological or grade interpretations and can overlook subtler, yet economically important, variations in mineralogical, geochemical, and metallurgical attributes that affect recovery predictions. Unsupervised clustering offers a more data-driven means of defining such domains. This study presents a machine-learning workflow applied to a porphyry copper dataset comprising geochemical and processing variables. The variables were standardised and weighted using Bayesian optimisation to improve domain definition. Weighted dissimilarities were then represented in a lower-dimensional space using multidimensional scaling and clustered with the K-means algorithm to generate candidate domains. Validity indices guided domain selection, while principal component analysis within each domain assessed internal consistency and dominant compositional patterns. Cluster assignments were then mapped back into drillhole space and interpreted against established porphyry copper alteration zonation. The resulting domains show reduced internal variability while remaining geologically plausible and broadly consistent with known alteration patterns. Accurate delineation of such domains can improve geometallurgical modelling and operational planning because more consistent processing behaviour provides a clearer basis for anticipating milling response and recovery variability across the orebody. This reproducible workflow links orebody heterogeneity to processing behaviour and provides a practical, data-driven framework for domaining in complex deposits.

Keywords Geometallurgy, domaining, porphyry copper, unsupervised clustering, multidimensional scaling

Introduction Resource estimation and mine planning depend on the assumption that grade and geometallurgical variables behave consistently enough within a modelling population for variograms, kriging, and conditional simulation to produce stable results (Deutsch et al., 2016). This assumption rarely holds across an entire orebody because geological controls introduce spatial trends, overprinting, and multiple statistical populations that weaken the basis for direct global modelling. Operations require reasonably consistent plant feed to support stable throughput, energy efficiency, and predictable metallurgical recovery; however, orebodies are inherently heterogeneous. Domaining partitions the deposit into smaller populations within which stationarity is more defensible and each population can support its own continuity model (Martin, Boisvert, 2020). Conventional domaining largely relies on expert interpretation of lithology, alteration, and grade patterns. This conventional workflow remains widely used and often performs well where geological contacts are clear. Problems arise where geological structures change, and boundaries are transitional or where alteration assemblages overprint earlier mineralogical signatures. Data-driven domaining applies statistical and machine-learning methods to analyse patterns in orebody datasets and define domains that are more consistent with multivariate structure, spatial continuity, and processing response (Bhuiyan et al., 2019; Niquini et al., 2025). A persistent limitation of distance-based clustering on assay data is that variables with larger variance or scale can dominate similarity calculations, obscuring subtler yet geologically meaningful distinctions (Tsai, Chiu, 2008). Purely statistical clusters may also be difficult to reconcile with established alteration zonation and may therefore not be trusted by geologists and metallurgists responsible for operational decisions (Keeney, Walters, 2011; Montoya-Lopera, 2014).

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Data-driven delineation of geometallurgical domains in porphyry copper systems Geometallurgical domains must reflect processing response as well as spatial continuity. Comminution performance, flotation behaviour, and concentrate grade depend on attributes such as mineral assemblage and alteration, all of which vary through space and may change over short distances. Geometallurgical programmes therefore seek to integrate geological and processing data so that material behaviour can be predicted and used for operational control (Dominy et al., 2018; Lishchuk et al., 2020). Porphyry copper systems provide a suitable setting for testing such a workflow because they are large-tonnage systems with marked geological and metallurgical heterogeneity. This paper presents a data-driven workflow for delineating geometallurgical domains in a South American porphyry copper orebody using multi-element geochemistry and metallurgical testwork data. It shows that geochemical signatures can be used to define geologically plausible and operationally relevant domains for blending and geometallurgical forecasting, while also highlighting the value of spatial validation of clustering outputs in geoscientific applications.

typically chalcopyrite ± bornite ± molybdenite, with lesser amounts of pyrite. Outboard of, and locally overlapping with, the potassic domain is the phyllic zone, marked by pervasive quartz-sericitepyrite alteration. It is commonly sulphur-rich because of pyrite enrichment and may also host elevated Cu where phyllic overprints affect earlier potassic zones (Sillitoe, 2010; Large, 2025). The system grades outward into propylitic alteration, characterised by chlorite– epidote–calcite assemblages. Propylitic zones typically carry only trace amounts of chalcopyrite and more abundant pyrite before grading into weakly altered or unaltered country rock. Locally, especially near the palaeosurface, an argillic overprint with clay minerals such as kaolinite and smectite may develop, commonly linked to late-stage magmatic-hydrothermal to meteoric fluid interaction along geological structures (Seedorff et al., 2005; Sillitoe, 2010). Many porphyry deposits also exhibit a secondary supergene enrichment profile. Circulating meteoric waters can leach copper from near-surface rocks to form a leached cap enriched in iron oxides and reprecipitate it at depth as secondary chalcocite (Cu2S) – covellite (CuS) blankets (Sillitoe, 2010; Arabpour et al., 2021).

Geological context

Data and methods

Porphyry copper deposits are large hydrothermal systems that form mainly in magmatic arcs above subduction zones. They supply ~60% of global copper (Cu) and over 50% molybdenum (Mo), with gold (Au) and silver (Ag) commonly recovered as by-products (Sillitoe, 2010; John, Taylor, 2016). Mineralisation occurs as stockwork veinlets and disseminations of sulphide minerals distributed through large volumes of altered host rock. These systems may occupy tens to hundreds of cubic kilometres of the upper crust. Copper is primarily hosted in minerals such as chalcopyrite (CuFeS2) and bornite (Cu5FeS4) (Seedorff et al., 2005; Sillitoe, 2010). Porphyry Cu deposits are characterised by concentric alteration shells, classically described by Lowell and Guilbert’s (1970) model of outward zonation (Figure 1). At the core, proximal to the causative intrusion, is the potassic zone, characterised by secondary K-feldspar and biotite replacing primary minerals. The potassic core tends to host the highest-temperature mineralisation,

The study integrates three nested datasets extracted from a South American porphyry Cu mine. The base dataset comprises 2,000 drill-core samples with assays for 18 major and trace elements, which provide the multivariate geochemical input for geological domaining. Two smaller embedded subsets comprise 60 comminution-test samples and 53 flotation-test samples (Table 1 and Figure 2). The comminution subset includes Graphite C, which was not part of the base assay dataset. The assay, comminution, and flotation tables were imported and aligned to a common schema. No additional compositing was applied before clustering; the exported assay records were analysed as provided because intervallength fields were not retained in the raw dataset. Variable names and units were standardised, numeric types were enforced, duplicate records were removed, and missing numeric values were imputed using the column mean. The clustering inputs were elemental assay variables; oxide variables were not used. Feature engineering involved deriving the drop weight test (DWT) parameter and Bond work index (BWI) from the measured comminution variables (Table 1). Together, these indices describe complementary aspects of ore hardness. The DWT captures impact breakage behaviour and how the rock responds to sudden impact

Figure 1—Generalised alteration-zonation schematic of a porphyry copper (Cu) system, showing potassic, phyllic, propylitic, and advanced argillic alteration zones, pyrite shells, ore zone, and distal polymetallic mineralisation. Abbreviations: Qtz = quartz; Kaol = kaolinite; Chl = chlorite; Ep = epidote; Carb = carbonate; Adul = adularia; Ab = albite; Ser = sericite; Py = pyrite; Kfs = potassium feldspar; Bi = biotite; Cpy = chalcopyrite; Pb = lead; Zn = zinc; Au = gold; Ag = silver (adapted from Sillitoe, 2010) 626

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Figure 2—Spatial distribution of the raw combined porphyry Cu dataset, showing drillholes coloured by Cu, with comminution and flotation testwork samples overlain The Journal of the Southern African Institute of Mining and Metallurgy

Data-driven delineation of geometallurgical domains in porphyry copper systems in the mill. In the Julius Kruttschnitt drop weight test (JKDWT), the relationship between breakage response and specific energy is expressed as (Morrell, 2004; Mwanga et al., 2015):

Table 1

Attribute table for porphyry Cu database [1]

where t10 is the percentage passing one-tenth of the original particle size, and A and b are ore-dependent impact breakage parameters. Ej is the specific input energy at the three standard energy levels j=1,2,3 used in the test. The measured responses were first normalised as: [2] where thj is the measured breakage response at energy level j. The parameters A and b were then estimated for each sample by minimising:

Description

BHID

Borehole ID

Easting coordinate

Northing coordinate

Vertical coordinate

Geochemistry fields Ag, Al, Au, C, Ca, Cl, Cu, F, Fe, K, Mg, Mn, Na, P, Pb, S, Th, U

Whole-rock assay geochemistry

Graphite C

Graphitic carbon

Comminution fields

[3] The impact breakage parameter used in the analysis was then calculated as: [4] BWI explains the overall grindability and provides a measure of how much energy is needed to reach the target product size. BWI was computed from the laboratory variables as follows (Bond, 1961; Nikolić et al., 2024): [5] where F80 and P80 denote feed and product 80% passing sizes in μm, M denotes the grindability index in grammes per revolution, and Pc denotes the closing screen size in μm.

Multidimensional scaling and clustering Each variable was standardised using the StandardScaler transformation prior to clustering and assigned a continuous weight. These weights were tuned using Bayesian optimisation to maximise the mean silhouette score (Rousseeuw, 1987; Snoek et al., 2012). The rationale is that not all variables should contribute equally to similarity. Feature weighting is a well-established way to improve partitioning quality in high-dimensional clustering by emphasising discriminative variables while suppressing irrelevant or redundant features (Tsai, Chiu, 2008). The optimised weights were used to compute a weighted Euclidean dissimilarity matrix on the standardised variables, where pairwise distance was calculated as the square root of the weighted sum of squared variable differences. These dissimilarities were embedded into a two-dimensional representation using multidimensional scaling (MDS), which seeks a configuration that best preserves the original distance relationships in fewer dimensions (Hout et al., 2013). The variable weights were optimised with Optuna by assigning one continuous weight per variable and searching each weight over the interval 0.001–1.000 for 15 trials. A 15-trial optimisation was used as a computationally efficient tuning step, and the resulting partition was then checked using validity indices and reseeding runs. The optimisation objective was the mean silhouette score of K-means solutions in the MDS The Journal of the Southern African Institute of Mining and Metallurgy

Variable

th1, th2, th3

T10 values

F80

Feed size at 80% passing

P80

Product size at 80% passing

Grindability index

Closing screen size

Flotation fields fr

Rougher mass pull

Rougher concentrate grade

LCT

Locked-cycle test recovery

space. K-means clustering was then applied in the embedded space to delineate domains. The final number of clusters was selected by evaluating the silhouette score (Rousseeuw, 1987), Calinski– Harabasz (Caliński, Harabasz, 1974), and Davies–Bouldin (Davies, Bouldin, 1979) indices across the candidate K values. A stability test was performed by rerunning clustering 100 times with different random seeds and quantifying reassignment as a simple check on partition robustness (Hennig, 2007), as illustrated in Figure 3. Clusters were mapped spatially and interpreted against established porphyry alteration assemblages and zonation models. Principal component analysis (PCA) was used as an internal validation step to assess whether each domain was internally coherent and to identify the dominant elemental associations that define it. Processing-response domaining was then performed on the comminution and flotation subsets using the same unsupervised clustering workflow to identify internally similar metallurgical behaviours. Finally, the geological and metallurgical domains were combined into integrated geometallurgical domains that can be translated into ore-routing and blending rules.

Results and analysis Pearson correlation analysis of the whole-rock geochemistry revealed that Cu correlates positively with S and other

Figure 3—Data-driven geometallurgical domain delineation workflow VOLUME 126

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Data-driven delineation of geometallurgical domains in porphyry copper systems transitional metals such as Fe, Ag, and Au, consistent with copper mineralisation being hosted mainly in Cu–Fe sulphides such as chalcopyrite and bornite (Figure 4). Alkali and alkaline earth elements group together and oppose the chalcophile suite, reflecting alteration-related mass transfer across the system (Seedorff et al., 2005; Sillitoe, 2010). The spatial distribution of copper grades across drillholes, as per Figure 2, shows a coherent pattern of higher-grade intercepts surrounded by lower-grade zones, and the positions of comminution and flotation samples span this range. This supports the representativeness of the geology and testwork subsets for subsequent domain interpretation.

