-
DETERMINATION AND PREDICTION OF THE ISOBARIC VAPOR-LIQUID-LIQUID
EQUILIBRIUM DATA
Koichi Iwakabe and Hitoshi Kosuge
Department of Chemical Engineering, Tokyo Institute of
Technology12-1, Ookayama-2, Meguro-ku, Tokyo 152-8552, Japan
ABSTRACT
The prediction method of the isobaric vapor-liquid-liquid
equilibria (VLLE) data for thesystem ethanol-water-1-butanol and
ethanol-2-butanol-water was studied. Theparameters for the activity
coefficients models were determined from the constituentbinary VLE
data. With the parameters, the isobaric ternary VLLE data were
predictedand compared with the experimental ones obtained in our
previous study.Furthermore, a prediction method based on successive
calculations of LLE at boilingpoint and VLE was studied. For this
method, the two sets of the parameters wereindependently determined
for LLE and VLE calculation. The VLLE data predicted bythis method
were in good agreement with the experimental ones.
INTRODUCTION
The phase equilibrium data is one of the basic and important
physical properties fordesign and analysis of phase separation
processes. For vapor-liquid equilibrium(VLE) and liquid-liquid
equilibrium (LLE), a number of measurements and predictionshave
been reported in literature and they seem to be successful. On the
contrary,vapor-liquid-liquid equilibrium (VLLE) data, which are
significantly important for theheterogeneous separation processes,
have been reported scarcely due to thedifficulty of their
measurements. The static method and the dynamic method havebeen
mainly used for the measurements. The static method, which is said
to be themost accurate method for the measurement of phase
equilibria and is widely used forthe isothermal measurements,
requires long time for settling the system. On thecontrary, the
dynamic method can achieve the equilibrium quickly and has
beengenerally used for the isobaric measurements. However, when it
comes to themeasurements of VLLE, there is a problem of the
recirculation of two liquid phasesthat appear in an apparatus for
the VLLE measurements. So far, treatments of thesetwo liquid phases
in the VLE measurement apparatus have been studied by
someresearchers. For example, a dynamic VLE still equipped with an
ultrasonichomogenizer in order to mix two liquid phases in the
boiling flask was proposed byGomis et al. [1]. In their apparatus,
LLE measurements at boiling points are doneindependently in the
water bath that is controlled at the boiling temperature. Also
in
-
our previous study [2], a simple dynamic VLE still has been
developed for both VLEand VLLE measurements. Although the still is
very similar to the ordinary Gillespietype VLE still, VLE and VLLE
measurements can be done in the same apparatus.
Another problem concerned with VLLE is its prediction. It is
well known that theprediction of VLLE is not as precise as those of
VLE and LLE. As studied by Lee etal. [3], the selection of the
parameters for the VLLE prediction is very important sincethe
parameters obtained from VLE data are very different from those
obtained by LLEdata. The predicted VLE or VLLE with the parameters
determined by LLE data aresometimes poor. There seem to be no good
indices to tell which parameters arebetter for prediction. In order
to explore the cause, the accurate VLLE measurementsare
indispensable.
The purposes of this study are to predict the VLLE precisely. In
our previous study,isobaric VLE and VLLE for binary and ternary
systems have been measured atatmospheric pressure. In this paper,
the parameters for the activity coefficientsmodels are determined
from the constituent binary VLE data. With the parameters,the
isobaric ternary VLLE data are predicted and compared with the
experimentalones Furthermore, a prediction method of ternary VLLE
for these systems, and aparameter estimation method for LLE
calculation are studied. The prediction methodis based on
successive calculations of LLE at boiling point and VLE. For
eachcalculation, the different sets of parameters of activity
coefficients models areindependently determined and used. The
predicted vapor phase compositions atVLLE are compared with the
experimental ones.
PARAMETER ESTIMATION FOR ISOBARIC BINARY VLE AND VLLE DATA
Parameters of activity coefficient models, NRTL [4] and UNIQUAC
[5], aredetermined by minimizing the following objective
function.
= =
=
m
k
n
i ki
calkikiOF1 1
2
exp,,
,,exp,,
(1)
where OF is the objective function, and i,k,exp and i,k,cal are
experimental andcalculated activity coefficient of component i of
the data number k, respectively, m isnumber of the data and n is
number of components. The objective function wasminimized by
Marquardt method [6] and Simplex method proposed by Nelder andMead
[7]. If the two sets of parameters determined by both methods are
different witheach other, the one whose objective function is
smaller than the other was taken. Inthis study, the non-randomness
parameter, , of the NRTL model was fixed at 0.2,and the values for
van der Waals volume and surface parameter, r and qrespectively,
were taken from the literature [8].
