1412 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
processing plant. While washability may not directly pre-
dict floatability, issues such as increased fines, higher clay
content, or the presence of sheared coal may also indicate
challenges during flotation processing.
In summary, understanding both the microscopic and
macroscopic characteristics of coal is crucial for predicting
its washability. Composition, mineral distribution, particle
size, density, porosity, hardness, and wettability are all fac-
tors that contribute to the success of the coal washing or
cleaning processes. Coal washing technologies are designed
to exploit these characteristics and achieve efficient separa-
tion of coal from its impurities for improved quality and
market value.
METHODOLOGY
Washability Numbers, Indices, and Parameters
Washability assessment methodologies, including ASTM
tests, were applied utilizing bulk coal samples. In this study,
Washability Number was employed to predict washability
from exploration and mine coal samples. Formulas for cal-
culating Washability Number (Wn) and degree of washing
(N) were utilized. Washability assessments were evaluated
based on yield, ash content, and near gravity material for
comprehensive insights. The separation of coal from near-
gravity material can be challenging because the differences
in specific gravity are not as pronounced as with other
impurities. As proposed by Holuszko (1994) and previ-
ously introduced by Sarkar et al., (1962) the following cal-
culation were used for the washability number:
Wn b
N 10 =
where b =the clean ash content at N (the same specific
gravity that was used to calculate N).
N= degree of washing
The degree of washing (N) can also be calculated using the
equation below:
N a
w a bh =-^
where:
a =feed ash content?
b =ash content of the clean coal at a given density
w =yield of the clean coal at the given SG of
separation
Generally, it has been accepted that when:
• Wn 40: difficult-to-wash coal
• Wn 40–50: sometimes good
• Wn 50–60: generally good
• Wn 60: very good washability
Mineral Matter in Coal and Clay Minerals
Characterization by Micro-FTIR, XRD and MLA
Mineral matter characterization, focusing on clay iden-
tification, utilized Fourier Transform Infrared (FTIR)
spectroscopy (Micro-FTIR). FTIR’s non-destructive, min-
eralogical characterization capability, exploiting vibrational
energy states, offered insights into both organic and inor-
ganic components (Chen et al., 2015). During this study,
this tool was not used for quantitative measurements. The
Micro-FTIR system tested the detection feasibility of min-
erals commonly found in studied coal seams, aiding quick
and reliable mineralogy evaluations. The potential for FTIR
to detect clay minerals and serve as a tool for onsite assess-
ments was explored, emphasizing its applicability for iden-
tifying oxidation and other mineral species of interest. The
liberation characteristics with textural associations between
the coal and mineral matter were quantitatively evaluated
by Mineral Liberation Analyzer (MLA). Liberation refers
to the process of separating individual components within
a coal sample. A machine-learning approach using X-ray
Powder Diffraction (XRD) data of a large subset of raw
coal and equivalent clean coal samples were employed to
quantify the clay mineral contents and build a library of
variability that would be expected both laterally and verti-
cally by seam.
It is important to note that FTIR, XRD and MLA are
tools that are used to measure the mineral components
directly in samples and are not geochemical tools such as
X-ray fluorescence spectroscopy (XRF) or Laser Induced
Breakdown Spectroscopy (LIBS). The data generated by the
latter two tools can be used as proxies for the mineral com-
ponents in samples/materials but this can become com-
plicated with multiple mineral phases that host the same
elements. Direct determination and identification of the
minerals are more dependable for more complex mineral
mixtures.
RESULTS
Particle Size Distribution (PSD)
The PSD data was acquired from various sources, includ-
ing reports from commercial labs that contained washabil-
ity data derived from RC chip samples or bulk samples. In
addition, PSD were obtained from samples collected from
stockpiles (Seam A, Table 1), while others originated from
blends of various seams or single-seam (Seam B, Table 1).
processing plant. While washability may not directly pre-
dict floatability, issues such as increased fines, higher clay
content, or the presence of sheared coal may also indicate
challenges during flotation processing.
In summary, understanding both the microscopic and
macroscopic characteristics of coal is crucial for predicting
its washability. Composition, mineral distribution, particle
size, density, porosity, hardness, and wettability are all fac-
tors that contribute to the success of the coal washing or
cleaning processes. Coal washing technologies are designed
to exploit these characteristics and achieve efficient separa-
tion of coal from its impurities for improved quality and
market value.
METHODOLOGY
Washability Numbers, Indices, and Parameters
Washability assessment methodologies, including ASTM
tests, were applied utilizing bulk coal samples. In this study,
Washability Number was employed to predict washability
from exploration and mine coal samples. Formulas for cal-
culating Washability Number (Wn) and degree of washing
(N) were utilized. Washability assessments were evaluated
based on yield, ash content, and near gravity material for
comprehensive insights. The separation of coal from near-
gravity material can be challenging because the differences
in specific gravity are not as pronounced as with other
impurities. As proposed by Holuszko (1994) and previ-
ously introduced by Sarkar et al., (1962) the following cal-
culation were used for the washability number:
Wn b
N 10 =
where b =the clean ash content at N (the same specific
gravity that was used to calculate N).
N= degree of washing
The degree of washing (N) can also be calculated using the
equation below:
N a
w a bh =-^
where:
a =feed ash content?
b =ash content of the clean coal at a given density
w =yield of the clean coal at the given SG of
separation
Generally, it has been accepted that when:
• Wn 40: difficult-to-wash coal
• Wn 40–50: sometimes good
• Wn 50–60: generally good
• Wn 60: very good washability
Mineral Matter in Coal and Clay Minerals
Characterization by Micro-FTIR, XRD and MLA
Mineral matter characterization, focusing on clay iden-
tification, utilized Fourier Transform Infrared (FTIR)
spectroscopy (Micro-FTIR). FTIR’s non-destructive, min-
eralogical characterization capability, exploiting vibrational
energy states, offered insights into both organic and inor-
ganic components (Chen et al., 2015). During this study,
this tool was not used for quantitative measurements. The
Micro-FTIR system tested the detection feasibility of min-
erals commonly found in studied coal seams, aiding quick
and reliable mineralogy evaluations. The potential for FTIR
to detect clay minerals and serve as a tool for onsite assess-
ments was explored, emphasizing its applicability for iden-
tifying oxidation and other mineral species of interest. The
liberation characteristics with textural associations between
the coal and mineral matter were quantitatively evaluated
by Mineral Liberation Analyzer (MLA). Liberation refers
to the process of separating individual components within
a coal sample. A machine-learning approach using X-ray
Powder Diffraction (XRD) data of a large subset of raw
coal and equivalent clean coal samples were employed to
quantify the clay mineral contents and build a library of
variability that would be expected both laterally and verti-
cally by seam.
It is important to note that FTIR, XRD and MLA are
tools that are used to measure the mineral components
directly in samples and are not geochemical tools such as
X-ray fluorescence spectroscopy (XRF) or Laser Induced
Breakdown Spectroscopy (LIBS). The data generated by the
latter two tools can be used as proxies for the mineral com-
ponents in samples/materials but this can become com-
plicated with multiple mineral phases that host the same
elements. Direct determination and identification of the
minerals are more dependable for more complex mineral
mixtures.
RESULTS
Particle Size Distribution (PSD)
The PSD data was acquired from various sources, includ-
ing reports from commercial labs that contained washabil-
ity data derived from RC chip samples or bulk samples. In
addition, PSD were obtained from samples collected from
stockpiles (Seam A, Table 1), while others originated from
blends of various seams or single-seam (Seam B, Table 1).