Coal Preparation » General
Micro-CT (Computed Tomography) technology provides fast 3 dimensional analysis of solid samples and this technique has been used to assess the particle characteristics of coal samples from the fine circuit of a coal preparation plant, coal maceral concentrates and float/sink samples.
The technique was used to detect maceral content, mineral content and particle density. The project explored different methods of presenting particles to the instrument and examined a range of available image analysis techniques. It was found that coal maceral components broadly appear to have different greyscale values in microCT and this offers an opportunity to provide petrographic information as well as mineral content and composition. It was also found that presenting particles as physically separated particles on a adhesive backed Styrofoam film (rather than packed bed) reduces analytical times down from 13 hours to 75 minutes. This offers opportunities as a genuine replacement for analytical float/sink analysis particularly for diagnostics in the fine circuit of a coal preparation plant-where timely response is important. In particular, a partition curve was generated in ~12 hours of analysis; consisting of 9 scans in total to acquire sufficient particle numbers. Likely this timed response could be further improved by optimising the number of particles per scan.
The characterisation of coal maceral concentrates was to enable a direct comparison with coal grain analysis; which had been previously reported for these selected samples. Across all four samples, the micro-CT produced density distributions that were tighter and less distributed than the CGA equivalent. This difference was considered due to the potential for stereological errors commonly associated with 2D image analysis. This occurs by assuming volumetric data from a single crosssectional slice, which tends to mis-characterise the size of internal components and potentially whole particle size. This difference appeared to be carried through to the characterisation of grain types, with the micro-CT data suggesting that the majority of grains in the sample were of Composite grain type (all components below 65%). The equivalent CGA data suggested that the majority of grains were of Dominant grain type (one component between 65-95%). It is noted that this analysis was only compared on a single sample and there is likely further work involved to determine if such observations are systemic and to quantify the extent.
During the project an image analysis method was generated for determining a particles surface composition. This may have implications for resource evaluation in terms of flotation potential and also for specific coal uses such coking behaviour. In this work, the average surface mineral content was ~2% but was observed to vary from 0-100% across the inertinite concentrate sample.
A focussed study on higher density minerals used a specific sample already well characterised by SEM-EDS based analysis (Tescan Integrated Mineral Analyser, TIMA). It was found that the micro-CT was able to detect three mineral groups of low (kaolinite+quartz), medium (siderite) and high density (iron oxide). There appeared to be very good agreement between the two techniques on quantified mineral abundance of the three dominant mineral groups. However, it did not appear to have the desired sensitivity to detect minor mineral constituents below 0.2%, nor to be able to discriminate them from the larger mineral groups. It was also observed that the actual measurement of density at these higher ends was not accurate, in part because of the elongation of density distribution towards higher density. This is an area for future improvement, though it must be stated that two component mineral systems such as silica/chalcopyrite were found to easily discriminate between minerals. Once a difference between greyscale values can be observed further data extraction such as mineral grain size distribution was easily obtained.
Overall, this technique offers an opportunity of exploring coal and mineral samples in a highly visual and immersive way, whilst the 3D data extracted with image analysis techniques offers a range of useful quantitative metrics for assessment. Given the relatively fast speed of analysis and ability for further automation, this technique has the potential for increased opportunities in coal preparation as a routine alternative to heavy medium float/sink analysis. It also shows great promise as a characterisation tool for coal in both preparation and utilisation studies.