The parameters determined from the binary VLE data for the
system ethanol (1) water (2) are shown in Table 1. Predicted VLE
for the system with the parameters inTable 1 are compared with the
experimental ones in Fig. 1. In the figure, the VLEdata for the
system in the literature [9-11] are also plotted. As can been seen
in thefigure, the experimental and the predicted VLE for the system
are in good agreementwith each other.
-
Table 1 Binary parameters of the NRTL and UNIQUAC models
System Model A12 A21 OF y1,avg y1,max Tavg TmaxEthanol-Water
NRTL -460.030 1791.37 0.0137 0.0044 0.0136 0.1507 0.4686
UNIQUAC -70.1587 398.992 0.0066 0.0029 0.0122 0.1123 0.3362
Water-1-Butanol NRTL 3409.83 -426.999 0.3818 0.0129 0.0289
1.1576 2.8259UNIQUAC 498.762 183.167 0.2616 0.0100 0.0425 0.9634
2.3324
2-Butanol-Water NRTL -552.206 3344.64 0.6257 0.0202 0.0479
0.5278 0.9990 UNIQUAC 79.2739 516.5409 0.4319 0.0165 0.0641 0.4177
2.6441
Aij = (gij-gjj) / R for NRTL, (uij-ujj) / R for UNIQUAC
Fig. 1 Boiling and dew point for the system ethanol (1) - water
(2) with those predicted byactivity coefficient models
The parameters for the systems water (1) 1-butanol (2) and
2-butanol (1) water(2) are also shown in Table 1. The comparison of
boiling and dew points between theexperimental and the predicted
ones for these systems are shown in Figs. 2 and 3,respectively. In
these figures, the VLE data in the literature [12-20] and
thosepredicted by ASPEN Plus are also plotted. For both systems,
the predictedcompositions of water in theorganic liquid phase at
VLLE are richer than theexperimental ones. In order to investigate
the cause of these deviations, various
0 0.2 0.4 0.6 0.8 1350
355
360
365
370
375
T [K
]
Jones et al.(1943) Kojima et al.(1969) Rieder et al.(1949) this
study Predicted by NRTL
x1, y1 [ - ]
-
types of the objective functions were tested. However, the
results showed the sametendency. From these results of predictions,
the sizes of two liquid phases regions atboiling points predicted
by the parameters determined with VLE data for these twobinary
systems tend to become larger than the experimental ones.
Fig. 2 Boiling and dew point for the system water (1) 1-butanol
(2) with those predicted byactivity coefficient models
0 0.2 0.4 0.6 0.8 1360
365
370
375
380
385
390
395
400
x1, y1 [ - ]
T [K
]
Solubility data Boublik et al. (1960) Ellis et al. (1960) Hessel
et al. (1965) Zong et al. (1983) this study predicted by NRTL
(ASPEN) predicted by NRTL (this study)
-
Fig. 3 Boiling and dew point for the system 2-butanol (1) water
(2) with those predicted byactivity coefficient models
PREDICTION OF ISOBARIC TERNARY VLLE WITH THE
PARAMETERSDETERMINED BY BINARY VLE DATA
Predictions of isobaric ternary VLLE for the systems ethanol (1)
water (2) 1-butanol (3) and ethanol (1) 2-butanol (2) water (3)
were done with the parametersdetermined by the isobaric constituent
binary VLE and VLLE data. Since isobaricVLE data for the system
ethanol 1-butanol and ethanol 2-butanol were notmeasured in this
study, the parameters for those systems were taken from
literature[8]. The result for the system ethanol water 1-butanol is
shown in Fig. 4. As canbe seen in the figure, the predicted two
liquid phase region at boiling point is largerthan the experimental
one. Experimental boiling points are compared with thepredicted
ones in Fig. 5. Although the predicted boiling points seems to be
in goodagreement in the figure, the liquid phase compositions are
very different from theexperimental ones. Thus, these parameters
cannot predict the isobaric VLLE data forthis system
accurately.
0 0.2 0.4 0.6 0.8 1355
360
365
370
375
380
x1, y1 [ - ]
T [K
] Solubility by Ochi et al. (1996) Altsybeeva et al.(1964) Boeke
et al.(1942) Yamamoto et al.(1959) this study Predicted by
NRTL(ASPEN) Predicted by NRTL(this study)
-
Fig. 4 Comparison between experimental and predicted
compositions at VLLE for the systemethanol (1) water (2) 1-butanol
(3)
0.4 0.5 0.6 0.7 0.8 0.9 10
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
x2, y2 [ - ]
x 1, y
1 [ -
]
Exp. Pred. Organic phase Aqueous phase
Vapor phase
Ethanol
Water 1-Butanol
0 10
10
1
-
Fig. 5 Comparison between experimental and predicted boiling
points at VLLE for the systemethanol (1) water (2) 1-butanol
(3)
The comparison for the system ethanol-2-butanol-water is shown
in Fig. 6. Similar toethanol-water-1-butanol system, the predicted
two liquid phases region at boilingpoints is different from the
experimental one, especially for the organic phase.Furthermore, the
predicted boiling points for the region are about 15 K higher
thanthe experimental ones, as shown in Fig. 7.
