Application of Computational Fluid Dynamics in Simulation and
Optimization of a Fluidized Sugar Bed Dryer
NABASIRYE SUSAN1, RICHARD O. AWICHI3, STEPHEN KADEDESYA1,
ANSELM O. OYEM1,2,*
1Department of Mathematics,
Busitema University,
UGANDA
2Department of Mathematics,
Federal University Lokoja,
NIGERIA
3Department of Mathematics and Statistics,
Kyambogo University,
UGANDA
*Corresponding Author
Abstract: - The application of computational fluid dynamics (CFD) in the design of industrial thermal process
equipment is of great importance. Food drying is an important process in the sugar processing industry as it
helps in the easy of transportation and storage, and also increases the life span of food. In this study a two-
dimensional (2D) fluidized bed dryer is designed in the Blockmesh Dict file an application in the OpenFOAM
with dimensions height 0.8m and diameter . The Navier-Stokes equations were solved to provide the flow
variation that occurs inside the fluidized bed dryer in terms of temperature and velocity. For optimization of
results, Taguchi analysis was considered and the results show that at a very low temperature below , the
sugar drying process is slow leading to much time being spent for effective sugar drying. Also an increase in
flow velocity results in a faster drying rate of sugar granules. During the optimization of the performance of the
fluidized sugar bed dryer, the percentage contribution of sugar granules diameter is more significant than other
factors and it was also noted that pressure has less significance on the drying process within the fluidized bed.
Key-Words: - CFD, OpenFOAM, Fluidization, ANOVA, Taguchi technique, 2D.
Received: June 3, 2023. Revised: October 27, 2023. Accepted: December 18, 2023. Published: December 31, 2023.
1 Introduction
Over the years computational fluid dynamics (CFD)
has become a beacon of research in several aspects
of human endeavors, say, industrial applications and
processes like, drying of food and beverages,
pharmaceutical drug production; environmental
processes in wastewater sludge, storages,
transportation. Technological advancements and the
current high demands in consumption and
production, have given room to the exploration and
usage of various innovative drying techniques and
utilization of drying equipment for productions,
ranging from applications in chemical, biochemical,
pharmaceutical, and agricultural sectors to drying of
various materials for a variety of industrial and
technological applications, making, [1], to
comprehensively, review the application of CFD in
both industrial and lab-based drying applications. It
was observed that CFD can be used as a tool to
predict hydrodynamic heat and mass transfer
mechanisms occurring in the processes. This made,
[2], look into improving the drying performance of
parchment coffee due to the newly redesigned
drying chamber. Furthermore, conducting a
simulation using CFD helped them attain a better
unit’s dryer air behavior, notable drying times
reduction, and improved air distribution. The pore
structure distribution and coupled heat and moisture
transfer during the drying process of grains using a
fixed-bed corn drying process were looked into by
[3].
Sugar bed dying in any processing industry is of
great importance because of the preservative
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procedures because of the notion that it saves time
and money, [4]. Globally, various methods have
been put in place to help in the sugar drying process
like the vibrative dryer, rotary drum dryer, and the
fluidized dryer, [5]. But, [6], gave a review of the
common dilemma plaguing spray drying modelling
using a plug-flow simulation approach to the
counter-current spray drying process with the CFD
technique. [7], looked into the development of a
fluidized bed dryer for drying a sago bagasse. They
observed that high temperature and air feed velocity
results in a rapid drying rate with the advantage of
reducing the power energy and cost supply.
A fluidized bed dryer (FBD) is one of the most
regularly used and established drying methods for
wet solid particles due to its high production rate of
heat and mass transfer which ensures a considerably
faster and homogeneous drying process. The
advantages of FBD are huge as a result of its
experimental or simulation successes recorded.
Some of these advantages include providing a large
contact area between solid and air, a high solid
mixing level, adequate heat and mass transfer
between solid and air, and providing better
temperature and operational control along the
drying process. In a fluidized bed dryer, the hot gas
is passed through the bed of solids at a velocity
sufficient to keep the bed in a fluidized state where,
mixing and heat transfer are very rapid and the
equipment works on a principle of fluidization of
the feed materials as shown in Figure 1, [8], [9].
