KONA Powder and Particle Journal No. 41 (2024) 242–253/https://doi.org/10.14356/kona.2024006

Original Research Paper

Simulation of the Classification of Manufactured Sands
in the Throat Air Classifier †Horacio A. Petit* and Edgardo Fabián Irassar
The Center for Research in Physics and Engineering of the Center of the Province of Buenos Aires (CIFICEN), Argentina

The scarcity of natural sands has triggered a considerable increase in the
consumption of manufactured sands for concrete production. In this regard,particle flaking and excessive fines are the main problems that should be
addressed when utilizing manufactured sands. The throat classifier is an
air classifier designed for the elimination of fine particles (smaller than 75
micrometers) from manufactured sands. The main features of the classifier havebeen presented in the literature, but the mechanism that drives the classification
has not been studied in detail. Therefore, this work explores the mechanismof classification of the throat classifier by using CFD-DPM and CFD-DEM
simulations. The accuracy and limitations of the methodologies were evaluatedby comparing the results against experimental data obtained at the pilot scale.
The simulations presented fair results in the representation of the airflow andthe particle classification inside the throat classifier. Differences between thepredictions using CFD-DPM and CFD-DEM methodologies under the simulated
conditions were found to be negligible. The results of the simulations allowed
for a more detailed understanding of the classification mechanism that occurs
inside the device and the influence of operational variables on the equipment
performance.
Keywords: manufactured sands, air classification, CFD-DEM, stone dust, aggregates

1. Introduction

The worldwide growth of population and wealth has triggered an increase in the construction of housing, facilities,
service cities, and infrastructure. The new urban building
environment (drinking water and sanitation, public transport, roads, etc.) is demanding large volumes of building
materials such as aggregates, cement, ceramics, lime, gypsum, glass, wood, steel, aluminum, and plastic. The demand
for such enormous volumes of natural resources is affecting
the sustainability of these industrial sectors (OECD, 2019).
Among the building materials, concrete and mortar are themost widely used, both consisting of cement, aggregates,
and water. Cement production represents about 5 % of the
global CO2 emissions worldwide. Therefore, the cement
and concrete industries are developing new approaches toincrease production with a lower carbon footprint and lessusage of natural resources. The main goal is to achieve net
zero emission by 2050 (GCCA, 2022). Despite cement,
aggregates also play an essential role in the eco-efficiency
of concrete; nearly 75 % of the concrete volume is repre†*

Received 14 September 2022; Accepted 25 October 2022
J-STAGE Advance published online 3 June 2023
Corresponding author: Horacio A. Petit;
Add: Pinto 399, 7000, Tandil, Argentina
E-mail: hpetit@fio.unicen.edu.ar
TEL: +54-2284-451055

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sented by coarse and fine aggregates (Tatari and Kucukvar,2012). Aggregates can be natural (gravel or river sand) or
manufactured (crushed stone or manufactured sand). At
the beginning of this century, governments and academic
communities were on alert about the depletion of natural
sources of sand and its increasing scarcity (Torres et al.,2017; UNEP, 2014). Manufactured sands had been foundto be an alternative for river sands (Donza et al., 2002;
Goncalves et al., 2007); thus, the replacement of natural
sands by manufactured sands became an intense researchsubject ever since.
There are two main shortcomings for the application of
manufactured sands in concrete: the particle shape and thehigh content of dust. Manufactured sands are produced bycrushing and sieving during coarse aggregate production.
The angular shape (sometimes flaky) of manufactured
sand particles causes a low packing density, increasing thewater demand and affecting the rheology of the mortar or
concrete (Cepuritis et al., 2016; 2017). The content of dust
is defined as the percentage of particles smaller than 75 μm
in the sands. It increases the water demand for mortar andconcrete and enhances other problems such as particulatematter emissions during handling (Petit et al., 2021) and inhomogeneity of the stock due to humidity. The performanceof mortar and concrete constituted by manufactured sands
can be improved by reducing the content of dust and the

Copyright © 2024 The Authors. Published by Hosokawa Powder Technology Foundation. This is an open accessarticle under the CC BY license (https://creativecommons.org/licenses/by/4.0/).

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flakiness of the sand particles (Ramos et al., 2013). Waterwashers are generally used for the elimination of dust.
Although water-washers are effective, they have several
problems such as unwanted sewage in the quarries, unsellable wastes, final wet products, and a large water footprint(Aasly et al., 2014).
New dry processes, such as magnetic (André et al.,2019) or air classification (Peng et al., 2022), can avoid theuse of water for dedusting manufactured sands. A novel
classifier, the throat classifier, was explicitly developed
for the dedusting of manufactured sands (Petit and Irassar, 2021). Its design focuses on simplicity of operation,
reduction of energy consumption, and low maintenance.
In this machine, the manufactured sand is fed using a
conveyor belt and classified into three products with different gradations. The advantage of this classifier is thatthe dust extracted from the manufactured sand remains dry
and can be stored in a silo for later use as filler in cement,
mortar, and concrete (Di Salvo Barsi et al., 2020), or otherindustrial applications such as filler in the plastic industry(Passaretti et al., 2019; Tawfik et al., 2022). The throatclassifier was tested at pilot scale as a replacement forwater-washers, giving excellent results (Petit et al., 2018a).
However, detailed knowledge about the airflow and particle classification occurring inside the device is needed
to improve its design. In this regard, Computational Fluid
Dynamics (CFD) and the Discrete Phase Method (DPM)
can be used to simulate the airflow and classification mechanism inside the chamber (Petit et al., 2020; Stone et al.,2019). However, this approach has limitations for systems
where the concentration of solids is high and particle–
particle interactions are of critical importance. The Discrete
Element Method (DEM) is similar to the DPM method but
takes into account the particle–particle interactions at the
micro level, thus representing the bulk behavior of the
granular flow (da Cunha et al., 2013).
In this paper, the airflow and particle classification inside
the throat classifier were simulated using the CFD-DPM
approach. The accuracy and limitations of the simulation
methodology were evaluated. Elements of the DEM
method were incorporated into the CFD-DPM methodology to compare the results and assess the necessity of modeling particle–particle interactions. The airflow behavior
and classification mechanisms inside the throat classifier
were studied and visualized by analyzing the simulation
results, improving the understanding of the influence ofthe main operational variables on the classification mechanisms and complementing the knowledge obtained byprevious experimental studies.

