Enhancing copper and cobalt hydrometallurgical leaching through deep learning-based process optimisation

Abstract

This study presents a comprehensive machine learning framework for the optimisation of metallurgical parameters for the grinding, leaching and solid/liquid separation in a Congolese Copperbelt hydrometallurgical plant. Three deep learning architectures namely Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) were designed, trained, and comparatively evaluated. Models were trained to predict Cu and Co leaching recoveries from ten critical physicochemical process variables: feed rate, particle size distribution (PSD), percentage solids at key settling or decantation stages (Leach feed thickener (LFT), Counter current decantation (CCD4), Leach discharge thickener (LDT)), metal losses in overflow streams, and flocculant addition rates. The DNN achieved superior predictive performance with MAE = 1.328 % and R 2 = 0.721 for Cu recovery, substantially outperforming CNN (MAE = 2.381 %, R 2 = 0.017) and LSTM (MAE = 2.421 %, R 2 = − 0.024). Permutation-based feature importance identifies PSD (granulometry) as the dominant predictor (ΔMAE = +0.368), followed by metal losses and thickener underflow densities. A 3-month DNN forecast projects Cu recovery improving from the 2024–2025 mean of 96.0 % to 97.2–97.8 % as gradual PSD and LDT improvements are implemented, with Co recovery reaching 79.8–80.0 %. The critical operational recommendation is to improve milling fineness from the current mean PSD of 61 % to the 75–80 % at 75 µm target range through SAG mill circuit optimisation.