1
PILOT PLANT DISTILLATION COLUMN: EXPERIMENTATION
AND SIMULATION
A. Aucejo*, N. Martínez, M. C. Burguet, M. Sanchotello, J.B.Montón
Departamento de Ingeniería Química, Escuela Técnica Superior de Ingeniería,
Universitat de València, 46100 Burjassot, Valencia, Spain.
Tel. 34 963544319; Fax: 34 963544898; e-mail: antonio.aucejo@uv.es
Abstract
A set of multicomponent distillation experimental data with a non-ideal system: water + 1propanol + 2-propanol in a pilot-scale column (tray bubble-cap column) is presented to fill
the gap of such data in literature. In this paper, working at total reflux, it has been studied
the topology of the ternary mixture and the overall geometric efficiency has been estimated.
Experimental composition profiles along the column height in continuous mode are
reported. The simulation results obtained using the equilibrium stage model corrected with
the estimated overall geometric efficiency have been compared with the experimental data.
Very good agreement was obtained.
Keywords: Pilot plant columns, efficiency, residue curve maps, simulation, n-propanol, isopropanol, water.
2
1. Introduction.
The distillation is still the separation process most used in the chemical industry, in spite
of its great power consumption. The recent studies tend, indeed, to reduce everything
possible this consumption and to improve the knowledge of the operation, optimizing their
structure and work conditions. In these studies the simulation programs appear as a powerful
tool due to the facility and speed whereupon allows to introduce changes in the operating
conditions and to analyze the simulated results. However, the use of simulators without a
critic analysis of the basic data (thermodynamic as well as operational) and the results, can
lead to decisions far away of the wished objective.
The currently available tools to simulate distillation columns are generally based on the
concept of equilibrium stages. The equilibrium stage model assumes that the liquid and
vapor phases that leave each stage are in thermodynamic equilibrium. This model can lead
to some erroneous conclusions. The non-equilibrium stage models seems to be a more
realistic approach, however they are computationally intensive and also requires much more
input information.
Alternatively, one usual way to correct the weakness in the equilibrium stage model is
the application of efficiency values. The overall efficiency for distillation columns was
defined by Lewis [1] as a relationship between the theoretical and real plates required to
perform a specified separation. Murphree [2] defined the plate efficiency relating the
behavior of a real plate with that of and ideal plate, through the contact degree between the
vapor and liquid phases. After the Murphree efficiency definitions, several methods for
efficiency estimation have been developed; however, there are still some difficulties to use
the available efficiency correlations.
Moreover, the difficulty of the simulation results analysis becomes bigger by the little
existing information of experimental data obtained in industrial or pilot plant columns. In
front of the plentiful bibliography of thermodynamic data (equilibrium, heat of mixture,
activity coefficients, etc) it is remarkable the small amount of papers you can find where a
real column was used. A well designed and executed pilot-plant program is a powerful tool
3
that can help process engineers to optimize their designs and can help to guide the plant
operation engineers toward a successful startup.
The aim of this work is to do a comparative study between the experimental results
obtained in a column of 30 real trays with those predicted with a commercial simulation
program, using an average geometric efficiency.
For this study a ternary system, whose thermodynamic equilibrium was studied by our
group in previous works [3, 4], has been selected. The system is formed by water (1), 1propanol (2), and 2-propanol (3). It does not present ternary azeotrope and has two binary
minimum boiling azeotropes: one in the water + 1-propanol binary system, with a boiling
point of 87,6 C and the second in the water + 2-propanol binary system, with a boiling point
of 78,9 C. We have carried out simulations with Aspen HYSYS® v3.2 of Aspen Technology
Inc., using the binary interaction parameters correlated from experimental data obtained for
all binary systems involved. In the cited papers, it was established that UNIQUAC model
estimates with sufficient precision the equilibrium data and the best values of the model
interaction parameters were calculated.