Geological domains Initial runs of the standard K-means algorithm on the geochemical data yielded some separation of the deposit but struggled to produce geologically meaningful clusters, as shown in Figure 5a. As a result, the initial K-means partition did not align well with the expected porphyry zonation because it merged the ore zone with parts of the alteration halo and produced only two domains. Applying MDS before clustering improved domain delineation. The transformation reduced the influence of variables that dominated the initial structure and allowed other geochemically informative features to contribute more effectively to domain separation (Figure 5b). Cu and S emerged as influential variables for domain separation, whereas Ca, Pb, and U were down-weighted. The MDSbased workflow delineated four geological domains, indicating that the transformed space captured the internal structure of the system more effectively than the standard approach. Clustering validity metrics confirmed the choice of four geological clusters. All three indices, namely the silhouette, Caliński–Harabasz (CH), and Davies–Bouldin (DB) scores, identified K = 4 as the optimal number of clusters (Figure 6). The mean silhouette coefficient peaks at four clusters, CH reaches a maximum at four, and DB reaches a minimum at four clusters. Beyond four clusters, the silhouette values decline, indicating that over-partitioning would create smaller or less coherent clusters. Likewise, fewer than four clusters would underfit the deposit’s complexity. The chosen four-cluster MDS–K-means model was tested for stability by rerunning the algorithm with different initial seeds; fewer than 5% of samples changed cluster assignment upon reseeding, indicating that the partition is stable. The four geological domains can be characterised as an outward-to-inward sequence of alteration facies, consistent with the

Figure 4—Pearson correlation matrix for the elemental assays 628

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classic porphyry Cu model. Domain 0 is enriched in Ca, Na, and Mg relative to the other clusters, while being comparatively depleted in Cu, S, and precious metals. Domain 0 has the lowest mean Cu (0.37 wt%), S (0.14 wt%) and Au-Ag of all clusters, but the highest combined Na+Ca content. It also contains elevated Th-U. Its PCA plot (Figure 7a) shows that Ca and Mg load heavily on the first principal component, whereas Cu and S form a minor component; this indicates that variation within domain 0 is dominated by plagioclase–epidote alteration mineralogy, with only minor introduction of sulphides. Figures 7b and c provide the average grades for each element, reinforcing the domain’s depletion in Cu–S. These traits collectively point to an outer propylitic alteration zone, which is the low-grade zone of the porphyry system. Domain 1 marks a gradational zone between the barren propylitic halo and the high-grade core. It is characterised by elevated Al (5.3 wt%) and Fe (22.5 wt%) relative to domain 0, together with moderate Cu (0.6 wt%) and S (~0.2 wt%). K is moderate (~1.2 wt%), while Ca and Na are strongly depleted compared to domain 0. This geochemical signature is consistent with a phyllic domain with possible argillic overprint. Plagioclase has been replaced by clay-sericite assemblages, and the moderate K content suggests that sericite is present, but less strongly developed than in a potassic zone (Seedorff et al., 2005; Sillitoe, 2010). The PCA for domain 1 (Figure 8a) shows a split: one subgroup trends toward the Cl-Na-K direction, whereas another subset trends toward the Cu-Fe-S vector, indicating incipient mineralisation. Domain 1 is therefore interpreted as a transitional argillic to early

Figure 5—Geological domains as classified by the a. standard K-means versus b. MDS K-means workflow The Journal of the Southern African Institute of Mining and Metallurgy

Data-driven delineation of geometallurgical domains in porphyry copper systems

Figure 6—Validity indices indicating the optimal clusters (K) for the dataset, comparing the standard and MDS K-means workflow

Figure 7—Elemental associations within domain 0 analysed by a. PCA and mean b. major and c. trace element statistics

phyllic zone, likely forming a shell around the core mineralisation. Domain 2 is the most mineralised cluster. It contains the highest average Cu (~1.1 wt%) and S (~0.4 wt%) of the four domains, as well as the highest average Au (0.8 ppm) and Ag (2.5 ppm). It is markedly low in Na and depleted in feldspar-related elements, consistent with intense hydrolytic alteration and a well-defined high-sulphide signature, likely with abundant chalcopyrite and pyrite. The PCA for domain 2 (Figure 9a) shows Cu, S, and Au The Journal of the Southern African Institute of Mining and Metallurgy

clustering together as the dominant component within this domain. Domain 2 therefore represents a sulphide-rich phyllic domain in the inner mineralised part of the orebody, rather than the potassic core itself. The abundance of sulphides in sericite-altered host rock explains its high Fe, Cu, and S, and likely contributes to its elevated Au (Sillitoe, 2010; Large, 2025). Domain 3 is characterised by elevated K (~1.6 wt% on average) and high Fe (~28 wt%), along with the second-highest Cu (~0.68 VOLUME 126

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Figure 8—Elemental associations within domain 1 analysed by a. PCA and mean b. major and c. trace element statistics

Figure 9—Elemental associations within domain 2 analysed by a. PCA and mean b. major and c. trace element statistics

wt%) and S (0.24 wt%) grades. These attributes point to a potassic alteration domain, generally associated with the inner part of the porphyry system. Potassic alteration introduces abundant K, and it can host substantial Cu mineralisation. Domain 3’s PCA (Figure 10a) reveals notable internal variability: one subset of samples loads strongly on Al, K, F, and Cl, indicative of biotite-rich potassic rock, while another subset trends toward the Cu-Fe-S direction, indicating that some domain 3 samples are transitional and share characteristics with the high-sulphide domain 2. Domain 3 therefore represents the potassic core of the system, likely including both strongly potassic-altered rock and potassic-phyllic transitional zones. It contains a substantial portion of the deposit’s copper resource, enveloped by domain 2 and grading outward to domains 1 and 0. In addition to the bulk assay dataset, the comminution and flotation testwork samples were examined in the context of these geological domains. The results confirmed that the testwork samples span the previously defined domains. One noteworthy observation is localised graphitic enrichment: a small cluster of comminution samples plots distinctly because of higher graphitic carbon content, with ~688 ppm graphite C and elevated Cu (~0.8 wt%) and Fe (~27 630

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wt%). This carbonaceous enrichment did not form a standalone geological domain in the deposit-scale clustering because it was identified only within the smaller comminution subset and did not show sufficient spatial continuity in the main geological domain model. It is, however, identifiable within the 60 comminution samples and may represent graphite-enriched material within the phyllic domain, highlighting the value of sub-domain analysis because even minor lithological variants can be isolated when focused subsets are examined.

Comminution domains Clustering was next applied to the comminution response variables to define comminution domains, thereby grouping samples with similar breakage and grinding behaviour. Using the MDS–K-means workflow on the comminution variables separated the 60 samples into two clear clusters. These two comminution domains can be described as “hard” and “soft” ore. Domain 0 (comminution) comprises 47 samples and has a notably higher BWI than domain 1 (comminution), indicating that domain 0 contains more competent rock that is harder to grind (Figure 11). The distribution of BWI values for domain 0 averages 20.6 kWh/t with a standard deviation The Journal of the Southern African Institute of Mining and Metallurgy

Data-driven delineation of geometallurgical domains in porphyry copper systems

Figure 10—Elemental associations within domain 3 analysed by a. PCA and mean b. major and c. trace element statistics

Figure 11—Metallurgical domains for comminution, showing the a. MDS K-means clusters, and a comparison of the b. BWI and c. DWT statistics per domain

(SD) of 2.6 kWh/t; this is about 1.5 kWh/t higher than domain 1, which averages 19.1 kWh/t (SD 1.4 kWh/t). Domain 0 samples also have lower DWT values, indicating harder, more competent rock. Domain 1, by contrast, shows greater breakage response and lower BWI. The assigned comminution domains correlate strongly with the geological domains defined earlier. The harder domain 0 (comminution) is dominated by samples from the propylitic, transitional argillic-phyllic, and potassic geological domains. In contrast, the softer domain 1 (comminution) draws mainly from the high-sulphide phyllic domain and samples characterised by graphitic enrichment.

Flotation domains The same unsupervised approach was applied to the flotation testwork data. This yielded two distinct flotation response domains (Figure 12). Domain 0 (flotation) comprises 35 samples and represents the higher-performance class, with an LCT recovery of ~88.7% (SD 4.4%), compared with ~86.3% (SD 6.5%) for domain 1. Domain 1 material comprises 18 samples and has a slightly higher rougher mass pull (Fr ~25.7% vs. 24.3%), meaning that more The Journal of the Southern African Institute of Mining and Metallurgy

material is floated in the first stage for domain 1 samples. The lower recoveries in domain 1 suggest the presence of flotation-penalty material, most plausibly clays or graphitic carbon. Cross-checking the geological compositions confirms that domain 1 (flotation) corresponds mostly to the high-sulphide phyllic and carbonaceous samples, whereas domain 0 (flotation) corresponds mainly to cleaner ore from the propylitic, transitional argillic-phyllic, and potassic zones. Domain 1 therefore captures penalty ore that yields sub-optimal metallurgical response. Domain 0, by contrast, represents ore that floats more efficiently and produces higher recovery. Identifying these two flotation domains early is important because a ~2.4% difference in Cu recoveries is substantial in a production context. Operators can anticipate lower recoveries when a larger proportion of domain 1 material is fed and can thus adjust reagent schemes or blending accordingly (Morales et al., 2019; Saavedra et al., 2025).

Geometallurgical domains Combining the geological and metallurgical perspectives, geometallurgical domains can be defined for the deposit (Table 2 VOLUME 126

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Figure 12—Metallurgical domains for flotation, showing the a. MDS K-means clusters, and a comparison of the b. LCT recovery per domain

Table 2 Geo

metallurgical domains Geomet domain I – hard/high recovery

II – soft/low recovery

Geological domain

Facies

Net processing behaviour

Argillic phyllic

Sericite-clay halo

Breakage similar to propylitic rock, acceptable Cu recovery.

Potassic

K silicate core

Hard rock, reliable Cu recovery; useful for blending.

Propylitic

Outer halo

Harder to break; Cu grades too low to influence plant performance.

Graphitic enrichment

Carbonaceous lens

Very soft; can dilute the concentrate grade.

Phyllic

High sulphide core

Breaks easily but loses Cu during flotation.

and Figure 13). The findings indicate two main geometallurgical domains that capture the contrast in processing behaviour: (I) a hard, high-recovery domain and (II) a soft, lower-recovery domain. The hard, high-recovery domain comprises rocks from the propylitic, potassic, and transitional argillic-phyllic portions of the orebody; these are relatively competent, with higher BWI and less deleterious carbonaceous or clay-rich materials. Although they may require more grinding energy, they deliver better flotation performance and therefore higher Cu recovery. The soft, lowerrecovery domain corresponds to the high-sulphide phyllic zones and associated graphitic enrichment. This material grinds more easily, often supporting higher mill throughput, but tends to suffer roughly 2% lower Cu recovery. In effect, domain 1 classes in comminution and flotation broadly represent the same altered and carbonaceous material population. The complementary set of samples, domain 0 in both comminution and flotation, corresponds to cleaner lithologies that, despite being harder to grind, yield better recoveries.

Discussion Clustering analysis identified distinct geochemical populations in 632

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the porphyry Cu dataset, and the domaining workflow refined these into operationally interpretable classes. Four geological domains were delineated from assay data, reflecting the natural alteration zoning of the porphyry system without manual facies labelling during clustering. This indicates that multi-element geochemistry contains sufficient signal to delineate alteration-related material classes objectively when clustering data is stabilised and made less sensitive to the dominance of high-variance variables. The geological domains were found to drive differences in comminution hardness and flotation response. The comminution results show that phyllic and carbonaceous material is generally softer than propylitic and potassic material. The magnitude of the observed BWI separation should not be interpreted as trivial, because even a moderate difference in hardness can translate into operational consequences. A 1.5 kWh/t gap corresponds to roughly 10% variation in SAG-mill throughput at constant power, consistent with operational observations (Keeney, Walters, 2011). The flotation results similarly align with established mechanisms, as potassic alteration is commonly associated with harder rock and comparatively cleaner copper mineralogy, whereas clay-rich alteration is typically softer but more deleterious to The Journal of the Southern African Institute of Mining and Metallurgy

Data-driven delineation of geometallurgical domains in porphyry copper systems

Figure 13—Final geometallurgical domains for the porphyry Cu database

flotation (Sillitoe, 2010; Yildirim et al., 2014). Higher rougher mass pull paired with reduced LCT recovery is consistent with increased entrainment and reduced selectivity, especially when fine gangue and clay minerals are present. Clay minerals reduce flotation performance through rheology effects, slime coatings, and altered froth behaviour, and this has been demonstrated in copper ore deposits (Uribe et al., 2016; Jeldres et al., 2019; Taner, Onen, 2021; Pan et al., 2022). At 100,000 t/d and 0.5% Cu feed, a 2% recovery deficit would equate to the loss of approximately 10 t Cu per day; depending on copper price and operating assumptions, this could translate into a substantial annual revenue penalty. Explicit flagging of such material is therefore warranted, even where it does not form a deposit-scale geological cluster.

