Modeling Batch Distillation
W. M. Clark, WPI, March 2008
This report documents my efforts to model a batch distillation process in an attempt to
support an educational collaboration between Professor Jim Henry at the University of
Tennessee at Chattanooga, Professor Marina Miletic at the University of Illinois UrbanaChampaign, and Professor David DiBiasio at Worcester Polytechnic Institute. The main
goal of this collaboration is to investigate the advantages and disadvantages of remote
operation of a distillation column for a chemical engineering laboratory component via
distance learning. The learning experiences of students doing hands-on batch distillation
experiments at WPI and Urbana-Champaign are being compared to those of other
students at these same locations who are conducting batch distillation experiments at
Chattanooga by remote control over the web. Adding the capability of modeling a batch
distillation process could potentially be useful for either the remote learners or the handson learners or both. In addition, it might be interesting to compare students who study
only a simulated batch distillation process to those who do hands-on or remote
experiments.
The initial thought was to try to develop a model of batch distillation similar in concept to
models we have recently developed for a membrane process and a heat exchanger using
Comsol Multiphysics finite element software. The goal was to have a model that
provides a visual representation of the temperature and concentration profiles that result
from solving the differential equations representing the process going on within the
distillation unit. I still believe that such a model would be better for promoting
conceptual understanding than a “black box” model where students provide input and get
the expected results based on behind-the-scene calculations that are not revealed to the
students. Modeling the complex and dynamic behavior of a multistage batch distillation
column proved to be overwhelming, however, and this attempt was abandoned in favor of
evaluating existing models such as ChemSep, MultiBatchDS, and Aspen BatchSep.
ChemSep was created by Professors Hendrik Kooijman and Ross Taylor at Clarkson
University as a teaching tool for courses in thermodynamics and separation processes. It
can perform calculations and plot result profiles for a variety of separation processes
including multistage, multicomponent distillation. Among the key features are a wide
range of built-in thermodynamic models for calculating physical properties and plotting
phase diagrams and the automatic generation of McCabe Thiele diagrams complete with
stepped stages. ChemSep can be purchased from the Computer Aided Chemical
Engineering (CACHE) Corporation for a $100 initial license and a $60 fee for annual
license renewal after the first year. There is also a free ChemSep-Lite version.
ChemSep does not have a built-in batch distillation simulator at present, but according to
the ChemSep book, “batch distillation is another area which would be possible to
investigate if we were able to specify an initial amount of each component present in the
reboiler. Startup would begin with a total reflux simulation … [then a dynamic simulation
could be run where] … the perturbation consists then of starting to draw a distillate
product.” ChemSep was purchased from CACHE and this process was attempted
without success.
The ChemSep book indicates that there are four modes of operation available: flash,
equilibrium column, non-equilibrium column, and dynamic column. The ChemSep-Lite
version that I downloaded for free only had flash and equilibrium column available.
Version 6.06 of ChemSep that I purchased along with the ChemSep Book from CACHE
in fall 2007 included non-equilibrium column and dynamic column but dynamic column
was grayed out and apparently still under development or unavailable through CACHE.
Noting that the ChemSep book I was sold is not consistent with Version 6.06 of the
software, that a new edition of the ChemSep book is under development with proofs
available on the web, and that batch distillation is still not addressed in the new edition,
attempts to model batch distillation with ChemSep were abandoned.
ChemSep did prove useful and easy to use for modeling steady state distillation processes
including total reflux operation. Figure 1 shows a McCabe Thiele diagram that was
generated with ChemSep for ethanol-water distillation at 1 atm operating pressure with a
60% stage efficiency, 12 stage (including a condenser and a reboiler) column operating at
total reflux. In addition to the number of stages and stage efficiency, required input
included the boilup rate (1e-4 kmol/s), the amount of subcooling in the condenser (40 K),
and the composition of the liquid in the condenser (0.78 mole fraction ethanol). Figures
2 and 3 show the corresponding temperature and composition profiles generated by
ChemSep for this total reflux condition.
Figure 1. McCabe Thiele diagram drawn by ChemSep for ethanol-water at total reflux
with operating conditions as specified in the text.
Figure 2. Column temperature profile for ethanol-water at total reflux for operating
conditions given in the text.
Figure 3. Column compostion profiles for ethanol-water at total reflux for operating
conditions given in the text.
Other results are also readily available in tabular or graphical form. With this software,
students can readily see the effect that changes in the operating conditions have on the
column at total reflux. One significant limitation is that the composition of the distillate
(or bottoms) at total reflux must be specified instead of that of the initial charge. This
software appears to be quite useful for illustrating steady state distillation operation with
known feed and product flow rates.
