Leveraging Machine Learning for Enhanced Efficiency in Mineral Processing Through Silica Estimation at Rosh Pinah Zinc Mine
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Date
2025-04-18
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Namibia University of Science and Technology
Abstract
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.
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Keywords
mineral processing, comminution, machine learning, ore quality estimation, silica content, AI in mining
Citation
Angula, T. K. (2025). Leveraging Machine Learning for Enhanced Efficiency in Mineral Processing Through Silica Estimation at Rosh Pinah Zinc Mine [Master’s thesis, Namibia University of Science and Technology].