This multidisciplinary report explores the transition potential of the Phalaborwa region, South Africa, from a mining-focused economy to one that integrates sustainable water, energy, and food (WEF) systems in a post extractive setting. Set within a semi-arid climate with significant resource challenges, the study assesses WEF capacities to propose strategic, sustainable development solutions. Analysing critical issues like water scarcity, renewable energy potential, and soil management, the study presents frameworks for sustainable agriculture, water management, and energy solutions to support post-mining economic resilience. Through a multidisciplinary methodology that integrates engineering assessments and urban planning, the report addresses critical biophysical resource issues. The findings emphasize the region’s unique resource inter dependencies, outlining frameworks for post-mining development that strengthen resilience to climate pressures and resource limitations. Ultimately, this study provides actionable insights for creating a balanced, sustain able future for Phalaborwa and the surrounding area. Although the study proposes frameworks that could inform similar transitions in other semi-arid, resource-constrained regions, it also emphasizes the importance of addressing the unique complexities of each area to ensure that solutions are appropriately tailored.

Mineral resource modelling (MRM) requires enough geological information to define the geological model. The success of a mining project is supported by the accuracy of this model and its interpretation. Major failures can occur as a result of an incorrect degree of uncertainty quantification in the geological/geometallurgical models. There are different techniques in the industry today to reduce uncertainty in MRM. All techniques respect the statistical and geostatistical properties of the constraining data, although they vary in the specifics and the approach and they all rely on the stationarity assumption, which is not a testable hypothesis but rather the choice to collect data from a certain area or domain. This paper aims at developing a framework for reducing associated uncertainties with MRM using Bayesian Evidential Learning (BEL). BEL enables to model posterior distributions in prior model spaces using predefined parameters. It provides an indication of how future data might appear, given the data and model. The Walker Lake dataset is used to test the framework. The objective is to reduce the uncertainty in the prediction of the hardness of the orebody. First, the model is built with data from lithology, mineralogy, penetration rate and grade. These properties are obtained from samples that are spatially correlated. Then, Monte Carlo realizations are obtained based on the exploration data and the assumed uncertainty range. A relationship needs to be obtained between lithology, grade and mineralogy and hardness variables. PCA is applied to get a better visualization by looking at the most influential properties. The observed data are used to compare and see if the prior model needs to get falsified. It is determined that the penetration rate and the lithology are the most influential properties. After that, Canonical Correlation Analysis (CCA) is applied to find the combination of the variables that have the maximized linearity between the penetration data and the prediction data. The predictions are made and then back-transformed to their original space. Finally, the hardness predictions are not falsified by the observed data from the drillholes. These predictions are used to domain the orebody into soft, medium or hard materials.

Mineral Resource Modeling (MRM) is used to predict the properties of an orebody, however it does not come without uncertainties. Multiple approaches can be used to reduce the latter. Due to limited knowledge about the subsurface, predictions are difficult to be made. In this thesis the application of Bayesian Evidential Learning (BEL), in order to reduce uncertainties on MRM, will be researched. The uncertainties present in MRM will be linked to the knowledge obtained from case studies in different geological domains where BEL has been applied successfully and reduced certain parameter uncertainties. The gap in todays industry is the knowledge and proof that using BEL for MRM will reduce uncertainties and risks, works effectively and will consume less money. The aim of this study is to show that BEL is a useful approach to reduce uncertainty of the predictions in MRM. The success of a project is supported by the accuracy of the model utilized and the geological interpretation. BEL is a framework based on statistical relationships between data and prediction variables. It will predict the posterior distribution of the prediction variable. The various case studies that have been discussed are (Hermans et. al, 2019), (Hermans et. al, 2018), (Thibaut et. al, 2021) and (Tadjer and Bratvold, 2021). BEL has successfully been able to reduce uncertainties related to geological problems of the following:
• The temperature in an alluvial aquifer
• The efficiency of the thermal energy storage capacity in an alluvial aquifer
• The wellhead protection areas surrounding the pumping well using tracing experiments as predictors
• The prediction of leakages of CO2 and the storage of CO2
Thus, BEL can be seen as a potential approach to also reduce uncertainties in a mineral resource domain; the research of this thesis. In attempt to prove this, a descriptive case study on Tropicana Gold Mine has been executed. The aim is to show that the use of BEL will, based on the geological and geochemical properties such as lithology, grade and mineral type, reduce the uncertainty in the prediction of a geometallurgy property, namely the hardness of a rock. The six steps of BEL’s framework will be followed consisting of Monte Carlo simulations, Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) in order to obtain relations between the data and predictor variables. Where the data variables are from exploration drillhole data and prediction variable the hardness of the rock. It shows that BEL is able to reduce the uncertainty of a geometallurgy property by using geological and geochemical properties. Meaning BEL can be applied to MRM to reduce uncertainties.