Applied Thermal Engineering 30 (2010) 2180e2186
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Applied Thermal Engineering
journal homepage: www.elsevier.com/locate/apthermeng
Thermodynamic simulation and evaluation of sugar refinery evaporators using
a steady state modelling approach
A.E. Lewis a, *, F. Khodabocus b, V. Dhokun b, M. Khalife c
a
Crystallization and Precipitation Research Unit, Department of Chemical Engineering, University of Cape Town, Cape Town, South Africa
Department of Chemical and Environmental Engineering, University of Mauritius, Reduit, Mauritius
c
BluESP (Mauritius) Limited, President John Kennedy Street, Port-Louis, Mauritius
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 27 October 2009
Accepted 27 May 2010
Available online 2 June 2010
In a sugar refinery, the juice is concentrated through evaporation, with the objective of concentrating the
juice to syrup as rapidly as possible. Because the heat of vaporization of water is relatively high, the
evaporation process can be highly energy intensive, and therefore the economical use of steam is
important in the refinery. This paper reports on the development of a simulation model for the evaporation sections of two Mauritian sugar refineries.
The first objective was to use the simulation model to carry out an energy balance over the evaporators
in order to assess the economy of steam usage over the refinery.
The second objective was to examine to what extent a fundamental steady state model, based on
thermodynamics (not kinetics) was capable of predicting the material and energy flows in two operating
sugar refineries and thereby to evaluate the applicability of the modelling framework.
The simulation model was validated using historical data as well as data from the plant DCS system.
The simulation results generally correlated well with the measured values, except for one of the evaporators on one refinery. Some suggestions were made as to the cause of the discrepancy. On balance, it
was found that both refineries are extremely efficient in terms of steam and equipment usage and that
there is not much scope for energy optimisation within the present configuration e nor for much spare
steam capacity for an additional refinery.
It was also shown that steady state process simulation, using thermodynamic models, can generate
a very useful representation of a working refinery. Besides being able to use the model to “benchmark”
the operation and thus evaluate its performance as a whole as well as across individual units, it could
also be used to evaluate refinery performance across refineries, nationally as well as globally.
Ó 2010 Elsevier Ltd. All rights reserved.
Keywords:
Sugar refinery
Modelling
Simulation
Aspen Plus
Steam consumption
Energy balance
1. Introduction
In a raw sugar or white sugar refinery, the juice is initially
concentrated through a process of evaporation, during which water
is removed from the solution by vaporization. The juice usually
enters the evaporator at approximately 12 mass percent sucrose
(12 Brix) and is concentrated to about 65 mass percent (65 Brix)
in the final evaporator [1]. The objective of the process is to
concentrate the thin juice to a syrup as rapidly as possible, at
temperatures that will minimise sucrose losses and colour formation, while at the same time minimise steam consumption. Because
the heat of vaporisation of water (latent heat) is relatively high
(2257 kJ/kg), the evaporation process can be highly energy
* Corresponding author. Tel.: þ27 21 650 4091; fax: þ27 21 650 5501.
E-mail address: Alison.Lewis@uct.ac.za (A.E. Lewis).
1359-4311/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.applthermaleng.2010.05.031
intensive. The traditional fuel of a sugar cane refinery is the final
bagasse discharged by the mill. Steam, generated by burning
bagasse under steam boilers, is then used for mechanical purposes
(live steam) and subsequently for heating (exhaust steam) [2].
Using steam economically in the sugar factory is as important as
generating it efficiently, but far more attention has been paid to the
generation step [3]. However, as sugar refineries become more
complex; as quality standards become more stringent; and as
energy consumption and wastage is of increasing concern, it has
become necessary to pay more attention to the efficient and
economical use of steam in the refinery itself.
An energy balance is also important to be able to investigate the
performance and capacity of the system and to compare performance with the original equipment specs. In addition, with the
commissioning of sugar refineries to further refine the raw sugar,
there will be additional demands for steam as a source of energy for
the refineries.
