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Effects of physical properties estimation on process design a case study using AspenPlus.

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ASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING
Asia-Pac. J. Chem. Eng. 2009; 4: 729–734
Published online 26 June 2009 in Wiley InterScience
(www.interscience.wiley.com) DOI:10.1002/apj.328
Special Theme Review
Effects of physical properties estimation on process design:
a case study using AspenPlus
Loic Cadoret,1 Cheng-Ching Yu,1 * Hsiao-Ping Huang1 and Ming-Jer Lee2
1
2
Department of Chemical Engineering, National Taiwan University, Taipei 106-17, Taiwan
Department of Chemical Engineering, National Taiwan University of Sci. Tech., Taipei 106-07, Taiwan
Received 31 October 2008; Revised 11 March 2009; Accepted 12 March 2009
ABSTRACT: This study focuses on the physical property model parameters estimation in order to accurately simulate
separation processes for a given set of components. The non-random two-liquid (NRTL) model was chosen and
parameters were calculated using different methods: experimental data regression and UNIFAC and COSMO-SAC
(conductor-like screening model, segment activity coefficient) predictive models. The vapor-liquid equilibrium (VLE)
obtained from these different models was compared and results showed that COSMO-SAC can be a reliable tool when
data or functional groups are missing. Results also showed that the use of UNIFAC to estimate activity coefficients at
infinite dilution can, in some cases, leads to inaccurate results and strongly impact process simulation results.  2009
Curtin University of Technology and John Wiley & Sons, Ltd.
KEYWORDS: process simulation; thermodynamic properties; parameters estimation
INTRODUCTION
In the last decades, simulation software have turned
out to be powerful tools to quickly design and improve
chemical process. However, the lack or the wrong estimation of physical properties can easily lead to inaccurate results and non-negligible deviation from potential
optimal process configurations.[1,2] The use of thermodynamic models can often provide good estimations
of molecule interactions and the choice of the model
appears to be of great importance. It will directly impact
phase equilibrium calculations and thus separation units
design.[3 – 6] Then, even with suitable thermodynamic
models, the uncertainties in the model parameters can
be significant. Through a literature review, Whiting[4]
showed the consequences of inaccurate input parameters
on process simulation results. According to the author,
one should consider the entire system of data generation, model choice, parameters regression/estimation
and simulation software choice to provide an actual
accurate process optimization. Starting from a broad
literature survey, Kister[7] identified the main sources
of discrepancies between plant simulations and plant
data. His study showed that for two-thirds of reported
cases, main issues were related to poor predictions of
vapor–liquid equilibrium (VLE) (especially for systems
*Correspondence to: Cheng-Ching Yu, Department of Chemical
Engineering, National Taiwan University, Taipei 106-17, Taiwan.
E-mail: ccyu@ntu.edu.tw
 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
exhibiting non-idealities and close-boiling molecules)
and to the use of inconsistent experimental data. The
author also recommended the use of graphical techniques (McCabe–Thiele and Hengstebeck diagrams)
for simulations troubleshooting. Barnicki[8] showed that
checking the quality of data was a critical step and that,
consistency tests should always be performed to ensure
accurate regressions and estimations of parameters.
When using activity coefficient models [Wilson, UNIQUAC, non-random two-liquid (NRTL)], one often
has to deal with a lack of experimental data and
binary interaction parameters have to be estimated. The
well-known UNIFAC group contribution method[9] is
commonly used to solve this issue. In this model,
molecules are subdivided into functional groups and
thermodynamic properties are obtained by summing
up contributions from all the pair-wise interaction
groups. UNIFAC is a reliable method which gives
good results, is extensively described in the literature, but has some drawbacks: experimental data are
needed for fitting interaction group parameters and some
functional groups remain missing. UNIFAC parameters are fit to a large number of data sets of components. By this averaging, for a given particular system,
accuracy is not guaranteed and can lead to unnecessary over-design costs.[8] Among more recent methods to estimate physical properties, the conductor-like
screening model-segment activity coefficient (COSMOSAC) method appeared to be very promising.[10,11] It
730
L. CADORET ET AL.
Asia-Pacific Journal of Chemical Engineering
is a new class of predictive model based on quantum chemical methods in which molecule surface is
subdivided into segments with equal area and the chemical potential is derived from interactions between the
surface segments of all molecules (a detailed description of the model can be found in Refs [11–13]). A
significant advantage of this method is that no experimental data are needed to fit model parameters and
thus, it can potentially handle any kind of system.
