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893 Copper Recovery Predictive Performance for Selected Machine Learning Algorithms B. Amankwaa-Kyeremeh University of South Australia, UniSA STEM, Future Industries Institute, Mawson Lakes, Adelaide, Australia C. McCamley BHP Olympic Dam, Adelaide, Australia R. K. Asamoah University of South Australia, UniSA STEM, Future Industries Institute, Mawson Lakes, Adelaide, Australia BHP Olympic Dam, Adelaide, Australia ABSTRACT: In this paper, the predictive performance of selected machine learning algorithms (e.g., support vector machine, Gaussian process regression, multi-layer perceptron artificial neural network, linear regression and random forest) is investigated for copper flotation recovery. The model performance, assessed using linear correlation (𝑟), root mean square error (RMSE), mean absolute percentage error (MAPE) and variance accounted for (VAF), showed that Gaussian process regression model makes the most precise rougher copper recovery prediction. The outstanding performance of the Gaussian process regression model is attributed to its ability to adequately model the noise patterns in the copper recovery data. Links with other systems and opportunities for further development has been discussed. Keywords: Froth flotation, Predictive algorithms, Gaussian process regression (GPR), Machine learning, cop- per flotation recovery, Random forest INTRODUCTION Copper is one of the first metals exposed to humans and has continued to find modern commercial applications due to its high electrical conductivity, resistance to corrosion and its ability to easily form alloys with other metals like nickel (cupronickel), zinc (brass) and tin (bronze) (Latorre et al., 2019). The electrical, building, transportation and elec- tronic industries are some of the main consumers of cop- per produced globally (Sverdrup et al., 2014). Copper can occur naturally in its pure state as native copper but mostly mined and processed from primary sulphide chalcopyrite (CuFeS2) and its oxidized states occurring near the surface as a result of weathering (Kesler et al., 2015). With the almost depleted oxide deposits, sulphides have remained the major source of copper with ever-decreasing ore grades and increasing mineralogical complexity. Most copper mining companies are now treating large tonnages of low grade copper ores in order to meet the rising global copper demand which is forecasted to increase by 2–3% per year between 2010–2050 (Kuipers et al., 2018). Froth flotation has received widespread applications in sulphide mineral processing, having its performance much