Machine learning for geoscientific data Equal-weight clustering assumes that all variables contribute equally to meaningful similarity. This assumption is rarely valid because geoscientific data differ in variance, scale, and measurement distribution. In this study, Bayesian optimisation efficiently tuned continuous weights under a defined objective function, avoiding manual trial-and-error and using a well-established framework for optimisation in complex parameter spaces (Tsai, Chiu, 2008; Snoek et al., 2012). Clustering outputs mapped back into drillhole space allow the resulting domains to be assessed for geological plausibility, reducing the risk of retaining purely statistical classes that are difficult to model and mine (Martin, Boisvert, 2020; Mohammadi et al., 2022). MDS helps visualise domain structure in a form that can be communicated across geoscience and processing teams. Stable domains, reflected here by low reassignment under reseeding, matter because unstable material codes undermine forecasting and reconciliation. A persistent constraint in geometallurgy is that metallurgical testwork is costly and sparse relative to drillhole geochemistry. Data-driven techniques can help address this by using more abundant proxy information, such as geochemistry, drilling-derived variables, or lithological classes, to define or predict processing-related classes and then calibrate them against limited comminution and flotation measurements (Van Duijvenbode et al., 2022; Niquini et al., 2025).

Operational use The Journal of the Southern African Institute of Mining and Metallurgy

The integrated geometallurgical domains make the relationship between throughput and recovery explicit in a spatial model. Such a model can be used to forecast periods of throughput increase or recovery penalty, design blending strategies that limit variability, and adjust reagent schemes when lower-quality material is scheduled (Dominy et al., 2018; Morales et al., 2019; Lishchuk et al., 2020). Geometallurgical models are specifically used to link ore variability to production planning and process response, while blending is widely used to manage feed variability and stabilise downstream performance. The results suggest three feed categories for planning. First, propylitic and potassic domains form a hard, competent ore that requires more grinding energy yet still yields high Cu recovery once processed. Second, the argillic-phyllic material falls in the mid-range for both hardness and recovery and is therefore well suited to smoothing daily variation in mill load and metal output (Both, Dimitrakopoulos, 2021). Third, high-S phyllic and graphite-enriched intervals form a softer feed that produces a lower-grade concentrate. Blending this softer, lower-recovery ore with the more competent, higher-recovery feed can help stabilise SAG-mill throughput and sustain overall copper production (JKTech, 2024). The domain model (Figure 13) therefore provides a clear, geologically grounded basis for ore scheduling and process optimisation.

Conclusion The data-driven workflow combining multidimensional scaling and K-means clustering delineated four spatially coherent geological domains in a porphyry copper deposit. These were then consolidated into two geometallurgical groupings representing hard, higher-recovery material and soft, lower-recovery material, which provide a practical basis for feed classification and planning. The domains align with established alteration zonation and explain differences in comminution and flotation response, demonstrating a direct link between orebody heterogeneity and plant behaviour. Even moderate differences in hardness (~1.5 kWh/t) and recovery (~2%) are operationally meaningful because they influence grinding energy, throughput, and metal recovery. The resulting framework supports scheduling, blending, and recovery planning while reducing reliance on subjective domaining decisions. The workflow VOLUME 126

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Data-driven delineation of geometallurgical domains in porphyry copper systems uses standard geoscientific databases and open-source software, making it readily transferable to other porphyry copper deposits where heterogeneity and non-stationarity complicate resource modelling and process forecasting.

Acknowledgements This work was funded by the Sibanye-Stillwater Digital Mining Laboratory (DigiMine), hosted at the Wits Mining Institute at the University of the Witwatersrand. Additional support was provided by the Julian Baring Scholarship Fund.

CRediT author statement TM: Conceptualisation, methodology, writing – original draft, visualisation, validation; GN: Writing – review and editing, methodology, conceptualisation, supervision, resources, funding acquisition.

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Drilling and Blasting Drilling and Blasting Short Course 2027 Data-driven delineation of geometallurgical domains in porphyry copper systems Short Course 2027

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PRESENTER Simon Tose Simon Tose is an expert in Blast Design from AECI Mining Explosives and willTose be sharing his expertise on this subject. Simon Simon Tose is an expert in Blast Design from AECI Mining Explosives and will be sharing his expertise on this subject.

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For further information contact: Gugu Charlie, Conferences and Events Coordinator E-mail: gugu@saimm.co.za For further information contact: Tel: 011 538 0238 Web: www.saimm.co.za Gugu Charlie, Conferences and Events Coordinator E-mail: gugu@saimm.co.za Tel: 011 OCTOBER 538 0238 636 2026 Web: www.saimm.co.za VOLUME 126

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Integrating diamond breakage into existing kimberlite geometallurgical frameworks: A perspective by S. Burness1, S. Bremner1, M. Becker1, G.H. Howarth², A.N. Mainza1 Affiliation: 1 Centre for Minerals Research, Department of Chemical Engineering, University of Cape Town, South Africa 2 Department of Geological Sciences, University of Cape Town, South Africa Correspondence to: S. Burness Email: sara.burness@uct.ac.za

Dates:

Received: 31 Mar. 2025 Revised: 05 Aug. 2025 Accepted: 02 Oct. 2026 Published: Oct. 2026 How to cite: Burness S., Bremner S., Becker M., Howarth, Mainza A.N. 2026.Integrating diamond breakage into existing kimberlite geometallurgical frameworks: A perspective. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 10, pp. 637 – 646 DOI ID: https://doi.org/10.17159/2411-9717/3945/2026 ORCiD: S. Burness https://orcid.org/0000-0003-0542-1480 S. Bremner https://orcid.org/0000-0002-6460-5434 M. Becker https://orcid.org/0000-0002-7025-137X G.H. Howarth https://orcid.org/0000-0001-5008-2399 A.N. Mainza https://orcid.org/0000-0002-9802-2720

Abstract

Global natural diamond production is in decline, with several major mines nearing depletion and few new Tier 1 kimberlite deposits being identified. As supply tightens, every recovered carat becomes more economically significant, particularly for larger high‑value stones. In this context, reducing diamond breakage during processing has become essential for sustaining value in modern kimberlite operations. Previous research indicates that the mechanical behaviour of natural diamonds is influenced by a range of intrinsic characteristics, including size, morphology, propensity for cleavage, inclusion size and abundance, and internal strain. These features influence the distribution of stress within the diamond and can affect the initiation and propagation of fractures during comminution. However, many of these characteristics are difficult to quantify systematically, particularly at the scale required for geometallurgical applications. The destructive nature and cost of mechanical testing further limit the extent to which diamond breakage behaviour can be directly characterised. Consequently, diamondspecific characteristics have received relatively limited consideration within conventional kimberlite geometallurgical approaches. This paper provides a perspective on the current understanding of diamond breakage in kimberlite processing, with consideration of the diamond characteristics that may influence mechanical response and the approaches available for investigating them. Controlled breakage studies using diamond simulants provide insights into energy thresholds, breakage probability, and fragmentation behaviour, including through approaches such as the T10 model, Fmat and progeny size distributions. In parallel, studies of natural diamonds provide evidence for the potential influence of characteristics such as morphology, cleavage expression, inclusion distribution, and internal features on breakage behaviour. Work currently being undertaken at the University of Cape Town provides a practical context for considering these approaches and their application to kimberlite geometallurgy. Together, the literature and these experimental proxies suggest that greater consideration of diamond-specific characteristics with warranted when assessing breakage and value loss during kimberlite processing. However, their relative influence under operational conditions remains poorly constrained, highlighting opportunities for further investigation.

Keywords diamond breakage, geometallurgy, impact energy, kimberlite processing, natural diamond properties, simulant testing

The evolving diamond sector in Africa Southern Africa has played a defining role in the development of the modern diamond industry. Following the discovery of alluvial diamonds along the Orange and Vaal rivers in the late nineteenth century, the recognition of diamond-bearing kimberlite established a primary source for diamond production and underpinned the development of the South African diamond industry (Janse, 1995). More than a century later, Africa remains central to global natural diamond production, although the distribution of production across the continent is changing. In 2025, Botswana and Angola produced 15.50 and 15.20 million carats, respectively, while South Africa produced 5.57 million carats (Kimberley Process, 2026). Angolan production increased from 9.75 million carats in 2023 to 15.20 million carats in 2025, while South African production has steadily declined from 9.72 million carats in 2021 to 5.57 million carats in 2025. These changes form part of a broader contraction in global mined diamond supply, which fell to 98.8 million carats in 2025, its lowest level in the Kimberley Process record since

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Integrating diamond breakage into existing kimberlite geometallurgical frameworks: 2015 (Kimberley Process, 2026). This contraction reflects both declining production from mature mining operations and the closure of major mines, including Australia’s Argyle mine, which ceased production in 2020 (Rio Tinto, 2020). Also, the natural diamond sector is facing increasing competition from laboratorygrown diamonds produced by high-pressure high-temperature (HPHT) and chemical vapour deposition (CVD) methods, of which the growing market presence has contributed to changing consumer demand and pricing dynamics (Natural Resources Canada, 2026; De Beers, 2026). Together, these supply- and market-side changes are increasing the importance of preserving the size, quality and value of diamonds recovered from existing natural-diamond operations. As production becomes increasingly constrained, the preservation of diamond value throughout the mining and processing chain becomes increasingly important. This is particularly relevant where deposits contain a significant proportion of large, high-value diamonds, such as those recovered from the Lulo area of Angola, where exceptionally large and premium Type IIa diamonds have been documented (Price et al., 2024). The industry has responded in part through changes to processing technology and flowsheet design intended to reduce the exposure of liberated diamonds to processing conditions that may promote breakage. X-ray transmission (XRT) recovery, for example, can be incorporated into diamond-processing circuits to identify and recover liberated large diamonds before further handling, pumping and crushing, thereby reducing their exposure to downstream processes that may cause fracture or breakage (Van Niekerk et al., 2016; Voigt et al., 2019; De Hager, Houghton, 2024). These developments reflect an increasing emphasis on not simply recovering diamonds but recovering them in a condition that preserves their size, quality, and value. At the same time, geometallurgy has developed as a means of accounting for the spatial variability of ore and its influence on processing performance. Rather than treating an orebody as a homogeneous feed, geometallurgical approaches integrate geological and material properties with process-response models to identify domains with different processing behaviour (Lechuti-Tlhalerwa

et al., 2019). In kimberlite, these properties can include rock type; rock-mass and strength characteristics such as rock quality designation (RQD), which describes the degree of fracturing and quality of the rock mass, and uniaxial compressive strength (UCS), which measures the strength of intact rock, as well as comminution parameters derived from drop-weight testing, including specific energy (t10), which describes the proportion of fragments generated below one-tenth of the original particle size, and A × b, a parameter used to characterise resistance to impact breakage. Chemical assays, grade, alteration, mineralogy, and other in situ characteristics may also be incorporated to capture spatial variability (LechutiTlhalerwa et al., 2018). These properties are spatially characterised to define domains with distinct processing responses, which can then inform mine planning and plant optimisation. At Orapa, for example, geometallurgical modelling has incorporated ore hardness (UCS), smectite clay content and DMS yield to identify treatability challenges and inform ore blending, with quantitative simulation subsequently used to evaluate throughput, equipment effectiveness, and power and water consumption, as illustrated in Figure 1 (Gilika, Van der Westhuyzen, 2023). Diamond populations are already fundamental to kimberlite evaluation and resource characterisation. Indicator minerals, particularly mantle-derived garnets, have long been used to assess diamond prospectivity and mantle source characteristics (Gurney, 1984; Grütter et al., 2004), while microdiamond and macrodiamond data provide essential information on diamond populations and their size distributions. Size-frequency distributions (SFD) are routinely used to characterise diamond populations and support assessment of diamond grade and value, while recognising the limitations associated with sampling, recovery, and the relationship between microdiamond and macrodiamond populations (Chapman, Boxer, 2004). The challenge is therefore not to establish the importance of diamond populations or breakage in kimberlite evaluation, but to integrate this knowledge within a geometallurgical framework that already accounts for the spatial variability of the kimberlite host rock and the processing conditions imposed by the comminution circuit. Such integration could

Figure 1—Cross section of the A/K1 (Orapa) resource showing geometallurgical heat maps of (a) ore hardness (UCD), (b) smectite clay content, and (c) DMS yield from Gilika and Van der Westhuyzen, 2023. 638

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Integrating diamond breakage into existing kimberlite geometallurgical frameworks: extend existing geometallurgical approaches by identifying where combinations of host-rock properties and processing conditions may increase the risk of diamond fracture or breakage, and how this may ultimately influence the recovered diamond population and associated value.