In a previous study, Athès et al .[10] used NRTL and
COSMO-SAC to predict VLE of aqueous mixture with
aroma compounds. They showed that COSMO-SAC
model was a reliable tool providing good estimations
of VLE.
Finally, for activity coefficient models, VLE predictions are very sensitive to activity coefficient (γ )
estimations, whatever the data sources (experimental data, estimation with UNIFAC or COSMO-SAC).
The infinite dilution approximation is often used
when activity coefficient values or not available for
the whole range of composition. Barnicki[8] showed
that an accurate estimation of γ at infinite dilution (γ ∞ ) for light-boiling component was a key
for an accurate distillation simulation. The importance of γ ∞ in aqueous solution containing many
aroma compounds was pointed out by Athès et al .[10]
Authors showed that even for small amounts of
aroma compounds, the total concentration of these
molecules may not be negligible and γ may significantly deviate from γ ∞ , due to a synergy effect. Thus,
the infinite dilution approximation has to be taken
carefully.
By comparing former cited models, this study aims
at showing the importance of this estimation step
in a process design. As a case study, a mixture
of six main components was considered (Table 1).
To illustrate impact of physical properties estimation, the separation of components by distillation was
simulated using AspenPlus[14] software. This example
was taken from a larger study conducted to design
a whole new process.[15] It mainly dealt with the
ammoximation of cyclohexanone, an intermediate step
in the production of caprolactam. The chosen components are the main components involved in this
process.
ESTIMATION OF MODEL PARAMETERS
The NRTL model[16] was chosen to represent both VLE
and liquid–liquid equilibrium (LLE). This choice was
justified by the presence of polar species, no electrolytes
and a relatively low pressure (less than 2 bars) in the
separation unit. The gas phase was considered as ideal
with no vapor phase association.
In the model (Eqns 1–3), binary interaction parameters (aij , bij and cij ) can be entered to accurately represent phase equilibrium (VLE and LLE) in the mixture:
xj τji Gji
j
ln γi = 
+
xk Gki
k
j

τij −

xj Gij
xk Gkj
k
xm τmj Gmj
m
xk Gkj




(1)
k
Gij = exp(−cij τij )
τij = aij +
(2)
bij
T
(3)
With γi the activity coefficient of component i ,
xi the mole fraction, T the temperature and aij , bij ,
cij the binary parameters are to be estimated. These
parameters are unsymmetrical, meaning that aij may not
be equal to aji . For the cij parameter, it is assumed that
cij = cji = 0.3 for all pairs of components, following
the recommendations of Renon and Prausnitz.[17] It
is worth noting that this is a simplified version of
the model since Gij and τij could be expressed using
six binary parameters.[14] A preliminary study showed
that the three other parameters could be neglected.
In some cases, the use of the six parameters even
led to inaccurate results. In the literature, influence
of the number of regressed parameters has already
been observed.[4,18] It has been shown that number
of parameters not only affects the VLE prediction
but also, the simulation results (even without apparent
discrepancies in VLE prediction).
Table 1. Recapitulating available data.
Boiling point
NH3 −33.4 ◦ C
t-butane
Water
Toluene
c-hexanone
Oxime
PCES/CS
O
PCES/CS
PCES/CS
CS
t-butanol 82.5 ◦ C
Water 100 ◦ C
Toluene 110 ◦ C
c-hexanone 155 ◦ C
Oxime 208 ◦ C
O
O
Data
CS
O
O
CS
O
Data
O
–
O, binary parameters available in the software; data, data regression; PCES and CS, binary parameters estimated with PCES software method
and with Cosmo-Sac, respectively.
 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
Asia-Pac. J. Chem. Eng. 2009; 4: 729–734
DOI: 10.1002/apj
Asia-Pacific Journal of Chemical Engineering
EFFECTS OF PHYSICAL PROPERTIES ESTIMATION ON PROCESS DESIGN
Figure 1. Different ways used to obtain binary parameters for NRTL model
in AspenPlus.
Table 2. AAD measurements for different binary systems from the mixture.