From the results of the predictions for these two ternary
systems, the parametersdetermined with the constituent binary VLE
data cannot be used for the prediction ofthe ternary VLLE data for
these systems. In particular, the predictions of thecompositions of
the organic liquid phase are significantly poor. It seems that
theparameters for LLE calculation is necessary for better
prediction.
90 91 92 93 94 9590
91
92
93
94
95
Texp [oC]
T cal [o
C]
+1.0oC
-1.0oC
Predicted from aqueous phasePredicted from organic phase
-
Fig. 6 Comparison between experimental and predicted
compositions at VLLE for the systemethanol (1) 2-butanol (2) water
(3)
0 0.1 0.2 0.3 0.4 0.50
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
x2, y2 [ - ]
x 1, y
1 [ -
]
Exp. Pred. Organic phase Aqueous phase
Vapor phase Tie line
0 10
10
1
Ethanol
2-Butanol Water
-
Fig. 7 Comparison between experimental and predicted boiling
points at VLLE for the systemethanol (1) 2-butanol (2) water
(3)
DETERMINATION OF THE PARAMETERS WITH THE ISOBARIC TERNARY
VLLEDATA
Determination of the Parameters for LLE CalculationBased on the
discussion above, the parameters for LLE calculation
areindependently determined from the LLE data of the isobaric
ternary VLLE data,namely the liquidcompositions. Though there are
six parameters to be determinedfor a ternary system, it was hard to
determine all of them at the same time. Recently,a regression
method of the parameters with ternary LLE data was proposed
byKatayama [21]. In the method, the parameters for the immiscible
binary pair arepreliminary determined from the binary LLE or
solubility data. By fixing theseparameters, the rest of the
parameters are determined from the ternary LLE data byusing the
modified Marquardt method proposed by Katayama [21]. In this study,
thisKatayama method was used to determine the parameters for LLE
calculation.
To determine the parameters from binary LLE data, the following
objective functionwas employed.
( )= =
=m
k
n
ikiki aaOF
1 1
2,2,,1, (2)
where ai,1,k and ai,2,k are the activities of the component i of
the data number k in thephase 1 and 2, respectively, and calculated
as follows.
85 86 87 88 89100
101
102
103
104
Texp [oC]
T cal
[oC
]
aqueous phase organic phase
-
caliii xa ,= (3)To determine the parameters from ternary LLE
data part of ternary VLLE data,equation (2) can be also used as the
objective function. However, the LLE datapredicted with the
parameters determined from the equation (2) were not accurate.So
the following objective function for the prediction of the ternary
LLE data wasemployed, which is the difference of the experimental
and predicted liquidcomposition in each phase.
( )= = =
=m
k j
n
icalkjikji xxOF
1
2
1 1
2,,,exp,,, (4)
where j is the phase 1 and 2, and m is the number of the
data.
First of all, the parameters for the immiscible binary pairs are
determined from thesolubility data. Since the solubility data are
not measured in this study, the data in theliterature [16, 20] are
used. The solubility data are smoothed by the polynomialfunction of
the temperature for each system and the LLE data for a temperature
canbe read from this smoothed curve. Then the rest of the
parameters were determinedby the modified Marquardt method proposed
by Katayama [21].
Parameter estimation and Prediction for the system
Ethanol-2-Butanol-WaterThe resultant parameters versus the
temperature for the system 2-butanol-water areshown in Fig. 8. The
parameters were correlated to the temperatures as a secondorder
polynomial. The coefficients for the polynomial function are shown
in Table 2.The rest of the parameters necessary for the ternary LLE
prediction were determinedby Katayamas method successfully. They
are shown in Table 3. The parameters forthe VLE calculations are
directly determined from the activity coefficients data forboth
systems. The parameters are shown in Table 4.
