1426 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
It is also important for plant operators to recognize that
their sizing operations can also contribute substantially to
particle misplacement and efficiency losses. For example,
poor fine wire sieve performance after a spiral will result
in the unwanted misplacement of high-ash fines into the
fine clean coal stream. To compensate for the poorer qual-
ity, operators often lower the density cutpoint in dense
medium cyclone circuits, leading to total lower yields and
potentially a higher total plant moisture. Therefore, effec-
tive metrics must also be developed and monitored for these
auxiliary operations to ensure preparation plants operate at
their optimal levels of efficiency and profitability.
In many cases, froth flotation circuits are found to
be the greatest contributor to coal losses. It is most com-
mon to find losses created by a failure of plant personnel
to track performance and to make adjustments as neces-
sary to maintain a high recovery of coals. Random sam-
pling cannot fix this issue since it tends to only be done
when cells are optimized and neglect often follows these
sporadic sampling programs. As such, routine sampling
using an automated/timed collection system is absolutely
essential for good and consistent flotation performance. To
illustrate this point, consider a large flotation circuit used to
recover minus 0.15 mm coking coal. Historically, the flota-
tion bank at this facility was only “visually” monitored and
detailed sampling was rarely performed. At the urging of
external consultants, this oversight was corrected by install-
ing an automatic sampler that was configured to take rep-
resentative samples from the feed, froth and tailing streams
from the bank of cells. The samplers were programmed to
collect timed cuts throughout the active duration of the pro-
duction shifts so that daily samples could be composited/
analyzed.
Figure 2 shows combustible recovery versus time
data obtained after the installation of the automatic sam-
plers. Prior to July, relatively little attention was paid to
the froth cells and as expected, the bank performance was
poor and highly erratic. The average recovery from early
June through mid-July was only 34.8% and, during some
periods, dropped to below 10% due to equipment issues.
Experience indicates that poor recovery values like these are
not unusual for unmonitored banks of flotation cells. In the
middle of July, plant management intensified the sampling
program and began sharing performance data with the
flotation operators on a shift-by-shift basis. Interestingly,
very little changed until the plant manager began plotting
the day-by-day recovery data as dollar “losses” or “gains”
(Figure 3). This form of data reporting showed that the
plant was losing more than $36,000 per day because of
poor operating practices. The reporting of flotation perfor-
mance as a “monetary” as opposed to an “efficiency” gain/
loss more effectively demonstrated the true magnitude of
the problem to the circuit operators. Consequently, flota-
tion recoveries began to steadily rise reaching an average
recovery of 86.3% by the beginning of September. The
plant manager now meets daily with the flotation opera-
tors to review performance and discuss the impacts of
operating practices such as proper chemical dosing, better
control of pulp levels, cleaning of air filters, and so forth.
Monetarybased data reporting helped the plant operators
Table 3. Partitioning performance for a fine coal spiral circuit (–1 +0.15 mm, dry basis)
Density Class
Feed Product Refuse PC
(%)%Wt Ash (%)%Wt Ash (%)%Wt Ash (%)
Float 1.60 69.11 6.62 96.67 6.44 17.74 7.42 91.20
S1.60 F1.80 2.91 35.67 1.73 35.70 2.52 34.25 56.63
S1.80 F2.00 1.49 42.75 0.86 42.15 3.58 43.68 31.36
2.00 Sink 26.49 80.54 0.74 66.67 76.16 80.59 1.81
Total 100.00 27.59 100.00 7.70 100.00 65.12 65.54
Table 4.
