Luis Martinez Tipe
Mine planning is more than just defining pit shells and sequences. It involves navigating a range of variables—geology, economics, processing capacity, and operational limitations. MiningMath solves key inefficiencies in conventional mine planning. At the heart of its solution is a custom Branch & Cut algorithm, designed to optimize directly from the block model in a single step.
Some true/false geostatistic and mine plan optimisation questions to warm up the brain (what do you reckon are these statements true or false?):
1. Kriging and conditional simulation are different processes that output different results.
2. N>>1 equally probable conditional simulations (realisation) of an orebody will generate N equally probable mine plans.
3. Kriging is an expected (average) metal grade block model.
4. The mine plan and design optimisation process is a non-linear process.
5. Kriging block grade model is the best block model to run the mine plan and design optimisation process where the (technical and economic) results, including the mine plan and design, are seen as expected (averages) results.
6. Run of Mine metal grade variability is because geological metal grade uncertainty.
7. Operational uncertainty has nothing to do with run of mine metal grade variability.
8. Cutbacks, or pushbacks, are generated/designed before the mine scheduling.
9. The main sources of uncertainty when planning and designing a mine project are: geological, capex, operational, and economic.
10. The mine project evaluation process (that includes the mine plan, design and valuation processes) is a forecasting process.