Live Stream: How to Use Pit Optimizer

Pit optimizer is used on open pit mines for strategic planning over long time intervals—typically exceeding one year. The module determines the optimal pit boundary and mining sequence to maximize net present value. Every deposit has unique characteristics requiring careful configuration of economic parameters, slope angles, and processing constraints before optimization calculations begin.

The optimization process starts with a block model containing deposit information—coordinates, block dimensions, rock types, quality indicators like iron content, and block weights. Each project can have multiple scenarios with different data sources to simulate various mining approaches. The example block model contains approximately 4 million blocks at 5×5×5 meter dimensions, with properties visible in the property vector editor.

K-MINE offers three approaches for configuring slope angles in pit optimization. The first method sets angles by horizontal azimuthal sections—for example, different values from 0-60 degrees versus 60-180 degrees. The second method uses three-dimensional wireframes to define spatial areas with specific slope values. The third method assigns slope angles based on rock type or other block model parameters, allowing the calculation to account for varying geotechnical conditions across the deposit.

Block evaluation calculates the difference between sales revenue and expenses for mining, transportation, and processing each block. Positive cash flow comes from finished product sales price multiplied by useful component content—whether expressed as weight, percentage, or absolute values. Multiple mineral products can be configured separately when an enterprise mines several commodity types. The set of blocks with highest economic valuation defines the optimal pit boundary.

Multiple price coefficients can be specified to generate numerous optimal boundaries in a single calculation—scenarios may include over 60 coefficients while simpler analyses use five or fewer. These coefficients multiply against the base price to simulate different commodity price scenarios. The resulting nested shells help identify which mining direction is most effective during initial design stages and establish the sequence for pushback development.

For ore types, users specify processing costs, recovery losses, and dilution percentages—in the example, types 2 and 4 have $12 processing cost with 2% losses and 3% dilution. Cut-off grade determines which blocks qualify as ore versus waste; blocks with magnetite iron below 16% are classified as overburden. Rock types without processing parameters automatically become waste in the optimization calculation.

Mining costs are assigned by rock type—$8 per ton for ore types and $6 for other materials in the demonstration. Transportation costs vary by elevation, with $3.50 per ton at 1000 meters and $3.25 between elevations 1100 to -100 meters. Custom formulas can express costs dependent on multiple variables like z-coordinate and rock weight through the built-in formula editor, accommodating deposit-specific expense structures.

After configuring minimum required parameters, the calculation generates optimal boundaries for each specified coefficient. Results display in multi-level tables showing total rock mass weight, concentrate weight by ore type, block counts, income, expenses, profit, and stripping ratio. Quality indicators show minimum, maximum, and average values for each mineral product. Coefficients yielding negative economics—like 0.59 in the example—indicate unprofitable mining scenarios.

Optimal pit boundaries can be visualized by hiding blocks outside each coefficient's shell or by creating separate wireframe objects. These wireframes become data sources for subsequent planning stages including calendar and operational scheduling. Different slope angle configurations produce different pit geometries, volumes, and economic indicators—all exportable as separate graphical objects with customizable layer assignments.

Users select multiple optimal boundaries representing deposit mining stages—for example, coefficients 0.59, 0.80, and 1.0 define three sequential phases. The inner boundary is mined first, then the pit expands to intermediate boundaries, and finally reaches the ultimate limit. Coefficient selection is arbitrary; users can choose all available coefficients or specific combinations based on operational requirements rather than strict boundary constraints.

Constant production capacity over the mining period can be configured by specifying target values for rock mass, ore tonnage, and quality parameters per period. Production fluctuations are accommodated by adjusting targets in the planning table—for instance, changing ore capacity starting from the third period. Different optimization criteria—pit area, rock mass volume, ore volume, concentrate volume, or mineral quality—influence the final mining sequence calculation.

Worksite width, incline slope angle, boundary lag distances, sinking speed, and minimum pit bottom dimensions constrain the optimization to operationally feasible results. The lag parameter controls how far internal boundaries can advance relative to outer boundaries—expressed as maximum and minimum sinking levels. Pit sinking speed limits depth increase per period, such as no more than four levels annually, preventing unrealistic excavation rates.

Three scenarios display side-by-side: the configured option with efficiency and technical parameters, the best-case following strict boundary sequences, and the worst-case mining by horizontal levels only. The best scenario mines optimal boundaries sequentially—0.59 coefficient first, expanding to 0.8, then to 1.0—without deviation. The worst scenario ignores nested shells entirely, dividing the ultimate pit by levels and mining sequentially regardless of economic optimization.

Selecting any planning period displays the corresponding open pit configuration on the block model. Wireframes can be created for each period showing pit geometry at that stage. Blocks mined during specific periods or cumulative ranges are highlighted separately. Mining and technical parameters like 30-meter bottom width and worksite width are maintained throughout all periods when properly configured.

All calculation tables export to Excel format preserving the multi-level data structure for further processing. PDF export creates formatted reports containing complete optimization results. Custom graphs display selected indicators with configurable axes, labels, colors, and chart types. Charts save as image files for inclusion in presentations and technical reports.

K-MINE uses the pseudoflow algorithm rather than Lerchs-Grossmann, providing faster computation for large block models with millions of blocks. The pit optimizer works with regular block models that can include topography, waste, and ore in a single file. Block models from other platforms import via CSV format, and most volumetric objects transfer into K-MINE. Slope angles must be pre-calculated in the stability analysis module before optimization. Typical calculation time ranges from three to six minutes depending on block model complexity.