Technical Market Support

Improved Understanding of Coal Exploration Samples by Coal Grain Analysis

Technical Market Support » Metallurgical Coal

Published: February 13Project Number: C20040

Get ReportAuthor: G O’Brien, B Firth, K Warren and P Hapugoda | CSIRO

Over the past six years, CSIRO has used optical imaging and Coal Grain Analysis (CGA) to obtain and use compositional information on individual coal grains for studies on product coals, raw coals and tailing samples. The output from a CGA measurement provides a reflectance fingerprint on the maceral groups, and compositional information on each individual grain. As the mass and ash value are calculated for each grain, the data obtained from the analysis of raw coal samples can be used to estimate the expected yield and the petrographic composition of the product at any target ash value. In this project the capability of the CGA system was expanded to also predict maceral chemistry information on individual particles.


Six Australian coals of different ranks, from different coal basins, were used to develop and validate correlations between maceral chemistries and coal rank. These coals were all "typical" Australian coals which were formed in non-marine environments and varied in mean random vitrinite reflectance from 0.75% to 1.75%. For each coal, fine coal float sink procedures were used to produce density fractions on which proximate analysis, ultimate analysis and Crucible Swelling Number (CSN) were conducted to appropriate Australian Standards. Whole coal reflectogram and Coal Grain Analysis assessments were also conducted on each density fraction. Two different statistical analysis methods, Regression Analysis and the optimisation routine implemented by MS Excel Solver were used to correlate the maceral composition with chemical attributes for the different density fractions for each coal to enable chemical predictors to be determined.


The whole coal reflectogram information that was obtained for each coal was used to determine the mean reflectance value for each maceral group. The maceral composition information obtained on each density fraction for each coal was used to determine maceral density.


Regression techniques were used to develop predictors for ash value, CSN, volatile matter and ultimate analysis composition and to also obtain maceral chemistry for the vitrinite, inertinite, and liptinite constituents of each coal. As ultimate analyses measures Carbon (C), Hydrogen (H), Nitrogen (N), Sulphur (S) directly and calculates Oxygen (O) by difference, these results also report a calculated Oxygen value. These chemical values were then correlated with mean random maceral reflectance values.


Validation tests on the chemical predictors were conducted on two separate borecore samples (for Coal A and B) and on mine shipment samples (Coal C and D). In general there was good agreement between the predicted values for the second borecores for the two coals and the measured values, excepting carbon and sulphur values for Coal A. Although these two borecores were collected from adjacent geographic positions, the second borecore contained about 2% less carbon and 0.3% more sulphur on a Dry Ash Free basis (DAF) than the density fractions for the first borecore. At this stage it is unclear why these two borecores showed this variability. The blind tests conducted on the two shipment samples also showed that the methods made reasonable predictions of proximate and ultimate properties of these samples.


For the second borecore samples for Coal A and B, estimates of the expected yield and the proximate and ultimate properties of the expected product for 4 product ash values (6%, 8%, 10%, 12%) were made. For both coals CSN decreased as product ash value increased. For the lower rank Coal A, carbon, and nitrogen contents remained relatively consistent, hydrogen, sulphur and volatile matter (DAF) contents decreased and oxygen content increased as product ash value increased. For Coal B, little variation in the DAF chemical properties of the product coal was determined as product ash value increased.


During the project it was investigated whether coals of the same rank which were formed in different basins under broadly similar conditions (non-marine) resulted in differences in macerals chemistry. This was done by comparing the predicted maceral chemistries of two pairs of samples of similar ranks. The predicted maceral chemistry properties for the first pair (one from the Hunter Valley and the second from the Rangal coal measures) which had mean vitrinite reflectance values of 0.75% generally showed similar trends, but somewhat different values. For both coals the liptinite contained the most amounts of hydrogen and volatile matter and the least amount of oxygen, and inertinite contained the lowest amounts of hydrogen and volatile matter and the most amount of oxygen. The second pair of coals, which came from the Rangal coal measures and the Illawarra coal measures, had slightly different coal ranks as measured by mean random vitrinite reflectance. Considering that there is a slight rank difference for these two coals, the maceral chemistry predictions for vitrinite and inertinite were quite similar. Vitrinite contained more hydrogen, and volatile matter and inertinite contained more oxygen.


The maceral reflectance and maceral chemistry information obtained for this suite of 6 "typical" Australian coking coals which were formed in non-marine conditions provides baseline information to which other coals, both domestic and international may be compared. For example, it would be valuable to establish if coals that are marine in origin show the same relationships as these typical non-marine Australian coals between maceral reflectance and maceral chemistry.


The information provided by this approach of correlating maceral chemistry with maceral reflectance may assist coal technologists to better understand why coals which have similar bulk maceral and or chemical attributes may exhibit different behaviours during utilisation. As CGA provides compositional information on individual particles, this method can provide an estimate of the major chemical composition of individual particles at the size that they are used. This means that maceral chemistry information can be obtained for the individual coal particles which have been crushed to a 4mm topsize for coke oven feed, or to a 0.1mm topsize for power generation.


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