Modelling uranium leaching kinetics

• Main Author: Sililo, Bernard Liswani
• Other Authors: Groot, D.R.
• Language: en
• Published: University of Pretoria 2017
• Subjects: UCTD; Uraninite; Leach kinetics; Oxidation-reduction potential; Integral method
• Online Access: http://hdl.handle.net/2263/61336; Sililo, BL 2016, Modelling uranium leaching kinetics, MSc Dissertation, University of Pretoria, Pretoria, viewed yymmdd <http://hdl.handle.net/2263/61336>

The uranium price decline has negatively impacted on the uranium mining industry. This decline in price requires that uranium metallurgical processes be made to operate more efficiently. Some key parameters that influence the dissolution and kinetics of leaching uraninite (one of the main minerals from which uranium can be extracted) are pH, oxidationreduction potential and iron concentration. A good understanding of the effect these parameters have on the leach kinetics would lead to an efficient operation of metallurgical processes. The objective of this work was therefore to investigate the effects of these key drivers on leach kinetics of Rӧssing Uranium ore. Added to this, was an attempt to come up with a mathematical model which can successfully replicate the leach kinetics. A series of laboratory leach experiments were performed on Rӧssing ore where the pH, oxidationreduction potential and total iron were varied, one at a time, to establish the effects they have on the leach kinetics and on the uranium extraction. Analysis of the data collected from this study showed that the leach kinetics are more dependent on the oxidation-reduction potential, followed by the iron concentration and least affected by the pH. It was further shown that oxidation-reduction potential is a function of total iron. An integral method was used to analyse the kinetic data. A literature study reveals that uraninite dissolution follows first order kinetics, but of interest in these results was that the uranium dissolution was found to closely follow the second order. Further research is recommended to look at ascertaining these results. Two models were developed, one using regression and the other by curve fitting method. Both models could fit the experimental data well enough. === Dissertation (MSc)--University of Pretoria, 2016. === Materials Science and Metallurgical Engineering === MSc === Unrestricted