The project, funded by the Australian Coal Association Research Program (ACARP), aims to integrate the Minerals and Coal Evaluation (MACE 300) system with the Mineral Liberation Analyser (MLA).
The strengths of the two systems will be brought together using fusion technology. By the end of the decade coal could be assessed using the most advanced analytical technology yet seen.
The research is being undertaken at the University of Queensland’s JK Centre with consultant Jenkins-Kwan Technology. It will be led by Dr Barry Jenkins, who specialises in automated light microscopy, a key feature of the MACE system.
“Our light microscopy methods can analyse the organic constituents of coal in great detail but we can’t do a comprehensive analysis of the minerals present,” Jenkins said.
The MLA analyses inorganic components of a coal sample while the MACE300 analyses the organic components.
Coal has a very low atomic number, making it not ideal for analysis with electron microscopy, as used by the MLA. But together with MACE, the MLA is potentially a powerful analytical tool for coal.
MACE creates characterised particle images based on the reflectance and texture of organic materials. In the case of thermal and coking coals, the reflectance rating of Vitrinite, the most important organic constituent, gives a measure of coal rank.
However MACE, as a light-based microscopy system, cannot distinguish very small particles and cannot assess the mineral composition of these particles. Coal particles can contain a vast variety of very fine mineral grains that therefore go undetected.
The MLA system will enable more detailed analysis of very fine particles such as those found in flotation feeds and pulverized fuel, based on its ability to image particles at sub-micron resolutions and provide mineral chemistry information.
The fusion of the two systems will allow analysis of raw and processed coals on a grain-by-grain basis with both minerals and the organic components characterised accurately. This has major significance for developers of coal utilisation, process optimisation and performance.
Yet to be achieved is how the two images or data sets will be combined to make one composite image of every grain in the coal sample.
Once this is achieved, the new data from the two systems will provide a revolutionary tool for performing tasks such as flotation diagnostics and fly ash analysis.
