Towards a Dynamic Optimisation of Comminution Circuit Under Geological Uncertainties
Bibliographic record
Abstract
Geometallurgical programmes are crucial for designing mineral processing plants that maximise comminution throughput. However, the variability of complex ore bodies, such as platinum group element (PGE) deposits, poses challenges in developing these programmes into profitable mine-to-mill production. This paper investigates the geological characteristics of different lithologies hosting the complex PGE orebody located in the Northern Limb of the Bushveld igneous complex in South Africa and assessed their impact on metallurgical efficiency in comminution circuits. Regression machine learning techniques were employed to analyse the ore mineralogical dataset from two lithologies (feldspathic pyroxenite and pegmatoidal feldspathic pyroxenite) and predict the Bond Work Index (BWI), a key comminution parameter for calculating processing plant throughput. The results indicated that BWI is strongly influenced by Chlorite, silicates, iron oxides, and the relative density of the PGE deposit. Using both simulated and laboratory-measured throughput values, a particle swarm optimisation (PSO) algorithm was applied to maximise the plant’s comminution throughput through tactical blending of low-grade and high-grade ore stockpiles. The PSO algorithm was shown to be an effective tool for stockpile management and tactical mine-to-mill operation in response to feed mineralogical variability. This first-time innovative approach addresses complex geological uncertainties and lays the groundwork for future geometallurgical studies. Potential areas for further research include incorporating additional lithologies for tactical ore stockpile blending and optimising parameters critical for ore mineral flotation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".