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Get the NPV upper bound better than the Best Case scenario!

Average

Estimated reading: 2 minutes 416 views

What is an Average constraint?

Average constraints are based on the average of any quantifiable parameter modeled block by block. To use this feature on MiningMath, the dataset must contain an auxiliary field/column which considers a value of what you wish to limit regarding the value of it in each block. Therefore, this feature controls the avarage value of the variable that has been modeled considering blocks that were mined in that single period. Since this feature is based on average parameters, the algorithm can use lower values to respect this target and increase the NPV with higher ones.

This feature is usually applied on blending to combine low-grade and high-grade blocks in order to increase the profitability. Although, it could have a lot of other applications. Basically, any variable which could be could be modeled considering these assumptions could be controlled.

Video 1: Blending and other constraints.

Some examples using average are listed below:

  • Grade of a contaminant on the plant.

  • Haulage distance, based on the destination each block.

  • Blasting material consumptions.

  • CO2 and noise emissions, energy and water consumption and other socio-enviromental.

The user can define:

  • Minimum and maximum average limits.

  • Different limits for different materials.

  • Different limits for different intervals.

  • Different limits for different destinations.

  1. Create auxiliary fields in the block model, quantifying the information to be controlled.

  2. During the importation, assign the column to be blended to Grade (Figure1).

  3. On the Average tab, input minimum and maximum limits for each variable (Figure 2a), period range (Figure 2b), other weights to be considered (Figure 2c) and destination (Figure 2d).

Figure 1: During the importation, Cu and Au are assigned to "Average".
Figure 2: Fields where the user can input limits (A), for each period range (B), Weights (C) and each destination (D).
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