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Un enfoque de Q-learning para la reducción de roca estéril en el diseño de minas a cielo abierto basado en principios

Naser Badakhshan¹, Ezzeddin Bakhtavar², Kourosh Shahriar³

¹Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran.

Scientific reports

|January 28, 2026

Resumen

Este estudio presenta un marco de Q-learning para optimizar el diseño de fosas mineras, reduciendo significativamente la extracción de roca estéril y los costos ambientales. El enfoque prioriza la sostenibilidad sobre el máximo beneficio, mejorando la eficiencia de los recursos en las operaciones mineras.

Área de la Ciencia:

Ingeniería de Minas
Inteligencia Artificial
Ciencias Ambientales

Sus antecedentes:

La minería a gran escala genera una cantidad sustancial de roca estéril, lo que plantea desafíos ambientales y económicos.
Los métodos actuales para el diseño del límite final de la fosa a menudo pasan por alto los costos ambientales, lo que afecta la sostenibilidad.
La reducción de la generación de residuos es crucial para alinear la minería con los objetivos de desarrollo sostenible.

Objetivo del estudio:

Desarrollar y validar un marco novedoso para la optimización del diseño del límite final de la fosa.
Integrar los costos ambientales en los cálculos del valor económico de los bloques.
Minimizar la extracción de roca estéril maximizando la recuperación de mineral y la rentabilidad.

Principales métodos:

Integración de modelado matemático con Q-learning, un algoritmo de aprendizaje por refuerzo.
Inclusión explícita de costos ambientales (prevención, mitigación, compensación) en el valor económico de los bloques.
Validación utilizando un depósito de mineral de cobre a gran escala y comparación con el algoritmo de Lerchs-Grossmann.

Principales resultados:

El marco de Q-learning redujo la extracción de roca estéril en 2,7 millones de toneladas.
Se observó una ligera disminución de 0,5 millones de toneladas en la recuperación de mineral.
El tiempo computacional se redujo de 7,2 a 5,8 horas y el marco priorizó el diseño realista de la fosa sobre el máximo beneficio.

Conclusiones:

El marco de Q-learning ofrece un enfoque más sostenible para el diseño del límite final de la fosa al incorporar costos ambientales.
Este método mejora la eficiencia de los recursos, minimiza los impactos ecológicos y promueve la producción más limpia en la minería.
La optimización basada en el aprendizaje por refuerzo promueve la sostenibilidad en la industria minera.

Palabras clave:

Optimización Aprendizaje por refuerzo Minería sostenible Límite final de la fosa Roca estéril

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