Table 2 Temperature dependent parameters for LLE calculation
System Pseudo Water(1)-1-Butanol(2) 2-Butanol(1)-Water(2) aij
bij cij aij bij cij
NRTLA12 1.978x107 -1.084x105 148.5 -1981 11.81 -2.224x10-2A21
-1.713x107 9.378x104 -128.3 -2367 17.64 -1.587x10-2
UNIQUACA12 1.212x107 -6.631x104 90.73 -174.7 1.655
-4.166x10-3A21 -9.343x107 5.111x104 -69.91 -1261 7.486
-8.115x10-3
Aij = aij + bij T + cijT2
-
Fig. 8 Temperature dependences of the model parameters for the
system 2-butanol (1) water (2)
250 300 350 400-1000
0
1000
2000
3000
T [K]
g12g21u12u21
gij, u
ij [K]
-
Table 3 Binary interaction parameters for LLE calculation
System Ethanol (1)-Water (2)-1-Butanol (3) Ethanol (1)-2-Butanol
(2)-Water (3)NRTL UNIQUAC NRTL UNIQUAC
A12 1193 485.1 -546.5 -186.3A13 74.99 1447 -42.96 -385.3A21
-1292 651.3 -1386 -1691A31 -1741 387.9 -3073 -1095
Aij=(gij-gjj)/R for NRTL, (uij-ujj)/R for UNIQUACTable 4 Binary
interaction parameters for VLE calculation
System Ethanol(1)-Water(2)-1-Butanol(3)
Ethanol(1)-2-Butanol(2)-Water(3) NRTL UNIQUAC NRTL UNIQUAC
A12 2433 -303.6 -469.4 -282.3A13 -178.8 6.880 -175.5 84.00A21
-19.77 631.1 1315 1001A22 1657 626.1 -295.4 75.78A31 749.3 378.0
851.6 94.44A32 -285.3 -200.0 1541 181.6
Aij=(gij-gjj)/R for NRTL, (uij-ujj)/R for UNIQUACThe prediction
method for the isobaric VLLE data is similar to the one proposed
byLiu et al. [22], that is, VLE and LLE data are separately
calculated with thecorrespondent parameters in Tables 2, 3 and 4.
The procedure is as follows. Firstly,an overall liquid composition
is given and the bubble point is set as the initialtemperature.
With this condition, isothermal LLE calculation is done and
thecompositions of the two liquid phases are calculated. For these
compositions, theboiling points and the vapor phase compositions
are independently determined bythe bubble point calculation. If
these boiling points are different with each other, theaverage
value is used for the next LLE calculation. The procedure is
repeated untilthe difference of the boiling points between the
former and the present iterationbecomes small enough for both
liquid phases. So the predicted vapor phasecompositions and boiling
points are presented for both organic and aqueous liquidphases in
this study. Since only the two liquid phases regions are studied in
thispaper, the phase stability analysis was omitted.
The predicted VLLE for the system ethanol-2-butanol-water by
this method areshown in Fig. 9. As can be seen in the figure, the
predicted LLE favorably coincideswith the experimental ones. The
predicted vapor compositions are also in goodagreement with the
experimental ones. Fig. 10 shows the comparison between
thepredicted and experimental boiling points. Almost all of the
predicted boiling pointsare within 1.0 K of the experimental
ones.
-
Fig. 9 Comparison between experimental and predicted isobaric
ternary VLLE for the systemethanol (1) 2-butanol (2) water (3) at
101.3 kPa
0 0.1 0.2 0.3 0.4 0.50
0.01
0.02
0.03
0.04
0.05
0.06
x2, y2 [ - ]
x 1, y
1 [ -
]Exp. Pred.
Organic phase Aqueous phase
Vapor phase Tie line
-
Fig. 10 Comparison between experimental and predicted
compositions at VLLE for thesystem ethanol (1) 2-butanol (2) water
(3)
Parameter Estimation and Prediction for the system
Ethanol-Water-1-ButanolFor the system water-1-butanol, if the
parameters determined by Katayamas methodare used for prediction of
ternary LLE at boiling points, the predicted LLE datadeviate from
the experimental ones as shown in Fig. 11. In the region where
thecomposition of ethanol in the liquid phase is more than 0.02
mole fraction, thesolubility of the organic liquid phase for this
ternary system changes a lot, while theboiling points for this
region doesnt change so much. The binary solubility data forthis
narrow temperature range changes slightly. Thus, the parameters
from binaryLLE data cannot predict this big solubility change.
In this study, thepseudo-binary LLE data are assumed by
normalizing thecompositions of water and 1-butanol in ternary LLE
data of VLLE data, and theparameters for this binary immiscible
pair are determined from the pseudo-binaryLLE data. The parameters
determined by this method are also shown in Table 2. Therest of the
parameters necessary for the ternary LLE prediction are estimated
byKatayamas method and shown in Table 3. The parameters for VLE
calculation aredetermined from the isobaric ternary VLLE data for
this system. They are shown inTable 4.
86 87 88 8986
87
88
89
Texp [oC]
T cal [o
C]
predicted for organic phasepredicted for aqueous phase
+1.0oC
-1.0oC
-
Fig. 11 Comparison between experimental and predicted isobaric
ternary LLE with theparameters determined by Katayamas method for
the system ethanol (1) water (2) 1-
butanol (3)
The predicted VLLE for the system ethanol-water-1-butanol are
shown in Fig. 12.The VLLE was calculated by the same way as for the
system ethanol-2-butanol-water. The predicted compositions are in
good agreement with the experimentalones. Fig. 13 shows the
comparison between the predicted and experimental boilingpoints.