CFD techniques are used to solve complex
engineering problems and their applications, fluid
flow, heat, and mass transfer problems. Hence, its
application and simulation use cannot be over-
emphasised especially in the drying processes. The
use of CFD in numerical analysis has shown good
results in solving problems of fluid flow with heat
and mass transfer, [10] and this has attracted many
researchers due to its industrial, environmental, and
scientific applications, [11], [12], [13], [14], [15],
[16], [17], [18]. [19], analyzed the process of using
CFD modeling with the developed three-
dimensional FBD model while, [20], studied a
bubbling flow in a 2D pulsed fluidized bed dryer
using the Eulerian approach, and their results
showed that pressure changes inside the bed, was
due to the formation of air bubbles at minimal
frequency pulsating inlet flow causing intermittent
fluidized state. [21], investigated the effect of
operating parameters of sucrose fluid bed drying
powder quality and drying time to optimize the
production of sucrose powder. They observed that
gas flow and sugar particle size had significant
effects on the quality of the sugar dried with
99.9699% drying time.
The effect of increasing the air inlet size and the
bed thickness on the thermal behavior of the dryer
was studied by [22] and their study showed that air
inlet size is proportional to the moisture extraction
and increasing the bed dryer prolongs the drying
period. Then, the airflow in a mixed-flow dryer was
studied by [23], to determine its influence on air
duct size, results indicated that small ducts yield
higher velocity rates.
Since clumping and discoloration of sugar as a
result of the sugar not properly being dried is one of
the key problems faced in sugar manufacturing
industries, this has led to poor quality and low value
of sugar produced and unsafe for storage. Fluidized
bed drying is one of the popular methods used for
drying sugar in industries, the drying mechanism
within the dryer depends on different factors like air
temperature, pressure, air velocity, and bed
thickness among others, and the combined effects
are not yet explored adequately.
Fig. 1: Concept of Fluidized bed drying
Hence, in the current study an OpenFOAM
software in CFD is applied and simulated to
establish the effect of air velocity, pressure, and
drying air temperature on the sugar drying process
within a fluidized bed dryer as well as to optimize
the performance of a fluidized sugar bed dryer and
validate the optimum process parameters for sugar
drying.
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2 Problem Formulation
A fluidized sugar bed dryer 2D geometry was
developed using the blockMesh in OpenFOAM 7
software, meshed, and the Navier-Stokes equations
discretized. A Newtonian fluid flow in the drying
chamber is incompressible, with constant physical
and chemical properties for airflow in the bed dryer
and for turbulence considered.
Based on the assumption that the governing
equations conserve all laws for each control volume
of the domain, the fluid flow and heat transfer of the
governing continuity, momentum and energy
equations are given below:
where is the velocity vector of airflow which
depends on the coordinates , .
and,
Where, is the density of the fluid,
is the velocity
component, is the fluid pressure, is dynamic
viscosity and is acceleration due to gravity.
In modeling of thermal processes, it requires that
the energy equation governing the heat transfer
within a fluid system is solved and this equation is
written as:
Where, is the specific heat capacity of hot air
is Temperature, is thermal conductivity and is
the thermal sink or source. The Fourier equation
governs heat transfer in an isotropic solid given by
[24], [25].
The convective mixing term for temperature, is
incorporated in Eq. (3). For a conjugate heat transfer
situation where evaporation at the sugar surface is
considered, and heat transfer coefficient is known,
the boundary condition for Eq. (4) is given by:
Where is temperature of the bulk fluid, is
temperature of surface, is water molar latent heat
of vaporization , is the emission factor
coefficient, is the Stefan Boltzmann constant and
is Nusselt number.
The heat transported by radiation and convection
from hot air to sugar, raises the sample temperature
and drives towards evaporating the free water at the
surface, [26]. Solution of Eq. (5) can be used on the
sugar surface to calculate the local heat transfer
coefficients, [27] below:
Since, sugar drying is a thermal process
associated with turbulent motion (high flow rates
and heat transfer interactions in the drying
chambers), consider the standard turbulence
model, which is a two-equation model making a
closure to Reynolds Averaged Navier-Stokes
(RANS) to model the fluid flow phenomenon in the
fluidized bed drying chamber. The RANS equations
play an important role in modelling both steady-
state and turbulent flow phenomena containing two
transport equations; turbulent kinetic energy and
dissipation rate of turbulent kinetic energy . For
the turbulence model, the turbulent viscosity
is given as:
showing that turbulence viscosity , depends on the
kinetic energy and its dissipation rate .