F/S
1 mm × 100M Feed Product Refuse
PC% Gravity %Wt %Ash %Wt %Ash %Wt %Ash
1.60 Float 60.40 6.77 93.19 6.49 1.99 9.08 98.97
1.80 Float 3.97 32.98 3.02 38.29 0.85 34.22 87.97
2.00 Float 4.11 49.14 2.04 48.44 1.90 47.70 68.84
2.00 Sink 31.52 76.99 1.75 65.72 95.26 78.31 3.64
COMP 100.00 31.69 100.00 9.34 100.00 75.98 67.29
It is also important for plant operators to recognize that
their sizing operations can also contribute substantially to
particle misplacement and efficiency losses. For example,
poor fine wire sieve performance after a spiral will result
in the unwanted misplacement of high-ash fines into the
fine clean coal stream. To compensate for the poorer qual-
ity, operators often lower the density cutpoint in dense
medium cyclone circuits, leading to total lower yields and
potentially a higher total plant moisture. Therefore, effec-
tive metrics must also be developed and monitored for these
auxiliary operations to ensure preparation plants operate at
their optimal levels of efficiency and profitability.
In many cases, froth flotation circuits are found to
be the greatest contributor to coal losses. It is most com-
mon to find losses created by a failure of plant personnel
to track performance and to make adjustments as neces-
sary to maintain a high recovery of coals. Random sam-
pling cannot fix this issue since it tends to only be done
when cells are optimized and neglect often follows these
sporadic sampling programs. As such, routine sampling
using an automated/timed collection system is absolutely
essential for good and consistent flotation performance. To
illustrate this point, consider a large flotation circuit used to
recover minus 0.15 mm coking coal. Historically, the flota-
tion bank at this facility was only “visually” monitored and
detailed sampling was rarely performed. At the urging of
external consultants, this oversight was corrected by install-
ing an automatic sampler that was configured to take rep-
resentative samples from the feed, froth and tailing streams
from the bank of cells. The samplers were programmed to
collect timed cuts throughout the active duration of the pro-
duction shifts so that daily samples could be composited/
analyzed.
Figure 2 shows combustible recovery versus time
data obtained after the installation of the automatic sam-
plers. Prior to July, relatively little attention was paid to
the froth cells and as expected, the bank performance was
poor and highly erratic. The average recovery from early
June through mid-July was only 34.8% and, during some
periods, dropped to below 10% due to equipment issues.
Experience indicates that poor recovery values like these are
not unusual for unmonitored banks of flotation cells. In the
middle of July, plant management intensified the sampling
program and began sharing performance data with the
flotation operators on a shift-by-shift basis. Interestingly,
very little changed until the plant manager began plotting
the day-by-day recovery data as dollar “losses” or “gains”
(Figure 3). This form of data reporting showed that the
plant was losing more than $36,000 per day because of
poor operating practices. The reporting of flotation perfor-
mance as a “monetary” as opposed to an “efficiency” gain/
loss more effectively demonstrated the true magnitude of
the problem to the circuit operators. Consequently, flota-
tion recoveries began to steadily rise reaching an average
recovery of 86.3% by the beginning of September. The
plant manager now meets daily with the flotation opera-
tors to review performance and discuss the impacts of
operating practices such as proper chemical dosing, better
control of pulp levels, cleaning of air filters, and so forth.
Monetarybased data reporting helped the plant operators
Table 3. Partitioning performance for a fine coal spiral circuit (–1 +0.15 mm, dry basis)
Density Class
Feed Product Refuse PC
(%)%Wt Ash (%)%Wt Ash (%)%Wt Ash (%)
Float 1.60 69.11 6.62 96.67 6.44 17.74 7.42 91.20
S1.60 F1.80 2.91 35.67 1.73 35.70 2.52 34.25 56.63
S1.80 F2.00 1.49 42.75 0.86 42.15 3.58 43.68 31.36
2.00 Sink 26.49 80.54 0.74 66.67 76.16 80.59 1.81
Total 100.00 27.59 100.00 7.70 100.00 65.12 65.54
Table 4.
F/S
1 mm × 100M Feed Product Refuse
PC% Gravity %Wt %Ash %Wt %Ash %Wt %Ash
1.60 Float 60.40 6.77 93.19 6.49 1.99 9.08 98.97
1.80 Float 3.97 32.98 3.02 38.29 0.85 34.22 87.97
2.00 Float 4.11 49.14 2.04 48.44 1.90 47.70 68.84
2.00 Sink 31.52 76.99 1.75 65.72 95.26 78.31 3.64
COMP 100.00 31.69 100.00 9.34 100.00 75.98 67.29