XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1427
to embrace, the large value they add to plant profitability.
They now take great pride in the impressive day-to-day per-
formance of their flotation cells.
CONVERSION OF EFFICIENCY DATA
INTO FINANCIAL OPPORTUNITIES
The above flotation example highlights the importance of
defining plant performance not only in terms of standard
misplacement indicators such as coal recovery, but also as
monetary gains and losses. This approach provides a “uni-
versal language” that is well understood by all levels of
management and operational personnel .Moreover, this
mode of reporting monetary gains/losses makes it possible
to identify where financial resources can be best expended
in terms of circuit upgrades, new instrumentation, operator
training, and so forth.
One effective way of converting efficiency data into
financial opportunities is through the development of key
performance indicators (KPIs) for major circuitry compo-
nents and by compiling these into a total plant potential
efficiency gain. By simulating plant performance using “best
practice” efficiency data, the expected plant performance by
0
10
20
30
40
50
60
70
80
90
100
Before Average =34.8%
After Average =86.3%
Figure 2. Impact of routine performance monitoring on flotation bank recovery
-$60,000
-$50,000
-$40,000
-$30,000
-$20,000
-$10,000
$0
$10,000
Average Loss
$36,370/Day
Figure 3. Impact of performance monitoring on flotation revenue losses/gains
noitatolFhtorF )%(yrevoceRelbitsubmoC
)yaD/$(niaGrossoLeuneveR

Previous Page Next Page

Extracted Text (may have errors)

XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 1427
to embrace, the large value they add to plant profitability.
They now take great pride in the impressive day-to-day per-
formance of their flotation cells.
CONVERSION OF EFFICIENCY DATA
INTO FINANCIAL OPPORTUNITIES
The above flotation example highlights the importance of
defining plant performance not only in terms of standard
misplacement indicators such as coal recovery, but also as
monetary gains and losses. This approach provides a “uni-
versal language” that is well understood by all levels of
management and operational personnel .Moreover, this
mode of reporting monetary gains/losses makes it possible
to identify where financial resources can be best expended
in terms of circuit upgrades, new instrumentation, operator
training, and so forth.
One effective way of converting efficiency data into
financial opportunities is through the development of key
performance indicators (KPIs) for major circuitry compo-
nents and by compiling these into a total plant potential
efficiency gain. By simulating plant performance using “best
practice” efficiency data, the expected plant performance by
0
10
20
30
40
50
60
70
80
90
100
Before Average =34.8%
After Average =86.3%
Figure 2. Impact of routine performance monitoring on flotation bank recovery
-$60,000
-$50,000
-$40,000
-$30,000
-$20,000
-$10,000
$0
$10,000
Average Loss
$36,370/Day
Figure 3. Impact of performance monitoring on flotation revenue losses/gains
noitatolFhtorF )%(yrevoceRelbitsubmoC
)yaD/$(niaGrossoLeuneveR

