3896 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
change in the time response of the buffer capacity such as
bin. These are time-varying operational issues which occur
in many circuits but often are limited at the design phase
of the comminution circuit using steady-state simulation.
In contrast, dynamic simulation capability provides the
opportunity to study the simultaneous effects of equipment
selection and design, buffer choice and control system
response for designing a new circuit layout. In particular,
this paper presents a methodology of integrating dynamic
process simulation for the design of a suitable size bin for
complex circuit configuration. The goal of the circuit is to
maximize overall process performance while minimizing
the size of bin choice in the circuit. A case study consisting
of a conceptual circuit with multiple crushers and HPGR
is used to demonstrate the design process.
DYNAMIC SIMULATION AND
MODELLING OF CRUSHING PLANT
The dynamic process modelling is capable of simulating
time-dependent interaction and effects of a crushing plant,
mimicking the real-time behaviour of the circuit perfor-
mance in production. The dynamic process modelling
approach can capture discrete and gradual changes occur-
ring in plant operation, such as delays in material flow,
start-up sequence, discrete events, and wear. Each equip-
ment model is based on the mathematical description of
mass m and properties γ in a derivative form with respect to
time, as shown in Equations 1 and 2. (Asbjörnsson, 2015
Sbárbaro &del Villar, 2010)
((t) (t)) dt
dm(t) m m
,in j,out =-o o (1)
(
(
()
(
dt
d
m t)
m t)
t
t)
where ,in
,in
n
h
c1(t)W
c
c(t) c c(t)), c(t) =-=
o
R
T
S
S
S
V
X
W
W
(2)
The dynamic simulation used in the research is based on
the previous development in the MATLAB/Simulink envi-
ronment (Asbjörnsson et al., 2013). The material flow is
regulated using interlocks and regulatory controllers (e.g.,
On/Off, PI and PID.). Bins, splitters, and stockpiles are
modelled using the perfect-mix principle while the con-
veyors are modelled using the state-space model represent-
ing the material delay units in dynamic process simulation
(Asbjörnsson, 2015 Bhadani et al., 2021). For a system
with multiple parallel flows, recirculating mass, larger lag
times, partial integration of different stages and a mismatch
between manipulated variables and control variables, sta-
bility can be harder to achieve (Asbjörnsson et al., 2022
Dorf &Bishop, 2016). The product size distribution
(PSD) of the crusher and HPGR model is backfitted using
equipment manufacturers’ data to a simplified population
balance model using the Whiten selection function and
Reid–Stewart breakage function (Bhadani et al., 2023
Duarte et al., 2021 Napier-Munn et al., 1996). The capaci-
ties are also mapped to the manufacturers’ data. The screen
model and edge splitter model are based on the Whiten
expression using a reduced partition curve (Bhadani et al.,
2021 Napier-Munn et al., 1996).
CASE STUDY FOR BIN SIZING
APPLICATION
The selection of equipment, control philosophy, and bin
sizing simultaneously play a role in generating suitable
conceptual design. Figure 1 presents the layout of the
comminution plant under study using dynamic process
simulations. The process represents the segment after the
products from the primary crusher in stockpile 1 (SP1).
The SP1 feed together with the products of the two crush-
ers (CR01 – secondary crusher), tertiary crusher (CR02)
and recirculation by edge splitter (ES01) are fed to the bin
(B01). The bin (B01) is regulated to maintain the level set-
point to feed the two parallel screens (S01) using a PI con-
troller. The oversize of the screen (+40 mm) is conveyed to
bin B02 as secondary crusher feed. The middle fraction size
(20–40 mm) is fed to bin B03 as a tertiary crusher feed. The
undersize fraction (0–20 mm) is conveyed to the bin (B04)
as HPGR feed. The process circuit is inspired by previous
research and industrial relevance (Asbjörnsson et al., 2016).
The two crushers CR01 and CR02 have the opera-
tional setpoints of closed side setting (CSS) 50 mm and
CSS 20 mm, respectively. The bins B02, B03 and B04 are
controlled using PI controllers. A safety interlock is added
to the stockpile SP1 in connection to the four bin levels
(B01-B04) exceeding 80% of the total capacity of any bins.
The nominal capacity of the 4 bins is based on the reference
design (nominal capacity referred to a value of 1) and the
challenge here is to study the plant performance based on
changing bin capacities. The key performance indicator for
this study is mass flow (throughput) at different points in
the circuit, crushers and HPGR utilization, control triggers
due to interlocks and dynamic behaviour. The models used
for KPIs are based on the previous development (Bhadani
et al., 2020). The process simulation for this case does not
include the effects of the discrete changes in the system
such as discrete failure, run-of-mine truck frequency, mate-
rial characteristics change, capacity influence of conveyors,
and wear in crushers and screens.
change in the time response of the buffer capacity such as
bin. These are time-varying operational issues which occur
in many circuits but often are limited at the design phase
of the comminution circuit using steady-state simulation.