2. Materials and methods
2.1 Experimental set-up

A scheme of the throat classifier is presented in Fig. 1(Petit, 2018; Petit and Irassar, 2021). The classifier pres-

Original Research Paper

ents two classification chambers separated by an inclined
plate and a vertical deflector. The right-side chamber isthe primary chamber, while the left-side chamber is thesecondary chamber. The space between the vertical deflector and the edge of the inclined plate is called the throat
and is the region where the primary classification takes
place. The primary classification at the throat is performedby an air stream that classifies particles according to its
aerodynamic diameter. The airflow that passes throughthe throat is composed of a main airflow and an auxiliary
airflow. The main airflow enters the classifier through theprimary inlet at the top of the primary chamber, while theauxiliary airflow enters the classifier through the auxiliary
air inlet located at the bottom of the primary chamber.
Both, the main and auxiliary airflows, merge at the throat
and flow towards the secondary classification chamber. The
auxiliary airflow is used to improve the performance of theclassifier and its use is optional. Therefore, the auxiliary
air inlet can be closed using the adaptable hopper. The
manufactured sand enters the classifier through the main
air inlet at the top. Sand particles fall by gravity and impact
against the inclined plate. Coarse particles bounce after the
impact, while fine particles slide over the inclined plate. All
particles reach the edge of the inclined plate with different
inertia, depending on the segregation and percolation thattakes place on the inclined plate. Particles are then classified by the airflow at the throat. Coarse particles are lesssensitive to the drag force and fall to the bottom of the
primary chamber, while fine particles are dragged by the
airflow to the secondary chamber. If the auxiliary airflow
is used, coarse particles exit the classifier as the primary
product through the primary product outlet. Otherwise, theprimary product is collected in the adaptable hopper. The
fine particles that were dragged to the secondary chamber
experience a secondary classification. The airflow reduces
its velocity in the secondary chamber, causing some ofthe particles to settle at the bottom and to be collectedin the secondary hopper as the secondary product of theclassification. Particles that did not settle in the secondary
chamber exit the classifier with the air and are collectedin an auxiliary equipment such as a cyclone classifier ora bag filter. These particles represent the tertiary productof the classification. The performance of the classifier iscontrolled by adjusting the velocity of the airflow and theposition of the vertical deflector. The position of the deflector is characterized by a set of coordinates (d and D) set
on the edge of the inclined plate with the y-axis direction
pointing downwards, as shown in Fig. 1.

2.2 Case studies

Two experimental studies are presented in this work. The
first study was performed to characterize the airflow inside
the equipment and to obtain validation data to be comparedto the results of the CFD simulations. In this regard, the243Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253

Original Research Paper

Fig. 1 Scheme of the throat classifier (Petit et al., 2019), and illustration of its main classification mechanism. Stations 1–4 locate the measurementsof the air velocity profiles.

main variables of interest are the pressure loss and the air
velocity at specific positions inside the equipment. The
pressure loss was measured with a differential manometer;
the first measurement point was set at the exit tube of theclassifier and the second at an arbitrary point outside theequipment. Hence, the pressure loss accounts for the loss ofenergy inside the equipment and the loss due to the acceleration of the atmospheric air while entering the classifier.
The velocity inside the equipment was measured using a
Pitot tube. Velocity profiles for the air were measured atfour different stations, as presented in Fig. 1. Station 1 was
set to measure the velocity profile inside the exit tube. Stations 2 and 3 were set at the inlet of the classifier. Station 4
was set perpendicular to the inclined plate, near the throat,in order to study the velocity profile near the classification
zone. The measurement of the velocity profile at the throat
was not possible due to the relatively large dimensions of
the Pitot tube, so only the mean air velocity was measured
at the throat.
The second study was carried out to characterize theclassification of sand particles using different set-ups forthe operational variables of the classifier. The obtained data
was then used to validate the results of the CFD-DPM and
CFD-DEM simulations. The classification study consistedof three classification cases that differed in the position of

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Table 1 Cases of study for the classification experiences.

Case

Position (P)uth [m/s]

Aux. Inlet (A)

1

P1

3.80

closed

2

P1

3.20

open

3

P2

7.75

closedthe vertical deflector (P), the velocity at the throat (uth), andthe utilization of the auxiliary air inlet (A), as presented inTable 1. Two different positions were used for the verticaldeflector. Position P = 1 is determined by the coordinates
D = 0.08 m and d = 0.02 m and position P = 2 by the coordinates D = 0.02 m and d = –0.02 m (Fig. 1). Case 1 and 2
differ in the utilization of the auxiliary air inlet, A = closed
for case 1, while A = open for case 2. Granitic manufactured sand was used for the study, presenting a solid densityof 2630 kg/m3, a dust content of 14.59 %, and a moisture
content of 0.24 %. Compared to the standard limits for fineaggregates in concrete (ASTM C33, 2018) (Fig. 2), themanufactured sand presents an excess of material passing
the 300 μm. For each case of study, 1 kg of granitic manufactured sand was fed to the classifier at a constant rate of
80 kg/h. After the classification, the primary and secondary
products of the classification were characterized by sieving

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253(µm)
Fig. 2 PSD of the granitic manufactured sand and comparison against
the standard limits (ASTM C33, 2018).

analysis. The partition curve and the cut sizes were computed by comparing the particle size distribution (PSD) of
the products and the feed. These cases were selected froma more complete study that the reader may find in previous
works by the authors (Petit, 2018; Petit and Irassar, 2021).

