Reduction in the Energy Cost of Minerals through at-the-face Comminution and Separation of Mineral and Waste [abstract]

Only abstract of poster available.Track III: Energy InfrastructureHigh-pressure waterjets penetrate into material through the pressurization and growth of small cracks within the target surface. In mineral ores the individual grains of the constituent components are defined by the grain boundaries and these provide such surface cracks. Eroding the ore by a stream of high-pressure water can thus exploit the cracks so that they grow, inter-connect and remove the ore on a grain by grain basis. This separates out the individual components of the ore, as the ore is mined. Because the properties of the different mineral grains differ, either in size, density or shape they can be separated, often quite easily, at the mining machine, as the grains are collected after being removed from the face. Thus, at the point of mining, the valuable components of the ore can be separated and collected. The remaining waste minerals can then be left adjacent to the mining face, potentially being re-cemented to provide support to the ongoing excavation. This joint mining and separation process saves the cost of transporting the waste rock out of the mine, and the costs of conventional separation of the valuable material at the surface. In current practice, all the ore mined is crushed, at the surface, to a very fine powder in order to achieve liberation of the valuable mineral. As well as requiring considerably more energy this also produces a very fine waste product, which is more expensive to dispose of, often behind large tailings dams at the surface, at an environmental cost. The use of pressurized cavitation to enhance the process, and reduce energy needs and process time is a part of this work. This new process is anticipated to drop the energy cost of mineral production by up to 60% and has been validated in laboratory and some field tests

Efficient Cable Shovel Excavation in Surface Mines

The cable shovel is widely used in surface mining. High operating and ownership costs necessitate efficient use of the cable shovel. Operator practices have long been suspected to contribute towards the inefficient use of the shovel. Crowd arm and hoist rope speeds are key measures of operator practices. the objective of this work is to find the crowd arm and hoist rope speeds for optimal shovel performance for given initial conditions and material properties. Shovel kinematics and dynamic modeling, using shovel geometry and the simultaneous constraint method, respectively, have been employed to build models of the excavation process. Dynamic models of the shovel payload and the material cutting resistance have also been developed using geometric simulation and passive soil pressures techniques, respectively. These models are solved numerically by combining Runge-Kutta and Gaussian elimination algorithms to compute the work done and the resistive forces during shovel excavation. the algorithms have been combined into a shovel simulator. the simulator has been used to simulate the P&H 2100BL shovel. the simulation results indicate that input energy and digging time increase with increasing crowd arm and decreasing hoist rope speeds. the input energy per unit loading rate is proposed as an appropriate measure of shovel performance. High energy per unit loading rate occurs for high crowd speeds and low hoist rope speeds. for the simulated conditions and crowd arm and hoist rope speeds ranging from 0.25 to 0.5 ms-1 and 0.5 to 0.7 ms-1, respectively, the optimal crowd arm and hoist rope speeds were found to be 0.25 ms-1 and 0.7 ms-1, respectively, and the objective function value was 0.21 KJs/kg. This work establishes, theoretically, the fact that operator practices have an effect on shovel performance and is useful in establishing optimum practices. the results are the initial steps towards full automation of the excavation process. © 2010 Springer Science Business Media B.V

Production Scheduling and Waste Disposal Planning for Oil Sands Mining Using Goal Programming

In oil sands mining, timely provisions of ore and tailings containment with less environmental footprints are the main drivers of profitability and sustainability. The recent Alberta Energy Resources Conservation Board Directive 074 requires oil sands waste disposal planning to be an integral part of mine planning. This requires the development of a well integrated strategy of directional mining and tailings dyke construction for in-pit and ex-pit tailings storage management. The objectives of this paper are to: 1) determine the order and time of extraction of ore, dyke material and waste that maximizes the net present value; 2) determine the destination of dyke material that minimizes construction cost; and 3) minimize deviations from the production goals of the mining operation. We have developed, implemented, and verified a theoretical optimization framework based on mixed integer linear goal programming (MILGP) to address these objectives. This study presents an integration of mixed integer linear programming and goal programming in solving large scale mine planning optimization problems using clustering and pushback techniques. Application of the MILGP model was presented with an oil sands mining case. The MILGP model generated a smooth and uniform mining schedule that generates value and provides a robust framework for effective waste disposal planning. The results show that mining progresses with an ore to waste ratio of 1:1.5 throughout the mine life, generating an overall net present value of $14,237M. This approach improves the sustainable development of oil sands through better waste management

