MiningMath

MiningMath

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One of the unavoidable steps for the next generation of Data Science and Artificial Intelligence technologies applied to mining

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Estimated reading: 4 minutes 1443 views

MiningMath doesn’t necessarily compete against mine scheduling optimization packages. The only concept we and all research centers worldwide recommend mining companies to overcome is the one related to Pit Optimization, due to the set of problems you have to face when dealing with such technology.

Figure 1: Lerchs-Grossmann/Pseudoflow

Therefore, even our simplest version has more features to generate nested pits with better control so that you could design better pushbacks and define a mine schedule using your preferred tool. The reason why this software can deliver such results is the Direct Block Scheduling methodology based on Mixed Integer Linear Programming (MILP) model and proprietary heuristics. Check other technical details and related research in our theory section.

Figure 2: Direct to block scheduling.

MiningMath also allows you to generate optimized pushbacks, which could facilitate your design process and guide your mine schedule while using other software packages. Notice that our tool is an optimizer that simply breaks the whole deposit (your block model) into smaller pieces, aiming for maximum Net Present Value, but respecting as many constraints as you wish:

Figure 3: Optimized pushbacks and optimized schedules.

A usual application of our technology is basically on strategy optimization for building decision trees. Once we run dozens/hundreds of scenarios of the yearly schedule optimization and fine-tune their parameters/constraints, our users take some of the resulting surfaces of MiningMath and use them to design some pushbacks so that they could integrate with other packages, such as MSSO, COMET, etc. This procedure could be accelerated/simplified by working with packages of years and finding shapes closer to pushbacks you’re used to.

The outputs of our software will serve basically as optimized pushbacks, searching for maximum NPV and controlling whatever variable you consider necessary. Once we manage to import MiningMath surfaces into the other package, they will serve as guidance and they should assist the other package in finding higher NPVs. Most of these packages also allow us to predefine the blocks’ destination, if we wish to use MiningMath optimized cutoff policy. Finally, the package should have “only” the duty to do the bench scheduling, according to your short-term operational/tactical needs.

Even if you decide, for any internal reason, that you have to use LG/Pseudoflow to define final pit limits, there is no problem at all. MiningMath is the only tool available in the market capable of performing complete strategic analysis by building decision trees unconstrained by predefined pushbacks. Please, check this short example (in Spanish) with dozens of scenarios just for the decision on CAPEX regarding processing capacities. Check also the second half of this video for a broader view on how to use the same concept to take strategic decisions on many other aspects related to mine projects or ongoing operations. I assure your managers will get much more interested in your reports once you start adding this sort of strategic analysis. Notice you could perform this sort of analysis either free of constraints or respecting any pre-existing (designed) ultimate pit or pushbacks.

Figure 4: Multiple scenarios to build.

Going one step beyond, we also have clients improving their adherence and reconciliation between long and short-term mine plans by using MiningMath as a complementary tool. Notice that, by using MiningMath in strategic mine planning, you could add more constraints from real-life operations, even if you decide just to check your current long-term plans. Also, notice you could place some surface limits, such as the designed surface of the next five years plan for example, and give some controlled freedom to short-term planners to rerun their mine plans, including more operational details, as long as they don’t change anything from period 6 on and they don’t affect the NPV negatively. Whenever they find an issue or an opportunity, short and long term teams have a way to collaborate and generate new joint configurations that account for all the strategic and tactical needs of the project simultaneously. All the remaining details, such as the designs, could be adjusted using the current mining packages available.

If you wish to skip such steps and go straight to your final designed plans, we can guide you through this process, which includes a loop of running MiningMath and designing surfaces, until reaching a reasonable and operational sequence. This is a much more innovative procedure, which tends to achieve higher NPVs.

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