Towards a geometallurgical framework for predicting diamond breakage Simplistically, a geometallurgical framework begins with numerical or categorical input features (primary variables), which can be quantified and grouped into geologically meaningful categories (domains), to model measurable outcomes (predicted responses or targets) that inform objectives such as metallurgical performance or recovery goals. Developing such a framework for predicting diamond recovery and breakage requires incorporating diamondspecific features with measurable strength and fracture properties of diamond, alongside more traditional host-rock properties, e.g., kimberlite facies or units, country rock dilution, density etc. The strength and fracture properties of natural diamonds are inherently difficult to measure, which may explain why these constraints are often omitted from existing geometallurgical models. Unlike most ore samples, where size, shape, and purity can be reasonably constrained, diamond crystals are typically small, with considerable range in shape, and can often contain internal strain; but more importantly, diamonds are not uniform or fungible. Assessing impurities and defects generally requires detailed visual investigations observed by trained individuals, which are often siloed from studies of physical responses during recovery. In addition, strength testing is costly, as it commonly involves damaging or destroying the diamonds. Despite these challenges, several key diamond characteristics and their influence on how diamonds respond to applied stresses can be identified. In some cases, the literature provides reasonably well-constrained numerical values for specific responses, while in others significant uncertainty remains, highlighting the need for further targeted research. This paper brings together the principal features and response variables controlling natural diamond breakage and identifies opportunities for further proxy testing to refine our understanding of breakage during kimberlite processing to inform geometallurgical modelling.

Fundamental diamond traits affecting resource estimation and fracture susceptibility Diamond grade and its spatial distribution are primary parameters in geometallurgical models, as they underpin both resource estimation (Bush, 2010) and downstream processing decisions. Microdiamonds, recovered from small (5 kg – 20 kg) drill core samples using caustic fusion or acid dissolution, provide highresolution data on stone counts per kilogram and enable detailed mapping of grade variability across a kimberlite at relatively low cost. In contrast, macrodiamonds, obtained from large-diameter drilling or bulk sampling, and recovered through crushing, dense media separation (diamond density = 3.52 g/cm3), X-ray recovery, and/or grease tables, provide measurements of grade in carats per tonne and an indication of the average diamond value. Geological models rely on calibrating microdiamond abundances to macrodiamond grades under the assumption that the sizefrequency distribution remains consistent within defined geological domains (Rombouts, 1995; Danoczi, Creighton, 2016; Stiefenhofer et al., 2016; Webb et al., 2024). However, this relationship may introduce uncertainty, as microdiamonds may not fully represent The Journal of the Southern African Institute of Mining and Metallurgy

the larger diamond population due to resorption effects and the underrepresentation of rare morphologies, while sampling bias, recovery efficiency, plant configuration, and data quality may further influence the comparability of the datasets and should therefore be considered when integrating them (Nowicki et al., 2018; Stiefenhofer et al., 2016; Haggerty et al., 2019). Diamond size (expressed in millimetres or carats) and the associated size-frequency distribution (SFD), typically presented as histograms or cumulative curves, describe the relative abundance of diamonds across different size classes (Sasman et al., 2018; Chapman, 2024). Diamonds generally follow a lognormal SFD (Rombouts, 1995), characterised by a high proportion of small diamonds and a progressively decreasing number of larger diamonds. Revenue can be highly influenced by the larger diamonds, particularly if these are of good quality. Size exerts a strong control on fracture behaviour and breakage susceptibility. Larger diamonds can act as stress concentrators within the host rock, increasing the likelihood of fragmentation during volcanic emplacement or blasting (Field, 1992; Fowler et al., 2001), and they experience higher absolute stresses during crushing and handling, making them more prone to breakage, particularly for diamonds greater than ~10 mm to 12 mm (Robb, 2005; Lusinga, 2023). In contrast, smaller diamonds (< 5 mm) are less likely to induce significant fractures in the surrounding rock, are generally liberated more efficiently with minimal damage, and contribute less to stress localisation within the kimberlite. While size is an important control on fracture behaviour and breakage susceptibility, it does not act in isolation, with factors such as diamond quality, internal defects, incomplete liberation, contact geometry, and processing conditions also contributing to breakage behaviour, as discussed below. A natural diamond has a Mohs hardness of 10, the highest on this relative scale, reflecting the exceptional strength of its covalently bonded carbon atoms and its resistance to scratching. As a more mechanically informative measure, Knoop hardness (Hk) measures a material’s resistance to permanent, localised deformation, typically determined by pressing an indenter of known geometry into the crystal surface under a specified load. It reflects the difficulty of scratching or denting a material and is also closely linked to atomic bond strength (Chaudhri, 2020). Diamond hardness varies with crystallographic orientation and structural characteristics (Brookes, 1979). Diamond hardness is anisotropic, meaning hardness depends on the direction of the applied load, with higher values generally observed along the {111} planes. In engineering measurements, Knoop hardness values for single-crystal diamonds typically range from ~70 GPa to 130 GPa, with variability arising from orientation, testing conditions, and defect density (Evans, Phaal, 1962; Sumiya et al., 2023; Chaudhri, 2020). Twinned or mechanically deformed diamonds may exhibit locally reduced hardness due to lattice distortions. Diamond type, defined by nitrogen and boron impurity contents and the aggregation state of the former, provides additional insight into thermal history and defect structure, and can influence mechanical behaviour. While intrinsic hardness is broadly similar across diamond types, differences in defect populations and internal strain may affect fracture susceptibility: In Type Ia diamonds, nitrogen aggregation and platelet development may contribute to fracture susceptibility; however, Type Ia should not be treated as a homogeneous population, as the nitrogen aggregation state differs substantially between IaA and IaB diamonds; In Type Ib diamonds, isolated nitrogen defects and limited annealing may similarly VOLUME 126

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Integrating diamond breakage into existing kimberlite geometallurgical frameworks: influence mechanical response, although their extreme rarity in natural diamond populations limits their practical relevance; Type IIa diamonds may be less susceptible to fracture owing to their low impurity content and relatively well-ordered lattice, although their more perfect cleavage may offset this effect, particularly in larger stones, leaving the net influence on fracture susceptibility uncertain. In Type IIb diamonds, boron-related lattice distortion may also modify fracture behaviour (Evans, Phaal, 1962; Sumiya et al., 2023). These interpretations are based on the anticipated effects of physical defect structures, rather than direct evidence of relative fracture susceptibility, and must be tested against systematic diamond breakage data. Laboratory-grown diamond studies provide some supporting evidence, with lower nitrogen concentrations associated with higher tensile strength, hardness, and abrasion resistance (Chrenko, Strong, 1975), although, whether these relationships translate to breakage behaviour in natural diamonds remains to be established. Crystal morphology, including crystal form (Figure 2), aspect ratio, and degree of resorption (Figure 3), exerts a key control on diamond fracture behaviour. Irregular geometries, elongate or thin crystal sections, and sharp edges act as sites of stress concentration and preferential crack initiation. Although quantitative thresholds

for these effects are not well constrained in natural diamonds, fracture mechanics principles indicate that sharp features can locally amplify stress by several times the applied load, promoting crack nucleation, particularly in a material with low fracture toughness (brittle behaviour) such as diamond (K₁c ~3 MPa m1/2 – 5 MPa m1/2) (Field, 2012). Natural cleavage planes in diamond also strongly influence breakage behaviour, with the {111} planes dominating due to their significantly lower surface energy (~10.6 J m-²) compared to {110} (~13 J m-²) and {100} (~18.4 J m-²), resulting in preferential fracture along these orientations (Telling et al., 2000). Internal structural features including dislocations, microcracks, healed or annealed fractures, and lattice strain represent pre-existing weaknesses that further facilitate crack propagation. These defects commonly develop during diamond growth, mantle residence, and subsequent transport, reinforcing their importance as intrinsic controls on breakage. However, the abundance, distribution, and combined effects of these factors remain poorly constrained in the literature. The type, abundance, and spatial distribution of mineral inclusions strongly influence diamond mechanics, as elastic mismatches at the inclusion–diamond interface can amplify local stresses and promote crack initiation (Puhan et al., 2025; Smith et al., 2022; Wirth et al., 2009). The most common solid inclusions are sulphides and mantle-derived silicate minerals, such as olivine, garnet, and pyroxenes, which define the principal paragenetic groups (Harris et al., 1979; Stachel et al., 2022). Metallic inclusions,

Figure 4—Three orientations of the same octahedral diamond showing a centrally located sulphide inclusion. The sulphide inclusion is surrounded by a rosette fracture system formed during differential expansion between the sulphide and the diamond during ascent to the surface. Figure 2—Octahedral and cubic forms are the fundamental crystal habits of single-crystal diamonds, with rhombic dodecahedral forms occurring less commonly. Miller indices indicate the principal crystal faces. Preferential growth can produce distorted, elongated, or flattened forms. Aggregates and twins comprise two or more intergrown crystals; aggregates contain randomly oriented crystals, whereas twins exhibit defined crystallographic relationships, such as macles with a Σ3 twin on the {111} plane. Redrawn and adapted from Tappert and Tappert (2011).

Figure 3—Evolution of diamond morphology during resorption, showing the transition from octahedral forms to tetrahexahedral (THH) or dodecahedral habits through modification of the {111} faces (taken from Fedortchouk, 2019). 640

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Diamond fracture and mechanical response to stress The mechanical response of diamonds to applied forces is a key target variable in geometallurgical modelling, as it governs how diamonds behave under typical blasting, mining, and comminution conditions. During these processes, diamonds may experience compressive, tensile, and shear stresses, with impact loading particularly important during comminution. Their response is described by the stress–strain relationship: at low stresses, diamonds respond elastically, accommodating applied loads through reversible deformation without permanent deformation. This elastic behaviour is characterised by Young’s modulus (E), which quantifies material stiffness and describes the relationship between applied stress and elastic strain. For natural diamond, Young’s modulus typically ranges from ~1050 GPa to 1200 GPa, depending on crystallographic orientation (Klein, 1992; Field, 2012). Young’s modulus therefore describes the stiffness of the diamond lattice under elastic loading but does not by itself define the stress at which irreversible deformation or fracture occurs. The latter is governed by the diamond’s strength and fracture behaviour, which are influenced by crystallographic orientation, defects, internal strain, and preexisting flaws. The Journal of the Southern African Institute of Mining and Metallurgy

Integrating diamond breakage into existing kimberlite geometallurgical frameworks: including Fe–Ni alloys, are comparatively rare and typically occur in superdeep diamonds formed under highly reducing conditions in the transition zone or lower mantle (Smith et al., 2016). Although only a small fraction of diamonds contains recognisable mineral inclusions (~1%), sulphides dominate this subset, providing critical insights into diamond formation ages, mantle sources, and deepEarth processes (Smit et al., 2022; Stachel et al., 2022). Despite their prevalence and geological significance, the effect of sulphide inclusions (Figure 4) on diamond fracture behaviour remains poorly understood. Diamond exhibits an exceptionally high resistance to mechanical failure, although its response is strongly influenced by crystallographic orientation, defects and other sources of structural weakness. The ideal compressive strength calculated for diamond is highly orientation dependent, with first-principles calculations yielding values of approximately 223 GPa – 470 GPa along the (100), (110) and (111) directions (Luo et al., 2010). Under tensile loading, the stress required to initiate fracture in diamond is approximately 3.75 GPa (Field, 1992), compared with 0.2 GPa – 0.8 GPa reported for olivine (Roedder, 1984; Johnson, Jenkins, 1991; Brett, 2009), illustrating the substantially greater resistance of diamond to crack initiation. However, natural diamonds are not defect-free, and impurities, inclusions, surface damage, internal stress, crystallographic orientation, and cleavage can influence the initiation and subsequent propagation of fractures. Consequently, the breakage behaviour of a natural diamond during comminution cannot be inferred from its intrinsic strength alone. Once a crack is initiated, its propagation is governed by the fracture properties of the material, including the energy required to create new fracture surfaces. The energy required to generate a unit area of new crack surface has been reported as approximately 5.5 J m-² for diamond, compared with ~1.1 J m-² for olivine (Field, Freeman, 1981; Field, 1992). Fracture mechanics provides a framework for describing this behaviour: the Mode-I stress intensity factor, (K1), characterises the stress field at the tip of an existing crack, while crack propagation occurs when (K1) reaches the critical stress intensity factor, (K₁c), or fracture toughness. Reported (K₁c) values for diamond are typically in the range of several MPa m½, although values vary with crystallographic orientation, diamond type, and measurement method (Field, 2012). Diamond fracture can be classified into three principal modes, which broadly represent increasing severity of breakage and associated potential value loss: ➤ Shattering: High-energy or rapid compressive loading produces extensive fragmentation, generating a broad progeny size distribution (PSD) as particles undergo multiple collisions and fractures until the applied energy is dissipated. This represents the most severe form of breakage and can result in substantial loss of diamond value through the generation of numerous small fragments. ➤ Cleavage: Fracture propagates preferentially along crystallographic or other pre-existing planes of weakness, producing relatively larger and more discrete fragments and a narrower PSD. Although less extensive than shattering, cleavage can still result in significant value loss where breakage reduces the size or integrity of a valuable stone. ➤ Chipping: Low-energy surface damage or attrition removes small fragments from the diamond without complete particle failure, generally producing few The Journal of the Southern African Institute of Mining and Metallurgy

progeny. Chipping therefore represents a lower-severity form of breakage, although repeated chipping can progressively reduce stone size and value.