A.E. Lewis et al. / Applied Thermal Engineering 30 (2010) 2180e2186
ZVv "
2181
#
RT
v
dV lnZm
V
T;V;niej
The particular focus of this paper is the expansion of two
Mauritian sugar factories to include a refining step. The refineries
will have additional steam requirements and thus place extra
demands on the current steam. For this reason, it is an important
exercise to be able ascertain firstly: to what extent the current
steam production is optimally used and secondly: if there will be
sufficient steam to cope with the extra demands.
From a broader and more academic perspective, developing
a model of the evaporator section of a sugar refinery has additional benefits. Sugar refineries are by and large run by very
experienced operators who have the benefit of many years of
plant expertise. However, partly because of this depth of experience, there is often not a very rigorous or fundamental
approach to the energy and the material balance on the plant.
Developing a model that is based on fundamental principles has
two benefits: firstly, in the process of developing the model, the
information about how the plant is configured and operated
must be established. This is often an illuminating and educative
exercise in itself. The second benefit is the ability to “benchmark”
the operation against some standard and thus to be able to
evaluate its performance as a whole as well as across individual
units.
Although it is the primary objective of this paper to answer the
specific questions about steam usage in the two case study plants,
the secondary objective is to see to what extent a fundamental
steady state model, based on thermodynamics (not kinetics) is
capable of predicting the material and energy flows in two operating sugar refineries and thereby to evaluate the applicability of
the modelling framework.
Aspen PlusÔ was chosen as the modelling tool, since it has the
capacity to generate steady state simulations of a range of process
plants. These simulations can then be used to investigate material
balance issues such as raw material conservation, product quality
optimisation or waste minimisation. They can also be used to
explore energy balance issues such as energy analysis and minimisation. Aspen Plus has been widely used as a tool to investigate
a range of energy questions, such as evaluation of steam power
plants [4] and heat integration [5]. In this case, the software was
used for the purposes of energy analysis. A process simulation of
the evaporator section of a sugar refinery was developed using
Aspen Plus. The simulation was then used to analyse the evaporator
sections of two Mauritian sugar refineries with a view to ascertaining the efficiency of the current steam consumption, and to
what extent the current steam generation will be able to satisfy the
additional steam demands of a refinery.
2. Thermodynamic modelling
2.2. Choice of thermodynamic property methods and models
The key thermodynamic property calculation that is performed
in order to carry out the thermodynamic modelling is the phase
equilibrium calculation. Besides the calculation itself, the phase
equilibrium method also determines how other thermodynamic
properties, such as enthalpies and molar volumes, are calculated.
The basic relationship for every component i in the vapour and
liquid phases of a system at equilibrium is:
The starting point for the development of the simulation model
is to ensure that the correct thermodynamic property methods and
models are selected. For these case studies, the most important
thermodynamic property is the correct prediction of the elevated
boiling point of the sucrose solution as it becomes progressively
more concentrated in each evaporator. A number of different Aspen
Plus thermodynamic models were tested and the predicted boiling
point elevation compared with data obtained from Tressler [6]. It
was also necessary to input the data for the Antoine equation
vapour pressure prediction and the heat of formation of crystalline
sucrose. The relevant data is given in the appendix. The most
accurate prediction was obtained using the UNIFAC thermodynamic model with the molecular structure of sucrose specified. The
structure of the sugar molecule is depicted in Fig. 1, and the thermodynamic data is tabulated in Table 1. The salt equilibrium
constant for sucrose crystallization was obtained from the regression of sucrose solubility data using Aspen Plus DRS.
fiv ¼ fil
(1)
Where:
f iv ¼ the fugacity of component i in the vapour phase
fil ¼ the fugacity of component i in the liquid phase.
For the sugarewater system, the calculation of the fugacities
from the phase equilibrium relationship in terms of measurable
state variables was carried out using an activity coefficient method.
