High purity distillation column: simulation and optimization

Isuru A. Udugama, Rob Kirkpatrick, Wei Yu* and Brent R. Young
Chemical & Materials Engineering, The Faculty of Engineering,
The University of Auckland, New Zealand
*Email: [email protected]
Abstract— Distillation columns with a high-purity product
(down to 7 ppm) have been studied. A steady state model is
developed using a commercial process simulator. The model is
validated against industrial data. Based on the model, three
major optimal operational changes are identified. These are,
lowering the location of the feed & side draw streams,
increasing the pressure at the top of the distillation column and
changing the configuration of the products draw. It is
estimated that these three changes will increase the throughput
of each column by ~5%. The validated model is also used to
quantify the effects on key internal column parameters such as
the flooding factor, in the event of significant changes to
product purity and throughput.
Keywords- high-purity distillation columns; steady state
model, operating condition optimization
Distillation is the dominant process for separating large multicomponent streams into high purity products. In the refining
and chemical industries it consumes approximately 40% of the
total energy used for operating the plant [1]. Optimization will
be an efficient way to improve the energy efficiency and
product quality.
Optimization of distillation columns is not a straightforward
process. Firstly, columns come in a variety of configurations
with different operating objectives. These differences cause
distinct dynamic behaviors and different operational degrees
of freedom. This situation becomes more complex when the
product purity is regulated down to the 10~1000 parts-permillion (ppm) range due to the complex dynamics and nonliner behavior. This highly nonlinear behavior as reported in
[2-3] will bring a great challenge for control and optimization.
Thirdly, column operations normally exhibit significant
interactions within the control loops (i.e composition control
loops) and have numerous constraints. Finally, disturbances
from up-streams or utilities may result in difficulties in
obtaining and/or maintaining the optimized conditions.
The economic optimization of a distillation column involves
the selection of the number of trays and feed location, as well
as the operating conditions to minimize the total investment
and operating cost. Continuous decisions are related to the
operating conditions and energy involved in the separation,
while discrete decisions are related to the total number of trays
and the tray positions of each feed and product streams. If we
can build a reliable model, it may be possible to quantify the
differences in total cost and energy consumption in order to
facilitate optimal design. As the number of components
increase, the number of possible alternatives can be enormous
and the selection of the correct sequence becomes a major
Many researchers have proposed several techniques to address
the optimization of the distillation column with high purity
products. From a control perspective, nonlinear controllers
were designed so that the distillation could be regulated with
disturbances [4-6]. The dynamic and controllability of highpurity distillation columns was studied in [7]. Optimal
distillation feed design for general distillation column was
proposed in [8]. Optimal energy usage and energy efficiency
of distillation columns were discussed in [9-10] and the
optimal design of distillation columns was addressed in [11].
This paper considers the operating conditions optimization of a
high purity distillation column. A comprehensive model of a
high-purity distillation column for producing high purity
ethanol was built using HYSYS, a commercial simulation
software package. This model was validated against industrial
data. Optimization was carried out based on the validated
model. Each optimal operating condition was considered one at
a time. Since the feed, top-product and side-stream draw
locations were left open during the design, their position was
tested to find the optimal solution. The effect of the condenser
pressure was also investigated, and the optimal pressure
selected based on the results of the simulation. The last factor
investigated was the product specification, and the effect of
reducing the purity of the top product on the potential increase
in throughput.
Each distillation unit in the industry consists of a topping
column and a refining column (as shown in Figure 1). The
crude methanol, consists of methanol (80%), ethanol (150ppm
Chemeca 2013, Brisbane, Australia, Setp. 29 - Oct. 2
w/w), butanol (95ppm w/w), water (20%) and other light
gases, is first pumped into the topping column where the light
gasses are taken off in the top stream. The bottoms stream is
sent through a series of condensers and heat exchangers and
then fed into an 88 tray refining column. Methanol (product) is
taken at the top of the refining column with 99.99% purity,
while water is taken from the bottom. Ethanol is
predominantly taken at the fusel draw, located near the middle
of the column. The product flow rate product is mainly
governed by the concentration of ethanol (ppm) in the product
stream, where a strict level is enforced. Failing to meet the
product ethanol specification would result in a substandard
product with a lower commercial value.
This paper focuses specifically at the optimization of the
refining column, whilst enforcing the overhead ethanol
same specifications as the industrial column; a purge stream
with zero flow had to be added for completeness.
The high purity of ethanol present throughout the column
made convergence difficult. Attempting to converge the model
directly to 10 ppm at first was unsuccessful, as the simulator
was unable to find a feasible solution that satisfied all
constraints in one go. Starting with a relaxed overhead ethanol
specification of 50 ppm allowed the model to first converge to
a relatively relaxed specification, allowing for the creation of a
reference point. Subsequent convergence to lower ppm
specifications uses this as a starting point. This method
allowed to gradually tighten the ethanol specification down to
7 ppm. Once the model was converged to 7ppm, it was very
robust in finding out new solutions.
