Sequenciamento e otimização de operações de perfuração e desmonte - um estudo de caso
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REV. MAMYM
Abstract
Las operaciones de perforación y voladura tienen un amplio rango de aplicabilidad para el procesamiento de minerales, requiriendo cada vez más el desarrollo de estudios mediante análisis críticos y analíticos encaminados a optimizar los resultados. Este trabajo buscó optimizar una unidad operativa atendiendo a la secuenciación del ciclo operativo de perforación y voladura, buscando tanto mejorar los equipos de perforación como el cumplimiento del plan minero ejecutado. Para validar este estudio, se llevó a cabo un análisis de planificación a corto plazo (semanalmente) que demostró el potencial de ganancias en la operación. Al finalizar el proyecto se pudo observar que hubo una reducción significativa en el equipo necesario para la operación, donde antes se necesitaban alrededor de 13 equipos de perforación, se encontró que solo se necesitaban 9 equipos, por lo que reduciendo los costos por hora trabajada y aumentar los ingresos operacionales de la operación. Por lo tanto, resultó factible extrapolar finalmente el escenario anual. Para interpretar y ayudar a una mayor clarificación de los números de equipos finales, realizamos una simulación de Monte Carlo, que permitió observar la probabilidad de que ocurra un determinado evento, como en este caso, la productividad general y el número de equipos. (observando la probabilidad de necesidad de equipo antes y después de la adaptación de los datos por parte del modelo completado).
Drilling and blasting operations have a wide range of applicability for mineral processing, increasingly requiring the development of studies through critical and analytical analyses aimed at optimizing results. This work sought to optimize an operational unit by dealing with the sequencing of the drilling and blasting operational cycle, seeking both to improve drilling equipment and adherence to the executed mining plan. For this study to be validated a short-term planning analysis was carried out - carried out weekly - demonstrating the potential for gains in the operation. At the end of the project, it was possible to observe that there was a significant reduction in equipment needed for the operation, where previously around 13 drilling rigs was needed and it was found that there was a need for only 9 pieces of equipment, thus reducing costs per hour worked and increasing the operational income of the operation. Therefore, it became feasible finally extrapolate the annual scenario. To interpret and assist with further clarification of the numbers of final equipment, we carried out a Monte Carlo simulation, making it possible to observe the probability of a certain event occurring, as in this case, the overall productivity and the number of equipment (observing the probability of the need for equipment before and after data adaptation by the completed model).
Drilling and blasting operations have a wide range of applicability for mineral processing, increasingly requiring the development of studies through critical and analytical analyses aimed at optimizing results. This work sought to optimize an operational unit by dealing with the sequencing of the drilling and blasting operational cycle, seeking both to improve drilling equipment and adherence to the executed mining plan. For this study to be validated a short-term planning analysis was carried out - carried out weekly - demonstrating the potential for gains in the operation. At the end of the project, it was possible to observe that there was a significant reduction in equipment needed for the operation, where previously around 13 drilling rigs was needed and it was found that there was a need for only 9 pieces of equipment, thus reducing costs per hour worked and increasing the operational income of the operation. Therefore, it became feasible finally extrapolate the annual scenario. To interpret and assist with further clarification of the numbers of final equipment, we carried out a Monte Carlo simulation, making it possible to observe the probability of a certain event occurring, as in this case, the overall productivity and the number of equipment (observing the probability of the need for equipment before and after data adaptation by the completed model).
Description
Vol. 8, No. 2