Mathieu Kayembe Musala, Matthieu Matthieu Tshanga
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 R2 = 0.721 for Cu recovery, substantially outperforming CNN (MAE = 2.381 %, R2 = 0.017) and LSTM (MAE = 2.421 %, R2 = -0.024). Permutation-based feature importance identifies PSD (granulometry) as the dominant predictor (Δ MAE = +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 graduation.
@article{afd76df6-4e78-4092-a99a-e4c89ffd34b7,
title={Enhancing copper and cobalt hydrometallurgical leaching thr 2027 Minerals En},
author={Mathieu Kayembe Musala and Matthieu Matthieu Tshanga},
year={2026},
language={en}
}TY - JOUR TI - Enhancing copper and cobalt hydrometallurgical leaching thr 2027 Minerals En AU - Mathieu Kayembe Musala AU - Matthieu Matthieu Tshanga PY - 2026 LA - en ER -
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