Browsing by Author "Angula, Tuhafeni Kiiga"

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    Leveraging Machine Learning for Enhanced Efficiency in Mineral Processing Through Silica Estimation at Rosh Pinah Zinc Mine
    (Namibia University of Science and Technology, 2025-04-18) Angula, Tuhafeni Kiiga
    In mineral processing, real-time silica estimation in ore samples remains a significant challenge which influences operational inefficiencies, increased costs and recovery rate. Traditional methods depend on periodic laboratory sample tests, which, while accurate, introduce delays that exacerbate issues related to delays on changes of the blending ratio to stabilise the silica entering the plant circuit. These conventional methods provide valuable insights into mineral content patterns but fail to timely deliver the information needed for optimal mineral processing efficiency. This study addresses these challenges by introducing machine learning techniques to estimate silica content in the ore feeds. Four commonly used Machine Learning (ML) models were considered, namely Multiple Linear Regression, Support Vector Regression, Random Forest, and Extreme Gradient Boosting. A dataset consisting of 5967 entries and 10 features collected from mining operations at Rosh Pinah Zinc Mine in Namibia for the past five years (2019-2023) was used to train and test the ML models. The study identifies the key features that significantly influence silica content estimation. Pearson correlation analysis was applied, which shows elements such as Sulphur (-0.54), Zinc (-0.49) and Manganese (-0.49) exhibiting strong negative correlation. Among the four ML models, Random Forest Regression emerged as the highly performing algorithm due to its ability to capture complex and non-linear trends, hence superior performance. The evaluation was obtained using standard machine learning performance metrics, namely Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²) scores. The Random Forest model demonstrated 85.4% estimation accuracy, with low error rates of MSE at 0.3821 and MAE at 0.2848. This study demonstrates the capability of machine learning to offer a timely data-driven approach to enhance ore blending and improve overall mineral processing efficiency. In addition, this enhances decision-making processes in mineral processing, reducing operational costs and increasing recovery rates.