Mathieu Kayembe Musala

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

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...

Optimization of leaching efficiency and anomaly detection in a copper–cobalt hydrometallurgical plant using random forest, gradient boosting, and k-nearest neighbors

Hydrometallurgical extraction of copper (Cu) and cobalt (Co) from oxide ores represents a critical industrial process in the Central African Copperbelt, yet operational variability consistently undermines recovery targets. This study presents a comprehensive data-driven analysis...