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 variations is important for determining the optimal mine configuration and for optimizing productivity and profitability.

In Multivariate Sensitivity Analysis, scenarios are created gradually increasing or gradually decreasing the values of the constraints within a desired range, covering all combinations of values. This allows users to have a comprehensive view of the impact of combinations of constraint values on the project’s performance.

Considering the nature of global optimization and the nonlinearity of the problem, it is expected that there will be variations in performance as certain parameter values are modified. Therefore, it is crucial to generate a large number of scenarios to obtain a comprehensive analysis of the impact of these variations on the project.

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.

Decision Tree (part 1) for Multivariate Sensitivity Analysis using the Marvin dataset

Decision Tree (part 2) for Multivariate Sensitivity Analysis using the Marvin dataset

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.

MiningMath

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Multivariate Sensitivity Analysis

Example

Required dataset

In-depth analysis

CONTENTS

MiningMath

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Multivariate Sensitivity Analysis

Example

Required dataset

In-depth analysis

Related articles

Multivariate Sensitivity Analysis

CONTENTS