XXXI International Mineral Processing Congress 2024 Proceedings Page 1368 (1392 of 3836)

XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1421
R² =0.8424
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%Ash in Clean Coal
Figure 10. Ash content (%)determined for the clean coal of the Seam A, B and C
composites compared against coal liberation (%)
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Seam A Seam B Seam C
Wn %Lib coal
Figure 11. Comparison between Wn and coal liberation (%)showing that they follow
the same trend for all Seam samples except for the second Seam A sample
%Liberated
C
l
%Lib
Coal
Wn

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XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1421
R² =0.8424
40
45
50
55
60
65
70
75
80
85
0 5 10 15
%Ash in Clean Coal
Figure 10. Ash content (%)determined for the clean coal of the Seam A, B and C
composites compared against coal liberation (%)
0
10
20
30
40
50
60
70
80
90
0
10
20
30
40
50
60
70
80
90
Seam A Seam B Seam C
Wn %Lib coal
Figure 11. Comparison between Wn and coal liberation (%)showing that they follow
the same trend for all Seam samples except for the second Seam A sample
%Liberated
C
l
%Lib
Coal
Wn

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1420 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
of binary coal particles associated with kaolinite, a clay
mineral.
Focused clay and mineral characterization, especially in
Seam B, revealed the effectiveness of FTIR spectroscopy in
identifying and mapping major clay minerals like kaolinite
and illite. This method not only distinguished between clay
minerals but also offered a mapping routine for detailed
discrimination between individual mineral grains. It should
be noted that this tool was not employed for quantitative
measurements but simply for mineral identification.
Automated mineralogy quantified mineral compo-
nents in RC chip samples, revealing higher abundances
of muscovite/illite/phengite in the feed coal sample from
Seam B. This observation raised questions about geological
Figure 9(a) Combined automated mineralogy BSE image, (b) equivalent mineral map and (c&d) EDS elemental maps showing
an example of liberated coal particles for Seam A (+300 µm material) and intricate mineral intergrowths (kaolinite and
muscovite-illite-smectite) in the cell lumen of the coal macerals
Table 5. Washability number, ash content, determined in the clean coal and percent liberated
coal for the composites from Seams A, B and C
Washability Number (Wn) %Ash Clean Coal Coal Liberation, %
Seam A 80 6.53 81.38
49 5.23 80.57
Seam B 70 7.23 70.44
45 8.86 72.34
47 9.12 67.82
33 11.02 60.00
Seam C 54 7.75 76.15
30 11.46 63.37

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1422 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
conditions, emphasizing the need for further explora-
tion and confirmation, possibly through shear strength
measurements.
The MLA methodology played a crucial role in under-
standing the impact of texture and coal liberation on pro-
cessing challenging seams. Positive correlations between
coal liberation and %ash in clean coal underscored the
importance of considering these factors in processing prob-
lematic seams.
Washability (Wn), evaluated by mine geologists, is rec-
ommended for predicting washability. A strong correlation
was found between Washability Number and the degree of
liberation for studied coal seams (A, B, and C), with Seam
B identified as the most challenging to clean.
In summary, a comprehensive approach involving Wn,
N-degree of washing parameters, PSD data, and identifi-
cation of clay minerals can serve as a warning system for
changing coal feed characteristics. To achieve accurate clay
identification, information on associated coal mineralogy
and liberation using FTIR and MLA is necessary. Micro-
FTIR or a machine-learning approach applied to XRD data
sets can enhance predictive characterization during explora-
tion, while automated mineralogy techniques can provide
detailed textural information on a smaller subset of samples.
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