Almost all of the predicted boiling points are within 1.0 K of the
experimentalones.
From the results above, the isobaric ternary VLLE data for these
two systems aresuccessfully predicted with the parameters obtained
in this study.
0.6 0.7 0.8 0.9 10
0.02
0.04
0.06
0.08
x2 [ - ]
x 1 [
- ]
Organic phase Aqueous phase Vapor phase NRTL UNIQUAC
-
Fig. 12 Comparison between experimental and predicted isobaric
ternary VLLE for thesystem ethanol (1) water (2) 1-butanol (3) at
101.3 kPa
0.6 0.7 0.8 0.9 10
0.02
0.04
0.06
0.08
0.1
0.12
x2, y2 [ - ]
x 1, y
1 [ -
]Exp. Pred.
Organic phase Aqueous phase
Vapor phase
-
Fig. 13 Comparison between experimental and predicted vapor
phase composition of 1-butanol at VLLE for the system ethanol (1)
water (2) 1-butanol (3)
CONCLUSION
The predictions of the isobaric ternary VLLE data for the
systems ethanol-water-1-butanol and ethanol-2-butanol-water with
activity coefficient models are studied. Theparameters determined
from binary VLE data for the constituent binaries cannot beused for
the prediction since the predicted LLE data at boiling points are
verydifferent from the experimental ones. Then the parameters for
VLE and LLEcalculation were independently determined from those
data. Katayamas method wasemployed to determine the parameters for
LLE calculation. For the system ethanol-2-butanol-water, the
parameters were obtained by the method. For the system
ethanol-water-1-butanol, however, the parameters for the binary
immiscible pair determinedfrom the binary LLE data were not suited
for the prediction of ternary LLE at boilingpoints. So the
pseudo-binary system was assumed by normalizing the compositionsof
binary immiscible pairs in the ternary LLE data. Using the
parameters, betterpredictions for the ternary LLE data at boiling
points were obtained. Finally, theisobaric ternary VLLE data are
predicted by the successive calculation of the LLEand VLE. The
predicted VLLE data were in good agreement with the
experimentalones.
90 91 92 93 94 9590
91
92
93
94
95
Texp [oC]
T cal
[oC
]
predicted for organic phasepredicted for aqueous phase
+1.0 oC
-1.0 oC
-
NOMENCLATURE
Aij = binary interaction parameter for component i and j [K]a =
activity [ - ]aij = coefficients for temperature dependent
parameter [K]bij = coefficients for temperature dependent parameter
[ - ]cij = coefficients for temperature dependent parameter [K-1]g
= binary interaction parameter of NRTL model [J mol-1]m = number of
the data [ - ]OF = objective function [ - ]q = van der Waals
surface area parameter [ - ]R = gas constant (=8.314 J mol-1 K-1)
[J mol-1 K-1]r = van der Waals volume parameter [ - ]T =
temperature [K]u = binary interaction parameter of UNIQUAC model [J
mol-1]x = mole fraction in the liquid phase [ - ]y = mole fraction
in the vapor phase [ - ]
Greeks = nonrandomness parameter of NRTL model [ - ] = activity
coefficient [ - ]
Subscriptcal = calculated valueexp = experimental valuei =
component ij = component jj = phase number, in eq. 4k = data
number
-
REFERENCES
1. V. Gomis, F. Ruiz, J. C. Asensi and M. D. Saquete (1997),
Fluid Phase Equilibria,129, 15-19.
2. K. Iwakabe and H. Kosuge (2001), Fluid Phase Equilibria, 192,
171-186.
3. L.-S. Lee, W.-C. Chen and J.-F. Huang (1996), J. Chem. Eng.
Japan, 29, 3, 427-438.
4. H. Renon and J.M. Prausnits (1968), AIChE Journal, 14, 1,
135-144.
5. D. S. Abrams and J. M. Prausnitz (1975), AIChE Journal, 21,
1, 116-128.
6. D. W. Marquardt (1933), J. Soc. Ind. Appl. Math, 11, 2,
431-441.
7. J. A. Nelder and R. Mead (1965), Computer J., 7, 308-313.
8. J. Gmehling and U. Onken (1977), Vapor-Liquid Equilibrium
Data Collection,DECHEMA Chemistry Data Series, Vol. I, DECHEMA,
Frankfurt.
9. C. A. Jones, E.M. Shoenborn and A.P. Colburn (1943), Ind.
Eng. Chem., 35, 666-672.
10. K. Kojima, K. Ochi and Y. Nakazawa (1969), Int. Chem. Eng.,
9, 342-347.
11. R. M. Rieder and A.R. Thompson (1949), Ind. Eng. Chem., 41,
2905-2908.
12. T. Boublik (1960), Collect. Czech. Chem. Commun., 25,
285.