When using an activity coefficient method, the liquid properties are
determined from summation of the pure component properties to
which a mixing term or an excess term is added. This is important
for systems where there is non-ideality present. The vapour phase
properties are derived from an equation of state. In the activity
coefficient method:
fil ¼ xi gi fi;l
(2)
fiv ¼ fvi yi p
(3)
lnfvi
1
¼ RT
N
vp
vni
Where R ¼ universal gas constant 8.315 J K1 mol1
T ¼ temperature (K)
V ¼ total volume (m3)
p ¼ pressure (Nm2)
ni ¼ mole number of component i
Z ¼ compressibility factor
2.1. Thermodynamic modelling using Aspen PlusTM
*,l
Where fi ¼ liquid fugacity of pure component i at mixture
temperature
xi ¼ mole fraction of component i in the liquid phase
gi ¼ liquid activity coefficient of component i
yi ¼ mole fraction of component i in the vapour phase
4vi ¼ fugacity coefficient and is calculated according to:
(4)
Fig. 1. Structure of sugar molecule.
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A.E. Lewis et al. / Applied Thermal Engineering 30 (2010) 2180e2186
Table 1
Numerical data used for the thermodynamic model.
Number of functional groups CH
Number of functional groups OH
Number of functional groups C
Number of functional groups CH2
Number of functional groups CHO (ether)
Vapour pressure constants for the Antoine
equation
1
2
3
4
5
6
7
8
9
Solid enthalpy of formation at 25 C for
crystalline sucrose [8]
Constants in the expression for the
calculation of Ksp
A
B
C
D
5
8
1
3
3
Units: bar and C
56.6050745
12433.0
0
0
6.0186
2.2518E-19
6
460.15
1063
DHs,frm531.9 kcal/gmol
233.53780
5602.2690
42.62540
0.08658790
The UNIFAC (UNIversal Functional Activity Coefficient) model is
an activity coefficient model that can be used to describe
vapoureliquid equilibria, liquideliquid equilibria and enthalpic
behaviour of highly non-ideal systems. The model is a semiempirical one that uses the functional groups present on the
molecules that make up the liquid mixture to calculate activity
coefficients. The UNIFAC model is one of a group that includes
WILSON, NRTL and UNIQUAC, all of which are used to model highly
non-ideal systems at low pressures.
Fig. 2 shows the comparison between the Boiling Point Elevation
as predicted by Aspen Plus and that generated using the experimental data. Since the maximum error is never greater than 1 C,
this prediction was deemed sufficiently accurate to model the
evaporator section. Since the Aspen Plus prediction of the Boiling
Point Elevation was slightly less than that of the experimental data,
the Aspen Plus simulation will tend to under predict the Boiling
Point Elevation and thus under predict the steam requirement.
However, given that the largest deviation between the two curves
3. The sugar refinery evaporator train
In both sugar refineries, the evaporator trains consist of
a number of vessels, each successive vessel operated at decreasing
pressure, thereby counteracting the boiling point elevation effect of
the increasing sucrose concentration of the solution. Steam
generated in the bagasse-fired boilers is used in the first evaporator
to evaporate the clarified juice. The juice is fed to the tube side of
each evaporator, with the vapours produced by the boiling juice
being collected and fed as the heating medium to boil up the juice
in the next evaporator, which is operated at lower temperature and
pressure. The steam is fed to the shell side, with the latent heat of
the condensing steam used to heat the juice and the condensate
being collected and removed from the vessel. This scheme is followed sequentially all the way down the evaporator train.
3.1. Case study 1
In the first refinery, the evaporator train consists of eight vessels,
the entire train evaporating approximately 94% of the water,
concentrating the juice from 12 Brix to about 70 Brix. The eight
evaporators are used in a quintuple evaporator arrangement. The
first and second effects are both Kestner type Evaporators; the third
effect is three Robert Evaporators in parallel; the fourth effect is
a single Robert evaporator and the fifth effect is two Robert Evaporators in parallel. The configuration of the evaporator train for case
study 1 is given in Fig. 3, with the measured Brix values for the juice
and the temperatures and pressures for the tube sides given on the
diagram.