2.3 Numerical set-up

The numerical method used for the simulation has beenused previously by the authors in the simulation of similar
air classifiers (Petit and Barbosa, 2017; Petit et al., 2018b,
2020). An overall description of the simulation methodology is presented here, but the reader may find the fulldescription of the model in the supplementary materials.
The air was treated as a continuum phase, and the turbulent
flow was simulated by solving the Unsteady Reynolds Averaged Navier-Stokes equations. These equations requirea closure model for the computation of turbulence, so two
different turbulence models were assessed in this work.
The first model is the realizable k – ε model which uses
conservation equations for the kinetic turbulent energy (k)and the turbulent dissipation rate (ε). The second model isthe Reynolds Stress Model, which is defined by a transport
equation for the Reynolds stress tensor.
Particles were modeled as a discrete phase using a
Lagrangian reference frame. Particles were grouped toform what is known as parcels, groups of particles withthe same size. The trajectory of each parcel was computed
in a similar manner as that of a single particle, using thecommon particle size of the parcel. The positions of allparticles that form the parcel were updated based on thistrajectory, thus reducing computation time. The mass flowrate of the parcel is given by the number of particles times
the mass of the component particles. The trajectories of theparcels were predicted by integrating the force balance,accounting for the gravity and drag forces. Manufactured
sand particles present irregular and flaky particles (Rolnyet al., 2015; Johansson and Evertsson, 2014); therefore, theOriginal Research Paper

drag coefficient was computed using the Haider and Levenspiel model (Haider and Levenspiel, 1989). The shapefactor (ratio between the surface area of a sphere having
the same volume as the particle and the actual surface areaof the particle) was computed using an optical microscope,presenting the mean value of ϕ = 0.7. The effect of turbulence on the particle trajectories was taken into accountby using the Random Walk–Eddy Lifetime model. The
discrete phase exchanges momentum with the continuum
phase through a momentum source term in the momentum
equations of the fluid phase.
Air classifiers are generally operated at low solid concentrations. However, in the case of the throat air classifier,the concentration of particles is high near the inclined plate
and the throat. Particle–particle interactions may present
an additional influence on the classification mechanism.
If relevant, a proper model for the particle–particle interaction should be used. One way to assess the relevanceof the particle–particle interactions was proposed by
Elghobashi (1994) and then used by Greifzu et al. (2016).
The assessment is carried out by defining the volume fraction occupied by the particles (αP) and the ratio between
the particle reaction time (τP) to the Kolmogorov (τκ).
However, the particle-laden flow inside the throat classifier
cannot be easily labeled based on these parameters. The
concentration of particles is high in the incline plate wheremost particle–particle collision occurs. The concentrationof solids diminishes away from the throat and becomes
negligible further away from this region. Moreover, thesizes of the manufactured sand particles range from a few
microns to a few millimeters, changing the values of theparticle reaction time by four orders of magnitude. The
reader may find more details about this assessment in the
supplementary materials or in Petit (2018). To summarize:a DEM model was used in order to account for particle–
particle interactions and to assess the necessity of a fourway coupled simulation under the tested conditions.
Collisions between particles were computed based on thework of Cundall and Strack (1979). The normal force acting on a particle experiencing a collision was represented
by a spring-dashpot model, which is defined by a spring
constant (K) and a damping coefficient (γ). The force acting
on the second particle was computed using the Newton’s
third law. On the other hand, the tangential forces werecomputed by using the Coulomb law of friction, defined bya friction coefficient (μf). Collisions between the particles
and the geometry were modeled by assuming a coefficientof restitution for the impact, which was decomposed into
a normal (CN) and tangential contribution (CT). The methodology proposed for the modeling of the particle–particle
interactions is an extension of the DPM method that was
solved using the built-in capabilities of the CFD solver
and presents several limitations such as the lack of particle
rotation and rolling friction effects. However, these effects

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can be considered secondary when particles are inside ahigh-velocity flow. Moreover, the calibration of the DEM
parameters is not as simple as the calibration of a full DEM
method due to the influence of the continuum phase. Therefore, the values of the DEM parameters (Table 2) were
estimated based on reported values for similar systems(Chu and Yu, 2008; Traoré et al., 2015; Zhou et al., 2014;
Boemer and Ponthot, 2016). These values were used in thesimulations of different air classifiers, giving fairly realistic
results (Petit and Barbosa, 2017; Petit et al., 2018b, 2020).
A mesh independence study was carried out using acoarse, a medium and a fine mesh. Each structured meshconsisted of approximately 3 × 105, 5 × 105, and 7 × 105
elements, respectively. Mesh refinement was done progressively, paying special attention to the region near the throat
where the largest gradients of velocity and pressure arepresent. The pressure-inlet condition was used at the inlet,
the air outside the classifier was set to an arbitrary relative
static pressure of PR = 0. The same boundary condition
was applied at the auxiliary air inlet when it was open. The
pressure-outlet boundary condition was used on the outlet
face where a negative static pressure was set according to
the measured pressure loss of the current case study. The
no-slip boundary condition and standard wall functions
were used at the walls of the classifier. All simulations
were carried out by following the same procedure. First,
the CFD simulation of the airflow was performed using asecond-order transient solver. The time step used for the
fluid equations was ∆t = 2 × 10–4 s. The simulations were
continued until the airflow inside the classifier reached asteady state condition. Secondly, particles were injected
into the fluid using the DPM or DEM method. The simulations were resumed, and the trajectories of the particles
were updated for every fluid time step using the trapezoidal
rule for the temporal integration. In the case of the DEM
method, the particle time step was set two orders of magnitude smaller than the fluid time step. The simulations were
continued until all particles in the domain escaped or settled in the hoppers. Finally, the positions of the remaining
particles were used to calculate the classification efficiency.

