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Geometallurgical modelling of manganese ore comminution behaviour at the kalahari manganese field.
Thesis (MEng)--Stellenbosch University, 2024.
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867614008779997184 |
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| access_status_str | Open Access |
| author | Mathebula, Ivan Pardon |
| author2 | Tadie, Margreth |
| author_browse | Mathebula, Ivan Pardon Tadie, Margreth |
| author_facet | Tadie, Margreth Mathebula, Ivan Pardon |
| author_sort | Mathebula, Ivan Pardon |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description |
Thesis (MEng)--Stellenbosch University, 2024. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/131826 |
| institution | Stellenbosch University (South Africa) |
| last_indexed | 2026-06-10T12:45:13.015Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/131826 Geometallurgical modelling of manganese ore comminution behaviour at the kalahari manganese field. Mathebula, Ivan Pardon Tadie, Margreth Von der Heyden, Bjorn Stellenbosch University. Faculty of Engineering. Dept. of Chemical Engineering. Manganese ores -- Geology Size reduction of materials Ore-dressing Geological modeling UCTD Thesis (MEng)--Stellenbosch University, 2024. ENGLISH ABSTRACT: Variability in orebody characteristics can control the comminution behaviour of ores, posing challenges that hinder operational efficiency. Geometallurgy seeks to predict comminution parameters based on ore properties reflecting the variability of an ore deposit. These parameters can be leveraged to improve a spatial understanding of ore comminution behaviour, which is crucial for managing the impacts of orebody variability and enhancing overall plant performance. This study aimed to explore the potential to predict manganese ore comminution behaviour from mineralogy and rock strength. Comminution tests were conducted on manganese ore samples from the Kalahari manganese field (KMF) to measure their comminution behaviour in terms of the uniaxial compressive strength (UCS), crushability index (CI), and Bond ball mill work index (BWI). The samples underwent X-ray diffraction (XRD) analysis to determine their quantitative mineralogy. The XRD and test work data were first analysed to assess the extent of variability among the samples. The analysis results indicated statistically significant variations in the samples’ mineralogy and comminution behaviour. Subsequently, a correlation analysis was conducted to evaluate the relationship between rock strength and comminution behaviour. The results indicated a positive correlation between UCS and the comminution indices of the tested samples, suggesting that rock strength could be a predictor of comminution behaviour. Additionally, correlation analysis examined the influence of mineralogy on comminution behaviour, and the findings revealed strong correlations between most minerals and the comminution behaviour of the tested samples. The XRD and test work data were then utilized to develop and validate machine learning models for predicting the comminution indices of the samples. The study explored six machine learning models: multiple linear regression (MLR), Ridge regression, Lasso regression, Elastic Net (ELN) regression, Random Forest regression (RFR), and Gradient Boosting regression (GBR). The modelling results indicated good model performance. Specifically, the RFR model demonstrated the highest predictive accuracy for the CI, with a root mean square error (RMSE) of 2.27 % and an R2 of 0.80, while the GBR model showed the lowest but comparable predictive accuracy, with an RMSE of 2.98 % and an R2 of 0.67. For the BWI, the ELN model exhibited the highest predictive accuracy, with an RMSE of 0.96 kWh/t and R2 of 0.94, while the MLR model indicated the lowest performance, with an RMSE of 2.49 kWh/t and R2 of 0.49. These findings led to the conclusion that the comminution behaviour of the samples could be accurately predicted from their mineralogy, suggesting the potential for the geometallurgical modelling of manganese ore comminution behaviour at the KMF. AFRIKAANSE OPSOMMING: Geen opsomming beskikbaar. Masters 2025-03-31T14:04:28Z 2025-03-31T14:04:28Z 2024-12 Thesis https://scholar.sun.ac.za/handle/10019.1/131826 Stellenbosch University xviii, 161 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Manganese ores -- Geology Size reduction of materials Ore-dressing Geological modeling UCTD Mathebula, Ivan Pardon Geometallurgical modelling of manganese ore comminution behaviour at the kalahari manganese field. |
| title | Geometallurgical modelling of manganese ore comminution behaviour at the kalahari manganese field. |
| title_full | Geometallurgical modelling of manganese ore comminution behaviour at the kalahari manganese field. |
| title_fullStr | Geometallurgical modelling of manganese ore comminution behaviour at the kalahari manganese field. |
| title_full_unstemmed | Geometallurgical modelling of manganese ore comminution behaviour at the kalahari manganese field. |
| title_short | Geometallurgical modelling of manganese ore comminution behaviour at the kalahari manganese field. |
| title_sort | geometallurgical modelling of manganese ore comminution behaviour at the kalahari manganese field |
| topic | Manganese ores -- Geology Size reduction of materials Ore-dressing Geological modeling UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/131826 |
| work_keys_str_mv | AT mathebulaivanpardon geometallurgicalmodellingofmanganeseorecomminutionbehaviouratthekalaharimanganesefield |