4
2. Experimental Section.
2.1. Chemicals
The chemical system selected for the experiments performed in this research was a
ternary mixture of water, 1-propanol and 2-propanol. 1-Propanol (>99,7 % molar) and 2propanol (>99,7 % molar) were purchased from Panreac. The used water was previously
degassed distilled water. These products were not subjected to further purification, because a
high quality is not necessary for this work, which tries to recreate the conditions of an
industrial plant
2.2. Experimental procedure
Experiments were carried out, at atmospheric pressure, in a pilot distillation column. A
flowsheet of the pilot column is given in Figure 1. The column (252 PC-50 Labodest model,
manufactured by Fisher) is made of Pyrex glass 2 mm. thick, which has a nominal diameter
of 5 cm and consist of 30 bubble cap trays (divided into three sections). The column was
properly insulated by a silvered vacuum jacket to minimize the heat losses. The boiler has a
capacity of 1.5 L. It is heated by means of an electrical resistance and has a heat flow control
system.
The column is fitted with extensive measurement equipment such that all the required
operating conditions can be registered. The temperature of the trays 3, 8, 13, 18 and 26 as
well as the relevant streams (feed, distillate, and residue) is measured using PT-100
resistance elements.
The system has measurement and control units and it is possible to set the values of the
most important operation parameters, as well as an alarm system and automatic stop if some
dangerous operation happen (interruption of the refrigeration water, for example). The
distillate flow is regulated by means of an automatic reflux dividing valve placed in the
column top and the bottoms flow is adjusted by means of a liquid-level control system in the
boiler. The distillate and the bottom products can be mixed and sent (feedback) again to the
feed system by means of a diaphragm pump, although in this case it has been preferred to
take away the distillate and bottom products and to feed the column with a previously
prepared mixture. In this way, the stationary conditions of the column are more stable.
5
The experiments, performed under total reflux, started filling the boiler with mixtures of
water, n-propanol and iso-propanol at different compositions. The column was allowed to
reach steady-state, indicated by a constant-temperature profile along the column. Once the
column reaches the steady-state and after two hours operating in this way, small liquid
samples (0.2 cm3) of trays, bottom and distillate products were withdrawn. The composition
of these samples is measured off-line with a gas chromatograph (GC).
To start up the experiments in the continuous process, the boiler was filled with the
mixture with similar composition than the feed stream. The electric immersion heater was
switched on and the column was operated at total reflux. Once the plates were filled with
liquid and steady-state reached the reflux regulator (an automatic reflux dividing valve), and
the boiler liquid-level controller were connected. At the same time, it was begun to
introduce the feed stream and to extract distillate and bottom products. The feed is
introduced in the selected stage by gravity fall from a reservoir. The feed stream is heated
until a temperature close to its boiling point. The feed flow is regulated by means of a flowcontrol valve and it is measured with a rotameter.
In these conditions it was waited again (three hours or more) until steady-state (constanttemperature profile along the column). Samples of the trays, bottom and distillate products
were withdrawn and analyzed by GC.
2.3. Column hydrodynamics.
One advantage of working with a pilot plant made of glass is that we have the chance of
seeing the hydrodynamic behaviour of the column for different operation conditions.
To establish the range of the most appropriate vapour flow rate to carry out this work,
several experiments were performed with the mixture under study. The vapour flow rate was
increased until the flooding, which is noticed by the increase of total tray pressure drop as
well as by the increase of the liquid level in the tray that can extend onto the tray above and
will progress to the point of filling the column. The results obtained in this hydrodynamic
study give a flooding flow rate of 0.041 kmol/h. On the other hand, the minimum vapour
flow rate was determined visually when no vapour flows in the top column. According to
the literature [5], the vapour flow rate is usually set at 60 to 80% of flooding. When the
vapour flow rate was approximately 60% of flooding, the behaviour of trays was correct, but
6
the time required to reach the steady-state was very high (more than 10 hours). Whereas if
the vapour flow rate was set at 80% of flooding, the steady-state was reached in not more
than 2 hours.
3. Results and Discussion.
3.1. Experiments at total reflux.
3.1.1. Feasibility and product distribution.
Operation at total reflux has no industrial importance, but by means of this kind of
operation, the feasible separations of ternary mixtures can be determined very easily and the
efficiency of the trays can be evaluated.