The evolution of kimberlite comminution circuits Early kimberlite processing circuits followed relatively simple flowsheets built around staged crushing, scrubbing, screening and dense media separation, with diamond recovery generally occurring downstream through conventional X-ray luminescence (XRL) and related X-ray recovery technologies (Clarke, 2018). Consequently, diamonds liberated during earlier stages could remain within the processing stream and be exposed to subsequent handling and comminution before recovery. Repeated exposure to high-energy crushing events can result in unnecessary breakage, including both immediate fracture and the accumulation of subcritical cracks associated with diamond fatigue (Bell et al., 1977). Traditional circuit design therefore prioritised efficient host-rock liberation and diamond recovery, while the potential value loss associated with repeated comminution of liberated diamonds received comparatively less attention. As understanding of breakage-related value loss developed, circuit configurations evolved to incorporate additional opportunities for diamond removal. Alongside established XRL recovery, X-ray transmission (XRT) technology has subsequently been adopted for diamond recovery and can be positioned within the flowsheet to reduce the subsequent exposure of liberated diamonds to comminution (Voigt et al., 2019). High-pressure grinding rolls (HPGR), also referred to as high pressure roll crushers (HPRC), have likewise been adopted in some operations as controlled-energy alternatives to conventional crushing technologies, providing a more controlled breakage environment (Popplewell, Baeletse, 2010; Olivier et al., 2010). For kimberlites known to yield exceptionally large diamonds, such as Karowe and Letšeng, XRT has also been applied upstream or in dedicated large-diamond recovery circuits to identify and remove large stones before further comminution, thereby reducing their exposure to potential breakage (Shor et al., 2015; Van Niekerk et al., 2016). These developments demonstrate that sensor-based recovery is now an established component of diamond processing, rather than a recent development. However, the effectiveness of different breakage-mitigation strategies remains dependent on orebody characteristics, diamond size distribution, circuit configuration, and the location and size range of sensor-based recovery stages. In particular, there remains a need to better quantify how these factors influence diamond breakage and value loss and to integrate this information into geometallurgical models of both the kimberlite host rock and the diamonds it contains.

Operational drivers affecting diamond breakage: Energy input, liberation efficiency, and ore dilution through waste rock incorporation Diamond breakage within a processing circuit is not solely a function of intrinsic diamond properties but is strongly influenced by operational conditions that determine how, where and at what magnitude stresses are applied. Among these, the magnitude and mode of energy input are important controls. Conventional crushing circuits (e.g., Figure 5) employ different combinations of compressive, tensile, shear and impact stresses depending on the equipment and operating conditions. Jaw and cone crushers are primarily compression-based devices, although particle–particle and particle–liner interactions can generate localised impact, shear, and tensile stresses that contribute to breakage. The resulting stress VOLUME 126

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Integrating diamond breakage into existing kimberlite geometallurgical frameworks:

Figure 5—Conceptual schematic illustrating a conventional kimberlite comminution and diamond recovery circuit alongside newer technologies and process configurations developed to improve diamond recovery and reduce exposure to processing conditions that may promote breakage, including high-pressure grinding rolls (HPGR) and X-ray transmission (XRT) recovery.

concentrations may exceed the fracture strength of individual diamonds, particularly where pre-existing weaknesses such as inclusions, ruts (resorbed cracks) or microfractures are present. The nature and distribution of the stress regime are therefore equally important. Compression-dominated systems such as highpressure grinding rolls (HPGR) introduce a different breakage environment from conventional crushing, promoting interparticle crushing within a confined particle bed, where the ore can preferentially break upon itself rather than being crushed directly against a steel surface (Daniel, Morley, 2010). However, localised stress concentrations within the particle bed can still develop, and their magnitude and distribution will depend on the properties, size distribution, and arrangement of the material being processed. The transition from one dominant breakage regime to another therefore does not eliminate diamond-breakage risk but changes the manner in which stresses are transmitted to diamonds. Understanding how different comminution devices and operating conditions partition energy and stress is consequently important for identifying where and under what conditions diamond breakage is most likely to occur within a circuit. Feed moisture provides another operational variable that can modify comminution behaviour. Changes in moisture content can influence particle interactions, bed packing, screening, handling, and the transmission of stresses through the material, with the resulting effect on diamond breakage being dependent on the ore and circuit conditions. Some kimberlites contain swelling smectites (Stripp et al., 2006), which can influence the physical breakdown and handling behaviour of the ore when hydrated. However, the abundance and behaviour of such clay minerals are highly orespecific, and their effects on liberation and diamond breakage should therefore be evaluated for individual deposits rather than assumed to be universal. Liberation efficiency can further influence diamond breakage 642

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risk through its effects on residence time and recirculation. Insufficient fragmentation may leave diamonds locked within host rock and, depending on circuit configuration, increase their exposure to subsequent comminution events. Repeated loading can progressively reduce the resistance of diamonds to fracture, with experimental studies demonstrating the development and propagation of cracks under repeated loading conditions (Bell et al., 1977). Conversely, excessive energy input may achieve rapid liberation at the expense of increased direct diamond breakage. The operating objective is therefore to achieve sufficient hostrock fragmentation and liberation while minimising unnecessary diamond breakage. The appropriate operating window is not fixed, but reflects the interaction between the diamond population, kimberlite characteristics, circuit configuration, and operating conditions, which may change with ore domain, feed characteristics, equipment settings, and equipment wear. Ongoing monitoring of recovered diamond populations and plant performance is therefore important for identifying changes in breakage behaviour through time and for informing geometallurgical models. Ore dilution, particularly through the incorporation of waste rock, may represent an additional control on diamond breakage, although its effect is dependent on the lithology, competency, abrasiveness, and proportion of the waste material. Harder or more competent waste fragments may alter the local stress environment during inter-particle crushing and can increase the severity of contacts experienced by diamonds under some conditions. Experimental and pilot-scale work reported by Lusinga (2023), for example, found increased diamond breakage when diamonds were surrounded by waste rock and identified potential benefits from upfront waste removal. These findings support consideration of waste dilution as a geometallurgical variable, but the magnitude and direction of its effect should be established through ore-specific test work rather than assumed from waste content alone. Where The Journal of the Southern African Institute of Mining and Metallurgy

Integrating diamond breakage into existing kimberlite geometallurgical frameworks:

Figure 6—Examples from drop-weight impact tests showing four possible outcomes for simulant liberation and breakage: (a) liberated simulant that is broken, (b) liberated simulant that remains intact, (c) partially liberated simulant, and (d) simulant retained within a partially broken host matrix. Pictures courtesy of Anotidaishe Dondo and Victor Spencer.

an adverse relationship is demonstrated, upfront waste removal, including sensor-based sorting, may provide an opportunity to reduce diamond exposure to unfavourable comminution conditions. Together with appropriate control of energy input and operating conditions, such strategies can contribute to maintaining conditions that favour effective liberation while limiting diamond breakage.

Identifying practical proxies for diamond breakage in geometallurgical models A major challenge in incorporating diamond breakage into geometallurgical frameworks is the need for simple, scalable proxies that reliably predict diamond response under different processing conditions. This is addressed through a combined approach integrating detailed mineralogical characterisation of diamond parcels (Burness et al., 2024) with controlled laboratory experiments using diamond simulants embedded in kimberlite analogues (Chele, 2021). Simulant-based testing provides a controlled and reproducible approach for investigating host-rock breakage, simulant liberation, and simulant fracture during comminution. Drop-weight, ultra-fast cell, split Hopkinson pressure bar, and piston-and-die tests can be applied to simulant-bearing composites designed to act as proxies for diamond-bearing kimberlite host rock, allowing the response of the composite system to be evaluated under defined loading conditions. The resulting progeny particle-size distribution (PSD) describes the sizes of particles produced during breakage, while (t10), defined as the percentage of progeny passing a size equal to one-tenth of the original particle size, provides a measure of the extent of fragmentation. The relationship between (t10) and specific comminution energy (ECS) can therefore be used to characterise the breakage response of the composite matrix. The extent of simulant liberation can be quantified using the fraction of simulants liberated from the matrix (Fmat), while (Ffragment) and breakage probability can be used to quantify the fracture response of the simulant population under the applied loading conditions. The resulting data defines an operational ‘safe window’ for comminution, in which the host rock fragments efficiently, while liberated simulants remain intact (see Figure 6). The Journal of the Southern African Institute of Mining and Metallurgy

The transferability of these results to natural-diamond fracture behaviour is necessarily limited. Simulant-bearing composites provide controlled measures of ‘kimberlite’ matrix breakage, simulant liberation, and fracture response, but cannot fully reproduce the heterogeneous defects and geological histories of natural diamonds, including inclusions, internal strain, healed fractures, resorption features, and twinning. Consequently, this data must be complemented by direct characterisation of natural diamond parcels. Diamond characterisation provides an important input to predictive geometallurgical modelling by capturing variability within the natural diamond population. In addition to established assessments of breakage, including shatter, cleavage, and chipping, characteristics such as morphology, crystallographic orientation, natural cleavage planes, visible strain, inclusions, and pre-existing fractures may provide indicators of fracture susceptibility (Burness et al., 2024). These characteristics can be assessed through systematic examination of recovered diamond parcels, typically within secure facilities, providing information on the structural and morphological variability of the recovered population. However, this assessment occurs only after recovery and therefore cannot provide real-time monitoring or directly identify the processing conditions under which individual diamonds fractured. The relationship between individual characteristics and breakage susceptibility also remains insufficiently constrained for any single trait to be considered predictive in isolation. Their potential value therefore lies in combining multiple characteristics to characterise the diamond population and identify associations with observed breakage outcomes. The resulting diamond characterisation can then be considered alongside established plant-based assessments and complementary recovery methods, such as secondary DMS and caustic fusion testing, which can help corroborate diamond recovery and identify material that may otherwise remain unrecovered. Together, these approaches provide complementary information on diamond breakage and recovery, while recognising that the classification criteria, observer reproducibility, and relationships between diamond characteristics and breakage outcomes require further validation. A tiered experimental approach could provide this validation (see Figure 7). The first tier would comprise testing of simulant-bearing composites used as proxies for diamond-bearing kimberlite host rock to investigate host-matrix effects, processing conditions, and energy thresholds, with breakage quantified using measures such as t10 and ECS. The second tier would involve targeted breakage testing of individual natural diamonds to measure fracture initiation energy and subsequent breakage distributions (Ffragment), including the influence of crystallographic orientation and other intrinsic characteristics. The third tier would involve characterisation of diamond parcels from different processing plants and localities to assess whether relationships identified through controlled testing are reflected in operational populations. Together, these datasets would progressively build the evidence required to develop and validate predictive geometallurgical models of diamond breakage, from controlled experiments through to operational populations.

Key takeaways and outlook ➤ Diamond breakage is an important source of potential value loss, particularly for large, high-value diamonds. Diamond characteristics such as size, morphology, inclusions, internal strain, and crystallographic features VOLUME 126

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Figure 7— Tiered approach for integrating diamond characteristics into kimberlite geometallurgy: (1) breakage testing of simulant-bearing composites, (2) breakage testing of individual natural diamonds, and (3) visual characterisation of diamond parcels from different plants and localities, together building the evidence to develop and validate predictive models of diamond breakage.

may influence fracture susceptibility and should therefore be considered as key geometallurgical inputs. ➤ Kimberlite host-rock properties and ore-domain characteristics provide a second set of key inputs. Facies characteristics, including mineralogy and texture, together with hardness, waste dilution, clay behaviour, and moisture content influence rock breakage, diamond liberation, and the stress conditions experienced during processing. ➤ Simulant-based testing provides a controlled means of investigating how host-matrix properties and processing conditions affect breakage. Energy thresholds, t10 relationships and energy–breakage responses can help define operating windows that balance effective kimberlite breakage with diamond preservation. ➤ Characterisation of natural diamonds can complement simulant testing by linking observable characteristics, such as morphology, crystallographic orientation, inclusions, internal strain, and pre-existing fractures, with fracture and breakage behaviour. But their value as predictors of breakage susceptibility requires further investigation. ➤ The proposed geometallurgical framework would integrate these datasets to link diamond characteristics, host-rock properties, and processing conditions with breakage probability, progeny size distributions, and potential value loss across ore domains. Future work should combine simulant testing, controlled experiments on natural diamonds, and characterisation of diamond parcels from different plants and localities to develop and 644

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test this framework, ultimately supporting value-focused circuit design, early recovery strategies, and improved energy management.

Acknowledgements We gratefully acknowledge colleagues in the Centre for Minerals Research and the Department of Geological Sciences at UCT for the constructive discussions that informed the development of the ideas presented in this paper.

