Abstract
The natural stratigraphic occurrence of Cu-bearing ore in the Copperbelt seam is characterized by complex mineralogy due to continuous changes in ongoing mining activities, making the extraction of Co and Cu challenging. The variability in mineralogy leads to drastically different behaviors during the dissolution of Co and Cu from these ores. Depending on whether the ore is oxidized or sulfide, the hydrometallurgy of these ores requires either reducing or oxidizing agents to create favorable thermodynamic conditions for their individual or combined leaching to promote their dissolution kinetically. Despite the oxidizing and reducing agents' effectiveness in dissolving Co and/or Cu from the ores, their availability and cost must be considered. Consequently, the direct leaching proposed in this study will involve a series of redox reactions, during which the galvanic mechanisms between oxides and sulfides will be examined. The galvanic leaching method provides distinct advantages regarding energy efficiency and economic benefits, highlighting its potential as a more sustainable and effective alternative to conventional leaching methods that utilize oxidizing and reducing agents.
This study focuses on the direct leaching of high-grade Cu and Co-bearing oxide and Cu-sulfide ore mixtures without using any oxidizing and/or reducing agents. The research aims to understand the leaching behavior of Co and Cu from Cu-Co oxides and Cu-sulfide ore mixtures in sulfuric acid and develop a suitable pathway for the effective recovery of Co and Cu from a non-supplemented mixture. Mechanisms underlying the dissolution of Co and Cu were examined from the thermodynamic and kinetic perspectives to achieve the purpose of the current investigation. Therefore, the electro-assisted leaching technique was employed to develop an effective and sustainable process route while working at elevated temperatures. Furthermore, statistical modeling tools based on designs of experiments (DOEs) and Bayesian conditional probability were used for optimization through response surface methodology (RSM) and prediction, respectively.