AAD
System
Regression
PCES
Cosmo-Sac
Consistency test
Oxime/toluene
Oxime/cyclohxanone
Water/t-butanol
Water/ammonia
t-butanol/cyclohexanone
0.012
0.193
0.0137
0.093
0.017
–
–
0.045
0.205
0.13
0.011
0.291
0.046
0.13
0.035
Failed
Failed
Passed
Failed
Passed
AspenPlus already provides parameter values for
some pairs of components and, different ways were
identified to obtain missing ones. The different procedures are summarized in Fig. 1.
When available, experimental data were regressed.
Thermodynamic consistency of data was first checked
through area and point tests.[19,20] From VLE x -y
curves, a mean deviation (AAD) between experimental
and calculated points was evaluated, given by:
AAD =
N
exp
1 |Yi − Yical |
exp
N i =1
Yi
(4)
exp
Were Yi and Yical represent respectively experimental (retrieved from the literature sources) and calculated
vapor molar fraction of component i , and N is the
number of used experimental points. If VLE data are
not available, binary parameters can be estimated based
on activity coefficients for infinite dilution and constant temperature. For these conditions, NRTL model
equations are simplified and the infinite dilution activity coefficient becomes only a function of the binary
interaction parameters[14] :
ln(γ1∞ , x1 → 0) = f (τ12 , τ21 )
(5)
ln(γ2∞ , x2 → 0) = f (τ12 , τ21 )
(6)
 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
Noting that for NRTL the third parameter cij
is fixed, an easy system of two equations with
two unknowns (aij , bij ) has to be solved. Obtained
parameters are then used to calculate activity coefficients over the whole composition. Activity coefficients at infinite dilution can be given as experimental or estimated data using UNIFAC (method
called PCES in AspenPlus). For this study, the more
recent modified UNIFAC-Dortmund model[21] was
used.
As previously mentioned, UNIFAC cannot be used if
some functional group is missing, and this is the case
in this study for the cyclohexanone oxime molecule
in which the ‘oxime group’ ( N-OH) is not available. In order to estimate interaction involving cyclohexanone oxime, the Cosmo-Sac model was used to
calculate activity coefficients over the entire mole
composition. Calculated coefficients are then used as
‘experimental data’ and regressed to calculate binary
parameters.
Depending on available data (Table 1), the different ways presented in Fig. 1 were tested and compared to each other via VLE calculation. AAD was
used to evaluate deviation of estimation from experimental points. Results are presented in the following
parts.
Asia-Pac. J. Chem. Eng. 2009; 4: 729–734
DOI: 10.1002/apj
731
732
L. CADORET ET AL.
RESULTS AND DISCUSSION
Parameters regression from available
experimental data
Figure 2(a) and (b) shows results of regression for
the two systems t-butanol/cyclohexanone and toluene/
oxime respectively. Mean deviations are recapitulated
in Table 2. At first, it can be seen that experimental
data did not pass thermodynamic consistency test for all
cases. This means that a ‘blind’ trust cannot be put on
retrieved data from literature. It then appeared useful to
plot equilibrium curves to get more qualitative results.
For the system toluene/oxime, despite the relatively
low calculated AAD (1.2%), one can see the regressed
bubble-point curve showing a significant deviation from
experimental points. Since given data did not pass the
consistency test, it becomes quite difficult to assess the
accuracy of regression and show that even if data are
available, efforts made to regress parameters do not
necessarily lead to satisfying results.
(a)
(b)
Figure 2. P-x-y VLE. (a) toluene/oxime pair (experimental data from Ref. [23]) (b) t-butanol/cyclohexanone pair
(experimental data from Ref. [22]).
 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
Asia-Pacific Journal of Chemical Engineering
For the t-butanol/cyclohexanone system, the AAD
is higher (1.7%), but calculated equilibrium curve
follows well the experimental curve shape. These results
suggest that a qualitative analysis of results is useful to
assess for regression quality (as already mentioned in
literature[22] ), especially when data are not consistent.
AAD should thus only be seen as a semi-quantitative
result, as it also strongly depends on the number of used
experimental points. It is worth noting that in any case,
compared to estimation methods, equilibrium retrieved
from data regression are always closer to experimental
points.