CRediT author statement SB: Led conceptualisation, diamond parcel characterisation, and writing. ShB: Contributed to conceptualisation and writing, and assisted with reviewing and editing. MB: Contributed to conceptualisation, and assisted with reviewing and editing. GHH: Contributed to diamond parcel characterisation, investigation. ANM: Developed the conceptual framework for simulant experimentation and reviewed the article.

References Bell, J.G., Stuivinga, M.E.C., Thornton, A.G., Wilks, J. 1977. The cracking and fatigue of diamond. Journal of Physics D: Applied Physics, vol. 10, no. 10, pp. 1379–1387. Brett, R.C. 2009. Kimberlitic Olivine. MSc thesis, University of British Columbia, Vancouver, Canada. Brookes, C.A. 1979. Indentation hardness of diamond. In: Field, J.E. The Journal of the Southern African Institute of Mining and Metallurgy

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In situ coarse-particle gold leaching from Witwatersrand ores using cyanide and glycine by J.J. Mahumana1, D. Groot², C. Adendorff1, V. Notole³,⁴, G.T. Nwaila³ Affiliation: 1 Milpark Business School, Milpark Education, Cape Town, South Africa. 2 Department of Civil, Mining and Process Engineering, Namibia University of Science and Technology, and Department of Materials Science and Metallurgical Engineering, University of Pretoria. 3 School of Geosciences, University of the Witwatersrand, South Africa. 4 School of Chemical and Metallurgical Engineering, University of the Witwatersrand, South Africa. Correspondence to: J.J. Mahumana Email: Joao.mahumane@gmail.com

Dates:

Received: 14 Apr. 2026 Revised: 10 Aug. 2026 Accepted: 30 Sep. 2026 Published: Oct. 2026 How to cite: Mahumana J.J., Groot D., Adendorff C. Notole V., Nwaila G.T. 2026. In situ coarse-particle gold leaching from Witwatersrand ores using cyanide and glycine. Journal of the Southern African Institute of Mining and Metallurgy, vol. 126, no. 10, pp. 647 – 654 DOI ID: https://doi.org/10.17159/24119717/3948/2026 ORCiD: J.J. Mahumana https://orcid.org/0009-0000-5787-5260 D. Groot https://orcid.org/0000-0002-3235-5231 C. Adendorff https://orcid.org/0000-0002-1520-9072 V. Notole https://orcid.org/0000-0002-6624-9701 G.T. Nwaila https://orcid.org/0000-0002-3974-9890

Abstract

In situ coarse-particle leaching could reduce surface disturbance relative to conventional mining, but its applicability to Witwatersrand conglomerates is poorly constrained. This study evaluates gold extraction from coarse (5 600 µm – 8 000 µm) Carbon Leader Middelvlei reef blends using cyanide and glycine under ambient bottle-roll conditions. Three blend ratios (7:3, 1:1, and 3:7; head grades 17 g/t – 32 g/t) were tested at pH 11 with 300 mg/L sodium cyanide or 1 500 mg/L glycine plus 1 500 mg/L potassium permanganate for 8 to 48 hours. Measured recovery remained low: the maximum 48 hour extraction was 15.3% for cyanide and 13.6% for glycine. In the Middelvlei-rich 3:7 blend, however, glycine provided faster early extraction (9.7% at 8 hours compared with 4.5% for cyanide). Glycine consumption (approximately 1.49 kg/t) was substantially higher than cyanide consumption (approximately 0.30 kg/t). Differences in matrix mineralogy, gold-grain compactness, and connected porosity provide plausible explanations for blend-dependent behaviour, but their individual effects were not isolated experimentally. The 48 hour endpoint was predefined for comparative screening of ore-property effects and earlystage kinetics, not for determination of maximum recovery. Accordingly, long-horizon model curves are presented only as unvalidated scenarios and do not establish an ultimate recovery or rate-limiting mechanism. Follow-up work will include at least one longer-duration experiment continued to a stable recovery plateau.

Keywords in situ leaching, gold, glycine, cyanidation, ore blending, Witwatersrand Basin

Introduction The Witwatersrand Basin in South Africa hosts the world’s largest gold deposits and remains a major gold province (Frimmel, Nwaila, 2020). Gold is conventionally recovered by cyanidation, which forms stable, water-soluble gold complexes and can extract gold from low-grade ores (Hilson, Monhemius, 2006; Marsden, House, 2006). Although cyanidation is operationally mature, cyanide toxicity requires strict handling, detoxification, and disposal controls (Acheampong et al., 2010; Johnson, 2015; Liu et al., 2022). These constraints have motivated investigation of alternative lixiviants (Aylmore, 2016; Wang et al., 2021). Glycine is a biodegradable amino acid that can complex gold in alkaline media (Altinkaya et al., 2020; Eksteen et al., 2018; Jamett et al., 2023; Oraby et al., 2019, 2020), although glycine-only systems commonly require an oxidant and may exhibit slower overall extraction than cyanidation (Oraby, Eksteen, 2015). In alkaline cyanide solutions, gold dissolves by oxidative complexation to dicyanoaurate. The overall stoichiometry is commonly represented by the Elsner reaction (4Au + 8CN- + O₂ + 2H₂O → 4[Au(CN)₂]+ 4OH-), so free cyanide concentration, dissolved oxygen, pH, and access to exposed gold surfaces control the leach response (Ilyas, 2018; Marsden, House, 2006). At pH 11, glycine is predominantly present as glycinate; an oxidant converts metallic gold to an oxidised state that is stabilised by gold– glycinate complexation. In the present tests, potassium permanganate supplied the oxidising capacity. Glycine leaching therefore depends on glycinate concentration, oxidant availability, alkalinity, competing base-metal reactions, and transport to exposed or connected gold surfaces. Because permanganate reduction products and gold–glycinate speciation vary with solution conditions, a single overall stoichiometric equation is not assumed for this system (Oraby, Eksteen, 2015; Oraby et al., 2020). In situ leaching, in which lixiviants are introduced into a fractured ore zone and the pregnant solution is recovered, may reduce surface disturbance and material handling (Martens et al., 2021; Sinclair, Thompson, 2015; Steven, 2009). This study uses laboratory bottle-roll tests on coarse (5 600 µm – 8 000 µm) Carbon Leader Middelvlei reef blends as a comparative screening investigation. Its

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In situ coarse-particle gold leaching from Witwatersrand ores using cyanide and glycine primary objective is to link ore mineralogy, texture, gold-grain morphology, and blend composition to early-stage coarse-particle leaching by cyanide and glycine. The study does not attempt to establish maximum recovery within the 48 h experimental window; diffusion-type and logistic models are used only as exploratory descriptions of the measured trends and long-horizon scenarios.

Geological setting The Archaean Witwatersrand Basin in northeastern South Africa forms part of the Kaapvaal Craton (Kröner et al., 2019), as illustrated in Figure 1. The succession is dominated by conglomerates, quartzites, shales, and minor volcanic units (Frimmel, Minter, 2002; Frimmel, 2019). The Witwatersrand Supergroup overlies a Palaeo to Mesoarchaean granitoid–greenstone basement and the Dominion Group, a bimodal volcanic succession with a thin basal siliciclastic unit (Armstrong et al., 1991; Marsh et al., 1989; Robb et al., 1992). The Witwatersrand Supergroup is subdivided into the West Rand and Central Rand groups. The West Rand Group (approximately 5 150 m thick; 2.985 Ga – 2.914 Ga) records repeated marine transgressions and regressions and comprises quartzite, conglomerate, shale, iron formation, and the Crown Formation volcanic unit (Armstrong et al., 1991; Frimmel, Nwaila, 2020; Kositcin, Krapež, 2004; Nwaila et al., 2021). The Central Rand Group (approximately 2 880 m thick; 2.90 Ga – 2.79 Ga) is dominated by fluvial and fluvio-deltaic sandstone and conglomerate deposited in braidplain and alluvial-fan systems (Catuneanu, 2001; Els, 1998; Gumsley et al., 2020). Gold- and uranium-bearing conglomerates near the basin margins account for most historical production (Buck, Minter, 1985; Frimmel, 2019; Frimmel, Minter, 2002; Nwaila et al., 2017). Prominent gold-bearing units in the Central Rand Group include the Carbon Leader and Middelvlei reefs. The Carbon Leader Reef rests on a palaeo-erosion surface at the base of the

Figure 1—Location and simplified geological map of the Witwatersrand Basin and its major goldfields. The inset at upper left shows the location of the basin in South Africa; younger Ventersdorp and Transvaal cover successions have been removed (modified from Beukes et al., 2024; Frimmel et al., 2005) 648

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Main Formation (Fuchs et al., 2016), as presented in Figure 2. Gold grades range from approximately 3 g/t to more than 100 g/t, with the highest grades associated with carbon-seam facies (Frimmel, Nwaila, 2020). Gold occurs with pyrite, carbonaceous matter, and minor sulphides in a matrix containing muscovite, chlorite, chloritoid, and pyrophyllite between quartz and chert clasts (Da Costa, 2019; Da Costa et al., 2020; Frimmel, 2019; Fuchs, 2015; Nwaila et al., 2020; Zhao, 1998). These features have direct leaching implications: carbonaceous matter may adsorb dissolved gold complexes (preg-robbing), sulphides can consume oxidant or cyanide, phyllosilicate-rich matrix material can restrict connected porosity, and natural fractures can expose otherwise inaccessible gold and provide transport pathways (Fleming et al., 2011; Ilyas, 2018; Nwaila et al., 2020). The Middelvlei Reef occurs approximately 60 m above the Carbon Leader reef (Jolly, 1984; Letanta, 2021; Nwaila et al., 2020), as can be observed in Figure 2. In the Carletonville area, it extends over more than 500 km² and comprises medium- to large-pebble oligomictic auriferous conglomerate interbedded with quartzite (Myers et al., 1993). The reef is typically 2 m – 4 m thick and contains thin pebble lags of approximately 10 cm (Letanta, 2021). Its gold grades are lower and more variable than those of the Carbon Leader reef, averaging approximately 5 g/t Au (Frimmel, Minter, 2002). Carbonaceous material is less abundant, and gold is commonly associated with quartz and pyrite (Els, 1987). The lower phyllosilicate and carbonaceous contents, higher quartz abundance, and fracture distribution provide testable contrasts in lixiviant penetration, oxidant demand, preg-robbing potential, and gold accessibility relative to the Carbon Leader reef.

Material and methods Ore samples and preparation Ore samples were collected underground from the Carbon Leader and Middelvlei reefs in Carletonville, South Africa, according to the sampling protocol used for the study. Approximately 80 kg of bulk material was obtained and dispatched to Super Laboratory Services (Johannesburg), the South African Nuclear Energy Corporation, and XRD Analytical & Consulting (Pretoria) for analysis. Samples were crushed into 5 600 µm – 8 000 µm, 1 700 µm – 5 600 µm, and

Figure 2—Simplified structural and stratigraphic relationships of the West Rand and Central Rand groups in the Carletonville goldfield. Younger Ventersdorp and Transvaal cover successions have been removed; the upperleft inset shows cross-section A–B, and the Ventersdorp Contact, Kloof, Middelvlei, Carbon Leader, and Government reefs are indicated The Journal of the Southern African Institute of Mining and Metallurgy

In situ coarse-particle gold leaching from Witwatersrand ores using cyanide and glycine <1 700 µm fractions and blended at Carbon Leader:Middelvlei ratios of 30:70, 50:50, and 70:30. Subsamples were analysed by fire assay to determine head grades. The present manuscript reports the 5 600 µm – 8 000 µm fraction.

Micro X-ray computed tomography (micro-XCT) Representative samples were scanned using a Nikon XTH 225L micro-XCT system at the South African Nuclear Energy Corporation to characterise three-dimensional mineral textures, fracture networks, and grain distributions (Bam et al., 2016, 2019; Becker et al., 2016). Samples were mounted, scanned at 80 kV, and reconstructed using CT Pro 3D and VG Studio Max 3.2. Mineral phases were segmented according to X-ray attenuation, enabling measurement of grain size, spatial distribution, morphology, and fracture connectivity relevant to lixiviant access.

X-ray powder diffraction (XRD) Pulverised fractions were analysed using a Malvern Panalytical Aeris diffractometer with cobalt Kα radiation at XRD Analytical & Consulting. Diffractograms were interpreted using X’Pert HighScore Plus to identify crystalline phases; minor unindexed peaks were treated as trace components.

determine the true recovery limit or rate-limiting mechanism.

