XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3 3955
Hence, the Jb/Jc ratio provides an excellent mea-
surement for detecting when the balance between grind-
ing media and ore is not optimal. When the ideal Jb/Jc
ratio target is achieved, optimal grinding operating condi-
tions occur for current ball charge. Advanced Analytical
Measurements can, therefore, be used to measure and
maintain a healthy ratio between media and total charge in
real-time, avoiding poor operation.
CONCLUSIONS
Advances in high-volume data processing and Advanced
Analytics Measurements (AAM) allow us to benefit from
vibration sensors attached to grinding mills in new ways
not previously discovered. The Impact Angle (IA) can be
accurately measured and indirectly indicates the liner wear,
as shown in Figures 4 to 7 earlier in the paper. Figure 4,
40 ft. SAG mill illustrates how liner wear affects IA over
time. Figure 5, 38 ft. SAG mill illustrates how IA can be
used to observe the overall effectiveness of increasing mill
speed from new to worn-out liners. Specifically, IA indicates
when the liners are worn out, they no longer throw, and the
mill has reached a point where increasing mill speed does
not improve grinding and instead wastes energy. Figure 6,
a fixed-speed 30 ft. AG mill, shows how IA is a function of
slurry volumetric fill level and liner wear. This allows for the
opportunity to optimize the mill by aligning the toe (FTA)
of the slurry with the actual impact angle (IA), as these are
both measured AAM signals. Figure 7, 32 ft. SAG, illus-
trates how the Impact Angle can be maintained automati-
cally for optimal grind using mill speed. It also illustrates
the point where the mill has reached maximum speed and,
therefore, can no longer be used to keep IA at the optimal
real-time operating point.
Advanced Analytics Measurements (AAM) can also
be used for real-time detection and measurement of both
optimal and sub-efficient operating cases. Figure 3 provides
examples of this with three different cases: Grinding media
overthrowing the slurry, media underthrowing the slurry
and optimal operation. Figure 3 also shows these three
modes of operation of the power curves.
Of the two cases, overthrowing and underthrowing are
the worst operating conditions for SAG and BM as they can
significantly increase the broken liner &grinding media
occurrences, peening, pegging, and blinding. Thus, to
reduce the amount of time needed to overthrow operations
and maintain optimal mill operations, Advanced Analytics
Measurements known as Liner Damage Level (LDL) and
Impact on Toe (IOT) are derived and monitored in real-
time. It has been found that overthrowing typically occurs
when the Liner Damage Level (LDL) is70 and Impact on
the Toe (IOT) is 15, as shown in Figure 9. Also shown
in this figure are times when the mill is overfilled or over-
loaded, resulting from an underthrowing operation. LDL
30 and IOT –15 are typically observed for this condi-
tion. Thus, LDL and IOT can be used in real-time to oper-
ate the mill more optimally between overthrowing and
underthrowing conditions. By monitoring these two AAM
variables, an operator or control system can quickly change
course to keep LDL ~50 and IOT~0. By staying out of
overthrowing operations, we provide a safer environment
for maintenance crews, reduce shutdown time and increase
the actual amount of time in normal production. By reduc-
ing the amount of time in underthrowing operation, we
avoid sudden throughput reductions (to avoid further
problems), improve power consumption safety and refrain
from operating the mill in an overfilled or overloaded man-
ner. Optimal operation can achieve maximum throughput
at low energy consumption rates and is the safest operating
point for both personnel and equipment.
AAM can also generate a reliable method for esti-
mating the relationship between the ball filling level (Jb)
and the total charge (Jc). The AAM signal Jb/Jc, shown
in Figure 10, can estimate the right amount of ore relative
to the current mill ball charge, further optimizing a mill’s
performance and efficiency.
This paper does not highlight the benefits of through-
put, energy savings, transfer size reduction, etc. The inten-
tion is to delve deeper into utilizing the Advanced Analytics
Measurements that will drive those benefits. More informa-
tion on these benefits is described in detail in previous pub-
lications, such as Nunez and Baron (2022) or Buchanan et
al. (2023).
In closing, the 1967 British statistician George Box
wrote, “All models are wrong, but some are useful.” Thus,
while Advanced Analytics Measurements (FLEN, LDL,
IA, FTA, IOT, and Jb/Jc) are not perfect models, they are
instrumental as they represent a real-time snapshot of the
process and enable the implementation of improved pro-
cess control strategies. These strategies include better meth-
ods of manual mill operation as well as automatic operation
with throughput, mill speed, and/or water control to pave
the path for achieving Operational Excellence on every
rotation of the mill.