13. S. R. M. Ellis and R.D. Garbett (1960), Ind. Eng. Chem., 52,
5, 385-388.
14. D. Hessel and G. Geiseler (1965), Zh. Phys. Chem. Leipzig.,
229, 199.
15. Z.-L. Zong, X.H. Yang, X.-Y. Zheng (1983), J. Chem. Eng.
Japan, 16, 1-6.
16. Nihon Kagakukai (Ed.), Chem. Soc. Jpn. (1975), Kagaku
Binran, Kisohen II,Maruzen, Tokyo, p. 835.
17. A. I. Altsybeeva, V.P. Belousov, N.V. Ovtrakht and A.G.
Morachevsky (1964), Zh.Fiz. Khim., 38, 1242-1247.
18. J. Boeke and K. H. Hanewald (1942), Rec. Trav. Chim.
PAYS-BAS, 61, 881-887.
19. Y. Yamamoto and T. Maruyama (1959), Kagaku Kogaku, 23,
635-640.
20. K. Ochi, T. Saito and K. Kojima (1996), J. Chem. Eng. Data,
41, 361-364.
-
21. H. Katayama (2001), Bulletin of Computational Science
Research Center, HoseiUniversity, 14.
22. F. Z. Liu, H. Mori, S. Hiraoka and I. Yamada (1993), J.
Chem. Eng. Japan, 26, 41-47
KEYWORDPhase equilibrium, Vapor-liquid-liquid equilibrium,
Prediction, Parameter estimation,Activity coefficient models
Navigation and PrintingTable of ContentIndexIndex of
AuthorsOrganizing Committee, International Scientific
CommitteeInternational Board of RefereesImpressumback to last
viewprint
PrefacePlenary LecturesPL1 WHAT CAUSED TOWER MALFUNCTIONS IN THE
LAST 50 YEARS?PL2 MODELLING SIEVE TRAY HYDRAULICS USING
COMPUTATIONAL FLUID DYNAMICSPL3 CHALLENGES IN THERMODYNAMICSPL4
EXPERIENCE IN REACTIVE DISTILLATION
Topic 1 Basic Data1-1 COMPUTER AIDED MOLECULAR DESIGN OF
SOLVENTS FOR DISTILLATION PROCESSES1-2 LARGE-SCALE DATA REGRESSION
FOR PROCESS CALCULATIONS1-3 IONIC LIQUIDS AND HYPERBRANCHED
POLYMERS PROMISING NEW CLASSES OF SELECTIVE ENTRAINERS FOR
EXTRACTIVE DISTILLATION1-4 PREDICTION OF DIFFUSIVITIES IN LIQUID
ASSOCIATING SYSTEMS ON THE BASIS OF A MULTICOMPONENT APPROACH1-5
KINETICS OF CARBON DIOXIDE ABSORPTION INTO N-METHYLDIETHANOLOAMINE
SOLUTIONS6-1 THERMODYNAMIC PROPERTIES OF DIMETHYL SULFOXIDE +
BENZENE OR + ISOPROPYLBENZENE MIXTURES6-2 DETERMINATION AND
PREDICTION OF THE ISOBARIC VAPOR-LIQUID-LIQUID EQUILIBRIUM DATA6-3
MASS TRANSFER COEFFICIENTS IN BATCH AND CONTINUOUS REGIME IN A
BUBBLE COLUMN6-4 A COMPARATIVE STUDY OF INTERFACIAL AREA OBTAINED
BY PHYSICAL AND CHEMICAL METHODS IN A BUBBLE COLUMN6-5
DETERMINATION OF BINARY VAPOR LIQUID EQUILIBRIA (VLE) OF REACTIVE
SYSTEMS
Topic 2.1 Equipment / Internals2.1-1 DISTILLATION COLUMNS WITH
STRUCTURED PACKINGS IN THE NEXT DECADE2.1-2 CHARACTERISATION OF
HIGH PERFORMANCE STRUCTURED PACKING2.1-3 MODIFICATIONS TO
STRUCTURED PACKINGS TO INCREASE THEIR CAPACITY2.1-4 CRYSTALLIZATION
FOULING IN PACKED COLUMNS2.1-5 FUNCTIONALITY OF A NOVEL
DOUBLE-EFFECTIVE PACKING ELEMENT2.1-6 RASCHIG SUPER-RING A NEW
FOURTH GENERATION PACKING OFFERS NEW ADVANTAGES2.1-7 PLATE DAMAGE
AS A RESULT OF DELAYED BOILING6-6 NEW HIGHSPEED MASS-TRANSFER
TRAYS6-7 DIFFUSIONAL AND HYDRAULIC CHARACTERISTICS OF KATAPAK-S6-8