[2]

where Ct is cumulative recovery (%) at time t, D is the effective diffusion coefficient, R is particle radius, and C0 is the fitted asymptotic recovery parameter (%). Equation 2 represents increasing diffusion resistance with particle size and assumes spherical particles, uniform internal transport, and no convective contribution. These assumptions do not reproduce the irregular particle shapes, surface roughness, and fracture networks observed by micro-XCT. [3] where f(t) is cumulative recovery (%), K is the carrying capacity, r is the intrinsic growth rate, and t0 is the inflection time. In the scenario shown, K was fixed at 100% solely to define an upperbound curve. Because the fit does not use observations beyond 48 h and has no long-duration validation, the fitted parameters must not be interpreted as a measured maximum recovery or a confirmed rate-limiting mechanism.

Results

Bottle-roll leaching experiments Triplicate bottle-roll tests were conducted for the ore blends. Slurries containing 30% solids were adjusted to pH 11 with lime and leached at ambient temperature (approximately 23 °C – 25 °C) using either 300 mg/L sodium cyanide or 1 500 mg/L glycine plus 1 500 mg/L potassium permanganate. Solution samples were collected after 8, 24, 32, and 48 h, and dissolved gold was measured by atomic absorption spectrometry. The 48 h endpoint was predefined for a comparative screening study intended to relate ore properties and blend composition to early-stage coarse-particle leaching; it was not selected to represent complete dissolution or maximum recovery. Tests were therefore terminated at 48 h even though recovery had not reached a plateau. Recovery at 8, 24, and 32 h was calculated relative to the measured 48 h-solution concentration and the independently determined 48 h recovery using Equation 1. At least one experiment continued until a stable recovery plateau is planned as follow-up validation. [1] where t is the sampling time (8, 24, or 32 h), and “final” denotes the measured value at the 48 h study endpoint. Equation 1 assumes proportionality between dissolved gold concentration and recovery over the measured interval. The term “final” does not imply that maximum or equilibrium recovery was reached.

Ore mineralogy and characteristics The Carbon Leader reef (CLR) is a well-sorted, auriferous, clastand matrix-supported conglomerate. Subrounded quartz clasts (0.2 mm – 10 mm) as shown in Figures 3a – c, occur in a fine-grained matrix (<0.4 mm) containing muscovite, chlorite, chloritoid, and pyrophyllite. Pyrite grains (0.02 mm – 0.5 mm) are concentrated in matrix interstices and locally form stringers approximately 2 mm wide. Trace phases include carbonaceous matter, uraniumbearing minerals, pentlandite, and visible gold. The Middelvlei reef (MVR) is also a well-sorted quartz-pebble conglomerate, as illustrated in Figures 3d – f, but its matrix contains less muscovite and pyrophyllite. Quartz- and calcite-filled fractures occur in both ores. Quantitative X-ray diffraction shows that the CLR samples average 86.7 wt.% quartz, 6.0 wt.% muscovite, 2.5 wt.% pyrophyllite, 2.4 wt.% chloritoid, and 2.4 wt.% pyrite, whereas the MVR samples average 96.8 wt.% quartz, 0.8 wt.% muscovite, 0.5 wt.% pyrophyllite, and 2.1 wt.% pyrite, with trace kaolinite, as per Table 1. Thus, the ores have similar pyrite contents but differ substantially in quartz

Modelling of leaching kinetics A diffusion-type expression was fitted to evaluate the sensitivity of recovery to particle radius and effective internal transport (Crank, 1979), as per Equation 2. A logistic curve was also fitted to the glycine data as an upper-bound scenario, as per Equation 3. For that scenario, the carrying capacity was fixed at 100% and the growth rate and inflection time were fitted using SciPy. Fixing the carrying capacity at 100% imposes a mathematical upper bound; it is not an estimate of experimentally achievable recovery. Both models assume idealised particle geometry and transport, and neither was validated beyond the 48 h experimental window. The fitted parameters are therefore used only to compare measured early-stage trends and illustrate extrapolation uncertainty, not to The Journal of the Southern African Institute of Mining and Metallurgy

Figure 3—Ore mineralogy and textures of the Carbon Leader reef (a–c) and Middelvlei reef (d–f) imaged by micro-XCT. In panels b and e, green denotes silicate minerals (predominantly quartz), orange denotes sulphide minerals (predominantly pyrite), and purple denotes uraninite VOLUME 126

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In situ coarse-particle gold leaching from Witwatersrand ores using cyanide and glycine and phyllosilicate abundance, which may affect oxidant demand, pore connectivity, surface coatings, and access to gold. Micro-XCT analysis characterised gold-grain morphology (Figures 4 and 5). CLR gold grains ranged from 0.01 mm to 0.5 mm (n = 1 813), with mean compactness of 0.47 and mean sphericity of 0.66 (Figure 4b). MVR gold grains ranged from 0.02 mm to 0.7 mm (n = 1 004), with mean compactness of 0.62 and mean sphericity of 0.72 (Figure 4a). The absolute difference in compactness (0.15) Table 1

Mineral contents determined by X-ray diffraction for selected Carbon Leader reef (CLR) and Middelvlei reef (MVR) samples; values are in wt.% Sample

CLR 1

CLR 2

MVR 1

MVR 2

Quartz

81.7

91.7

96.8

96.7

Pyrite

2.2

2.6

2.1

2.0

Muscovite

7.1

4.9

0.6

0.9

Pyrophyllite

4.2

0.8

0.5

Kaolinite

trace

Chloritoid

4.8

is greater than the difference in sphericity (0.06). These descriptors were not varied independently, so they cannot be assigned a causal effect on leaching. The larger compactness contrast, together with the measured matrix-mineralogy differences, provides a more plausible hypothesis for blend-dependent accessibility than sphericity alone.

Coarse-particle leaching The 5 600 µm – 8 000 µm fraction was leached with cyanide and glycine at CLR:MVR blend ratios of 7:3, 1:1, and 3:7 (as per Figures 6a and b and Tables 2 to 3). Extraction was low for both lixiviants. At 48 h, cyanide recovery was 12.63%, 13.09%, and 15.28% for the 7:3, 1:1, and 3:7 blends, respectively, compared with 10.88%, 9.98%, and 13.63% for glycine. Glycine produced faster 8 h extraction in the 1:1 and 3:7 blends (6.04% and 9.70%, respectively) than cyanide (4.35% and 4.52%), whereas the 7:3 blend gave similar 8 h values (5.23% for glycine and 5.57% for cyanide). Cyanide therefore provided higher 48 h recovery in all three blends, while glycine provided faster early extraction only in the blends containing at least 50% MVR. Maximum measured reagent consumption was approximately 0.31 kg/t for cyanide and 1.49 kg/t for glycine (Figures 6c and d). These data demonstrate measurable coarseparticle extraction but do not support describing either system as efficient at the tested contact time.

Figure 4—Box-and-density plots of gold-grain diameter, surface area, compactness, and sphericity for (a) the Middelvlei reef and (b) the Carbon Leader reef, determined by micro-XCT. The datasets contain 1 004 and 1 813 grains, respectively; IQR denotes interquartile range

Figure 5—Micro-XCT gold-grain relationships for the Carbon Leader and Middelvlei reefs: (a) radius versus surface area with quadratic fits, y = intercept + B₁x + B₂x², and (b) diameter versus compactness; RSS denotes residual sum of squares 650

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In situ coarse-particle gold leaching from Witwatersrand ores using cyanide and glycine

Figure 6—Measured gold recovery during (a) cyanide and (b) glycine leaching of Carbon Leader reef (CLR)–Middelvlei reef (MVR) blends, with corresponding consumption of (c) cyanide and (d) glycine

Table 3 Coarse-particle gold leaching with glycine; reagent consumption and recovery are reported

Table 2

Coarse-particle gold leaching with cyanide

Ore blend

Time (h)

Glycine consumed (kg/t)

Au in solution (mg/L)

Au recovery (%)

7CLR:3MVR

8.8

0.00

7CLR:3MVR

10.7

1.05

1.36

5.23

7CLR:3MVR

10.7

1.25

1.79

6.88

7CLR:3MVR

10.7

1.49

1.96

7.54

7CLR:3MVR

10.7

1.49

2.83

10.88

1CLR:1MVR

8.9

0.00

1CLR:1MVR

10.8

1.10

1.15

6.04

1CLR:1MVR

10.8

1.20

1.54

8.09

1CLR:1MVR

10.8

1.49

1.65

8.67

1CLR:1MVR

10.8

1.49

1.90

9.98

3CLR:7MVR

8.8

0.00

3CLR:7MVR

10.8

1.00

1.43

9.70

3CLR:7MVR

10.8

1.20

1.91

12.95

3CLR:7MVR

10.8

1.49

2.00

13.56

Ore blend

Time (h)

Cyanide consumed (kg/t)

Au in solution (mg/L)

Au recovery (%)

7CLR:3MVR

8.8

0.00

7CLR:3MVR

10.7

0.26

1.50

5.57

7CLR:3MVR

10.7

0.28

2.98

11.07

7CLR:3MVR

10.7

0.305

3.32

12.33

7CLR:3MVR

10.7

0.31

3.40

12.63

1CLR:1MVR

8.9

0.00

1CLR:1MVR

10.8

0.26

0.89

4.35

1CLR:1MVR

10.8

0.28

2.33

11.38

1CLR:1MVR

10.8

0.3

2.55

12.46

1CLR:1MVR

10.8

0.3

2.68

13.09

3CLR:7MVR

8.9

0.00

3CLR:7MVR

10.8

0.25

0.68

4.52

3CLR:7MVR

10.8

0.28

1.87

12.42

3CLR:7MVR

10.8

0.301

2.15

14.28

3CLR:7MVR

10.8

0.302

2.30

15.28

Exploratory logistic-growth simulations Figure 7 shows logistic curves extrapolated to 400 h for the glycine tests. Because the carrying capacity was fixed at 100%, the curves The Journal of the Southern African Institute of Mining and Metallurgy

approach 90% – 97% recovery by approximately 200 h. These values are a mathematical consequence of the imposed upper bound and VOLUME 126

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In situ coarse-particle gold leaching from Witwatersrand ores using cyanide and glycine the fitted growth parameters; they are not experimental evidence of near-complete recovery. Only four non-zero observations were available up to 48 h, and no test was continued to a plateau. The extrapolations are therefore non-unique and cannot identify the true recovery limit or rate-limiting step. Possible mechanisms for later exposure—diffusion through connected pores, access along existing or newly opened microfractures, and slow dissolution of partly exposed grains—were not measured in this study and remain hypotheses. The curves are retained only to illustrate extrapolation uncertainty and to motivate a follow-up experiment continued until recovery stabilises.

Discussion The ore-characterisation data identify several plausible controls on coarse-particle leaching, but they do not isolate any single causal variable. CLR and MVR have similar mean pyrite contents (approximately 2.4 wt.% and 2.1 wt.%, respectively), but their overall mineralogy is not similar: CLR contains markedly more muscovite, pyrophyllite, and chloritoid, whereas MVR contains approximately 10 percentage points more quartz. The compactness means differ by 0.15, compared with a sphericity difference of only 0.06. Accordingly, the larger compactness contrast and the phyllosilicate-rich CLR matrix are more credible contributors to differences in connected porosity and surface accessibility than sphericity alone. Carbonaceous matter may also promote pregrobbing, and pyrite or other reactive phases may consume oxidant or cyanide (Fleming et al., 2011; Ilyas, 2018). These effects were not tested independently and are presented as mechanisms for future validation rather than demonstrated causes. The kinetic comparison is blend- and time-dependent. Glycine gave higher 8 h recovery than cyanide for the 1:1 blend (6.04% compared with 4.35%) and the 3:7 blend (9.70% compared with 4.52%), but not for the CLR-rich 7:3 blend (5.23% compared with 5.57%). By 48 h, cyanide recovery exceeded glycine recovery for every blend: 12.63% compared with 10.88% for 7:3, 13.09% compared with 9.98% for 1:1, and 15.28% compared with 13.63% for 3:7. Cyanide also required substantially less reagent on a mass basis (approximately 0.30 kg/t compared with 1.49 kg/t glycine). It is therefore inaccurate to describe cyanide as having universally faster kinetics or glycine as giving comparable final extraction. The data instead show faster initial glycine extraction in MVR-rich blends, followed by higher 48 h cyanide recovery in all blends. The trials were terminated at the predefined 48 h screening

endpoint before maximum recovery was reached. This design was suitable for the primary objective of linking ore properties and blend composition to early-stage leaching of 5.6 mm – 8.0 mm particles, but it cannot establish the ultimate recovery or controlling mechanism. The diffusion-type and logistic fits should therefore be viewed as descriptive and scenario-based, respectively. In particular, fixing logistic carrying capacity at 100% precludes using the fitted curve as validation of near-complete extraction. A followup study will include at least one longer-duration test continued until successive measurements show a stable plateau, with reagent residuals and solution chemistry monitored to distinguish transport limitation from reagent depletion or passivation. Subsequent work may also evaluate controlled fracture enhancement or flow stimulation to test whether increased connected surface area improves coarse-particle recovery.

