Center for Preparatory Studies

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Browsing Center for Preparatory Studies by Subject "geometallurgy"

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    MODELING COMPLEX RELATIONSHIPS IN GEOMETALLURGICAL VARIABLES: ENHANCING METHODS WITH ACCEPTANCE-REJECTION AND HIERARCHICAL GAUSSIAN CO-SIMULATION
    (Nazarbayev University School of Mining and Geosciences, 2024-04-16) Kuanyshev, Shingiskhan
    Resource estimation is the basis of efficient and sustainable mining. Accurate mineral resource estimation is critical to optimizing mine planning, minimizing waste, and ensuring the economic viability of mining operations. Traditionally, resource estimation focused primarily on grade, the concentration of valuable minerals in ore bodies. However, the evolving complexity of modern mining requires a holistic approach that includes not only the grade but also the geometallurgical properties of the ore. Geometallurgical properties, which include attributes such as mineralogical composition, texture, and work index, play a key role in shaping mining operations. Understanding and modeling these properties is essential to unlocking the full potential of mineral deposits. In the context of mining, geological complexity often leads to complex non-linear bivariate relationships between different attributes of ore bodies. These relationships can pose significant challenges for resource modeling, as traditional methods such as Principal Component Analysis (PCA) and Minimum/Maximum Autocorrelation Factor (MAF) are ill-suited to handle such complexities. These methods are inherently linear and may fail to capture the nuanced interactions and dependencies within geologic datasets. This research paper presents a new approach to address the complexity of resource estimation in mining, particularly when dealing with non-linear bivariate relationships between geometallurgical properties. The proposed method combines the accept-reject method with hierarchical sequential Gaussian co-simulation. This approach enables the careful modeling of complex relationships (non-linearity) within geological data, leading to more accurate resource estimates and better-informed mining decisions. A case study is presented in which the acceptance-rejection method with hierarchical sequential Gaussian co-simulation is applied to the modeling of two geometallurgical properties, recovery and chalcopyrite. The study shows how this innovative approach increases resource estimation accuracy by capturing non-linear dependencies and spatial variability between these two variables. Due to the hierarchical nature of geological data, the method adapts to different scales of variability, resulting in more realistic and practical resource models. The findings of this research not only highlight the importance of integrating geometallurgical properties into resource estimation, but also provide a valuable solution for solving complex nonlinear bivariate relationships in geostatistical analysis of other regionalized variables. This approach has the potential to revolutionize resource modeling practices in the mining industry, leading to more sustainable, efficient, and economically viable mining operations. As mining continues to face evolving challenges and requirements, it is essential to incorporate advanced techniques such as the accept-reject method with hierarchical sequential Gaussian co-simulation to exploit the full potential of the Earth's mineral resources.