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La fragmentación de rocas por voladura es fundamental en el sector minero. Este proceso busca optimizar el tamaño de la roca para su posterior extracción, transporte y procesamiento. Predecir esta fragmentación se vuelve una tarea crucial para mejorar la eficiencia operativa; sin embargo, la aplicación de fórmulas simplificadas carece de precisión y adaptabilidad a las variaciones litológicas. Este estudio propone el uso de inteligencia artificial (IA) para la estimación precisa del diámetro promedio de rocas ígneas, sedimentarias y metamórficas. A partir de un conjunto de 97 datos de fragmentación y voladuras reales a nivel mundial, se evaluaron los algoritmos de Random Forest (RF), Support Vector Regressor (SVR) y Kernel Ridge Regression (KRR), junto con la ecuación de Kuz-Ram, ampliamente utilizada en la industria. Los resultados indican que los modelos de IA superan notablemente la ecuación convencional. RF ofrece la mayor precisión con valores de MSE de 0.0017 y R2 de 95.38 %. En contraste, SVR alcanza valores de 0.0064 y 83.13 %, mientras que KRR obtiene 0.016 y 69.60 %. El adecuado desempeño de estos algoritmos ha motivado el desarrollo de una aplicación que facilita a los usuarios la visualización de mallas de perforación, establecer dimensiones concretas y comparar las proyecciones del tamaño medio de las rocas. Esta herramienta facilita la toma de decisiones informadas, mejorando los procesos de minería, fomentando resultados más confiables y sostenibles en distintos contextos operativos.
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
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