Conclusion Cyanide and glycine produced measurable but low gold extraction from 5.6 mm – 8.0 mm Witwatersrand ore particles. After 48 h, cyanide recovery was 12.63% – 15.28%, compared with 9.98% – 13.63% for glycine. Glycine showed faster initial extraction in the 1:1 and 3:7 blends, but lower 48 h recovery, and its consumption was approximately five times that of cyanide on a mass-per-tonne basis. Ore characterisation showed similar pyrite contents but substantial differences in quartz and phyllosilicate abundance; the compactness contrast also exceeded the sphericity contrast. These associations plausibly explain blend-dependent accessibility, but causality was not established. The predefined 48 h endpoint was designed to link ore properties to early-stage coarse-particle leaching, not to determine maximum recovery. Long-horizon model curves remain unvalidated scenarios. Follow-up work will include at least one longer-duration test continued to a stable recovery plateau, with monitoring to distinguish diffusion, reagent depletion, passivation, and fracture-controlled access.

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Figure 7—Unvalidated upper-bound logistic scenarios (grey curves) for glycine leaching of CLR:MVR blends of (a) 7:3, (b) 1:1, and (c) 3:7; red points are the measured data through 48 h. The 100% carrying capacity was imposed and is not an experimentally determined recovery limit 652

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In situ coarse-particle gold leaching from Witwatersrand ores using cyanide and glycine Kolehmainen, E., Yliniemi, K., Lundström, M. 2020. Leaching and recovery of gold from ore in cyanide-free glycine media. Minerals Engineering, vol. 158, article 106610. Armstrong, R.A., Compston, W., Retief, E.A., Williams, I.T., Welke, H.J. 1991. Zircon ion microprobe studies bearing on the age and evolution of the Witwatersrand triad. Precambrian Research, vol. 53, nos. 3–4, pp. 243–266. Aylmore, M.G. 2016. Alternative lixiviants to cyanide for leaching gold ores. Advances in Gold Ore Processing. 2nd ed. Adams, M.D. (ed.). Elsevier. Bam, L.C., Miller, J.A., Becker, M., Basson, I.J. 2019. X-ray computed tomography: Practical evaluation of beam hardening in iron ore samples. Minerals Engineering, vol. 131, pp. 206–215. Bam, L.C., Miller, J.A., Becker, M., De Beer, F.C., Basson, I. 2016. X-ray computed tomography—determination of rapid scanning parameters for geometallurgical analysis of iron ore. See AusIMM (2016), pp. 209–219. Becker, M., Jardine, M.A., Miller, J.A., Harris, M. 2016. X-ray computed tomography: A geometallurgical tool for threedimensional textural analysis of drill core. See AusIMM (2016), pp. 231–240. Beukes, N.J., Guy, B.M., Thiele, S.T. 2024. Stratigraphy and basin analysis of the West Rand Group, Witwatersrand Supergroup. South African Journal of Geology, vol. 127, no. 2, pp. 239–270. Buck, S.G., Minter, W.E.L. 1985. Placer formation by fluvial degradation of an alluvial fan sequence: The Proterozoic Carbon Leader placer, Witwatersrand Supergroup, South Africa. Journal of the Geological Society, vol. 142, no. 5, pp. 757–764. Catuneanu, O. 2001. Flexural partitioning of the late Archaean Witwatersrand foreland system, South Africa. Sedimentary Geology, vol. 141, pp. 95–112. Crank, J. 1979. The Mathematics of Diffusion. 2nd ed. Oxford University Press, Oxford, UK. Da Costa, G., Hofmann, A., Agangi, A. 2020. A revised classification scheme of pyrite in the Witwatersrand Basin and application to placer gold deposits. Earth-Science Reviews, vol. 201, article 103064. https://doi.org/10.1016/j.earscirev.2019.103064 Da Costa, G.V. 2019. Evaluation of syngenetic gold deposition in syn-sedimentary pyrite of the Witwatersrand Basin. Thesis, University of Johannesburg, South Africa. Eksteen, J.J., Oraby, E.A., Tanda, B.C., Tauetsile, P.J., Bezuidenhout, G.A., Newton, T., Trask, F., Bryan, I. 2018. Towards industrial implementation of glycine-based leach and adsorption technologies for gold–copper ores. Canadian Metallurgical Quarterly, vol. 57, no. 4, pp. 390–398. Els, B.G. 1987. The auriferous Middelvlei Reef depositional system, West Wits Line, Witwatersrand Supergroup. Thesis, University of Johannesburg, South Africa. Els, B.G. 1998. The auriferous late Archaean sedimentation systems of South Africa: Unique palaeo-environmental conditions. Sedimentary Geology, vol. 120, no. 1–4, pp. 205–224. Fleming, C.A., Mezei, A., Bourricaudy, E., Canizares, M., Ashbury, M. 2011. Factors influencing the rate of gold cyanide leaching and adsorption on activated carbon, and their impact on the design of CIL and CIP circuits. Minerals Engineering, vol. 24, The Journal of the Southern African Institute of Mining and Metallurgy

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In situ coarse-particle gold leaching from Witwatersrand ores using cyanide and glycine Letanta, D. 2021. Application of sedimentological and gold-assay data for resource estimation: A case study of the Middelvlei Reef, Witwatersrand Basin, South Africa. PhD thesis, University of the Witwatersrand, South Africa. Liu, Z.W., Guo, X.Y., Tian, Q.H., Zhang, L. 2022. A systematic review of gold extraction: Fundamentals, advancements, and challenges toward alternative lixiviants. Journal of Hazardous Materials, vol. 440, article 129778. Marsden, J., House, I. 2006. The Chemistry of Gold Extraction. 2nd ed. Society for Mining, Metallurgy, and Exploration, USA. Marsh, J.S., Bowen, M.P., Rogers, N.W., Bowen, T.B. 1989. Volcanic rocks of the Witwatersrand Triad, South Africa. II: Petrogenesis of mafic and felsic rocks of the Dominion Group. Precambrian Research, vol. 44, no. 1, pp. 39–65. Martens, E., Prommer, H., Sprocati, R., Sun, J., Dai, X., Crane, R., Jamieson, J., Tong, P.O., Rolle, M., Fourie, A. 2021. Toward a more sustainable mining future with electrokinetic in situ leaching. Science Advances, vol. 7, no. 18, article eabf9971. Myers, R.E., Zhou, T., Phillips, G.N. 1993. Sulphidation in the Witwatersrand Goldfields: Evidence from the Middelvlei Reef. Mineralogical Magazine, vol. 57, no. 388, pp. 395–405. https://doi.org/10.1180/minmag.1993.057.388.04 Nwaila, G., Frimmel, H.E., Minter, W.E. 2017. Provenance and geochemical variations in shales of the Mesoarchaean Witwatersrand Supergroup. Journal of Geology, vol. 125, no. 4, pp. 399–422. Nwaila, G.T., Bourdeau, J.E., Jinnah, Z., Frimmel, H.E., Bybee, G.M., Zhang, S.E., Manzi, M.S.D., Minter, W.E.L., Mashaba, D. 2021. The significance of erosion channels on gold metallogeny in the Witwatersrand Basin (South Africa): Evidence from the Carbon Leader Reef in the Carletonville goldfield. Economic Geology, vol. 116, no. 2, pp. 265–284. Nwaila, G.T., Ghorbani, Y., Becker, M., Frimmel, H.E., Petersen, J., Zhang, S. 2020. Geometallurgical approach for implications of ore blending on cyanide leaching and adsorption behaviour

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of Witwatersrand gold ores, South Africa. Natural Resources Research, vol. 29, pp. 1007–1030. Oraby, E.A., Eksteen, J.J. 2015. The leaching of gold, silver, and their alloys in alkaline glycine–peroxide solutions and their adsorption on carbon. Hydrometallurgy, vol. 152, pp. 199–203. https://doi.org/10.1016/j.hydromet.2014.12.015 Oraby, E.A., Eksteen, J.J., Karrech, A., Attar, M. 2019. Gold extraction from palaeochannel ores using an aerated alkaline glycine lixiviant for consideration in heap and in situ leaching applications. Minerals Engineering, vol. 138, pp. 112–118. Oraby, E.A., Eksteen, J.J., O’Connor, G.M. 2020. Gold leaching from oxide ores in alkaline glycine solutions in the presence of permanganate. Hydrometallurgy, vol. 198, article 105527. https://doi.org/10.1016/j.hydromet.2020.105527 Robb, L.J., Davis, D.W., Kamo, S.L., Meyer, F.M. 1992. Ages of altered granites adjoining the Witwatersrand Basin with implications for the origin of gold and uranium. Nature, vol. 357, no. 6380, pp. 677–680. Sinclair, L., Thompson, J. 2015. In situ leaching of copper: Challenges and future prospects. Hydrometallurgy, vol. 157, pp. 306–324. Steven, N. 2009. Potential in situ leach exploitation of backfilled Witwatersrand gold mines: Parameters and flow-rate calculations from a Zambian Copperbelt analogue. Proceedings of the World Gold Conference. Southern African Institute of Mining and Metallurgy, Johannesburg. pp. 193–196. Wang, J., Faraji, F., Ramsay, J., Ghahreman, A. 2021. A review of biocyanidation as a sustainable route for gold recovery from primary and secondary low-grade resources. Journal of Cleaner Production, vol. 296, article 126457. Zhao, B. 1998. A mineralogical and geochemical study of alteration associated with the Ventersdorp Contact Reef in the Witwatersrand Basin, South Africa. PhD thesis, Wits University, South Africa. u

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ABOUT THE CONGRESS We are excited to be hosting Hoist & Haul 2027 in beautiful Cape town, South Africa. Mining is a global industry, with mining companies owning mines across the world. The large engineering companies and suppliers who provide design and equipment to the mines also operate internationally. So we hope to welcome delegates and accept technical papers from a wide spectrum of different countries. As the conference is being held in South Africa, we will also aim to highlight South African achievements in mine shaft hoisting, and technical visits will showcase excellence in current South African mining operations. Fifty years ago, the world, led by South Africa, was at the dawn of ultradeep mining. BMR hoists were being developed, accepted factors of safety were being challenged and updated on a more rational basis, much deeper single hoist shafts were replacing multiple shaft systems. These were exciting times for hoisting and gave birth to the Hoist and Haul series of conferences, in 1973 in Johannesburg, under the title “Hoisting: Men, Materials and Minerals”. Similar challenges exist today. Exploration has proved significant ore bodies at depths exceeding 4000 m. However, the cost and time required for extraction using current hoisting technology makes their exploitation unattractive, emphasizing the need for faster, more costefficient hoisting systems. But a whole new set of challenges has arisen from increasing societal demands for safety, sustainability, energy efficiency and environmental responsibility. And, whilst the world cannot develop and thrive without mining products, these demands are likely to escalate rapidly over the next fifty years. Mines that have been in operation for fifty years must upgrade and retrofit hoisting system to adapt to changing demands, and planning for new mines must aim to anticipate what will be necessary if they are to be socially acceptable from inception to closure in fifty years’ time.

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10TH SULPHUR AND SULPHURIC ACID CONFERENCE | 2027

10 MAY 2027 - WORKSHOP | 11-12 MAY 2027 - CONFERENCE 13 MAY 2027 - TECHNICAL VISIT PROTEA HOTEL POLOKWANE RANCH RESORT, POLOKWANE “Sulphur and sulphuric acid are no longer by products they must now be considered as strategic resources.” – Acuity Commodities, Aug 2026 Sulphuric acid is used in nearly every commercial process around the globe and for this reason is known as the king of chemicals. Southern Africa’s resources are in high demand from uranium, copper and cobalt to rare earths, manganese and lithium. Processing all of these requires sulphuric acid. Sulphuric acid production and consumption will both increase in the region over the next few years.

WHAT WILL BE DISCUSSED? The recent closure of the Strait of Hormuz has both highlighted the frailty of international trade routes and the importance of sulphur and sulphuric acid to global commerce. The southern African region has been one of the markets that has remained very active during this period, paying the highest prices for sulphur imports, largely to keep the buoyant central African copper boom going. New trade routes have been created to ensure sulphur flows into the region remain consistent. Will trade flows return to pre war patterns or will risk aversion and supply diversity rule the new order? Sulphur and sulphuric acid are no longer by products they must now be considered as strategic resources.

THESE ISSUES AND DYNAMICS WILL BE DISCUSSED AT THE CONFERENCE. Mining companies in Southern Africa have been looking to expand mining activities for some years with growth in copper, uranium, rare earths and battery chemicals set to accelerate over the remains of this decade as new projects get announced and commissioned. Nearly every country in the region will experience growth in sulphuric acid supply and demand as a result. Orebodies are changing and the emergence of more and more smelters and roasters is changing the sulphuric acid landscape. More tons will need to move around the region.