High Speed Production of Large Coal to Facilitate Easier and More Effective Cleaning

Track I: Power GenerationIncludes audio file (14 min.)Due to technical difficulties, the audio portion of this presentation is joined in progress.Most modern mining equipment extracts coal through grinding it from the solid using a set of rotating picks. This produces coal that is quite small in average size and generates a lot of dust in the process. The coal is also more expensive to collect and process to remove contained undesirable components. The use of high-pressure water jets as a cutting tool has been shown to provide a product that is larger in size, while con-commitantly eliminating the generation of dust (which carries with it the risk of ignition and explosion) and reducing the energy required for the mining process. Two different mining machines are described, one for use on longwall faces and one in room and pillar mines, and the potential for their development is discussed

Responsiveness of Mining Community Acceptance Model to Key Parameter Changes

The mining industry has difficulties predicting changes in the level of community acceptance of its projects over time. These changes are due to changes in the society and individual perceptions around these mines as a result of the mines\u27 environmental and social impacts. Agent-based modeling can be used to facilitate better understanding of how community acceptance changes with changing mine environmental impacts. This work investigates the sensitivity of an agent-based model (ABM) for predicting changes in community acceptance of a mining project due to information diffusion to key input parameters. Specifically, this study investigates the responsiveness of the ABM to average degree (total number of friends) of the social network, close neighbour ratio (a measure of homophily in the social network) and number of early adopters ( innovators ). A two-level full factorial experiment was used to investigate the sensitivity of the model to these parameters. The primary (main), secondary and tertiary effects of each parameter were estimated to assess the model\u27s sensitivity. The results show that the model is more responsive to close neighbour ratio and number of early adopters than average degree. Consequently, uncertainty surrounding the inferences drawn from simulation experiments using the agent-based model will be minimized by obtaining more reliable estimates of close neighbour ratio and number of early adopters. While it is possible to reliably estimate the level of early adopters from the literature, the degree of homophily (close neighbour ratio) has to be estimated from surveys that can be expensive and unreliable. Further, work is required to find economic ways to document relevant degrees of homophily in social networks in mining communities

Identifying the Presence of AMD-Derived Soil CO₂ in Field Investigations Using Isotope Ratios

Recent incidents of hazardous accumulations of CO2 in homes on or adjacent to reclaimed mine land have been shown to be linked to neutralization reactions between acidic mine drainage and carbonate material. An efficient and economic method is necessary to identify the presence of acid mine drainage- (AMD-) derived CO2 on reclaimed mine land, prior to construction. One approach to identify the presence of AMD-derived CO2 is to characterize stable carbon isotope ratios of soil CO2. To do so, a viable method is necessary to acquire soil gas samples for isotope ratio analysis. This paper presents preliminary investigations of the effectiveness of two methods of acquiring gas samples (sampling during soil flux measurements and using slam bar) for isotope analysis. The results indicate that direct soil gas sampling is cheaper and provides better results. Neither method is adequate without accounting for temporal effects due to changing gas transport mechanisms. These results have significant implications for safe post-mining land uses and future investigations of leakages from geologic carbon sequestration sites

The Impact of Clean Energy Technology Incentives on Mineral Demand: An Economic Framework for Measuring Response