Table 2 DEM parameters used in the present work.

Parameter

Units

Value

Spring constant (K)N/m

1 × 10–5

Damping coefficient (γ)—

0.9Friction coefficient (μf)—

0.3Normal restitution (CN)—0.3

Tangential restitution (CT)

—

0.7

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Original Research Paper

3. Results and discussion
3.1 CFD results

The results of the CFD simulations for case 1 are presented in Fig. 3, and compared against the measured velocity profiles measured at the four stations. The overall resultspresent a fair agreement with the experimental values. It canbe observed that the velocity profile of Station 1 is underpredicted, the utilization of a more refined mesh or adifferent turbulence model did not improve the results.
Compared to the values of Station 1, the profiles obtainedin Station 2 and 3 present a better agreement with theexperimental values and a better estimation of the velocity.
However, the value of the velocity near the walls presents
differences with the experimental values, which can be
attributed to the limitations of the Pitot tube used forthe measurements for such low velocities. In the case of
Station 3, the positive values of the velocity near the walls
could not be measured with the available measurement device. Similarly to Station 1, the utilization of a refined meshor the RSM turbulence model did not improve the results in
Station 2 and 3. The results for Station 4 present the worst
agreement between simulations and experiments. The velocity near the inclined plate is severely underpredicted andthe same occurs for distances from the plate (ℓ⊥) greaterthan 0.03 m. A slight improvement in the results is obtainedby using a refined mesh but is not sufficient to compensate
for the increase in the computational effort. This region of
the classifier is of great importance for an accurate modeling of the classification mechanism. The results show thatthe boundary layer of the flow near the throat, at the end of
the inclined plate, is not accurately modeled by using the
proposed approach. In addition, the utilization of a more
complex turbulence model does not correct this major
limitation of the simulation set-up. Other alternatives suchas the utilization of different coupling algorithms (PISO
instead of SIMPLEC), or different discretization schemes(QUICK scheme instead of second-order upwind scheme)
were tested without any improvements (Petit, 2018). A
more accurate approach may be to switch the first cellheight from y+ ≈ 30 to y+ ≈ 1 and to resolve the boundary
layer instead of modeling it by using wall functions. However, this approach would demand a computational effort
that could not be matched by the available computational
power at the time of the study. The same stands for the utilization of more complex turbulence models such as LES.
Therefore, the limitations of the current methodology wereassumed and further simulations were performed using thecoarse mesh and the realizable k – ε model.
The results of the CFD simulations, in terms of thepressure loss for the classifier, are presented in Fig. 4.
The simulations were performed using the two different
positions of the vertical deflector, with and without theauxiliary airflow. For each condition, the simulations wererun using: –200, –115, –70, –35 and –10 Pa for the outlet

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253

Original Research Paper◦

Fig. 3 Comparison between the experimental data ( ) and the CFD predictions for case 1, using the coarse (•), medium (•), and fine (•) meshes andthe realizable k – ε turbulence model. Values obtained with the Reynolds stress model and the fine mesh (×) are also presented. Error bars represent
minimum and maximum values while ℓ⊥ and u‖ represent the normal distance to the wall and the velocity parallel to the wall, respectively.

thFig. 4thComparison between the experimental data and the CFD predictions for the pressure loss for different conditions.

boundary condition. Then, the velocity at the throat wascomputed for comparison against the experimental values.
The simulations results present a fair agreement with theexperimental values. Both the experimental and numericalresults predict a higher pressure loss when the auxiliary air
inlet is closed. The pressure loss obtained by simulations
is overpredicted in almost all conditions. For P = 1, thepressure loss predicted by CFD is considerably higher

than that observed experimentally. These differences arehigher when the auxiliary air inlet is closed with errors as
high as 20 %. For the same position, the error committed
when the auxiliary air inlet is open is smaller, being as highas 14 %. For position P = 2, the error is smaller than thatobserved for position P = 1, as high as 5 %. In this position,
the simulations overestimate the pressure drop when theauxiliary air inlet is closed and underestimate the loss when247

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the auxiliary air inlet is open. Fig. 3 shows that the simulation cannot represent in full extent the acceleration of
the flow near the throat, which impacts on the conversionof mechanical energy in the flow and the computation of
the energy losses. Other sources of error can be attributed
to the numerical diffusion, the unresolved boundary layer
near the wall and the effect of adverse pressure gradients.
Despite the limitations of the methodology, the utilization of the CFD simulations can be used to gain valuable
insights about the behavior of the flow field inside theclassifier. Contours of velocity magnitude for different
conditions for the airflow inside the classifier are presented
in Fig. 5. The results are presented for a pressure boundary
condition of –200 Pa, and velocity contours for differentpressure conditions presented a self-similar behavior.
The trajectory of the airflow inside the classifier can beclearly identified. It can be observed that the behavior of
the airflow is different depending on the position of thevertical deflector and the utilization of the auxiliary airflow.
Therefore, differences in the classification of particles are
expected due to the different flow behavior. For position
P = 1 and the auxiliary air inlet closed, the airflow entersat the primary inlet and accelerates towards the throat. The
airflow reaches the end of the inclined plate and changes
its direction almost 180°, describing a U-shaped trajectoryand entering the secondary chamber. Secondary flows canbe observed near the main flow on both sides of the verticaldeflector, which are stronger in the secondary chamber andmay affect the secondary classification. For position P = 2,
the velocity patterns are similar to those observed for position P = 1. However, the change of direction at the throat
for position P = 2 is more abrupt. The air still changes its
direction about 180° but the radius of the swirl is smaller
than that observed for position P = 1. The flow area ofthe throat is smaller for position P = 2; therefore, the air
P
A