Help

Destination page number Search scope Search Text

loading

1428 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
circuit can be baselined on a circuit-by-circuit basis. This
information can then be compared to actual performance.
By calculating actual yield losses versus the benchmark
model yield (and knowing from plant feed size distribu-
tion), potential tonnage gains can be calculated. Then,
using market values for the coal sold, monetary opportuni-
ties can then be established.
To illustrate the use of this concept, consider the KPI
assessments conducted for eight actual different preparation
plants (Table 5). The results of these comparisons (actual vs
best practice) indicate that several substantial financial ben-
efits can be realized by improving efficiency and reducing
coal losses for the first four plants (i.e., Plants A-D). Plant
A had the greatest overall losses of potentially recoverable
coal at 27,385 tonnes. This facility also corresponded to the
highest monetary loss at just over $4.1 million per month.
Although Plant D had a significantly greater loss of coal
than Plant C (i.e., 5,042 vs 3,406 tonnes), the higher coal
sales price for Plant C resulted in these plants having simi-
lar $losses (i.e., $0.323 and $0.303 million). A detailed
examination of the data showed that for these inefficient
plants, the efficiency opportunity exceeded the total plant
operating costs. In contrast, the last four plants (i.e., Plants
E-H) cited in Table 5 were found to operate with negative
losses. This indicates that these operations had attained a
level of performance that exceeded the expectations of their
benchmark best practice targets, thereby limiting opportu-
nities for improving profitability at these sites.
This approach can also be used effectively for evalu-
ating single plant performance over time. For example,
Table 6 shows monthly KPI metrics for a large plant. The
“Base” case represents the initial evaluation of the plant
taken prior to circuit efficiency improvements. This data
indicated that an opportunity existed to recover an addi-
tional 17,710 tonnes per month from this facility. Over the
next several months, steps were taken to correct operational
and maintenance issues that were identified as the probable
causes of coal losses. New KPI evaluations conducted in
January, April, and June, showed a steady improvement in
plant efficiency and a corresponding decline in the losses
of saleable clean coal. Compared to the base case, a gain
of nearly $1 million per month was achieved at this site
as a result of (i) detailed sampling programs that identi-
fied and quantified the substantial coal tonnage losses, (ii)
Table 5. Key monthly performance indicators (KPIs) for seven CPPs
Circuit Loss (dry tonnes) Plant A Plant B Plant C Plant D Plant E Plant F Plant G Plant H
Coarse Circuit 9,138 9,567 3,203 –5,271 4,580 –143 –3,354 –204
Small Circuit 0 –3,856 –635 –4,559 –6,471 0 –2,843 –9,423
Fine Circuit –7,344 –2,459 –2,737 –2,213 –5,916 –301 1,800 –2,764
Froth Circuit 25,586 27,724 1,151 0 –6,885 –1,121 0 1,502
+0.15 mm In Fine Refuse 0 955 0 1,696 0 0 0 0
–0.15 mm Contamination 5 49 2,424 15,389 4,335 0 45 0
Total Coal Loss 27,385 31,980 3,406 5,042 –10,357 –1,565 –4,352 –10,889
Net Coal Lost 27,385 31,980 3,406 5,042 0 0 0 0
Coal Market Price ($/tonne) $150 $110 $95 $60 — — — —
Lost Value ($× 1000) $4,108 $3,518 $323 $303 $0 $0 $0 $0
Loss Raw ($/tonne Feed) $1.81 $2.06 $0.13 $0.10 $0.00 $0.00 $0.00 $0.00
Table 6. Key performance indicators (KPIs) for a single CPP
Circuit Coal Loss, tonnes Base January April June
-$200
$0
$200
$400
$600
$800
$1,000
$1,200
Baseline Jan April June
Coarse Circuit 5,830 7,340 2,930 1,455
Small Circuit 939 –15 –139 –604
Fine Circuit 2,645 –113 –112 –132
Froth Circuit 3,835 1,473 –298 1274
+0.15 mm In Fine Refuse 292 2,754 –2,709 195
–0.15 mm Contamination 282 39 –57 53
Net Coal Lost 17,710 11,478 –385 2,240
Coal Market Price, $/t $60.00 $60.00 $60.00 $60.00
Lost Value, ×$1,000 $1,063 $689 ($23) $134
Loss/Ton Raw Feed, $/t $1.77 $1.15 ($0.04) $0.22
)0001x$(eulaVtsoL

Previous Page Next Page

Extracted Text (may have errors)

