Minimization of CO2 Emissions in Openpit Mines by Using Stochastic Simulations

Modifications in European environmental legislation requiring the minimization of the environmental footprint of mining operations have resulted in increased environmental costs and fewer investments in new surface mines. Due to the significant dependence of the global economy on mining, which provides raw materials and energy for most industries, it is essential to develop the necessary technologies for reducing pollutant emissions and exploitation costs. In open pit or underground mining operations, the highest cost comes from loading and hauling the extracted ore. Hence, the optimal combination of loading and hauling equipment has a significant impact on the production rate of the mine/quarry. The primary aim of this research is to improve the production of a surface mining operation by modifying the operational parameters (different dumping positions of materials) of the loading-hauling equipment in such a manner as to reduce fuel consumption and emitted pollutants. This aim is achieved by optimizing the hauling cycle by examining different scenarios utilizing stochastic simulation based on queue theory. The queue theory is a stochastic method commonly used to simulate the shovel-truck haulage system of a mine operation. This method has been implicated to estimate pollutants emitted in the atmosphere and propose alternative scenarios for reducing emissions normalized with the hauled material. The method is validated against actual data from a large open pit. The implementation of queue theory and estimating fuel consumption and greenhouse gas (GHG) emissions are derived from the $$M/M/1$$ and $$M/M/2$$ queue models. These two scenarios were examined for two different transportation routes and dumping positions. Based on these scenarios, it was found that the case $$M/M/1$$ with one electrical shovel having the nearest dumping position gives the minimum GHG emissions. Regarding the environmental impact, in all examined cases, the optimum truck fleet was the one with five trucks. From the productivity point of view, for the $$M/M/1$$ and $$M/M/2$$ scenarios, the number of trucks was six and eight, respectively. The greatest production was achieved in $$M/M/2$$ scenarios, with the one having the maximum distance to the dumping position being the one closest to actual data from the mine site. Another scenario, considering the implementation of the Trolley Assist haulage (TA) system resulted in a local minimum decrease by 54% for the $$M/M/2$$ TA Route 1 (longest) and for the $$M/M/2$$ TA Route 2 (shortest) by 62% of kg CO2 per tons of hauled material for the selected truck fleet size.