P

P
A

P

Original Research Paper

velocity is higher.
The utilization of the auxiliary airflow modifies the flow
pattern, as the contribution of the ascending stream of air
in the primary chamber decreases the contribution of the
primary airflow entering at the top. Both streams merge
at the throat as expected. However, the trajectory of the
airflow entering the secondary chamber changes with thepresence of the auxiliary airflow. In the case of position
P = 1, the air entering the secondary chamber describes asmoother trajectory when the auxiliary airflow is present.
In addition, the presence of secondary flows in the secondary chamber is reduced, thus expecting a cleaner secondaryclassification. These changes are not as clear for position
P = 2; nonetheless, the influence of the auxiliary airflow
on the velocity pattern at the throat can still be observed.
The upward auxiliary airflow at the bottom of the primarychamber suggests that particles that could not be classified
at the throat may experience a second counter-current classification in this region; therefore, improving the classifier
performance.
The velocity contours show that the main pressure loss
is due to the strangulation of the airflow at the throat.
Therefore, position P = 2, with a smaller flow area presents
higher pressure drops than that observed for position P = 1.
The utilization of the auxiliary airflow reduces the swirl
intensity of the flow at the throat; therefore, cases with theauxiliary air inlet open present less energy dissipation andlower pressure losses. In this regard, the conclusions drawn
from the observation of the velocity contours match thetrends in the experimental values presented in Fig. 4.

3.2 CFD-DPM and CFD-DEM results

Snapshots of the position of the particles, obtained by
CFD-DPM simulations, for the three case studies are presented in Fig. 6. It can be observed that the feed (sand) falls
P
A

P

P
A

P

Fig. 5 Contours of velocity magnitude for the throat classifier for two different positions of the vertical deflector (P = 1 and P = 2), with and without
the utilization of the secondary air inlet (A = open and A = closed, respectively).

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Fig. 6

Original Research Paper

Snapshots of the particle positions inside the classifier at different times after the injection.

through the inlet creating a curtain of particles. The behavior of the feed varies depending on the flow conditions.
It can be observed in cases 1 and 3 that after entering theclassifier, fine particles are dragged by the airflow and reachthe end of the inclined plate before coarse particles. Thisbehavior is observed in cases 1 and 3 where the auxiliary
air inlet is closed and the primary airflow moves downward
with high velocity. On the other hand, the auxiliary air inlet
is open in case 2 and the contribution of the primary air
inlet is less intense. Thus, the segregation of fine particles
due to the high velocity of the primary airflow in case 2 is
not as clear as in cases 1 and 3.
After falling, particles bounce against the inclined plateand change the direction of their trajectories. It was foundexperimentally that the trajectory after the impact depends
on the particle size and shape. Coarse particles present
more inertia than fine particles and are less affected by thedrag force (Petit and Irassar, 2021; Petit, 2018). Moreover,fine particles present more flaky shapes, increasing thedrag force and forcing them to follow the airflow. Thisbehavior is only partially represented in the simulations.
The main issue regarding the current simulation set-up isthat particles of different sizes share a single coefficient ofrestitution and shape factor. However, the effect of the dragon the particle is modeled sufficiently well to recreate thedifferent trajectories after the impact. The differences in249

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the trajectories for different particle sizes are more clearlyobserved in case 2, where the auxiliary air inlet is open.
The classification of particles at the throat can beobserved in the three simulated cases. Fine particles near
the throat are dragged by the air and pass to the secondary
chamber. Particles that cannot follow the air impact against
the vertical deflector, which agrees with the large amountof particles found in the deflector after the experimental
runs (Petit, 2018). Cases 1 and 3, where only the primary
airflow is used, present a more abrupt change in the flow
direction, and fewer particles are able to pass to the secondary chamber. On the other hand, the presence of theauxiliary airflow modifies the flow at the throat (as seen in
Fig. 5), reducing the intensity of the swirl and forcing moreparticles to pass to the secondary chamber. Therefore, theutilization of the auxiliary air inlet increases the cut size ofthe primary classification. After passing to the secondary
chamber, some particles settle into the secondary hopper,
while others exit the classifier. As expected from the flow
visualizations presented in Fig. 5, the auxiliary airflow
allows a more ordered classification in the secondary
chamber. On the other hand, the strong secondary flows incases 1 and 3 disperse the particles, making the settlement
more difficult. The effect of the auxiliary airflow can also
be observed at the bottom of the primary chamber in case 2.
The auxiliary airflow enters the classifier at the bottom and(µm)