The knowledge of the form and topology of the residual curves (RC) allows making a
study in depth of the rectification as has been shown in numerous papers [6, 7]. This
experimental study can be made in a very precise way with distillation columns working at
total reflux. Some commercial simulation programs have implemented an option to
represent the residual curves on a diagram (for ternary or quaternary systems) or to calculate
a numerical table of their evolution. This is the case, for example, of DISTIL version 5.0,
under licence of Aspentech.
The usefulness of the RC maps lies in the fact that the composition profiles of continuous
distillation columns approximate the composition trajectories of the RC. The topology of the
system under study can be easily deduced from the number and nature of the pure
components and azeotropes (singular points). In Table 1 these data are shown. To start from
these data it is easily infered that this ternary system presents a distillation boundary and two
associated regions, as it is shown in Figure 2.
3.1.2. Comparison of simulated profiles with experimental data.
Owing to the importance that the exact knowledge of the distillation curves shape has, it
is very suitable to have information about the similarities between the distillation curves
estimated by the simulator and the real ones obtained in the column.
7
To evaluate the simulated results, the obtained profiles were compared with the
experimental data. The simulation was undertaken with DISTIL using UNIQUAC model. In
Figure 3 the simulated and experimental data are shown. The discontinuous lines represent
the estimation obtained using the parameters of the simulator data base, while the
continuous lines are the residual curves with the parameters calculated from the
experimental equilibrium data [3, 4]. The experimental points corresponding to the
composition of different trays along the column, working at total reflux and in steady-state,
have been also drawn.
It can be observed that the experimental points agree very well with the residual curves
predicted by the simulator. The existence of the two distillation regions, as has been
indicated by the system topology, appears clearly. The distillation curves are pointed at
different vertex depending on the feed composition.
3.1.2. Average geometric efficiency.
It is common to assume that the vapour and liquid phases are in equilibrium with each
other in the computer-based simulation and design of distillation columns. Real distillation
processes, however, almost operate away from equilibrium. In fact, the degree of separation
actually attained depends on the rates of mass transfer between vapour and liquid phases.
One of the purposes of this paper is to explore the relationships between the equilibrium
composition profile (calculated with ideal stage model) and the composition trajectories
(experimental profiles obtained working at total reflux) in tray columns to determine the
average efficiency of the column.
To estimate the global efficiency of the column, two experiments, at total reflux, were
carried out for different compositions loads in the boiler working at the optimum vapour
flow rate ( 80% flooding). For this purpose, the plate efficiency, defined as the change in
real compositions in the liquid phase divided by the change predicted by the equilibrium [7],
as can be seen in Figure 4, has been determined, according to the equation:
8
3
 
AB

AC
 x
i 1
3
 x
i 1
 x n  real
2
n 1
n 1*
x

where: AB
=
vector module representing the real plate separation
AC
=
vector module representing the equilibrium separation
xn+1
=
real liquid composition of plate n+1
xn
=
real liquid composition of plate n
x n 1* ≡
(1)
2
n ideal
ideal liquid composition of plate n+1
Finally, the global column efficiency was calculated as an average value of the individual
tray efficiencies previously estimated. In all experiments the obtained value was around 60
%. Figure 5 shows the experimental and calculated with equilibrium composition profiles
for the two experiments.
3.2. Continuous process.
3.2.1. Accomplishment of the experiments.
Five different experiments have been carried out changing the column operation
conditions. The time needed for the column to reach the steady-state and for the all
measured variables to stabilize lasted for at least two hours.
The feed and distillate flow and the compositions of these streams where determined
experimentally, whereas the bottom flow and composition where calculated by performing a
mass balance on each of the components. Specified and measured variables for the different
experiments are summarized in Table 2. All the experiments where carried out with a relux
ratio of 10.
