RESUMO
Small-scale mining usually operates under high geological uncertainty conditions. This turns mine planning into a complex and sometimes inaccurate task, resulting in low productivity and substantial variability in the quantity and quality of the mineral products. This research demonstrates how the application of a novel methodology that relies on traditional and low-cost geophysical methods can contribute to mine planning in small-scale mining. A combination of resistivity and induced polarization methods is applied to enhance mine planning decision-making in three small-scale mining operations. This approach allows for the acquisition of new data regarding local geological settings, supporting geological modelling and enhancing decision-making processes for mine planning in a timely and low-cost fashion. The results indicate time savings of up to 77% and cost reductions of up to 94% as compared with conventional methods, contributing to more effective mine planning and, ultimately, improving sustainability in small-scale mining.
RESUMO
The mining operations of loading and haulage have an energy source that is highly dependent on fossil fuels. In mining companies that select trucks for haulage, this input is the main component of mining costs. How can the impact of the operational aspects on the diesel consumption of haulage operations in surface mines be assessed? There are many studies relating the consumption of fuel trucks to several variables, but a methodology that prioritizes higher-impact variables under each specific condition is not available. Generic models may not apply to all operational settings presented in the mining industry. This study aims to create a method of analysis, identification, and prioritization of variables related to fuel consumption of haul trucks in open pit mines. For this purpose, statistical analysis techniques and mathematical modelling tools using multiple linear regressions will be applied. The model is shown to be suitable because the results generate a good description of the fuel consumption behaviour. In the practical application of the method, the reduction of diesel consumption reached 10%. The implementation requires no large-scale investments or very long deadlines and can be applied to mining haulage operations in other settings.