In-Depth MiningMath

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This tutorial provides a detailed guidance to the pages in the knowledge base for new MiningMath users. A shorter tutorial can be found here with a set of must read articles. In this tutorial, a larger number of pages is contextualized and recommended for those with no previous experience using MiningMath but who wish to gain a more advanced knowledge.

Software requirements

System Requirements and Recommended Hardware for MiningMath: Verify if your computer has all the minimum/recommended requirements for running the software.
Installing, Activatind and Running MiningMath: Here you’ll have all the necessary instructions to install, activate and run MiningMath.

Set up the block model

The next step after installation is to understand the home page interface and import your project data. The following pages go over these in detail.

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Home page: MiningMath automatically starts on this page. It depicts your decision trees, recent projects and model information.
Import your block model: import your csv data, name your project, set fields and validation.
Modify the block model: this window aims to help you to modify your block model accordingly with what is required for your project and also allows you to “Export” the block model to the CSV format to be used with any other software.
Calculator: calculate and create new fields by manipulating your project inside MiningMath.

Handling unformatted data

If you don’t have a block model ready to be imported you might want to create a new one. The following pages can guide you through this process.

Define the scenario and run

Once you have started your block model defined, there are several options to set up your project’s parameters before running a scenario.

Scenario tab: set densities, economic parameters, slope angles, stockpiles, add/remove processes and dumps, production inputs, geometric inputs and so on.
Save as: save the scenario's configuration once it has been configured.
Run: the Run tab is the last step before running your project’s optimization. Change the scenario name, set a time limit, and set up results files.

Results

After running your scenario it is important to analyze and understand the given results.

Output files and 3D viewer: by default, MiningMath generates an Excel report summarizing the main results of the optimization. It also creates outputs of mining sequence, topography, and pit surfaces in csv format so that you can easily import them into other mining packages. The 3D viewer enables a view of your model from different angles.
Export model: export your model as a csv file. This can be used in new scenarios or imported in other mining packages.

Extensive set-up

MiningMath offers a lot of customization. You might use pre-defined scenarios to learn with standard parameters. Otherwise, the following pages of the knowledge base detail several important parameters that might need to be fine tuned in your project.

Advanced content

Complex projects might need advanced configurations or advanced knowledge in certain topics. The following pages cover some subjects considered advanced in our knowledge base.

Theory

In order to understand the theory behind MiningMath’s algorithm, a set of pages is provided to describe mathematical formulations, pseudo-code, and any rationale to justify the software design.

Workflows

ManingMath acknowledges and supports different workflows. This knowledge base provides a set of articles aimed at showing how MiningMath can be integrated into other workflows or have its results used by different mining packages.

There is a wealth of content to explore and apply, and the possibilities are endless. From this point on, you are ready to use our solution independently and achieve extraordinary results.

This guide ends here, but your journey as a MiningMath user is just beginning. If you ever need support, rely on our Knowledge Base to refresh or expand your knowledge. Engage with our AI assistant, MIMA, or join discussions in our forum to connect with other community members and find solutions together.

MiningMath

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