In Figure 6 the obtained complete profiles, once the column reached the steady-state,
have been represented for the five experiments of Table 2. In this figure it can be seen
clearly that compositions of feed, distillate and bottom fulfill the mass balance (within
9
experimental error < 5 %). Also it is clear that the tray composition profiles always stay
within one of the distillation region. Depending on the feed composition, the bottom product
was pure water (experiment 5) or to pure 1-propanol (experiments 1, 2, 3 and 4).
3.2.2. Comparison of simulated profiles with experimental data.
In order to compare the simulated and experimental results, it has been represented in
figures 7 and 8 the composition profile of two experiments (as an example).
The simulation of the distillation process was carried out with a commercial process
simulator (Aspen HYSYS® v3.2 of Aspen Technology Inc.), which can simulate the
operation of distillation columns, among other process units. Rigorous distillation
calculations were carried out assuming ideal equilibrium stages model. To evaluate the
simulated results, and to compare with the experimental data, it was necessary to use the
average tray efficiency in the rigorous solution of the distillation column obtained with
HYSYS simulator.
The operation variables, which must be specified to carry out the simulation, are the
number of equilibrium stages, temperature, pressure, flow, compositions and inlet plate of
the feed, and column pressure drop. In order to make the HYSYS simulation the freedom
degrees of the column have been consumed by the operational reflux ratio and one
composition of the bottoms. The average efficiency of the column has been set on 60 %
(estimated previously). The simulation output provides information on the temperature, flow
rate, and composition of overhead and bottom streams. As can be observed in figures 7 and
8 the shape of estimated and experimental profiles is similar, but the simulation results
would not be extrapolated to industrial scale.
4. Conclusions.
The study of the process in pilot equipment will be always an interesting option (and
sometimes essential) that will allow to know the hydrodynamic behavior of the column to
set the optimum mass transfer conditions, to estimate the overall column efficiency and to
understand more accurately the results of the simulation. Simulation predict qualitatively the
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influence of the operation variables on the column performance but are not accurate enough
for design purposes.
Acknowledgements
Financial support from the Ministerio de Ciencia y Tecnología of Spain, through Project
no. CTQ2004-04477/PPQ
References
[1]
W. K. Lewis. J. Ind Eng Chem. 1922, 4, 492-
[2]
E. V. Murphree. Ind Eng Chem, 1925, 17, 747-
[3]
C. Gabaldon; P. Marzal; J. B. Montón; M. A. Rodrigo. J. Chem. Eng. Data, 1996, 41,
1176-1180
[4]
C. Gabaldon; P. Marzal; J. B. Montón; M. A. Rodrigo. J. Chem. Eng. Data, 1996, 41,
1379-1382
[5]
C. R. Branan. Rules of thumb for chemical engineers. Gulf Professional Pub. 2002
[6]
J.G. Stichlmair, J.R. Fair. Distillation. Principles and Practice, Wiley-VCH, New
York, 1998
[7]
M.F. Doherty, M.F. Malone. Conceptual Desing of Distillation Systems, McGraw
Hill, New York, 2001
11
Figure captions
Figure 1. Diagram of the pilot plant column.
Figure 2. Distillation boundary and associated regions for the water (1) + 1-propanol (2) +
2-propanol (3) system.
Figure 3. Residue curve map for the water (1) + 1-propanol (2) + 2-propanol (3) system:
○, ●, , □, ∆, ▼, ▲, experimental results for different composition load in the
boiler: (
data base; (
)
estimated results obtained using the parameters of the simulator
) estimated results obtained using the parameters calculated from
the experimental equilibrium data [3, 4]
Figure 4. Illustration of the difference between the change in real composition ( AB ) and
that one predicted by the equilibrium ( AC ).
Figure 5. Experimental and calculated (with equilibrium) composition profiles for two
experiments.