Parameter estimation methods
Figures 2 and 3 illustrate calculated VLE for different binary systems, using the different methods cited
in previous part. These figures are representative of
all results obtained with the different pairs listed in
Table 1. As previously mentioned, experimental data
are not available for all interacting pairs of components
and UNIFAC could not be used for all pairs involving
the cyclohexanone oxime molecule.
In Fig. 2(a), (b) and 3(a), it can be seen that CosmoSac reproduces quite well experimental behaviors. For
the t-butanol/water pair (Fig. 3(a)), the Cosmo-Sac
model predicts well the azeotrope, with an AAD of
4.6%. When looking at curve shape, it even gives a relatively better result than UNIFAC, in a qualitative point
of view. In these three cases, Cosmo-Sac remains very
close to UNIFAC estimation. Figure 3(b) shows the
result obtained for the ammonia/t-butanol pair. Here,
the two models give quite different results. It can be
observed that the use of UNIFAC with estimation of
activity coefficients at infinite dilution (PCES) give
quite peculiar results for small mole fraction of ammonia. This behavior was also observed using PCES for
the ammonia/cyclohexanone pair. The reason for this
result still do not appear clearly but shows that, this
way of estimating parameters failed for these two systems, in these conditions. In literature, the sensitivity
of results to activity coefficients at infinite dilution has
already been observed. Athès et al .[10] showed that special care should be taken when employing the ‘infinite
dilution’ approximation. The discrepancy observed on
Fig. 3(b) could be related to this approximation when
using UNIFAC to estimate γ ∞ (PCES). However in literature, discrepancies are usually observed for highly
non-ideal system, close-boiling components[8] or when
a synergy effect occurs.[10] In the presented case, the
estimated VLE with COSMO-SAC do not exhibit a
strong non-ideality and the two molecules do not have
close boiling points. The two components are relatively
‘common’ and such result with UNIFAC is quite surprising. These results reinforced the fact that the use of
Asia-Pac. J. Chem. Eng. 2009; 4: 729–734
DOI: 10.1002/apj
Asia-Pacific Journal of Chemical Engineering
EFFECTS OF PHYSICAL PROPERTIES ESTIMATION ON PROCESS DESIGN
(a)
(a)
(b)
(b)
Figure 3. Vapor–liquid equilibrium. (a) t-butanol/water
pair (experimental data from Ref. [22]) (b) ammonia/tbutanol pair. P = 0.5 atm.
γ at infinite dilution has to be handled with care since
it can lead to poor VLE estimations.
As mentioned in the introduction, previous studies in
the literature[10,11] showed that COSMO-SAC was able
to provide accurate VLE estimations. Presented results
are in agreement with former studies and show that
COSMO-SAC can be more suitable than UNIFAC, for
the specific mixture of interest. Thus, the model appears
to be a useful alternative to the UNIFAC limitations
(lack of functional group, inaccurate estimations).
Impact on simulation
Distillation of a four-component mixture was simulated with AspenPlus (Fig. 4). The separation takes
place between water and the water/t-butanol azeotrope.
Impact of the binary parameter estimation model was
checked by keeping the same column configuration
(reboiler duty and reflux ratio are kept constant) and
changing the estimation method for one or more binary
system.
 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
Figure 4. Simulation results for the distillation of
the ammonia/t-butanol/water/oxime mixture. For all
component pairs, binary parameters were estimated
following information given in Table 1, except for
the ammonia/t-butanol pair, which was estimated via
Cosmo-Sac (a) or PCES (b).
Figure 4(a) and (b) shows results obtained by respectively using Cosmo-Sac and PCES for the ammonia/tbutanol system binary parameter estimation. With the
use of Cosmo-Sac Fig. (4a), simulation predicted a clear
separation with no ammonia and t-butanol in the bottom
stream.
The use of PCES Fig. (4b) led to a significant amount
of these two light components in the bottom stream.
This observed behavior with PCES is probably due to
the peculiar VLE predicted for the ammonia/t-butanol
system, as shown previously in Fig. 3. The strongly
inaccurate estimation with PCES led to poor simulation
results. It is worth noting that trusting PCES estimations
would lead to a complete different approach for the
Asia-Pac. J. Chem. Eng. 2009; 4: 729–734
DOI: 10.1002/apj
733
734
L. CADORET ET AL.
Asia-Pacific Journal of Chemical Engineering
process design and for the further separation of water
and oxime.
