#### MiningMath

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

#### How should the block model be formatted in MiningMath?

MiningMath empowers mining engineers to improve their strategic analysis through risk assessments, starting with importing and formatting their block model data. The essential requirements for using block model data effectively in MiningMath are:

• CSV format.
• Regularized block model.
• No air blocks.
• Block coordinates in 3 dimentions.
• Azimuth rotation value (only for rotaded models).

Each block must include essential data such as coordinates, grade, and economic value (including processes and wastes). For detailed guidelines, refer to this documentation. For a comprehensive example in CSV format, download it here. Below is a concise illustration featuring a block model and two example blocks.

#### What other field types can MiningMath handle?

MiningMath accepts a large variety of field types for importing and formatting complex block model data. Optional fields include:
• Average field type: controls variables by minimums and maximums, like grades and haulage distances.
• Density field type: used to calculate block tonnage.
• Slope field type: allows for varying slopes by block, providing flexibility by lithotype and sectors.
• Recovery field type: accounts for varying block recoveries.
• Sum field type: similar to Average, but considers variables by their total sum.
• Predefined destinations field type: sets fixed values for destinations, useful for defining pushbacks or applying lithologic restrictions, though it may limit optimization potential.
• Uncertain field type: stores data from stochastic models with varying information from simulation to simulation, typically for grade fields.
Other optional fields can include any additional information for exported outputs.

#### Can MiningMath generate derived fields?

Certainly! MiningMath’s internal calculator allows users to manipulate their projects inside MiningMath, enabling field adjustments and creation. For example, different economic values can be defined for individual periods or to analyze how each constraint, such as the ore price, affects that year’s production and benefits or not the global project. Moreover, the use of derived fields combined with Decision Trees enables a broader view of your project and a deeper understanding of the impacts of each variable. This comprehensive approach can provide valuable insights, facilitating more informed decision-making and optimizing project outcomes.

#### How do I visualise an imported block model in MiningMath?

MiningMath’s 3D Viewer enhances your workflow by providing comprehensive visualization of your block model from various angles. This tool allows for the filtering of any imported field, offering a quick and efficient overview of your data.

MiningMath’s 3D Viewer

#### Start using now!

Windows 64-Bit (x86_64) - 121 MB

Windows 64-Bit (x86_64) - 121 MB