Multi-mine projects often have hidden opportunities to maximize value. Accordingly with the natural level of complexity, it is pretty commom to make a few simplifications that might reduce value, producing sub-optimal solutions from the mathematical perspective, while using the traditional LG/Pseudoflow methodology.
MiningMath has global optimization algorithm which can overcome such challenges, when working with integrated multi-mine projects to find solutions that regards all the pits been mined or mixed at the same time, instead of an individual pit optimization within a multi-mine project, which provides a totally different overview. To handle with such projects, the block model must contain all the mining regions which should be considered to the simultaneous optimization, thus, if you have the pits mapped in different datasets, it is important to follow the steps suggested below:
Work with a single block model or single pit first, run the initial tests and understand this region before handling the block model modification.
Try to eliminate meaningless blocks, which would not affect the solution and could increase complexity.
Join a second model/pit* and understand the manipulation process to work with multi-mine projects.
- Play with surfaces, if you wish to refine the results, filter regions to not mine, and any other guide. Since the surface files on MiningMath has always the same order, a good way to work with it could be using an excel file, available here, which is pretty useful to such modifications.
- Play with Mining Fronts, if you want to control the material which is extracted from each region.
Add the other regions and start use everything that you wish.
*This joint block model file should fullfill the same requirments of a single one, as mentioned at the formating data page.
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The current version of MiningMath consider the same Vertical Rate, Bottom Width and Mining Width for the entire block model. However, in a multiple pit overview, each one could have different geometric parameters that influence the levels of selectivity. Regarding these scenarios, the main recommendation is to set the parameter from one pit, fix solutions of this one, since force+restrict mining has the highest level of priority, and start the optimization of the others which would already consider the mass that would be extracted from the first pit fixed.
An efficient workflow is to run the first scenario without geometric parameters, which should be the validating or the Best Case, for scheduling optimization. Then set up a scenario with geometric parameters from the most selective mine which means, the smallest widths and biggest vertical rate (VR), which would be the least constrained scenario considering geometric aspects. Therefore, each surface generated by this approach will be used to fix solutions for Mine 1.
For instance, you could take the surface 1 generated and decrease the elevation in the other areas to regard the mass from the period one of mine 1 and what could be mined in the second pit. By having these results you can refine either surfaces or mining fronts to get the best results and do a sensitivity analysis of geometric parameters for multi-projects still preserving the global optimization.