Comparative Analysis of Machine Learning Regression Methods for Geometallurgical Modeling in the Sungun Copper Porphyry Deposit

Geometallurgical modeling (GM) plays a crucial role in the mining industry, enabling a comprehensive understanding of the complex relationship between geological and metallurgical factors. This study focuses on evaluating metallurgical varibles at the Sungun Copper mine in Iran by measuring and predicting process properties, including semi-autogenous power index (SPI), recovery (Re), and concentration grade. To overcome the additivity limitations of geostatistical methods, we utilized machine learning algorithms for enhanced predictive modeling, aiming to improve decision-making and optimize mining operations in geometallurgy. The research incorporates crucial data inputs such as sample coordinates, grades, lithology, mineralization zones, and alteration to assess the accuracy and reliability of different machine learning regression methods. The Relative Standard Deviation (RSD) is highlighted as a significant metric for comparing the accuracy of predicted process properties. Evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R۲) further confirm the superiority of specific modeling methods in certain scenarios. The K-Nearest Neighbors (KNN) method exhibits superior accuracy, lower error metrics (RMSE and MAE), and a higher R۲ for modeling the SPI test. For modeling Cu grade in concentrate, Support Vector Regression (SVR) proves to be effective and reliable, outperforming the Multilayer Perceptron (MLP) method. Despite MLP's high R۲, its higher RSD suggests increased uncertainty and variability in the predictions. Therefore, SVR is considered more suitable for modeling Cu grade in concentrate. Findings optimize operations at Sungun Copper mine, improving decision-making, efficiency, and profitability.

Keywords:

Geometallurgical modeling , process properties , semi-autogenous power index (SPI) , Machine learning algorithms , Sungun Copper Mine