Best Case

  1. Get The NPV Upper Bound

In order to get the best result that the project could generate, this scenario explores the whole solution space without any other constraints than processing capacities, in a global multiperiod optimization. As MiningMath optimizes all periods simultaneously, it might find higher NPVs than LG/Pseudoflow nested pits procedures, given the NPV maximization, cutoff policy optimization, and stockpiling. This execution provides the best striping ratio on your project since MiningMath in free to uncover the best NPV considering the relation between ore and waste, and serves as a reference for when you are comparing different best cases obtained in other softwares and the optimized block destinations could be imported back into any mining package, even if the purpose is to run LG with a DBS cutoff policy in place.

Example:

    • Processing capacity: 10 Mt per year.

    • Stockpiling parameters on.



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1.1 Further information and Refinements

It is important to mention that if you have multiple destinations, extra processing, or dump routes, could be added for proper cutoff optimization. Beyond that, the surfaces obtained here could be used in further steps or imported back into any mining package for pushback design and scheduling.

A refinement of the best case could be done by adding more constraints, preferably one at the time to evaluate each impact in โ€œreservesโ€, potential conflicts between them, and so on. You can try to follow the suggestions below for this improvement:

  • All blending constraints

  • All restrict mining aspects due to forbidden areas

  • Extra processing or dump routes for proper cutoff optimization

  • Sum variables (with caution) just in case some aspect must be controlled for the whole LOM at once.

  • In case more efficiency is needed, the resulting surface obtained in the Constraints Validation step could be used as restrict mining for the runs here.

These scenarios might take longer and the main recommendation is to use powerful machine to run it, in parallel, while other optimizations are performed.


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