Fig. 7flows upwards, dragging fine particles that were kept in thebulk of the coarser particles and could not pass through thethroat to the secondary chamber.
The prediction of the CFD-DPM and CFD-DEM simulations in terms of the particle size distribution of the
products is presented in Fig. 7, where the standard limits
are presented in the plot for reference. The yield of the
products and the values of the cut sizes for the three casesof study are presented in Fig. 8 and Fig. 9, respectively.
It can be observed that the PSD of the primary product isaccurately estimated by the simulations, with a slight under
prediction observed in case 2. On the contrary, the PSD of
the secondary and tertiary products is not as well describedas for the primary products. An exception occurs in case 2
where the overall results for the PSD are in fair agreementwith experimental values for all products. The results indicate that the simulation methodologies are able to describe
the main classification mechanism that takes place in the
primary chamber but not the secondary classification in thesecondary chamber. The results in Fig. 8 support this affirmation, the yield of the primary product is well estimatedin all the three cases, while errors appear for the yields ofthe secondary and tertiary products. The same can be observed in Fig. 9, where the primary cut size (x50) is in better
agreement with the experimental values than that observed
for the secondary cut size (y50).

(µm)

(µm)

PSDs of the primary (PP), secondary (SP), and tertiary (PP) products obtained experimentally or by simulations.

Fig. 8250

Original Research Paper

Yield of the products of classification and comparison against the CFD-DPM and CFD-DEM results.

Original Research Paper

(µm)

(µm)

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253

Fig. 9 Cut sizes of the classification and comparison against the CFD-DPM and CFD-DEM results. Variables x50 and y50 represents the primary andsecondary cut sizes, respectively.

The limitations of the CFD-DPM methodology were
tested by fine-tuning some parameters such as the coefficient of restitution (CN), the shape factor (ϕ), and the
Lagrangian time coefficient (CL) of the particles. The
results of the procedure are omitted here for brevity but
can be found in Petit (2018). In summary, reducing thevalues of the coefficient of restitution and increasing thevalue of the Lagrangian time coefficient slightly improved
the results. However, it was found that the increase in theaccuracy of the representation of the secondary classification deprecated the representation of the primary classification. This shows that one of the major limitations of themethodology is the impossibility of using size-dependent
parameters. In addition, the utilization of more classes torepresent the PSD of the feed was tested without any significant changes with the exception of more computational
effort. The influence of the Lagrangian coefficient, which
dominates the particle dispersion due to turbulent effects,
suggests that the turbulent dispersion of the particles is notcorrectly modeled. The results support this assumption, ascase 2 presents the best agreement with the experimental
values, being the case that was found to present a lessturbulent airflow in the secondary chamber (Fig. 5) and amore ordered secondary classification (Fig. 6).
The utilization of the CFD-DEM methodology does notpresent any improvements over the CFD-DPM methodology. Only minor differences in the PSD of the products,
the cut sizes, and the yield of the products were observed
between the two methodologies. In the case of the cut
sizes, all predictions using the CFD-DEM method present
a coarser cut size, for both the primary and secondary classifications. In any case, the increase in the computational
demand for the utilization of the DEM method does notbring an increase in the accuracy of the simulations for theconcentrations of solid used in this study. It is clear thatthe limitations of the simulation are associated with themodeling of the fluid–particle interaction and not with theparticle–particle interactions.
Despite the limitations, the simulations gave a good
description of the primary classification in the different

cases and offered predictions of the influence of the position of the deflector and the auxiliary airflow. Regarding
the auxiliary airflow, the simulations showed that the
upstream airflow at the bottom of the primary chamber incase 2 (Fig. 5) enables a better classification of the particles(Fig. 6), resulting in a coarser primary product (Fig. 7) anda greater primary cut size (Fig. 9). Regarding the positionof the vertical deflector, the CFD simulation described thecomplexity of the swirl that occurs in the throat in case 3(Fig. 5) and the finer classification that occurs due to themore abrupt change of direction in the airflow (Fig. 6).
Thus, resulting in finer particles in the primary product(Fig. 7) and a lower primary cut size (Fig. 9).

4. Conclusions

The airflow inside the throat classifier was simulated byusing the CFD methodology. The overall results showedthat the simulation provided fair predictions of the airflow
behavior with some limitations regarding the descriptionof velocity profiles in some locations. Changing the turbulence models or numerical schemes did not improve theresults.
Simulations of particle classification were performed
using both CFD-DPM and CFD-DEM methodologies. The
simulations showed reasonable results in the representationof the overall performance of the classifier and excellent
results in describing the primary classification products.
The utilization of the DEM method did not bring any improvements in the accuracy of the simulations. The major
limitations of the simulation methodology, for the concentration of solids used in this study, reside in the modeling of
the fluid–particle interactions.
Despite these limitations, the simulation methodology
allowed for an explanation of the previously collected
experimental results. Valuable information was gatheredregarding the description of the airflow inside the classifier
which explains the mechanisms of particle classificationthat occur inside the device. In addition, the utilization of
the simulations allowed to study in detail the influence of
operational variables on the performance of the classifier,251

Horacio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253

such as the position of the vertical deflector and the utilization of the auxiliary air inlet.

Nomenclature
CFD

Computational Fluid Dynamics

DEM

Discrete Element Method

DPM

Discrete Phase Method

PP

Primary product

PSD

Particle size distribution

SP

Secondary product

TP

Tertiary productA

Opening of the auxiliary air inlet (-)

CL

Lagrangian time constant (-)

CN

Normal coef. of restitution (-)

CT

Tangential coef. of restitution (-)

D

Vertical position of deflector (m)dHorizontal position of deflector (m)K

Spring constant (N/m)

kTurbulent kinetic energy (m2/s2)ℓ⊥Normal distance to the wall (m)P

Position of the vertical deflector (-)

PR

Relative static pressure (Pa)ui

Velocity components (m/s)

uth

Fluid velocity at the throat (m/s)

u‖Velocity parallel to the wall (m/s)

x, y, zCartesian coordinates (m)x50

Primary cut size (m, μm)y+Wall dimensionless distance (-)

y50

Secondary cut size (m, μm)

αpVolume fraction of particles (-)∆tTime step (s)∆PPressure loss (Pa)γ

Damping coefficient (kg/s)

εTurbulent dissipation rate (m2/s3)

ϕParticle shape factor (-)τ

Particle relaxation time (s)τPParticle reaction time (s)τκKolmogorov time scale (s)Data Availability Statement

The data from the comparison between experimental andnumerical investigation of the throat air classifier is available publicly in J-STAGE Data (https://doi.org/10.50931/
data.kona.23272763).