CONCLUSION
Using AspenPlus simulation tool, the estimation of
interaction parameters can be performed using different
ways. At first, the regression of available data is not
so obvious and has to be performed carefully. For
missing parameters, UNIFAC and COSMO-SAC model
were then compared. The COSMO-SAC model gave
quite good results and appears to be of great interest
when UNIFAC cannot be used. It was also shown
that the estimation of activity coefficient at infinite
dilution with UNIFAC could lead to inaccurate results
and significantly, impact the process simulation. The
influence of infinite dilution approximation on VLE
predictions has already been mentioned in literature but
still needs to be clarified in a future work. It can lead to
poor estimations, even for components which do not
exhibit close boiling point or strong non-ideality, as
shown in this study. Finally, depending on the system
of interest, every possible option should be considered
to estimate parameters when data are not available,
in order to avoid model failure and ensure process
simulation accuracy.
[2] S.G. Huang, C.C. Yu. J. Chin. Inst. Chem. Engrs., 2003; 34(3),
345–355.
[3] A.R. Nelson, J.H. Olson, S.I. Sandler. Ind. Eng. Chem. Proc.
Des. Dev., 1983; 22, 547.
[4] W.B. Whiting. J. Chem. Eng. Data, 1996; 41, 935.
[5] Y. Xin, W.B. Whiting. Ind. Eng. Chem. Res., 2000; 39, 2998.
[6] P.M. Mathias, H.C. Klotz. Chem. Eng. Prog., 1994; 90(6), 67.
[7] H.Z. Kister. Can We Believe Simulation Results? Chemical
Engineering Progress, October 2002; 52–56.
[8] S.D. Barnicki. How Good Are Your Data? Chemical
Engineering Progress, June 2002; 58–67.
[9] A.a Fredenslund, G. Gmehling, P. Rasmussen, Vapor-Liquid
equilibria using UNIFAC, Amsterdam, Elsevier, 1977.
[10] V. Athès, P. Paricaud, M. Ellaite, I. Souchon, W. Furst, Fluid
Phase Equilib., 265, 139–154.
[11] S.-T. Lin, S.I. Sandler. Ind. Eng. Chem. Res., 2002; 41,
899–913.
[12] A. Klamt. J. Phys. Chem, 1995; 99, 2224–2235.
[13] A. Klamt, F. Eckert. Fluid Phase Equilib., 2000; 172, 43–72.
[14] Aspen Technology Inc.. ASPEN Plus User’s Manual, Aspen
Technology Inc., Cambridge, 2000.
[15] M. Kitamura, Y. Shimazu, M. Yako. Sumitomo Chemical
Company, Limited. Continuous Process for Producing EpsilonCaprolactam by Gas-Phase Beckmann Rearrangement of
Cyclohexanone Oxime, Patent Number EP 570,110, August
16, 2000.
[16] J.M. Prausnitz, R.N. Lichtenthaler, E.G. de Azevedo. Molecular Thermodynamics of Fluid-Phase Equilibria 3rd edn,
Taiwan, Pearson Education 2004.
[17] H. Renon, J.M. Prausnitz. AIChE J., 1968; 14, 135–144.
[18] A.K.S. Murthy, D. Zudkevitch. Inst. Chem. Eng. Symp. Ser.,
1979; 56, 1.1-51–1.1-78.
[19] O. Redlich, A.T. Kister. Ind. Eng. Chem., 1948; 40, 345.
[20] M.M. Abbott, H.C. Van Ness. AIChE J., 1975; 21, 62.
[21] U. Weidlich, J. Gmehling. Ind. Eng. Chem. Res., 1987; 26,
1372–1381.
[22] J. Gmehling, U. Onken. Vapor-Liquid Equilibrium Data
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[23] K. Suehnel. Z. Phys. Chem., 1985; 9999.
REFERENCES
[1] E.C. Carlson. Don’t Gamble with Physical Properties for
Simulations, Chemical engineering progress, October 1996.
 2009 Curtin University of Technology and John Wiley & Sons, Ltd.
Asia-Pac. J. Chem. Eng. 2009; 4: 729–734
DOI: 10.1002/apj
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