Abstract
Declining global ore grades and sustainability are two fundamental challenges the mining industry will face for generations to come. Intelligent high resolution ore sorting, via a smart-shovel system that provides real-time ore characterization and grade data variability on every bucket, has been extensively field-tested and proven to maximize ore recovery and minimize dilution. The mine's extraction face, where the ore is most heterogeneous, is where this unique data has the greatest environmental and profit impact.
Every bucket full is a data collection event that unlocks smart, informed truck routing decisions, reduces ore loss and dilution, and provides the ability to optimize downstream processing. Additional benefits also support environmental, social, and governance (ESG) objectives by reducing the use of energy, water, and reagents per tonne of metal.
MineSense's ShovelSense® system measures a range of elements grade such us Cu, Fe, Ni, Zn, As and others in real-time, using a system of proprietary hardware and software data analytics and algorithms. The system is installed in the bucket of existing production shovels or front loaders without the need for additional capital equipment spend or footprint. Real-time data collection capability allows smart routing decisions for each shovel bucket to be accurately classified as ore or waste before being hauled, increasing operational efficiency, improving the mine plan and profitability.
Automated, smart decision-making optimizes profits by reducing the loss and dilution of ore at the mine face. Additional benefits include optimized operational and sustainability performance in recovery, production, and waste, which are critical to mining operations and stakeholders. In addition, smart decision-making can also increase mine life through the improvement of ore recovery from the existing ore body and target new ore bodies with new geological trend knowledge.
ShovelSense’s Global Navigation Satellite System (GNSS) defines the bucket grade and ore characterization for each bucket location. This combination provides a previously unavailable spatial resolution of the ore body. This data adds value in mine planning, by improving the predictive analytics for various downstream operations as shown in the Copper Mountain case study, through the identification of differences in Cu grade not reflected in the mine plan.