THE MVG TRAY WITH TRUNCATED DOWNCOMERS: RECENT PROGRESS6-9 MASS
TRANSFER AND HYDRAULIC DETAILS ON INTALOX PhD PACKING
Topic 2.2 Equipment / Flow2.2-1 EFFECT OF BED LENGTH AND VAPOR
MALDISTRIBUTION ON STRUCTURED PACKING PERFORMANCE 2.2-2 THE EFFECT
OF MALDISTRIBUTION ON SEPARATION IN PACKED DISTILLATION
COLUMNS2.2-3 INFLUENCE OF VAPOR FEED DESIGN ON THE FLOW
DISTRIBUTION2.2-4 ENTRAINMENT AND MAXIMUM VAPOUR FLOW RATE OF
TRAYS2.2-5 EXPERIMENTAL CHARACTERISATION AND CFD SIMULATION OF GAS
DISTRIBUTION PERFORMANCE OF LIQUID (RE)DISTRIBUTORS AND COLLECTORS
IN PACKED COLUMNS2.2-6 PROGRESS IN UNDERSTANDING THE PHYSICAL
PROCESSES INSIDE SPINNING CONE COLUMNS 2.2-7 SYSTEM LIMIT: THE
ULTIMATE CAPACITY OF FRACTIONATORS6-10 COMPUTATIONAL FLUID DYNAMICS
FOR SIMULATION OF A GAS-LIQUID FLOW ON A SIEVE PLATE: MODEL
COMPARISONS6-11 NUMERICAL CALCULATION OF THE FLOW FIELD IN A BUBBLE
COLUMN CONSIDERING THE ABSORPTION OF THE GAS PHASE6-12 MASS
TRANSFER IN STRUCTURED PACKING6-13 EXPERIMENTAL STUDY OF RIVULET
LIQUID FLOW ON AN INCLINED PLATE6-14 EFFECT OF THE INITIAL GAS
MALDISTRIBUTION ON THE PRESSURE DROP OF STRUCTURED PACKINGS6-15 A
NEW PRESSURE DROP MODEL FOR STRUCTURED PACKING
Topic 3.1 Process Synthesis3.1-1 SYNTHESIS OF DISTILLATION
SEQUENCES FOR SEPARATING MULTICOMPONENT AZEOTROPIC MIXTURES3.1-2
DESIGN TECHNIQUES USED FOR THE DEVELOPMENT OF AN AZEOTROPIC
DISTILLATION PROCESS WHICH USES A BINARY ENTRAINER FOR SEPARATION
OF OLEFINS FROM ACIDS AND OTHER OXYGENATES3.1-3 DESIGN AND
SYNTHESIS OF DISTILLATION SYSTEMS USING A DRIVING FORCE BASED
APPROACH3.1-4 THE NEW APPROACH TO ISOPROPYLBENZENE DISTILLATION
FLOWSHEET SYNTHESES IN PHENOL-ACETONE PRODUCTION3.1-5 A NOVEL
FRAMEWORK FOR SIMULTANEOUS SEPARATION PROCESS AND PRODUCT
DESIGN3.1-6 CASE-BASED REASONING FOR SEPARATION PROCESS
SYNTHESIS6-16 THE FUNDAMENTAL EQUATION OF DISTILLATION6-17
HYDRODYNAMICS OF A GAS-LIQUID COLUMN EQUIPPED WITH MELLAPAKPLUS
PACKING6-18 DYNAMIC BEHAVIOR OF RECYCLE SYSTEM: REACTOR
DISTILLATION COLUMN6-19 DISTILLATION REGIONS FOR NON-IDEAL TERNARY
MIXTURES6-20 SELECTIVE AMINE TREATING USING TRAYS, STRUCTURED
PACKING, AND RANDOM PACKING
Topic 3.2 Process Simulation3.2-1 INFLUENCE OF UNEQUAL COMPONENT
EFFICIENCIES ON TRAJECTORIES DURING DISTILLATION OF A QUATERNARY
AZEOTROPIC MIXTURE3.2-2 SHORTCUT DESIGN OF EXTRACTIVE DISTILLATION
COLUMNS3.2-3 SIMULATION OF HETEROGENEOUS AZEOTROPIC DISTILLATION
PROCESS WITH A NON-EQUILIBRIUM STAGE MODEL 3.2-4 PLATE EFFICIENCIES
OF INDUSTRIAL SCALE DEHEXANISER3.2-5 DESIGN OF AN EXPERIMENTAL
PROCEDURE TO INVESTIGATE EFFICIENCY IN THE DISTILLATION OF AQUEOUS
SYSTEMS6-21 EFFICIENT APPROXIMATE METHOD FOR PACKED COLUMN
SEPARATION PERFORMANCE SIMULATION6-22 SIMULATION OF THE SIEVE PLATE
ABSORPTION COLUMN FOR NITRIC OXIDE ABSORPTION PROCESS USING NEURAL