1428 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
circuit can be baselined on a circuit-by-circuit basis. This
information can then be compared to actual performance.
By calculating actual yield losses versus the benchmark
model yield (and knowing from plant feed size distribu-
tion), potential tonnage gains can be calculated. Then,
using market values for the coal sold, monetary opportuni-
ties can then be established.
To illustrate the use of this concept, consider the KPI
assessments conducted for eight actual different preparation
plants (Table 5). The results of these comparisons (actual vs
best practice) indicate that several substantial financial ben-
efits can be realized by improving efficiency and reducing
coal losses for the first four plants (i.e., Plants A-D). Plant
A had the greatest overall losses of potentially recoverable
coal at 27,385 tonnes. This facility also corresponded to the
highest monetary loss at just over $4.1 million per month.
Although Plant D had a significantly greater loss of coal
than Plant C (i.e., 5,042 vs 3,406 tonnes), the higher coal
sales price for Plant C resulted in these plants having simi-
lar $losses (i.e., $0.323 and $0.303 million). A detailed
examination of the data showed that for these inefficient
plants, the efficiency opportunity exceeded the total plant
operating costs. In contrast, the last four plants (i.e., Plants
E-H) cited in Table 5 were found to operate with negative
losses. This indicates that these operations had attained a
level of performance that exceeded the expectations of their
benchmark best practice targets, thereby limiting opportu-
nities for improving profitability at these sites.
This approach can also be used effectively for evalu-
ating single plant performance over time. For example,
Table 6 shows monthly KPI metrics for a large plant. The
“Base” case represents the initial evaluation of the plant
taken prior to circuit efficiency improvements. This data
indicated that an opportunity existed to recover an addi-
tional 17,710 tonnes per month from this facility. Over the
next several months, steps were taken to correct operational
and maintenance issues that were identified as the probable
causes of coal losses. New KPI evaluations conducted in
January, April, and June, showed a steady improvement in
plant efficiency and a corresponding decline in the losses
of saleable clean coal. Compared to the base case, a gain
of nearly $1 million per month was achieved at this site
as a result of (i) detailed sampling programs that identi-
fied and quantified the substantial coal tonnage losses, (ii)
Table 5. Key monthly performance indicators (KPIs) for seven CPPs
Circuit Loss (dry tonnes) Plant A Plant B Plant C Plant D Plant E Plant F Plant G Plant H
Coarse Circuit 9,138 9,567 3,203 –5,271 4,580 –143 –3,354 –204
Small Circuit 0 –3,856 –635 –4,559 –6,471 0 –2,843 –9,423
Fine Circuit –7,344 –2,459 –2,737 –2,213 –5,916 –301 1,800 –2,764
Froth Circuit 25,586 27,724 1,151 0 –6,885 –1,121 0 1,502
+0.15 mm In Fine Refuse 0 955 0 1,696 0 0 0 0
–0.15 mm Contamination 5 49 2,424 15,389 4,335 0 45 0
Total Coal Loss 27,385 31,980 3,406 5,042 –10,357 –1,565 –4,352 –10,889
Net Coal Lost 27,385 31,980 3,406 5,042 0 0 0 0
Coal Market Price ($/tonne) $150 $110 $95 $60 — — — —
Lost Value ($× 1000) $4,108 $3,518 $323 $303 $0 $0 $0 $0
Loss Raw ($/tonne Feed) $1.81 $2.06 $0.13 $0.10 $0.00 $0.00 $0.00 $0.00
Table 6. Key performance indicators (KPIs) for a single CPP
Circuit Coal Loss, tonnes Base January April June
-$200
$0
$200
$400
$600
$800
$1,000
$1,200
Baseline Jan April June
Coarse Circuit 5,830 7,340 2,930 1,455
Small Circuit 939 –15 –139 –604
Fine Circuit 2,645 –113 –112 –132
Froth Circuit 3,835 1,473 –298 1274
+0.15 mm In Fine Refuse 292 2,754 –2,709 195
–0.15 mm Contamination 282 39 –57 53
Net Coal Lost 17,710 11,478 –385 2,240
Coal Market Price, $/t $60.00 $60.00 $60.00 $60.00
Lost Value, ×$1,000 $1,063 $689 ($23) $134
Loss/Ton Raw Feed, $/t $1.77 $1.15 ($0.04) $0.22
)0001x$(eulaVtsoL

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

Previous Page Next Page

Extracted Text (may have errors)

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