Figure 6. Experimental composition profiles in the continuous process: (●) Exp. 1; (○)
Exp. 2; (▲) Exp. 3; (∆) Exp. 4; (□) Exp. 5; The big points represent the feed
composition of each experiment
Figure 7. Composition profile for experiment number 2. Experimental points: (■) water;
(▲) 1-propanol; (●) 2-propanol. Simulated results: (
)
Figure 8. Composition profile for experiment number 4. Experimental points: (■) water;
(▲) 1-propanol; (●) 2-propanol. Simulated results: (
)
12
LEGEND
4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
3
5
1
1
6
7
1
2
1
8
1
2
9
10
2
11
1
12
1
13
15
14
16
17
18
20
19
PT 100 Column
Bubble cap column
Condenser
Feed vessel
Reflux valve
Intermediate receiver
Distillate cooler
Manometer
Immersion heater
Distillation control device
Temperature control device
Safety cooler
Distillate receiver
Mixing chamber
Reboiler
Cooling water safety device
Residue product cooler
Residue receiver
Distillate pump
Residue pump
13
1-Propanol
(96.60 ºC)
0.0
1.0
0.2
0.8
0.4
0.6
0.6
Region I
Region I
(87.62 ºC)
0.4
0.8
0.2
(78.91 ºC)
1.0
2-Propanol
(81.70 ºC)
0.0
Region II
Region II
0.0
0.2
0.4
0.6
0.8
1.0
Water
(100.00 ºC)
14
1-Propanol
0.0
1.0
0.2
0.8
0.4
0.6
0.6
0.4
0.8
0.2
1.0
2-Propanol
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Water
15
1-Propanol
0.0
1.0
0.2
0.8
0.4
0.6
C
n+1*
B
0.6
A
0.4
n+1
n
0.8
0.2
1.0
2-Propanol
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Water
16
1-Propanol
0.0
1.0
1
2
2*
0.2
0.8
3
3*
1
0.4
4*
0.6
2
0.6
2*
3
0.4
3*
4*
0.8
0.2
1.0
2-Propanol
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Water
17
1- Propanol
0.0
1.0
0.2
0.8
0.4
0.6
0.6
0.4
0.8
0.2
1.0
2- Propanol 0.0
0.0
0.2
0.4
0.6
0.8
1.0
Water
18
1.0
0.8
0.6
xi
0.4
0.2
0.0
0
5
10
15
20
25
30
25
30
35
Tray number
1.0
0.8
0.6
xi
0.4
0.2
0.0
0
5
10
15
20
Tray number
35
19
Table 1.
Boiling point temperature and nature of the pure components and azeotropes
T (ºC)
Point Type
Water (W)
100
Node Stable
1-Propanol
96.6
Node Stable
2-Propanol
81.7
Saddle
Water + 1-Propanol
87.62
Saddle
Water + 2-Propanol
78.91
Node Unstable
Pure components:
Azeotropes:
20
Table 2.
Experimental results for the continuous process with water (1) + 1-propanol (2) + 2-propanol (3) ternary system.
Experiment number
1
2
3
4
5
Feed
Inlet stage
10
10
10
14
14
Temperature (ºC)
85
Molar flow (kmol/h)
85
-2
85
-2
2.267·10
85
-2
2.140·10
85
-2
4.770·10-2
2.838·10
2.615·10
Water (1)
0.5088
0.5327
0.5887
0.5854
0.1016
1-propanol (2)
0.1380
0.1399
0.2064
0.2964
0.1260
2-propanol (3)
0.3532
0.3274
0.2049
0.1182
0.7724
Molar flow (kmol/h)
5.7·10-3
5.1·10-3
6.5·10-3
8.03·10-3
6.07·10-3
Water (1)
0.1935
0.1580
0.0555
0.0132
0.0030
1-propanol (2)
0.3469
0.3907
0.5834
0.7005
0.7014
2-propanol (3)
0.4596
0.4513
0.3611
0.2863
0.2956
2.268·10-2
2.105·10-2
1.617·10-2
1.337·10-2
4.163·10-2
Water (1)
0.7102
0.7315
0.8691
0.9291
0.1559
1-propanol (2)
0.0073
0.0083
0.0195
0.0537
0.0346
2-propanol (3)
0.2825
0.2602
0.1114
0.0172
0.8095
Mole Fraction
Distillate
Mole Fraction
Bottoms*
Molar flow (kmol/h)
Mole Fraction
* Values obtained by mass balance