Fragmentación de Roca por Voladura en Minería a Cielo Abierto: Predicción con IA

• Toapanta-Delgado, Steven ^[1] ; Guerrero-Rodriguez, Byron ^[1] ; Mejia-Escobar, Christian ^[1]
  1. [1] Universidad Central del Ecuador

     Universidad Central del Ecuador

     Quito, Ecuador

     
• Localización: RTE: Revista Tecnológica Espol, ISSN-e 1390-3659, ISSN 0257-1749, Vol. 37, Nº. Extra 1, 2025 (Ejemplar dedicado a: REGULAR VOLUME (JUN,2025) - Special Section: Social Justice in the Contemporary World, Open Multidisciplinary Section: Science for Development), págs. 38-54
• Idioma: español
• DOI: 10.37815/rte.v37nE1.1356
• Títulos paralelos:
  • Rock Fragmentation by Blasting in Open-Pit Mining: an AI Predictive Model
• Enlaces
  • Texto completo
• Resumen
  • español

    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.

  • English

    Rock fragmentation by blasting is fundamental in the mining sector. This process seeks to optimize rock size for subsequent extraction, transport, and processing. Predicting this fragmentation becomes a crucial task for improving operational efficiency; however, the application of simplified formulas lacks precision and adaptability to lithological variations. This study proposes the use of artificial intelligence (AI) for the accurate estimation of the average diameter of igneous, sedimentary, and metamorphic rocks. Based on a set of 97 real fragmentation and blasting data from around the world, the Random Forest (RF), Support Vector Regressor (SVR), and Kernel Ridge Regression (KRR) algorithms were evaluated, along with the Kuz-Ram equation, widely used in the industry. The results indicate that AI models significantly outperform the conventional equation. RF offers the highest accuracy with MSE of 0.0017 and R2 of 95.38%. In contrast, SVR achieves values of 0.0064 and 83.13%, while KRR achieves 0.016 and 69.60%. The adequate performance of these algorithms has led to the development of an application that allows users to view drilling grids, set specific dimensions, and compare projections of average rock size. This tool facilitates informed decision-making, improving mining processes and promoting more reliable and sustainable results in different operational contexts.

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