Multivariate Sensitivity Analysis
Multivariate Sensitivity Analysis is the process of creating and analyzing scenarios based on a range of possible values for selected constraints. Analyzing the impact of constraints variation is important for determining the optimal mine configuration and for optimizing productivity and profitability.
With this method, scenarios are created by gradually increasing or decreasing the values of the constraints within a desired range, covering many combinations of values. It allows users to have a comprehensive view on the impact of combinations of constraint values on the project’s performance.
Considering the nature of the global optimization and the non-linearity of the problem, it is expected that there will be variations in performance (NPV, production, mining fronts, geometries, etc.) as parameter values are modified. Therefore, by generating a large number of scenarios, this behavior will be used in your favor, finding a diverse range of brand-new solutions unseen by any stepwise method.
Only with MiningMath, you can achieve such a broad understanding of how different parameter values affect the project’s overall performance and always have the chance to find better and better solutions.
Example
Required dataset
Preinstalled Marvin deposit. It can also be downloaded here.
Consider the base scenario overview and the respective decision tree built for a Multivariate Sensitivity Analysis depicted below.
All the scenarios in decision tree were executed and a set of best and worst results were chosen to be depicted in the graphs below.
The evaluation was conducted by analyzing both cumulative NPV and production stability. The results indicate that:
- A diverse range of cumulative NPVs can be achieved when compared to the Base scenario.
- Certain productions can be more stable than others.
- Certain productions can be violated, such as the dump (MSA_BW100_MW140, period 3).
These observations demonstrate the importance of performing a Multivariate Sensitivity Analysis.
In-depth analysis
The example provided illustrates a Multivariate Sensitivity Analysis, offering a basic evaluation based on cumulative NPV and production, while varying minimum bottom/mining width. More in-depth analyses may require a visual inspection and/or the use of additional indicators, such as vertical rate of advance. MiningMath reports warnings for certain violations as described here.