3.2. Case study 2
In the second refinery, the evaporator train consists of ten
vessels connected in series, the entire train evaporating approximately 92% of the water, concentrating the juice from 13 Brix to
about 67 Brix. The ten evaporators are used in a quintuple evaporator arrangement. The first and second effects both comprise
three vessels, the third effect is two columns, and the fourth and
fifth are each single evaporating columns. The configuration of the
evaporator train for case study 2 is given in Fig. 4, with the
measured Brix values for the juice and the temperatures and
pressures for the tube sides given on the diagram.
110
108
106
Boiling Point (°C)
is 0.78 C (which occurs at 50.4 Brix), the error deriving from this
slight deviation will not be significant.
Boiling Point Elevation is a colligative property, meaning that it
depends only on the number of dissolved particles and not the
identity of the particles. The physical significance of the boiling
point elevation in sugar crystallization is that, as the sugar solution
becomes more concentrated, there is an increase in its boiling
point. This means that, with increasing juice concentration, there is
a concomitant increase in the requirement of energy in order to
effect boiling and thus solvent evaporation.
104
102
100
98
3.3. Modelling of the evaporators at the sugar refineries
96
BP Aspen Plus calculation
94
BP (Tressler Data)
92
90
0
20
40
Brix (°)
60
80
Fig. 2. Boiling point of sucrose solution with increasing concentration in water
(experimental data compared to Aspen Plus calculation).
The multiple effect evaporator trains at both sugar refineries
were simulated using Aspen PlusÔ. The Aspen Plus flowsheet for
the evaporator sections is presented in Fig. 5.
The shell sides of the evaporators (the steam side) were
modelled using condensers, which calculate the duty required to
produce a saturated liquid at the specified pressure. This heat duty
was then transferred to the tube side of each evaporator in the form
of latent heat, as illustrated in Fig. 5.
A.E. Lewis et al. / Applied Thermal Engineering 30 (2010) 2180e2186
2183
Fig. 3. Schematic representation of the evaporator configuration for Case Study 1.
The tube side of the evaporators (the juice side) were modelled
using flash equilibrium vessels, which perform rigorous two-phase
(vapoureliquid) phase equilibrium calculations and calculate the
compositions and flows of the two outgoing streams based on the
specified pressure and the heat duty (from the latent heat).
The steam vents were modelled with simple flow splitters.
3.4. Model validation
The data used in model validation was that obtained from
a combination of the plant DCS (Distributed Control System) and
historical recorded data [7]. The tube side (juice) pressure for each
evaporator was fixed at the measured or recorded values. The
temperature on the tube side of each evaporator was then adjusted
by varying the quantity of vapour fed to the shell side such that the
calculated temperature of the juice matched the measured value.
The Brix of the product syrup was then calculated using the Aspen
Plus simulation, assuming that the syrup was at its bubble point
and that the overhead steam was saturated. These calculated Brix
values were then compared with those measured on the plant. The
comparison between calculated and measured values is given in
Fig. 6 for both Case Study 1 and for Case Study 2.
Fig. 4. Schematic representation of the evaporator configuration for Case Study 2.
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A.E. Lewis et al. / Applied Thermal Engineering 30 (2010) 2180e2186
Vent
Steam
Condenser 3
Condensate
Vent
Steam
Condenser 2
Condensate
Vapour
out
Flash
Equilibrium
Vessel 1
Vapour out
from stage 5
Latent
heat
Vapour out
from stage 4
Condensate
Vapour
out
Condensate
out
Latent heat
Condensate
Flash
Equilibrium
Vessel 3A
Steam
Condenser 1
Steam in
Steam
Condenser 4
Latent
heat
Vent
Steam
Condenser 0
Vent
Vapour out
from stage 3
Latent
heat
Flash
Equilibrium
Vessel 4
Flash
Equilibrium
Vessel 3B
Flash
Equilibrium
Vessel 2
Juice4
Juice2
Juice in
Flash
Equilibrium
Vessel 5A
Flash
Equilibrium
Vessel 5B
Juice1
Juice5
Flash
Equilibrium
Vessel 3C
Juice3
Fig. 5. Schematic representation of the Aspen Plus flowsheet for Evaporator Train for Case Study 1.