The turbulence kinetic energy transport equation
is expressed as:
with dissipation rate of turbulence kinetic energy
given as:
Where, is the production rate of turbulent kinetic
energy per unit mass and , , , , are
model constants, [28], with coefficient values as
shown in Table 1. Solving Eqs. (8) and (9)
numerically, yields the turbulence kinetic energy
and the kinetic dissipation rate respectively.
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Table 1. The turbulence model coefficients.
Constant
Value
Performing numerical simulation, the geometry
for the computational domain of the 2D fluidized
bed dryer as developed using the inbuilt Blockmesh
file in the system directory of OpenFOAM software
is shown in Figure 2. A cylindrical fluidized bed
dryer of bed height and internal diameter
is considered to determine the sugar profile of
the sugar bed dryer at varying times establishing the
effect of varying air velocity, temperature, and
pressure on bed height of drying sugar.
Fig. 2: Geometry of a 2D fluidized bed dryer
Using the blockMesh and snappyHexMesh
utilities in OpenFOAM 7.0., the geometry mesh is
presented in Figure 3. The blockMesh utility
generates elementary meshes of blocks with
hexahedral cells. A sizable background mesh was
created from the OpenFOAM Blockmesh dictionary
file which is found in the system directory and runs
in paraFoam ParaView. Since the simulation is
transient, smaller sized meshes are considered in
OpenFOAM such that the Courant number is close
to 1 for accuracy and numerical stability.
Fig. 3: A sizable computational mesh of the
fluidized bed dryer
In this paper, initial inlet air velocity of
, air pressure of and air
temperature of are considered in the
simulation because they are ideal ones which can
move the fluidized sugar during the drying process
while the boundary conditions in OpenFOAM used
in the simulation is shown in Table 2.
Table 2. Boundary conditions for simulation in
OpenFOAM, [14]
Boundary field
Inlet
Outlet
Air velocity
Fixed value
Pressure Inlet
Outlet value
Air temperature
Total
temperature
Total temperature
Air pressure
Fixed flux
pressure
Fixed value
3 Results and Discussion
For simulation of the mass transfer reaction of the
hot air (gas) phase and moist sugar phase in the
fluidized bed dryer, a reacting two-phase
EulerFoam, a multiphase solver in OpenFOAM 7.0
was used. Mass transfer involves the transport of
species among phases through diffusion (physical)
and, or chemical reactions in a special unit operation
(reactor), allowing such a process to take place. A
chemical reaction is used to haste up the mass
transfer rate, whenever species of different chemical
potentials are brought into contact, with hot air and
moist sugar. According to [28], to capture the
interphase between the two phases, reacting two-
phase EulerFoam uses the volume of fluid (VOF)
method. To know where the interphase is:
is worked out. Where,
is the mean velocity and
is the volume fraction within a cell. The model
employs volume fraction to denote the individual
phases. The volume fraction represents a
computational cell that is completely filled with
water and for represents a cell completely
filled with air. The liquid-gas interface arises within
mesh cells where takes on values between and
.
The solver works out both the continuity and
Naiver-Stokes equations for two incompressible,
isothermal, immiscible, transient, and turbulent
fluids where, the material properties such as the
density, viscosity, and specific heat capacity are
constant in the region filled by one of the two fluids
except at the interphase, [29]. Visual and graphical
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results were then generated using ParaView as seen
in simulated sugar volume fraction profiles of 2D
bed at seconds and displayed
in Figure 4, Figure 5, Figure 6, Figure 7 and Figure
8.
Fig. 4: Sugar at t = 0s
Fig. 5: Sugar at t = 0.5s
Fig. 6: Sugar at t = 1s
Fig. 7: Sugar at
Fig. 8: Sugar at
From Figure 4 and Figure 5, sugar is represented
by the red colour, while the blue color represents the
gas-phase. It is observed that at , the sugar
granules are static (not yet fluidized) as the
fluidization process begins when a particular inlet
air velocity threshold value exceeds to
maximum fluidization at . Maximum
fluidization occurs when all the sugar granules are
in a fluidized state. After the moisture content in the
sugar reached wet basis, turbulent fluidization
was realized and the sugar drying process was
considered complete. At time , the wet sugar
granules are introduced in the bed dryer, and there is
no drying hot air introduced, so the sugar granules
appear to be stationary. Furthermore, at ,
hot air starts entering the fluidized bed dryer
through the inlet at the bottom, and is seen raising
the sugar slowly since the sugar is so moist .
