University of Lubumbashi

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...

Heavy metal human health risk assessment and consumer perception of vegetables in Lubumbashi, Democratic Republic of the Congo

In the mining city of Lubumbashi (Democratic Republic of the Congo), intensive mining activities have led to heavy metal contamination of food crops, posing serious public health risks. Using an interdisciplinary framework, this study addresses a critical knowledge gap by quantif...

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...

Early-stage leaf litter decomposition of miombo tree species is more strongly driven by leaf litter chemistry than by charcoal-induced disturbance in forests around Lubumbashi, DR Congo

Litter decomposition is a key ecosystem process because it regulates carbon storage and nutrient availability, yet it remains poorly documented in miombo woodlands. This study assessed how leaf litter quality affects decomposition rates and whether these effects are modified by c...