Thesis Análisis y proyección del beneficio y gasto que se obtiene con mineria continua
Loading...
Date
2025
Journal Title
Journal ISSN
Volume Title
Program
Ingeniería Civil de Minas
Campus
Campus Santiago San Joaquín
Abstract
En el presente trabajo se abarca el tema de análisis y proyecciones del beneficio y gasto obtenido utilizando equipos de minería continua. El estudio toma lugar en la mina Pampa Hermosa, perteneciente a la faena Nueva Victoria de la empresa SQM, ubicada en el Norte de Chile. La empresa proporciona la información y bases de datos las cuales serán analizados mediante la herramienta Excel.
Este trabajo busca determinar variables relevantes que permitan analizar el beneficio del bloque, el costo basado en el gasto de puntas para así finalmente lograr predecir como se comportara el gasto de puntas en función de la dureza del suelo con sus respectivos costos.
El análisis comienza con una limpieza de los datos entregados, buscando no toparnos con datos erróneos que puedan intervenir con los cálculos a realizar. Una vez realizada la limpieza se analizan los indicadores de productividad para buscar una relación significativa entre la dureza que presenta el suelo de la mina, con las toneladas de material extraído y costo de este.
A continuación, se calcula el beneficio que se obtendría del modelo de bloques sin tener en cuenta la dureza del suelo y se compara con el resultado obtenido del cálculo considerando la dureza del suelo. De esta comparación se logra observar que el porcentaje de diferencia que hace este análisis de la dureza no es tan significativo a una escala global, es decir, se tiene una diferencia del -0.03%, por otro lado, si solo se considera el costo mina al momento de calcular el beneficio del bloque, nos encontramos con una diferencia que llega hasta un 12%. Si bien estos valores obtenidos no son tan altos como para realizar una modificación a nivel de planificación minera, si pudiese ser relevantes al nivel del departamento de gestión de mina.
Luego, para lograr una proyección de gasto se dividen los datos en 24 escenarios, según turnos, marcas de puntas y dureza del suelo, con estos escenarios podemos observar que los datos se distribuyen de forma lognormal. Una vez conocido como se distribuyen los datos se procede a realizar simulaciones de Monte Carlo para cada escenario, con el propósito de predecir como se comportará el gasto de puntas y su costo en cada dureza. De esto último se puede ver que a medida que el suelo es más duro, hay un mayor gasto de puntas, un mayor costo y un menor tonelaje extraído, lo cual se evidencia en el pronóstico de gasto de puntas con su respectivo costo estimado.
Finalizando este estudio, se recomienda tener una mayor claridad de donde se ubican las zonas más duras en el pit a extraer y delimitarlas, de esta forma se podrá tener un mejor control del gasto de puntas y así de los costos que estás conllevan.
This study addresses the analysis and projections of benefits and expenses associated with the use of continuous mining equipment. The study takes place at the Pampa Hermosa mine, part of the Nueva Victoria operation owned by SQM, located in northern Chile. The company provides the information and databases, which are analyzed using Excel as the primary tool. The objective of this study is to identify relevant variables that enable the analysis of block profitability and the costs based on cutting tool expenditures, ultimately aiming to predict how these expenditures will behave as a function of soil hardness, along with their corresponding costs. The analysis begins with the cleaning of the provided data to ensure that erroneous data, which could interfere with calculations, are excluded. Once the data is cleaned, productivity indicators are analyzed to establish a significant relationship between the hardness of the mine’s soil, the tons of material extracted, and the associated costs. Subsequently, the benefit derived from the block model is calculated without considering soil hardness and compared to the results obtained when soil hardness is taken into account. This comparison reveals that the percentage difference introduced by including soil hardness in the analysis is not globally significant, with a difference of -0.03%. However, if only mine costs are considered in the block benefit calculation, the difference reaches up to 12%. While these results are not substantial enough to warrant modifications at the mine planning level, they could be relevant at the mine management department level. To achieve an expenditure projection, the data is divided into 24 scenarios based on shifts, cutting tool brands, and soil hardness. These scenarios show that the data follows a lognormal distribution. Once the data distribution is identified, Monte Carlo simulations are performed for each scenario to predict the behavior of cutting tool expenditures and their associated costs for each hardness level. The results indicate that as soil hardness increases, there is a corresponding increase in cutting tool consumption, higher costs, and lower tonnage extracted. This trend is evidenced in the forecast of cutting tool expenses and their estimated costs. In conclusion, it is recommended to improve the identification and delineation of harder zones within the pit to be extracted. This would allow for better control of cutting tool consumption and, consequently, the associated costs.
This study addresses the analysis and projections of benefits and expenses associated with the use of continuous mining equipment. The study takes place at the Pampa Hermosa mine, part of the Nueva Victoria operation owned by SQM, located in northern Chile. The company provides the information and databases, which are analyzed using Excel as the primary tool. The objective of this study is to identify relevant variables that enable the analysis of block profitability and the costs based on cutting tool expenditures, ultimately aiming to predict how these expenditures will behave as a function of soil hardness, along with their corresponding costs. The analysis begins with the cleaning of the provided data to ensure that erroneous data, which could interfere with calculations, are excluded. Once the data is cleaned, productivity indicators are analyzed to establish a significant relationship between the hardness of the mine’s soil, the tons of material extracted, and the associated costs. Subsequently, the benefit derived from the block model is calculated without considering soil hardness and compared to the results obtained when soil hardness is taken into account. This comparison reveals that the percentage difference introduced by including soil hardness in the analysis is not globally significant, with a difference of -0.03%. However, if only mine costs are considered in the block benefit calculation, the difference reaches up to 12%. While these results are not substantial enough to warrant modifications at the mine planning level, they could be relevant at the mine management department level. To achieve an expenditure projection, the data is divided into 24 scenarios based on shifts, cutting tool brands, and soil hardness. These scenarios show that the data follows a lognormal distribution. Once the data distribution is identified, Monte Carlo simulations are performed for each scenario to predict the behavior of cutting tool expenditures and their associated costs for each hardness level. The results indicate that as soil hardness increases, there is a corresponding increase in cutting tool consumption, higher costs, and lower tonnage extracted. This trend is evidenced in the forecast of cutting tool expenses and their estimated costs. In conclusion, it is recommended to improve the identification and delineation of harder zones within the pit to be extracted. This would allow for better control of cutting tool consumption and, consequently, the associated costs.
Description
Keywords
Minería continúa, Dureza del suelo, Proyección de costos