Then, , the rate of hot air entering the
fluidized bed dryer is seen to increase moving the
moist sugar granules to a higher height within the
fluidized bed dryer. Though, the fluidization process
of the sugar granules is seen to start slowly within
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the bed but, at time , as more hot air
continuously enters into the bed dryer with
increasing velocity, sugar granules get into more
fluidization and they move with turbulent motion
within the bed dryer causing the sugar to dry in a
homogeneous way. At time , the sugar goes
on drying as the moisture content reduces and it
moves more vigorously in turbulent motion in the
fluidized form as a result of more drying hot air
entering the bed with a higher velocity
The air which is initially entering at ,
decreases to at a fluid bed height of .
This effect is due to the drag force of sugar particles
on each particle as sugar remains in close
configuration. The velocity of air decreases further
to to a height of . The velocity of
air further decreases because the air continues to
absorb moisture from the sugar making it move to a
height of . The velocity is then seen to
increase drastically from at a height of
to at a height of . This
makes the particles move away from each other and
become suspended within the fluidized bed dryer,
causing the bed to expand. As the air velocity
increases, the wall shear stresses increase and the
pressure begins to increase, hence pressure increases
with gas velocity. Between the height of to
, there is an increase in the drying rate of the
sugar and hot air hence decrease in the moisture
content as illustrated in the Figure 9.
Fig. 9: Gas velocity distribution within a fluidized
bed chamber
The pressure within the bed is initially at
at a height of and the pressure
significantly decreases from to
at a height of . The decrease is due
to the sugar having more moisture thus being
dominated by inter-particle frictional forces. Then,
pressure increases from to at
a height of . The increase is due to the
expansion of the bed and decrease in the amount of
sugar moisture which makes the sugar granules
move upwards from the bottom of the bed as a result
of being fluidized (Figure 10).
Fig. 10: Pressure distribution within a fluidized bed
chamber
The temperature at which air enters the bed was
and the temperature decreased to at a
bed height of . The decrease is caused by a
lot of moisture in the sugar granules which absorbs
the heat of the hot drying air causing a drastic
decrease in temperature. The temperature of
is maintained from a bed height of to
and remains constant within that range of bed height
due to the uniform mixture of the hot air and moist
sugar. Subsequently, temperature of air increases
from at a height of to at a bed
height of . This is due to more hot air
entering the bed with reduced moisture in the sugar.
The temperature is maintained from a bed
height of to a bed height of and this is
attributed to the homogeneous drying of sugar
granules to moist content of about 1%, as shown in
Figure 11.
Fig. 11: A graph showing air temperature
distribution within a fluidized bed chamber
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3.1 Optimization of a Fluidized Sugar Bed
Dryer Performance
In optimizing the performance of fluidized sugar
bed dryers, a technique that handles large number of
samples with fewer trials is used. Using the Taguchi
technique (T), the results were analyzed by ranking
the factors (Table 3) that affect the energy
utilization ratio in the fluidized sugar bed dryer. The
Taguchi L9 orthogonal array of the simulations is
shown in Table 4.
The level of interaction in Table 5, represents the
interaction between factors listed in Table 3, where,
both inlet air temperature and sugar granule
diameter have the greatest interaction. Inlet air
temperature-inlet air velocity and sugar granule
diameter-inlet air velocity are the pairs whose
interactions are significant though to a lesser extent.
Using ANOVA analyses, the parameters of the
response by the decomposition of the total variation
and its appropriate components by measuring their
relative effects were determined (Table 6).
From Table 6, the sugar granule diameter factor
(B) contributed the highest percentage to the factor
effects. Based on Taguchi technique, the expected
energy utilization ratio, which is the sum of the total
contribution from all factors and the grand average
value is in Table 8. The difference between
the grand average value and the average effect of
each factor corresponding to its optimum level is the
contribution of each factor from Table 7, [30].
