The mining industry is a significant contributor to greenhouse gas emissions and operational costs. The transportation of materials within mining operations, especially using diesel-powered trucks, accounts for a substantial portion of both emissions and costs. Optimizing fuel consumption in this context is crucial for environmental sustainability and financial efficiency. While there is some existing research on minimizing fuel consumption in mining operations, most of these studies prioritize production over fuel efficiency. Additionally, there is a lack of solutions addressing real-time dispatch problems while also minimizing fuel consumption. This thesis proposes the development of a model to optimize truck speeds in mining operations, with the primary goal of reducing fuel consumption. The focus is on minimizing waiting times for trucks at loading stations by adjusting their speeds using a metaheuristic algorithm, specifically Simulated Annealing (SA). To achieve this, a discrete event simulation (DES) model made in HaulSim to replicate a simplified mining site in Nevada, USA. The simulation includes Caterpillar 793 series trucks. The SA algorithm is applied to find optimal truck speeds, with the aim of reducing fuel consumption with the same level of production. The hypothesis is that lowering truck speeds in mining environments with queueing trucks can significantly reduce fuel consumption. The study explores how speed optimization impacts efficiency across different mining conditions, identifies the most effective optimization techniques, and quantifies potential fuel savings. The research reveals a 27-32% reduction in fuel consumption across various truck scenarios. However, these findings should be interpreted cautiously due to limitations in data constraints and modelling simplifications. In conclusion, the study highlights the effectiveness of SA in optimizing truck speeds and the potential for substantial fuel savings in mining operations. We recommend the integration of Artificial Neural Networks (ANNs) for a more nuanced approach to fuel consumption estimation and optimization, considering factors like road maintenance intervals and payload. This synergistic approach, combining metaheuristics and machine learning techniques, aligns with sustainable practices in the mining industry and offers promising avenues for further research and application.

Up until now, global mining companies have failed to create a fatality free environment for their employees, whether it be through increasingly difficult conditions or inadequate safety management approaches. Desiring to maintain the social license to operate, these companies are now looking into alternate ways of safety management with the aim of mitigating fatalities in the workplace.
In this study, a new risk management approach was applied to mitigate fatal incidents through the utilization of critical controls. The aim of this study was to create a scalable, minimally invasive proof-of-concept for AngloGold Ashanti that can successfully be implemented at any of the company’smining operations.
The foundation for this implementation was laid through a literature study blending contemporary safety and risk management approaches with Business Intelligence systems thinking and a perspective on industry best-practices. Subsequently, a framework of organizational requirements was set up based on 26 interviews with stakeholders in various departments within the company. Both design and implementation were conducted on-site at an AngloGold Ashanti operation in Western Australia.

The system was designed by adhering to organizational requirements, and ensuring that it is suitable to any mining environment. The designed Critical Control Management System was subsequently implemented at Sunrise Dam, one of AngloGold Ashanti’s Australian mining operations. To ensure that critical controls were also assessed at the operational level, a workplace inspection process was modified to generate control data. All sources of data subsequently were fed into a Business Intelligence environment enabling insight into critical control performance to all company stakeholders. Doing so informs decision-making on safety priorities company-wide, based on real-time data generated on the operational level.

Two case studies were performed to assess two of the most significant hazards at Sunrise Dam. The studies showed that the effectiveness of reactive controls changes irrespective of their compliance and performance. Furthermore, the influence of human factors within risk management remains difficult to quantify. Finally, it demonstrates the potential for integration of incident data into the Critical Control Management System, thus creating both leading and lagging indicators for safety performance.

The conclusion of this study is that an effective and scalable Critical Control Management System can be successfully implemented in a mining operation if the right conditions are generated. The approach of integration in existing processes demonstrates that companies can achieve greater control over fatality prevention without the need for an additional safety management system. On this basis, it is recommended that other operations are supported in creating an environment suitable for adaptation before Critical Control Management is implemented.

The metal cobalt is essential in creating a sustainable society, as it is an important component in lithium- ion batteries and wind energy turbines. About half of the Earth’s land-based cobalt reserves can be found in the Democratic Republic of Congo (DRC). This study focusses on characterising cobalt-bearing copper ore from a DRC based mining operation and linking characteristic ore properties to the comminution behaviour. Portable X-ray Fluorescence (pXRF) is used to determine the geochemistry, while QEMSCAN, CT scan and InfraRed (IR) spectroscopy are used to assess mineralogy on different length scales. In terms of geotechnical properties, the surface hardness of the ore is determined using a rebound hardness tester. The comminution behaviour is assessed by crushing selected ore samples below 3.35 mm and by grinding the crushed samples for 21 minutes using a ball mill. The energy consumption required for different ore types is quantified using the crushing and sieving simulation package Bruno.

The chemical characterisation of ore samples showed significant variation in cobalt content, as the cobalt grade measured in the samples using pXRF varied from 0% to 33.5%. By applying QEMSCAN analysis to samples coming from different locations within the deposit, two different mineralogical zones have been identified: a weathered, and a sulphide zone, with large differences in modal mineralogy. In the weathered zone kolwezite and heterogenite are the principal cobalt bearing minerals, in the sulphide zone carrollite is the only mineral containing cobalt. The CT scan confirmed the unique mineralogy of certain samples, as the mineralogy of pure carrollite nodules found using QEMSCAN showed to continue in 3D. Both QEMSCAN and CT scan were able to detect differences in mineral texture of the studied samples.
In terms of geotechnical properties, differences between surface hardness of the samples were observed using the rebound hardness tester. An empirical formula has been derived to adjust the measured surface hardness of drill core samples with a mass below 300 grams. This formula aided in identifying the differences in surface hardness for the distinct mineral textures of the sulphide zone. Influence of mineralogy on the sur- face hardness has been observed, as areas containing only carrollite showed to have a lower surface hardness than areas with a mixture of Mg silicates, quartz and carrollite.

Fourier Transformed IR (FTIR) spectroscopy was used to identify a range of gangue minerals within the samples. Carrollite could be identified if it was present in significant proportions and no other IR active minerals were present. FTIR spectroscopy also allowed to refine mineralogical data, as it was possible to differentiate between talc or serpentine and muscovite or illite in the studied samples, which was not possible using QEMSCAN. In total 8 minerals were identified using IR spectroscopy. The relative proportions of the modal mineralogy showed to be in agreement with the mineralogy found through QEMSCAN.

The comminution experiments led to the identification of three different comminution classes. These three classes can again be linked to the identified mineral texture types. The surface hardness showed to be correlated to the p80 of samples with the same mineral texture. After 21 minutes of milling, the samples with a higher surface hardness had a coarser p80. Simulation showed that the crushing energy requirements differ up to a maximum of 27% for different comminution classes. This research provides a foundation towards identification of geometallurgical domains in the sulphide section of the investigated cobalt-bearing copper deposit.

This MSc thesis was carried out at Camborne School of Mines (University of Exeter) as part of the NERC funded Cobalt: Geology, Geomicrobiology and Geometallurgy (CoG3) project.