Use of X-ray computed microtomography to measure the leaching behaviour of metal sulphide ores

• Main Author: Lin, Qingyang
• Other Authors: Neethling, Stephen
• Published: Imperial College London 2015
• Online Access: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.749073

Heap leaching is an important hydrometallurgical method to extract valuable metals from ores, especially low grade ores. The main disadvantages of heap leaching are the long processing time and low extraction efficiencies. Currently, a major barrier in fully understanding the leaching process is the study of the mass transport and surface chemistry at individual ore particle and mineral grain scale. This thesis describes a combined experimental and modelling approach to visualise, quantify and predict the leach behaviour based on X-ray Computed Microtomography (XMT, or micro-CT). An automatic image processing package was developed to process the 3D volume data. Individual ore particles as well as individual mineral grains can be tracked using a centroid tracking algorithm and a novel fast tracking algorithm respectively. The systematic and random errors and uncertainties in the image volume measurements were quantified. It was found that both the systematic and random errors are a strong function of the grain size relative to the voxel size. The random error can be reduced by combining the results from either multiple scans of the same object or scans of multiple similar objects while the systematic error can be eliminated by using volume standards. The leach performance for a leaching column was quantified at different scales and it was found that the leach behaviour and its variability were difficult to quantify at large scales (column and individual ore particle scale), but can be quantified at mineral grain scale by using a novel statistical analysis method. The tracked grains were divided into different size-distance categories to analyse the average leach performance and the variation for each category. Both grain size and distance dependencies were observed. The size dependency is more dominant at the early stage of leaching whereas the distance dependency can significantly influence the ultimate recovery. A method for using the data to estimate the variability in the in-situ surface kinetics was also developed. A model for simulating the grain dissolution and the resultant kinetics based directly on XMT based 3D volume is introduced. The simulations were able to accurately predict both the overall leaching trends, as well as the leaching behaviour of mineral grains in classes based on their size and distance to the particle surface.