References

Aasly A.K., Danielsen S., Wigun J., Norman S.H., Cepuritis R., OnnelaT., Review report on dry and wet classification of filler materials for

252

Original Research Paper

concrete, COIN Project report, 52 (2014).
André F.P., Miceli H., Moura L.C., Neumann R., Tavares L.M., Upgrading a manufactured fine aggregate for use in concrete using dry
rare-earth magnetic separation, Minerals Engineering, 143 (2019)105942. DOI: 10.1016/j.mineng.2019.105942
ASTM C33, Standard Specification for Concrete Aggregates, ASTM
International, 2018. DOI: 10.1520/C0033_C0033M-18
Boemer D., Ponthot J.P., DEM modeling of ball mills with experimental
validation: influence of contact parameters on charge motion andpower draw, Computational Particle Mechanics, (2016) 1–15. DOI:
10.1007/s40571-016-0125-4
Cepuritis R., Jacobsen S., Pedersen B., Mørtsell E., Crushed sand in
concrete – effect of particle shape in different fractions and filler
properties on rheology, Cement and Concrete Composites, 71 (2016)
26–41. DOI: 10.1016/j.cemconcomp.2016.04.004
Cepuritis R., Jacobsen S., Smeplass S., Mørtsell E., Wigum B.J., Ng S.,
Influence of crushed aggregate fines with micro-proportioned particle size distributions on rheology of cement paste, Cement and Concrete Composites, 80 (2017) 64–79. DOI: 10.1016/j.cemconcomp.
2017.02.012
Chu K., Yu A., Numerical simulation of complex particle–fluid flows,
WCPT5 Papers presented at the 5th World Conference of Particle
Technology (WCPT5), Orlando, Florida, April 23–27 2006, 5thWorld Conference of Particle Technology (WCPT5), 179 (2008)
104–114. DOI: 10.1016/j.powtec.2007.06.017
Cundall P.A., Strack O.D.L., A discrete numerical model for granularassemblies, Géotechnique, 29 (1979) 47–65. DOI: 10.1680/geot.
1979.29.1.47
da Cunha E.R., de Carvalho R.M., Tavares L.M., Simulation of solids
flow and energy transfer in a vertical shaft impact crusher using
DEM, Minerals Engineering, 43–44 (2013) 85–90. DOI: 10.1016/j.
mineng.2012.09.003
Di Salvo Barsi A., Trezza M.A., Irassar E.F., Comparison of dolostone
and limestone as filler in blended cements, Bulletin of Engineering
Geology and the Environment, 79 (2020) 243–253. DOI: 10.1007/
s10064-019-01549-4
Donza H., Cabrera O., Irassar E., High-strength concrete with different
fine aggregate, Cement and Concrete Research, 32 (2002) 1755–
1761. DOI: 10.1016/S0008-8846(02)00860-8
Elghobashi S., On predicting particle-laden turbulent flows, Applied
Scientific Research, 52 (1994) 309–329. DOI: 10.1007/BF00936835
GCCA (Global Cement and Concrete Association) , 2050 Net Zero
Roadmap Accelerator Program, 2022. <https://gccassociation.org/
netzeroaccelerator/> accessed11052023.
Gonçalves J., Tavares L., Toledo Filho R., Fairbairn E., Cunha E.,
Comparison of natural and manufactured fine aggregates in cement
mortars, Cement and Concrete Research, 37 (2007) 924–932. DOI:
10.1016/j.cemconres.2007.03.009
Greifzu F., Kratzsch C., Forgber T., Lindner F., Schwarze R., Assessmentof particle-tracking models for dispersed particle-laden flows
implemented in OpenFOAM and ANSYS FLUENT, Engineering
Applications of Computational Fluid Mechanics, 10 (2016) 30–43.
DOI: 10.1080/19942060.2015.1104266
Haider A., Levenspiel O., Drag coefficient and terminal velocity of spherical and nonspherical particles, Powder Technology, 58 (1989) 63–70.
DOI: 10.1016/0032-5910(89)80008-7
Johansson R., Evertsson M., CFD simulation of a centrifugal air classifier
used in the aggregate industry, Computational Modelling, 63 (2014)
149–156. DOI: 10.1016/j.mineng.2014.03.013
OECD, Global Material Resources Outlook to 2060 Economic Drivers
and Environmental Consequences, OECD publishing, 2019. DOI:
10.1787/9789264307452-en
Passaretti M.G., Ninago M.D., Paulo C.I., Petit H.A., Irassar E.F., Vega
D.A., Villar M.A., López O.V., Biocomposites based on thermoplastic starch and granite sand quarry waste, Journal of RenewableHoracio A. Petit et al. / KONA Powder and Particle Journal No. 41 (2024) 242–253
Materials, 7 (2019) 393–402. DOI: 10.32604/jrm.2019.04281
Peng S., Wu Y., Tao J., Chen J., Airflow velocity designing for air classifier of manufactured sand based on CPFD method, Minerals, 12(2022) 90. DOI: 10.3390/min12010090
Petit A., Cordoba G., Paulo C.I., Irassar E.F., Novel air classification process to sustainable production of manufactured sands for aggregate
industry, Journal of Cleaner Production, 198 (2018a) 112–120. DOI:
10.1016/j.jclepro.2018.07.010
Petit H., Barbosa M., Simulación de un clasificador de polvo de flujo
cruzado con altos contenidos de polvo, Revista Internacional deMétodos Numéricos para Cálculo y Diseño en Ingeniería, 33 (2017)262–270. DOI: 10.1016/j.rimni.2016.06.003
Petit H.A., Diseño de separadores de partículas finas empleando modelización de flujos complejos, Ph.D. thesis, Universidad Nacional delCentro de la Provincia de Buenos Aires, 2018. Olavarría - Argentina.
http://hdl.handle.net/11336/93493
Petit H.A., Irassar E.F., The throat classifier: a novel air classifier for thecontrol of dust in manufactured sands, Powder Technology, 390(2021) 417–427. DOI: 10.1016/j.powtec.2021.05.049
Petit H.A., Irassar E.F., Barbosa M.R., Evaluation of the performanceof the cross-flow air classifier in manufactured sand processing
via CFD-DEM simulations, Computational Particle Mechanics, 5(2018b) 87–102. DOI: 10.1007/s40571-017-0155-6
Petit H.A., Irassar E.F., Barbosa M.R., Desempolvador de arena triturada
mediante via seca, Patent (2019) AR110376A1. https://patents.
google.com/patent/AR110376A1
Petit H.A., Paulo C.I., Cabrera O.A., Irassar E.F., Modelling and optimization of an inclined plane classifier using CFD-DPM and the Taguchi
method, Applied Mathematical Modelling, 77 (2020) 617–634. DOI:
10.1016/j.apm.2019.07.059
Petit H.A., Paulo C.I., Cabrera O.A., Irassar E.F., Evaluation of the dustiness of fugitive dust sources using gravitational drop tests, Aeolian
Research, 52 (2021) 100724. DOI: 10.1016/j.aeolia.2021.100724