In the verification test phase, an additional
simulation was done using the optimum conditions
and the results obtained were compared with the
expected results at optimum conditions. The
confidence interval of the energy utilization ratio for
this extra simulation was and it was within
the confidence level of the predicted optimal
values, which meant that the prediction by the
Taguchi method is reliable (Table 8)
Table 3. The various parameters selected and their
respective levels.
Factors
Level
1
Level
2
Level
3
A: Inlet air
temperature
40
60
90
B: Sugar granule
diameter
0.04
0.06
0.08
C: Inlet air velocity
2.5
4
6
Table 4. Taguchi L9 orthogonal array design
Simulation number
A
B
C
1
1
1
1
2
1
2
2
3
1
3
3
4
2
1
2
5
2
2
3
6
2
3
1
7
3
1
3
8
3
2
1
9
3
3
2
Table 5. Interaction between factors
Interaction factor pairs
Interaction severity index
A – B
28.05
A – C
21.02
B – C
11.34
Table 6. ANOVA Results
Factor
Degree of
freedom (DOF)
Sum of
squares (S)
Variance
(V)
F-ratio
(F)
Pure sum
(S)
Percent, P (%)
A: Inlet air temperature (K)
B: Sugar granule diameter (m)
C: Inlet air velocity (m/s)
Other / error
2
2
2
2
0.002
0.012
0.003
0.004
0.001
0.006
0.001
4.843
18.712
3.011
0.002
0.018
0.001
20.13
54.114
7.61
18.146
Total
8
0.021
100
Table 7. Energy utilization ratio in Taguchi tests
Simulation number
Energy utilization ratio
1
0.040
2
0.028
3
0.012
4
0.161
5
0.120
6
0.101
7
0.231
8
0.141
9
0.196
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Table 8. Estimation of the optimum condition
(maximum energy utilization ratio)
Factor
Level
Contribution
A: Inlet air temperature
3
0.071
B: Sugar granule diameter
1
0.042
C: Inlet air velocity
3
0.020
Total contribution from all
factors
0.133
Current grand average of
performance
0.140
Expected result at optimum
condition
0.273
4 Conclusion
Fluidized bed dryer simulation using Computational
Fluid Dynamics (CFD) was considered to
investigate the effect of different parameters such as
inlet air temperature, inlet air velocity and sugar
granule diameter on energy utilization ratio at three
levels using Taguchi technique. The data and results
were obtained basically through simulation using an
OpenFOAM application called blockMesh Dict file
to fluidized bed dryer with the flow variables being
velocity and temperature, and the optimized results
were done using Taguchi analysis. From the
analysis, the following conclusions are made:
1. Very low temperature below results in a
slower sugar drying process leading to much
time being spent on the effective sugar drying
process.
2. Pressure build-up within the fluidized sugar bed
dryer increases with time as the hot air is being
pumped into the bed and the sugar granules tend
to be more spaced as the moisture content is
lost.
3. The velocity of the air within the fluidized bed
dryer as well as the velocity of the sugar
granules generally increases with time
specifically, after the minimum fluidization
velocity is obtained.
4. The percentage contribution of the sugar
granule diameter is more significant than the
other factors and the pair inlet air temperature-
sugar granule diameter has the maximum
interaction with each other, with the pair sugar
granule diameter - inlet air velocity having the
least/minimum interaction on each other.
5. Moderate the temperature of the drying air from
to ensure effective drying without
melting the sugar granules as well as not
consuming a lot of time and energy during the
drying process.
The constant use of CFD in fluidized bed drying
processes is becoming complex, and as such, further
research can be carried out in areas of improved grid
convergence, obtaining the thermophysical
parameter effects on the fluid flow, and exploration
of a different application to provide room for
comparison, improved simulation and obtaining the
error analysis.
Acknowledgement:
The authors hereby acknowledge the effort put forth
by the reviewers and editorial board toward a
successful manuscript.
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WSEAS TRANSACTIONS on HEAT and MASS TRANSFER
DOI: 10.37394/232012.2023.18.25
Nabasirye Susan, Richard O. Awichi,
Stephen Kadedesya, Anselm O. Oyem
E-ISSN: 2224-3461
295
Volume 18, 2023