In contrast, dynamic simulation capability provides the
opportunity to study the simultaneous effects of equipment
selection and design, buffer choice and control system
response for designing a new circuit layout. In particular,
this paper presents a methodology of integrating dynamic
process simulation for the design of a suitable size bin for
complex circuit configuration. The goal of the circuit is to
maximize overall process performance while minimizing
the size of bin choice in the circuit. A case study consisting
of a conceptual circuit with multiple crushers and HPGR
is used to demonstrate the design process.
DYNAMIC SIMULATION AND
MODELLING OF CRUSHING PLANT
The dynamic process modelling is capable of simulating
time-dependent interaction and effects of a crushing plant,
mimicking the real-time behaviour of the circuit perfor-
mance in production. The dynamic process modelling
approach can capture discrete and gradual changes occur-
ring in plant operation, such as delays in material flow,
start-up sequence, discrete events, and wear. Each equip-
ment model is based on the mathematical description of
mass m and properties γ in a derivative form with respect to
time, as shown in Equations 1 and 2. (Asbjörnsson, 2015
Sbárbaro &del Villar, 2010)
((t) (t)) dt
dm(t) m m
,in j,out =-o o (1)
(
(
()
(
dt
d
m t)
m t)
t
t)
where ,in
,in
n
h
c1(t)W
c
c(t) c c(t)), c(t) =-=
o
R
T
S
S
S
V
X
W
W
(2)
The dynamic simulation used in the research is based on
the previous development in the MATLAB/Simulink envi-
ronment (Asbjörnsson et al., 2013). The material flow is
regulated using interlocks and regulatory controllers (e.g.,
On/Off, PI and PID.). Bins, splitters, and stockpiles are
modelled using the perfect-mix principle while the con-
veyors are modelled using the state-space model represent-
ing the material delay units in dynamic process simulation
(Asbjörnsson, 2015 Bhadani et al., 2021). For a system
with multiple parallel flows, recirculating mass, larger lag
times, partial integration of different stages and a mismatch
between manipulated variables and control variables, sta-
bility can be harder to achieve (Asbjörnsson et al., 2022
Dorf &Bishop, 2016). The product size distribution
(PSD) of the crusher and HPGR model is backfitted using
equipment manufacturers’ data to a simplified population
balance model using the Whiten selection function and
Reid–Stewart breakage function (Bhadani et al., 2023
Duarte et al., 2021 Napier-Munn et al., 1996). The capaci-
ties are also mapped to the manufacturers’ data. The screen
model and edge splitter model are based on the Whiten
expression using a reduced partition curve (Bhadani et al.,
2021 Napier-Munn et al., 1996).
CASE STUDY FOR BIN SIZING
APPLICATION
The selection of equipment, control philosophy, and bin
sizing simultaneously play a role in generating suitable
conceptual design. Figure 1 presents the layout of the
comminution plant under study using dynamic process
simulations. The process represents the segment after the
products from the primary crusher in stockpile 1 (SP1).
The SP1 feed together with the products of the two crush-
ers (CR01 – secondary crusher), tertiary crusher (CR02)
and recirculation by edge splitter (ES01) are fed to the bin
(B01). The bin (B01) is regulated to maintain the level set-
point to feed the two parallel screens (S01) using a PI con-
troller. The oversize of the screen (+40 mm) is conveyed to
bin B02 as secondary crusher feed. The middle fraction size
(20–40 mm) is fed to bin B03 as a tertiary crusher feed. The
undersize fraction (0–20 mm) is conveyed to the bin (B04)
as HPGR feed. The process circuit is inspired by previous
research and industrial relevance (Asbjörnsson et al., 2016).
The two crushers CR01 and CR02 have the opera-
tional setpoints of closed side setting (CSS) 50 mm and
CSS 20 mm, respectively. The bins B02, B03 and B04 are
controlled using PI controllers. A safety interlock is added
to the stockpile SP1 in connection to the four bin levels
(B01-B04) exceeding 80% of the total capacity of any bins.
The nominal capacity of the 4 bins is based on the reference
design (nominal capacity referred to a value of 1) and the
challenge here is to study the plant performance based on
changing bin capacities. The key performance indicator for
this study is mass flow (throughput) at different points in
the circuit, crushers and HPGR utilization, control triggers
due to interlocks and dynamic behaviour. The models used
for KPIs are based on the previous development (Bhadani
et al., 2020). The process simulation for this case does not
include the effects of the discrete changes in the system
such as discrete failure, run-of-mine truck frequency, mate-
rial characteristics change, capacity influence of conveyors,
and wear in crushers and screens.