The calculated steam consumption necessary to achieve the
product Brix values is given in Fig. 7 for Case Study 1 and Fig. 8 for
Case Study 2, where t/h refers to metric tonnes. The steam fed to the
first evaporator is the only fresh steam fed to the evaporator
sections of both plants. The remaining evaporators (two through to
five) all use the vapours generated by the boiling juice in the
previous evaporator. The theoretically calculated steam requirement according to Rillieux’s First Principle [2], which states that
every tonne of steam used should evaporate a tonne of juice, is also
given in Fig. 7, Fig. 8 and Fig. 9.
Fig. 6. Measured vs calculated product Brix for Case Studies 1 and 2.
4. Discussion
4.1. Brix values
4.1.1. Case study 1
As expected, both the calculated and measured Brix values
increase monotonically with evaporator number. The rate of
increase of the Brix slows down slightly over Evaporators 4 and 5,
indicative of the increase in energy requirements as the sugar
solution becomes more concentrated. The point at which this curve
begins to level off corresponds with the point at which the slope of
Fig. 7. Measured vs calculated steam usage for Case Study 1.
A.E. Lewis et al. / Applied Thermal Engineering 30 (2010) 2180e2186
2185
Although the measured and calculated product Brix values are
generally in good agreement, the measured Brix from Evaporator 4
is 12.7% higher than the calculated value. This discrepancy arises
from the fact that that there is not sufficient vapour produced in
Evaporator 3 to satisfy the steam requirement for the juice
concentration in Evaporator 4, despite all vapour being used and
none vented. The problem originates further upstream, with the
steam requirement in Evaporator 3. This will be discussed further
below.
4.2. Steam requirements
For the steam requirements, in both case studies, the errors in
measured vs calculated values are slightly larger than those for the
Brix, ranging from 0 to 60.2%. This is partly due to the fact that the
Brix measurement is a less sensitive parameter than the steam
usage, with the boiling point elevation curve being relatively flat
over the entire operating range.
4.3. Steam requirementsecase study 1
Fig. 8. Measured vs calculated steam usage for Case Study 2.
the boiling point elevation curve in Fig. 2 suddenly increases. In
other words, the sharp increase in boiling point elevation is
translated into the flattening off of the Brix curve in Fig. 6.
The measured and the calculated product Brix values were
generally in good agreement, with errors between 0 and 6.1% for all
evaporators in the case study. It is to be expected that the calculated
product Brix will be higher than the measured value, as the
calculation is based on 100% efficient use of the steam and heat
transfer area. In reality, it is likely that the steam usage is not 100%
efficient and that there will be some wastage in the evaporators,
and thus the measured Brix will not be as high as the theoretical
value.
4.1.2. Case study 2
In Case Study 2, the calculated and measured Brix values also
increase monotonically with evaporator number. However, the
levelling off of the Brix curve with evaporator number is not as
apparent as it was for Case Study 1. This is potentially due to the
lack of vapour generated in Evaporator 3, which means that the Brix
out of Evaporator 3 is lower than expected. If this value had been in
the normal range of 52e56 Brix, then the characteristic flattening
off of the Brix curve would have been apparent.
Fig. 9. Comparison of normalised steam usage for the Case Studies 1 and 2.
In Case Study 1, the 124 t/h of steam necessary to achieve the
measured temperature and pressure in Evaporator 1 results in
a vapour flow rate out of the evaporator of 118 t/h. The ratio of 1 t
steam to approximately 1 t vapour correlates with values given in
literature [2] also known as Rillieux’s First Principle. For Case Study
1, the agreement between the calculated and measured steam
usage is generally good, with the calculated usage being less than
the measured value for all of the evaporators, except for Evaporator
4, where it is slightly more. The discrepancy for Evaporator 4 is
most likely due to the limit of accuracy in the measurement of the
steam flow rate; with a measured steam flow rate of 8.8 t/h and
a calculated value of 8 t/h. There would only need to be a 10% error
in the measured value for the calculated value to match exactly.
The theoretical steam usage in each evaporator was also calculated using Rillieux’s First Principle, with the results correlating
well with both the calculated and measured steam usage values.
