optimize milling time of each block Levinson et al. 2023 Maximize DCF, penalize deviations from production targets, minimize cost of preconcentration facility use Longterm SMIP CPLEX, metaheuristics with ML (reinforcement learning) Another potential application of geometallurgical data in mining enterprises is smart optimization of the milling circuit based on energy supply. In operations that are connected to national grids with a dynamic energy pricing policy, the feed can be adjusted to capitalize on lower electricity prices during off-peak hours to process harder material, reducing costs. Similarly, if the operation has access to renewable energy sources, such as PV panels or wind turbines, the material with less desirable grinding characteristics can be scheduled for milling in peak production periods, minimizing emissions. Pamparana et al. (2017) presents one such example. However, this approach does not acknowledge the geological uncertainty of the feed material and relies on sampling the stockpiled material. The combination of geometallurgy and stochastic optimization of mining complexes can further be developed by integrating direct feedback on material qualities from the processing stage. In light of the increasing application of real-time mining techniques, the geometallurgical data collected downstream can be used to update the extraction models (Anvari and Benndorf 2025). This would offer an opportunity to decrease uncertainty and generate more robust, dynamically adjusted short-term schedules that could better align with the feed requirements of the processing stage. 3.2 Transport optimization Similar to comminution, material moving constitutes another energy-intensive process in the mining industry. Given the fact that the vehicle fleet accounts for a large portion of capital and operational costs for the mining operation, multiple methodologies exist to address the issue of fleet sizing and management. However, the application of stochastic methods in the field is still relatively limited, despite their potential. Current developments involve the inclusion of the trucking cost in the extraction optimization frameworks, effectively changing the schedule to better manage the fleet size and hauling routes. This approach leads to a decrease in active material movement, lowering the fuel consumption and more efficient utilization of purchased equipment, eliminating the emissions associated with an oversized fleet. Another notable development is the inclusion of fuel consumption directly into the optimization function. The summary of the discussed methodologies is available in Table 3. The work by Spleit and Dimitrakopoulos (2017) mentioned in the waste management section aims to decrease the haulage fleet requirements while optimizing the extraction sequence. This is done by minimizing trucking costs in the objective function
RkJQdWJsaXNoZXIy MTM0Mzk2