Mining

The mining industry faces the critical challenge of balancing economic profitability with environmental responsibility. Traditional mine planning models often prioritise financial gains, particularly Net Present Value (NPV), while placing less emphasis on environmental impacts, such as carbon emissions. This research presents a comprehensive multi-objective optimisation model for production scheduling in sublevel stoping operations. The model simultaneously aims to maximise NPV and minimise carbon emissions, providing a more sustainable framework for decision-making. The carbon emission objective comprehensively accounts for energy consumption across all key mining activities, including drilling, blasting, ventilation, transportation, crushing, and backfilling, using a “top-down” accounting method. The multi-objective problem is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which generates a set of Pareto-optimal solutions representing the trade-off between the two conflicting goals. The model is applied to a conceptual copper deposit with 200 stopes. The results demonstrate a clear trade-off: schedules with higher NPV inevitably lead to higher carbon emissions, and vice versa. For instance, one solution yields a high NPV of $312.94 million but with 23,602 tonnes of CO₂ emissions. In contrast, another, more environmentally friendly solution reduces emissions by 26.5% to 18,647 tonnes, resulting in only a 1.21% reduction in NPV. This research concludes that integrating environmental objectives into mine planning is not only feasible but essential for promoting sustainable mining practices, offering a practical tool for operators to make informed, balanced decisions.

Fires in underground mines pose significant risks to worker safety. In this study, a digital twin of an underground mine was created, and the heat, gas distribution, and airflow dynamics were investigated during and after the fire using computational fluid dynamics (CFDs) methods at three different locations. While traditional methods did not indicate any problems, the results from the CFDs analyses revealed some important findings. One of the key findings of the study was the change in airflow direction caused by the changing thermodynamic conditions caused by the fire. The digital twin allows us to demonstrate how a fire at any point within the mine can affect the entire mine under these changing thermodynamic conditions. The digital twin enables the real-time monitoring of underground events. Additionally, it facilitates strategic planning to anticipate potential incidents during a fire in an underground mine, allowing for necessary precautions to be implemented.

With the weathering of iron sulfide minerals, acid rock drainage (ARD) emanates from the 60-millon tonne Main Waste Stockpile (MWS) at the Red Dog Mine. Following completion of the stockpile, a collection trench was constructed in 2012–2013 to capture and treat a portion of the ARD, and a cover system was emplaced from 2021 to 2025 to cover 90% of the stockpile. Select wells in the collection trench are associated with the different cover phases. Analysis of the water chemistry of samples collected at the wells indicates increased pH and decreased dissolved solids with each phase of the cover along with significant changes in flow and solutes such as aluminum, iron, sulfate, and zinc. Although the cover should continue to decrease ARD volume, acidity, and solute concentrations, an evaluation of historical acid production and iron sulfide consumption in the stockpile indicates a likely majority of the iron sulfide content remains available for weathering and acid production. Continued MWS ARD monitoring is necessary to evaluate the multi-year effect of the cover because of the variability of the pre-cover ARD, identification of seasonal and multi-year precipitation influences on ARD generation, and a yet to be determined influence of the cover on the volume of infiltrating precipitation.