Given that energy costs are a significant component of overall processing costs in mineral plants, reducing these costs through process optimization or technology adoption enhances the technical and financial feasibil...Given that energy costs are a significant component of overall processing costs in mineral plants, reducing these costs through process optimization or technology adoption enhances the technical and financial feasibility of a deposit. Geometallurgical modeling plays a key role in understanding the relationship between material characteristics, mine planning, and processing stages, ultimately contributing to more efficient resource management and cost reduction in mineral processing. This study aims to develop a block model for evaluating comminution energy consumption (CEC) and identifying blocks with the highest energy usage potential during the grinding process in a specified region. Therefore, by applying advanced geostatistical techniques, including joint estimation and simulation based on geometallurgical data from multiple mineral processing stages, we predict CEC across the study area. The dataset encompasses 2.754 drill samples and a block model with 4.680 blocks. In this effort, imulation techniques, such as Plurigaussian and Turning Bands, provided more realistic outcomes than cokriging, considering the unique characteristics of geometallurgical data and the limitations of kriging methods.展开更多
文摘Given that energy costs are a significant component of overall processing costs in mineral plants, reducing these costs through process optimization or technology adoption enhances the technical and financial feasibility of a deposit. Geometallurgical modeling plays a key role in understanding the relationship between material characteristics, mine planning, and processing stages, ultimately contributing to more efficient resource management and cost reduction in mineral processing. This study aims to develop a block model for evaluating comminution energy consumption (CEC) and identifying blocks with the highest energy usage potential during the grinding process in a specified region. Therefore, by applying advanced geostatistical techniques, including joint estimation and simulation based on geometallurgical data from multiple mineral processing stages, we predict CEC across the study area. The dataset encompasses 2.754 drill samples and a block model with 4.680 blocks. In this effort, imulation techniques, such as Plurigaussian and Turning Bands, provided more realistic outcomes than cokriging, considering the unique characteristics of geometallurgical data and the limitations of kriging methods.