This correlation between steam input and vapour generated is
preserved over the whole evaporator train. The relative magnitudes
of the three values is also expected, with the theoretical value
calculated using Rillieux’s First Principle being the lowest, followed
by the value calculated using the thermodynamic model and lastly
followed by the measured value being the highest.
4.4. Steam requirementsecase study 2
For Case Study 2, 110 t/h of steam generates 107 t/h of vapour in
the first evaporator. The approximately 1 t/h steam to 1 t/h vapour
correlation is also preserved over the whole evaporator train. The
correlation between measured and calculated steam usage is
reasonable for all of the evaporators except for Evaporator 3, where
the error is very significant e 45 t/h steam measured compared to
18 t/h predicted by the model. There are two possible causes of this
discrepancy: (1) either there is a significant inefficiency in the
evaporator, which causes it to use almost double the quantity of
steam expected or (2) it is most likely that this is an error in the
measured steam consumption, since a simple material balance over
the evaporator shows that, where Evaporator 3 to use approximately 45 t/h steam, then the syrup product from the evaporator
would be approximately 55 Brix, far more than the measured Brix
value out of Evaporator 3.
For Case Study 2, the theoretical steam usage in each stage,
when calculated using Rillieux’s First Principle, correlates well with
both the calculated and the measured values (except for Evaporator
3, which has been previously discussed).
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A.E. Lewis et al. / Applied Thermal Engineering 30 (2010) 2180e2186
4.5. Overall steam requirements per tonne of juice treated
When evaluating the steam usage per tonne of juice treated, and
comparing the two refineries, there are two aspects that are
apparent. The first is that Case Study 1 is more slightly efficient than
Case Study 2 in terms of steam usage, using 0.92 as opposed to 0.98
tonne steam per tonne juice treated. This could be due to the fact
that the refinery in Case Study 1 is 30% larger than the refinery in
Case Study 2, and the improved efficiency might be due to an
economy of scale. The second aspect that is apparent is that there is
not a significant difference between the actual steam usage and the
calculated values, which assume 100% efficiency of both steam and
equipment. For Case Study 1, Rillieux’s First Principle predicts 0.8
tonne steam per tonne of juice, compared to the actual value of
0.92. For Case Study 2, Rillieux’s First Principle predicts 0.85 tonne
steam per tonne of juice, compared to the actual value of 0.98.
These differences are relatively small.
This indicates that, in fact, both refineries are extremely efficient
in terms of steam and equipment usage and that there is not much
scope for steam optimisation within the present configuration. The
efficiencies were much better than expected, given that much of
the equipment is old and many of the control systems date from the
1950s. In practice, it is often the many years of experience of sugar
technologists that keep the plants running at high efficiencies,
despite the fact that the equipment is not the most modern.
However, the implication of this study is that, since the existing
steam is being used with such good economy and relatively optimally, there is not much spare capacity for extracting extra steam
out of the existing system. Therefore, on commissioning of the new
sugar refineries, it is likely that additional steam will need to be
generated.
5. Conclusions
For product Brix and steam usage, the measured values and
those calculated using the Aspen Plus model generally correlated
well. The reasonably good agreement extended over the entire
evaporator train for both case studies, except for Evaporator 3 in
Case Study 2. Some suggestions were made as to the cause of this
particular discrepancy.
On balance, it was found that both refineries are extremely
efficient in terms of steam and equipment usage and that there is
not much scope for steam optimisation within the present configuration. The implication is that, since the existing steam is being
used with such good economy and relatively optimally, there is not
much spare capacity for extracting extra steam out of the existing
system. Therefore, on commissioning of the new sugar refineries, it
is likely that additional steam will need to be generated.
Although the primary objective was to answer the specific
questions about steam usage in the two case study plants, the
secondary objective was to see to what extent a fundamental
steady state model was capable of predicting the material and
energy flows in two operating sugar refineries. This is perhaps the
more interesting and significant outcome.
Acknowledgements
Acknowledgements are due to the staff of a number of Sugar
Factories and Refineries in Mauritius, for their willing assistance
and provision of valuable data and advice.
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