Naturaleza no lineal de los parámetros
Reflejos
Understand why small changes in geometric parameters can lead to unexpected variations in NPV, and how MiningMath transforms this instability into a strategic optimization advantage through massive scenario generation and comparison.
Context: The Nonlinear Instability of Geometry Parameters
In traditional mine planning methodologies, geometric parameters such as Ancho mínimo de minería o Minimum Mining Length are typically treated with rigidity. It is commonly assumed that incremental changes to these parameters will produce proportional and predictable impacts on the project’s economic outcomes, particularly the Valor presente neto (VAN). Sin embargo, practical experience using MiningMath shows that this is often not the case, and no other technology is capable of handling such geometric aspects within a single global optimization process.
The NPV’s variability in response to seemingly minor adjustments, such as a small change in the minimum mining width, can be nonlinear, unstable, and counterintuitive. A small change might unexpectedly increase or decrease results, defying the deterministic logic that typically governs the industry.
Rather than viewing this instability as a flaw, it can be leveraged as a strategic opportunity, provided the planning tool supports rapid evaluation of multiple scenarios, as is the case with MiningMath’s approach.
The MiningMath Solution:
Evaluating Sensitive Parameters through Massive Scenario Generation
MiningMath enables users to systematically explore the unstable behavior of geometric variables by queuing multiple scenario generations and/or accessing MiningMath’s servers to run scenarios in parallel. By progressively varying parameters such as Minimum Width, Longitud mínima, o Ancho inferior mínimo, the system can produce different scheduling results along with their respective NPVs.
Por ejemplo, changing the minimum mining width from 30m to 80m in 10-meter increments may reveal a nonlinear response curve. Instead of an expected smooth fall in NPV, results fluctuate, highlighting patterns that only become visible through dozens of executions.
Examples varying three different parameters:
Analyzing Opportunities Using Charts and Tables
Note from the graphs that, after running multiple scenarios, users can create Excel charts to identify performance peaks that represent operational and economic opportunities that would likely go unnoticed in more restrictive approaches.
While MiningMath is currently working to automate chart creation, it provides all the necessary outputs for quick visual analysis, enabling robust, data-driven evaluations.
Integration with Decision Trees
By using the Execution Queue to vary geometric parameters systematically, MiningMath supports the creation of a structured decision tree, enabling:
- Visualization of NPV distributions across scenarios
- Selection of optimal paths based on operational criteria (p.ej, fewer warnings) and economic indicators
Iterative exploration: once favorable results are identified for one variable, others can be varied to refine the solution further
Comparison with Traditional Approaches
| Aspect | Metodología Tradicional | MiningMath Approach |
|---|---|---|
Width Exploration | Defined in pushbacks and fixed for the next steps | Through multiple scenarios, considering all constraints simultaneously in a single-step optimization. |
Impact Evaluation | Not possible, as pushbacks do not consider scheduling constraints | Through comparisons of NPVs, advertencias, and geometric aspects of multiple potential solutions |
System Response | Predictable but limited | Unpredictable, revealing other possible solutions |
When to Use | For a standard, well-known, and stable base case mine planning scenario | For deeper studies to improve NPV and/or other indicators |
Benefits and Applicability
- Discovery of unexpected solutions with a través de la optimización de un solo paso de MiningMath destinada a maximizar el flujo de caja descontado de su proyecto
- Reduction of operational warnings by using more robust geometries
- Flexibility to adapt designs to varying objectives (p.ej, mezcla, fleet management, socioenvironmental aspects)
- Practical and visual análisis de sensibilidad, aiding communication with management
- Strategic decision-making support by highlighting optimal and suboptimal points with potential for improvement
Conclusión
Instability in the behavior of geometric parameters should not be seen as a limitation; it is a gateway to uncovering hidden optimization opportunities. Sólo con MiningMath, users have the autonomy to generate and compare as many scenarios as needed, turning uncertainty into informed decisions.
Explore this behavior in your next project and discover how simple changes might unlock better geometries and greater economic value.