NETWORKS6-23 DISTILLATION SIMULATION WITH COSMO-RS6-24 BATCH
DISTILLATION: SIMULATION AND EXPERIMENTAL VALIDATION
Topic 3.3 Heat Integration3.3-1 OPTIMISATION OF EXISTING
HEAT-INTEGRATED REFINERY DISTILLATION SYSTEMS 3.3-2 INTEGRATION OF
DESIGN AND CONTROL FOR ENERGY INTEGRATED DISTILLATION3.3-3
IMPLEMENTATION OF OPTIMAL OPERATION FOR HEAT INTEGRATED
DISTILLATION COLUMNS3.3-4 THEORETICAL AND EXPERIMENTAL STUDIES ON
STARTUP STRATEGIES FOR A HEAT-INTEGRATED DISTILLATION COLUMN
SYSTEM3.3-5 INTERNALLY HEAT-INTEGRATED DISTILLATION COLUMNS: A
REVIEW6-25 AN ENGINEERING ANALYSIS OF CAPACITY IMPROVEMENT IN FLUE
GAS DESULFURIZATION PLANT6-26 ANALYSIS OF SEPARATION OF
WATER-METHANOL-FORMALDEHYDE MIXTURE6-27 MINIMUM ENERGY AND ENTROPY
REQUIREMENTS IN MULTICOMPONENT DISTILLATION
Topic 3.4 Control / Dynamics3.4-1 MODEL PREDICTIVE CONTROL OF
INTEGRATED UNIT OPERATIONS CONTROL OF A DIVIDED WALL COLUMN3.4-2
SIMULATION AND EXPERIMENTAL ANALYSIS OF OPERATIONAL FAILURES IN A
METHANOL - WATER DISTILLATION COLUMN3.4-3 MODEL-BASED DESIGN,
CONTROL AND OPTIMISATION OF CATALYTIC DISTILLATION PROCESSES6-28
OPTIMISATION, DYNAMICS AND CONTROL OF A COMPLETE AZEOTROPIC
DISTILLATION: NEW STRATEGIES AND STABILITY CONSIDERATIONS
Topic 4 Integrated Processes4-1 DEVELOPMENT AND ECONOMIC
EVALUATION OF A REACTIVE DISTILLATION PROCESS FOR SILANE
PRODUCTION4-2 SEPARATION OF OLEFIN ISOMERS WITH REACTIVE EXTRACTIVE
DISTILLATION4-3 TRANSESTERIFICATION PROCESSES BY COMBINATION OF
REACTIVE DISTILLATION AND PERVAPORATION4-4 INVESTIGATION OF
DIFFERENT COLUMN CONFIGURATIONS FOR THE ETHYL ACETATE SYNTHESIS VIA
REACTIVE DISTILLATION4-5 SYNTHESIS OF N-HEXYL ACETATE BY REACTIVE
DISTILLATION4-6 THERMODYNAMIC ANALYSIS OF THE DEEP
HYDRODESULFURIZATION OF DIESEL THROUGH REACTIVE DISTILLATION4-7
DISTILLATION COLUMN WITH REACTIVE PUMP AROUNDS: AN ALTERNATIVE TO
REACTIVE DISTILLATION4-8 HYBRID PERVAPORATION-ABSORPTION FOR THE
DEHYDRATION OF ORGANICS4-9 NOVEL HYBRID PROCESSES FOR SOLVENT
RECOVERY6-29 SCALE-UP OF REACTIVE DISTILLATION COLUMNS WITH
CATALYTIC PACKINGS6-30 CONCEPTUAL DESIGN OF REACTIVE DISTILLATION
COLUMNS USING STAGE COMPOSITION LINES
Topic 5 Novel Processes5-1 DEVELOPMENT OF A MULTISTAGED FOAM
FRACTIONATION COLUMN5-2 OPERATION OF A BATCH DISTILLATION COLUMN
WITH A MIDDLE VESSEL: EXPERIMENTAL RESULTS FOR THE SEPARATION OF
ZEOTROPIC AND AZEOTROPIC MIXTURES5-3 SIMULTANEOUS OPTIMAL DESIGN
AND OPERATION OF MULTIPURPOSE BATCH DISTILLATION COLUMNS5-4
SEPARATION OF TERNARY HETEROAZEOTROPIC MIXTURES IN A CLOSED
MULTIVESSEL BATCH DISTILLATION-DECANTER HYBRID5-5
ENTRAINER-ENHANCED REACTIVE DISTILLATION 5-6 NOVEL DISTILLATION
CONCEPTS USING ONE-SHELL COLUMNS5-7 INDUSTRIAL APPLICATIONS OF
SPINNING CONE COLUMN TECHNOLOGY: A REVIEW6-31 FEASIBILITY OF BATCH
EXTRACTIVE DISTILLATION WITH MIDDLE-BOILING ENTRAINER IN
RECTIFIER