MiningMath

MiningMath

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All parameters simultaneously handled,delivering multiple scenarios

MiningMath optimizes all periods simultaneously, without the need for revenue factors. Hence, its super best case has the potential to find higher NPVs than traditional procedures.

Usually when finding nested pits, other technologies might employ the Lerchs-Grossmann (LG) algorithm, the Pseudoflow algorithm, direct block scheduling or even more recent heuristic mechanisms. However, most of them will not account for processing capacities (gap problems), destination optimization and discount rate at this stage.

Nested pits: Traditional technologies  vs IconMiningMath

Nested pits

Traditional technologies vs IconMiningMath

Traditional technologies
MiningMath
Traditional technologies
MiningMath

See the Super Best Case in action with MiningMath!

Super Best Case benefits

There are many advantages of using MiningMath Super Best Case over other traditional technologies. 

In modern/traditional technology, large size differences between consecutive periods may render them impractical, leading to the “gap” problem. Such a gap is caused by a scaling revenue factor that might limit a large area of being mined until some threshold value is tested. MiningMath allows you to control the entire production without oscillations due to our global optimization.

In the modern/traditional methodology the decisions on block destinations can be taken following some techniques such as: fixed predefined values based on grades/lithologies post-processing cutoff optimization based on economics post-processing based on math programming or even multiple rounds combining these techniques. With MiningMath the destination optimization happens within a global optimization in a single step, maximizing NPV and accounting simultaneously for capacities, sinking rates, widths, discounting, blending, and many other required constraints.

Modern technology is restricted to pre-defined, less diverse sequences because it is based on step-wise process built upon revenue factor variation, nested pits, and pushbacks. These steps limit the solution space for the whole process. MiningMath performs a global optimization, without previous steps limiting the solution space at each change. Hence, a completely different scenario can appear, increasing the variety of solutions.

Due to tonnage restrictions, modern technology might need to mine partial benches in certain periods. With MiningMath’s technology, there isn’t such a division. MiningMath navigates through the solution space by using surfaces that will never result in split benches, leading to a more precise optimization.

Due to tonnage restrictions, modern technology might need to mine partial benches in certain periods. With MiningMath’s technology, there isn’t such a division. MiningMath navigates through the solution space by using surfaces that will never result in split benches, leading to a more precise optimization.

Modern approaches present a difference between the optimization input parameters for OSA (Overall Slope Angle) and what is measured from output pit shells, due to the use of the “block precedence” methodology. MiningMath works with “surface-constrained production scheduling” instead. It defines surfaces that describe the group of blocks that should be mined, or not, considering productions required, and points that could be placed anywhere along the Z-axis. This flexibility allows the elevation to be above, below, or matching a block’s centroid, which ensures that MiningMath’s algorithm can control the OSA precisely, with no errors that could have a strong impact on transition zones.

With MiningMath you are able to challenge the Best Case obtained by other means, including more recent academic/commercial DBS technologies available, as depicted below.

Add it to your workflow

The block periods and destinations optimized by MiningMath can all be exported in .csv and .xlsx files. In turn this can be imported back into your preferred mining package, for comparison, pushback design or scheduling purposes.

Clients who have benefit from MiningMath

Clients MiningMath: Antofagasta, Cemex, Codelco, MMG, RioTinto, Aclara, Vale, Kinross, Capstone, Rarex, Allkem, Glencore.

The Super Best Case is available for free!

Check our Knowledge Base for more documentation, or you can also use our forums to interact with the community and have any questions answered. MiningMath’s approach has been applied for years by our customers, with an increasing number of licenses sold worldwide, press releases and academic research also proving the consistency of the implementation.

With constant developments since 2013, MiningMath has reached a mature and robust state, providing solutions straight from the block model. It is the first and only single-step mining optimization engine available in the market!

Windows 64-Bit (x86_64) - 121 MB

Windows 64-Bit (x86_64) - 121 MB

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