MultiBatchDS is a commercial software package developed by Urmila Diwekar at
Carnegie Mellon University for simulating batch distillation processes. An educational
version of the software is available from CACHE Corp. for a $90 initial license and a $50
fee for annual license renewal after the first year. Several factors including (1) my
experience with the limitations of the educational version of ChemSep purchased from
CACHE, (2) the statement that the software comes on “two diskettes for the PC” on the
current CACHE order form, (3) a quick web search indicating that the software is
apparently not widely used, and (4) acquisition of ASPEN BatchSep software, caused me
to postpone, perhaps forever, any study of the MultiBatchDS software.
Aspen BatchSepTM is a core element of AspenTech’s aspenONETM Process Engineering
applications specifically for simulation of batch distillation. We have been using Aspen
Plus for teaching design at WPI for a number of years but until now, we were unaware
that this additional program was available to us. Aspen BatchSep runs separately and
needs to be installed separately from Aspen Plus but it appears that our site license for
Aspen Plus also allows us to install and use Aspen BatchSep. The BatchSep program is
similar to Aspen Plus but different enough and complex enough that it presents a
significant learning curve even for someone familiar with Aspen Plus. The expense of
Aspen software might be a deterrent for students at locations that don’t currently have a
license. We can probably make results from a limited set of simulations done under our
license available to students over the web, but we cannot allow students at other locations
to use the software under our license.
An undergraduate student working with Professor DiBiasio has obtained data on our
distillation column working in batch mode to recover an ethanol-rich overhead product
from an initial charge of 1 weight percent (0.0039 mole fraction) ethanol in water. This
low concentration was selected because it allowed a batch run to be completed within a
three hour time limit. The column has 12 stages including the condenser and reboiler and
was operated at 1 atm pressure at total reflux before distillate was removed at a reflux
ratio of 7.5. Experimental results led to the conclusion that the murphree stage efficiency
was approximately 60 % for this experiment. Figure 4 shows the distillate concentration
versus time as measured by the student. Figure 5 shows the corresponding temperatures
at the top and bottom stages versus time.
Ethanol in Distillate
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0
0.5
1
1.5
Time (hr)
Figure 4. Experimental results for mole fraction ethanol in distillate as a function of
time.
105
Temperature
100
95
90
85
80
75
70
0
0.5
1
1.5
Time (hr)
Figure 5. Experimental results for temperature at the top, stage 2, (blue diamonds) and
bottom, stage 11, (red squares) of the column as a function of time.
Note that the initial conditions at total reflux are similar to those modeled above using
ChemSep. The Chemsep values were 0.78 mole fraction ethanol and T = 78 oC at the
top of the column.
Although it required some guesswork and trial and error to estimate some of the required
input, I was able to simulate, at least qualitatively, the batch process described above
using Aspen BatchSep. This comprehensive software can incorporate many details like
holdup volumes, efficiencies, and heat losses on trays, but if these are not known, they
must be estimated appropriately. Figure 6 shows the simulation results for distillate
composition as a function of time beginning at total reflux. Note that the composition of
the initial charge of 0.0039 mole fraction ethanol was specified for the simulation and the
total reflux results were calculated. Figure 7 presents the simulation results for
temperature as a function of time at 4 locations (stages 2,5,8, and 11) including the ones
at the top and the bottom of the column. A wide range of other results in either tabular or
graphical form can be readily obtained from the simulation output.
Figure 6. Aspen BatchSep simulation results for distillate composition as a function of
time.
Figure 7. Aspen BatchSep simulation results for temperature at 4 locations as a function
of time.
Comparing Figures 4 and 5 to Figures 6 and 7 indicates that the current simulation results
are qualitatively similar to the experimental results. Discrepancies between the two can
be attributed to a number of factors including experimental error and uncertainties in
estimated parameters for the model. Part of the problem is likely to be the fact that the
low amount of ethanol in the system makes the composition of ethanol at the lower stages
very small and makes the simulation (and the experiment) sensitive to small changes in
process parameters. Simulation of a column with more ethanol would likely be easier.
After several failed attempts to adjust the model so that it would agree with the
experiment, it was concluded that an exact match between model and experiment may not
be necessary. Conceptual understanding of the process can probably be obtained from a
model that is not in complete agreement with the experiment. In fact, it might be better if
simulation results are provided to the students at conditions that are different from the
experimental conditions. Students could study the simulation results for conceptual
understanding and expected trends but not be tempted to “dry lab” the experiment
because they know the expected results.
What shall we do next? Is Aspen Plus and/or Aspen BatchSep available at the other
schools? Is there any merit to having students run simulations or look at simulated results
either before, during, or after they run the lab? Are there other results that we should
focus on besides temperature and distillate composition? Do we need to have a
simulation that gives good agreement with experiment to make it credible to the students?
Do we want to include some modeling results in the upcoming talk? Do we want to try
for some add-on funding for our current grant aimed at simulating laboratory
experiments? Do we want to write a separate proposal that includes the distance versus
hands-on experimentation and the modeling?