Original Research Paper

Ramos T., Matos A.M., Schmidt B., Rio J., Sousa-Coutinho J., Granitic
quarry sludge waste in mortar: effect on strength and durability,
Construction and Building Materials, 47 (2013) 1001–1009. DOI:
10.1016/j.conbuildmat.2013.05.098
Rolny D., Sovarso L., Cruz Palma J., Coyoupetrou L., Cremaschi G.,
Etcheverry R., Forte L., Estudio Preliminar de la Forma, Textura
Superficial y Distribución del Tamaño de Partículas de Arenas detrituración, in: Actas del Segundo Congreso Argentino de Aridos,
San Isidro, 2015, pp. 233–240.
Stone L., Hastie D., Zigan S., Using a coupled CFD–DPM approach topredict particle settling in a horizontal air stream, Advanced Powder
Technology, 30 (2019) 869–878. DOI: 10.1016/j.apt.2019.02.001
Tatari O., Kucukvar M., Eco-efficiency of construction materials: dataenvelopment analysis, Journal of Construction Engineering andManagement, 138 (2012) 733–741. DOI: 10.1061/(ASCE) CO.19437862.0000484
Tawfik M.E., Eskander S.B., Ahmed N.M., Potential applications of
granite waste powder as an add-value polymer composite product,
Polymers and Polymer Composites, 30 (2022) 09673911221110138.
DOI: 10.1177/09673911221110138
Torres A., Brandt J., Lear K., Liu J., A looming tragedy of the sand commons, Science, 357 (2017) 970–971. DOI: 10.1126/science.aao0503
Traoré P., Laurentie J.C., Dascalescu L., An efficient 4 way coupling
CFD–DEM model for dense gas–solid particulate flows simulations,
Computers & Fluids, 113 (2015) 65–76. DOI: 10.1016/j.compfluid.
2014.07.017
UNEP (United Nations Environment Programme), Sand, rarer than one
thinks: UNEP global environmental alert service (GEAS) , 2014.
<https://wedocs.unep.org/20.500.11822/8665> accessed11052023.
Zhou F., Hu S., Liu Y., Liu C., Xia T., CFD–DEM simulation of thepneumatic conveying of fine particles through a horizontal slit,
Particuology, 16 (2014) 196–205. DOI: 10.1016/j.partic.2014.03.015

Authors’ Short Biographies
Horacio A. Petit
Horacio Andrés Petit is a Chemical Engineer and obtained a Ph.D. in Engineering at the Universidad Nacional del Centro de la Provincia de Buenos Aires. Worked as a postdoc research assistantat the Universidade Federal de Rio de Janeiro (UFRJ). Now works as a researcher at the CIFICEN
research unit (UNCPBA - CICPBA - CONICET). His research interests are the design and simulationof mineral processing units. His works focuses on the utilization of CFD and DEM methods for thesimulation of air classifiers and other mineral processing equipment.

Edgardo Fabián Irassar
Edgardo F Irassar is a Full Professor of Civil Engineering Department at the Universidad Nacionaldel Centro de la Provincia de Buenos Aires, Olavarria, Argentina. He is Head of the INMAT Research Group of the Engineering Faculty and Vice Chair of the CIFICEN Research Unit (UNCPBA- CICPBA - CONICET). His research interests are the cementitious building materials (Concrete,mortar, cement) for sustainable concrete including durability, new supplementary cementing materials, aggregates beneficiation, recarbonatation and LCA evaluation.

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