Hence, the Jb/Jc ratio provides an excellent mea-
surement for detecting when the balance between grind-
ing media and ore is not optimal. When the ideal Jb/Jc
ratio target is achieved, optimal grinding operating condi-
tions occur for current ball charge. Advanced Analytical
Measurements can, therefore, be used to measure and
maintain a healthy ratio between media and total charge in
real-time, avoiding poor operation.
CONCLUSIONS
Advances in high-volume data processing and Advanced
Analytics Measurements (AAM) allow us to benefit from
vibration sensors attached to grinding mills in new ways
not previously discovered. The Impact Angle (IA) can be
accurately measured and indirectly indicates the liner wear,
as shown in Figures 4 to 7 earlier in the paper. Figure 4,
40 ft. SAG mill illustrates how liner wear affects IA over
time. Figure 5, 38 ft. SAG mill illustrates how IA can be
used to observe the overall effectiveness of increasing mill
speed from new to worn-out liners. Specifically, IA indicates
when the liners are worn out, they no longer throw, and the
mill has reached a point where increasing mill speed does
not improve grinding and instead wastes energy. Figure 6,
a fixed-speed 30 ft. AG mill, shows how IA is a function of
slurry volumetric fill level and liner wear. This allows for the
opportunity to optimize the mill by aligning the toe (FTA)
of the slurry with the actual impact angle (IA), as these are
both measured AAM signals. Figure 7, 32 ft. SAG, illus-
trates how the Impact Angle can be maintained automati-
cally for optimal grind using mill speed. It also illustrates
the point where the mill has reached maximum speed and,
therefore, can no longer be used to keep IA at the optimal
real-time operating point.
Advanced Analytics Measurements (AAM) can also
be used for real-time detection and measurement of both
optimal and sub-efficient operating cases. Figure 3 provides
examples of this with three different cases: Grinding media
overthrowing the slurry, media underthrowing the slurry
and optimal operation. Figure 3 also shows these three
modes of operation of the power curves.
Of the two cases, overthrowing and underthrowing are
the worst operating conditions for SAG and BM as they can
significantly increase the broken liner &grinding media
occurrences, peening, pegging, and blinding. Thus, to
reduce the amount of time needed to overthrow operations
and maintain optimal mill operations, Advanced Analytics
Measurements known as Liner Damage Level (LDL) and
Impact on Toe (IOT) are derived and monitored in real-
time. It has been found that overthrowing typically occurs
when the Liner Damage Level (LDL) is70 and Impact on
the Toe (IOT) is 15, as shown in Figure 9. Also shown
in this figure are times when the mill is overfilled or over-
loaded, resulting from an underthrowing operation. LDL
30 and IOT –15 are typically observed for this condi-
tion. Thus, LDL and IOT can be used in real-time to oper-
ate the mill more optimally between overthrowing and
underthrowing conditions. By monitoring these two AAM
variables, an operator or control system can quickly change
course to keep LDL ~50 and IOT~0. By staying out of
overthrowing operations, we provide a safer environment
for maintenance crews, reduce shutdown time and increase
the actual amount of time in normal production. By reduc-
ing the amount of time in underthrowing operation, we
avoid sudden throughput reductions (to avoid further
problems), improve power consumption safety and refrain
from operating the mill in an overfilled or overloaded man-
ner. Optimal operation can achieve maximum throughput
at low energy consumption rates and is the safest operating
point for both personnel and equipment.
AAM can also generate a reliable method for esti-
mating the relationship between the ball filling level (Jb)
and the total charge (Jc). The AAM signal Jb/Jc, shown
in Figure 10, can estimate the right amount of ore relative
to the current mill ball charge, further optimizing a mill’s
performance and efficiency.
This paper does not highlight the benefits of through-
put, energy savings, transfer size reduction, etc. The inten-
tion is to delve deeper into utilizing the Advanced Analytics
Measurements that will drive those benefits. More informa-
tion on these benefits is described in detail in previous pub-
lications, such as Nunez and Baron (2022) or Buchanan et
al. (2023).
In closing, the 1967 British statistician George Box
wrote, “All models are wrong, but some are useful.” Thus,
while Advanced Analytics Measurements (FLEN, LDL,
IA, FTA, IOT, and Jb/Jc) are not perfect models, they are
instrumental as they represent a real-time snapshot of the
process and enable the implementation of improved pro-
cess control strategies. These strategies include better meth-
ods of manual mill operation as well as automatic operation
with throughput, mill speed, and/or water control to pave
the path for achieving Operational Excellence on every
rotation of the mill.