The existing literature establishes the link between clean energy technology (CET) expansion and rising mineral demand but lacks a comprehensive analysis of the transmission mechanisms through which CET policy incentives particularly production and consumption subsidies affect mineral markets. This study addresses the gap by developing an economic model where a CET producer optimally determines mineral input demand and production volume, where the concept of elasticity is used to measure the response of mineral demand to policy incentives. Analytical results demonstrate that the elasticity of mineral demand with respect to CET policies is highly sensitive to returns to scale of CET production, mineral prices, and CET market size. Model implementation using most current data (2023/2024) from the US electric vehicle battery market suggests that subsidies have a stronger impact on mineral demand with declining but not constant returns. Further extrapolations show that the estimated elasticity changes with changes in market size and mineral prices but not with technical efficiency. These findings highlight the importance of integrating mineral market considerations into CET policy design to ensure sustainable resource availability for clean energy transitions

YOLO-Based Miner Detection Using Thermal Images In Underground Mines

Well-designed and effective in-mine robots can expedite miner self-rescue during emergencies and reduce fatalities. These in-mine robots for miner self-rescue can carry out diverse tasks such as scouting (including object detection and autonomous navigation), and payload delivery. However, robots that can effectively detect humans in a dark underground mine do not yet exist. This paper investigates challenges in the design of object detection algorithms for in-mine robots using thermal images, especially to detect people in real-time, in low-light conditions. The research team collected 500 thermal images in the Missouri University of Science & Technology Experimental Mine with the help of student volunteers using the FLIR TG 297 infrared camera, which they pre-processed and split into training and validation datasets with 450 and 50 images, respectively, using tenfold cross-validation. The research retrained two state-of-the-art, real-time object detection models, namely YOLOv5 (You Only Look Once version 5), and YOLOv8 (You Only Look Once version 8), using transfer learning techniques on the training dataset for 50 epochs. On the validation dataset, the re-trained YOLOv8 outperforms the re-trained YOLOv5. These trained models as well as the original models were then applied to a simulated mine fire emergency to assess their performance in emergency situations. The results show that the mAP of the YOLOv8 variants improved drastically after transfer learning. For instance, YOLOv8n improved from 13.90 to 74.70%. And that of the YOLOv5 variants improved significantly. For instance, YOLOv5n improved from 42.10 to 68.30%

Eliciting Drivers of Community Perceptions of Mining Projects through Effective Community Engagement

Sustainable mining has received much attention in recent years as a consequence of the negative impacts of mining and public awareness. The aim of this paper is to provide mining companies guidance on improving the sustainability of their sites through effective community engagement based on recent advances in the literature. It begins with a review of the literature on sustainable development and its relationship to stakeholder engagement. It then uses the literature to determine the dominant factors that affect community perceptions of mining projects. These factors are classified into five categories: environmental, economic, social, governance and demographic factors. Then, we propose a new two-stage method based on discrete choice theory and the classification that can improve stakeholder engagement and be cost-effective. Further work is required to validate the proposed method, although it shows potential to overcome some of the challenges plaguing current approaches

Corporate Social Responsibility: Understanding the Mining Stakeholder with a Case Study

The social responsibility of corporate mining has been challenged by a significant socio-political risk from local communities. These issues reduce shareholder value by increasing costs and decreasing the market perception of corporate social responsibility. Community engagement is the process of understanding the behavior and interests of a group of targeted mining communities through surveys and data analysis, with the purpose of incorporating mining community acceptance into the mining sustainability. While mining organizations have discussed community engagement to varying degrees, there are three main shortcomings in current studies, as concluded in the authors\u27 previous research. This paper presents a framework to apply discrete choice theory to improve mining community engagement and corporate mining social responsibility. In addition, this paper establishes the main technical challenges to implement the developed framework, and presents methods to overcome the challenges for future research with a case study. The contribution of this research will transform mine sustainability in a fundamental way by facilitating the incorporation of effective community engagement. This will lead to more sustainable mines that local communities support