The output data from these steady state models were then
compared with industrial data. After the validation of the
steady state results with industrial data, this model was used
for the optimization process where an adjuster block was
introduced to streamline the optimization process. The selector
block was assigned the task of changing the reboiler duty to
bring the product ethanol concentration down to the prespecified limit of 7 ppm. This is shown as ADJ-1 in Figure 2.
Figure 1: Distillation process
A. Modelling
To identify throughput optimizing operational changes that
could be made, it was necessary to build a steady state model
of the distillation column. This steady state model was then
used to test different operational changes and quantify the
effect they would have on the throughput and internal column
factors, such as flooding. Based on the results obtained from
the steady state model, plant trials were conducted using a
subset of operational changes tested with the simulations that
were practical to carry out at the plant. In the absence of a
steady state model, as developed in this instance, every single
operational change would need to be tested in an industrial
setting, which can result in significant production losses, as
well as safety issues. Moreover, the steady state model gives a
better insight into how operational changes affect internal
column parameters than carrying out a simple plant trial.
Figure 2: Distillation column model using HYSYS simulation
The process simulation software package HYSYS (Figure 2)
was chosen to build the preliminary model to match the steady
state data provided by industry. The property package WilsonVirial/ Poy was used to simulate the column, which had the
Chemeca 2013, Brisbane, Australia, Setp. 29 - Oct. 2
carries some error and that most of the simulated component
splits are well within the limits of error.
A. Steady State Model Validation
It was decided to specify outlet flow rates, reboiler duty and
the pressure profile. These variables resulted in a stable
simulation and allowed a streamlined optimization process.
The fluid package was adjusted within acceptable limits to
arrive at the observed component distribution across the three
The industrial data estimated the tray efficiencies to fluctuate
between 75 – 85%. Assuming all trays in the column have the
same efficiency, the tray efficiencies were adjusted to get a
better match on the component distribution.
Table 1 shows the results obtained with tray efficiency
adjusted to 80%.
Table 1: Tray efficiency
The percentage error associated with the butanol distribution
in the fusel draw is significant. However this discrepancy did
not hinder the validation of this model for the following
 The industrial information available on the butanol
concentration in both the fusel and bottoms draws are
estimates only.
 The butanol concentration profile forms the expected
bell shape, with peaks near the bottom of the column.
A slight shift in this profile will result in significant
changes in the predicted butanol value which would
not be accounted for by the industrial estimate.
Industry generalizes all alcohols heavier than ethanol
as iso-butanol, this generalization may be incorrect.
 The industry does not have limitations imposed on
the butanol distribution.
The absolute error associated with all component splits can be
regarded as minor and does not affect the ability of the
simulator to carry out optimization work. It is also important
to note that the data gathered from the industrial process
The analysis of the output data against the benchmark steady
state data from industry revealed that the process simulation
program HYSYS was well suited to modeling the behavior of
these refining columns.
The simulated column also showed the expected non-liner
behavior between the reboiler duty and the product ethanol
ppm specification.
The initial focus of the optimization work was to arrive at
operational changes that could be implemented without
significant plant interruptions or modification, using existing
piping and equipment. Many different variables were
methodically tested to see the effects on throughput. A
theoretical approach to optimization was also adopted, to find
ways to increase throughput without sacrificing product
quality. The validated HYSYS model was employed to see if
the changes resulted in improvements. If a certain operational
change reduced the required reboiler duty, it was considered a
positive operational change, as reboiler duty is directly
proportional to internal tower traffic. Thus, reduced reboiler
duty reduced tower traffic, which enables a greater
throughput. A large number of solutions were found at the end
of this process.
To proceed further with these solutions it was necessary to
look into the practical and operational impact of these
solutions. Upon closer evaluation, it was apparent that most
changes would not be practical. The handful of solutions left
were then carefully scrutinized in all aspects. The model was
used to accurately predict the expected throughput increase
from each operational change. Key personnel at industry were
also informed of the proposed changes and their inputs were
taken into consideration. It was assumed that any new
throughput optimizing changes had to maintain the current
ethanol product specification. This process identified the
following key operational changes as practical and easily
A. Changing the condenser pressure.
B. Changing the feed and fusel flow locations.
C. Changing the location of the product draw.
A. Changing condensor pressure
Based on both the steady state simulation and process
engineering principals, operating a distillation column at a
lower pressure makes the methanol/ethanol separation easier.
Thus, reducing the reboiler duty required to make the 7ppm
specification. However lowering the pressure at the top of the
tower increases the flooding factor of the trays at the top
significantly. The top is the most vulnerable part for flooding,
thus reducing the pressure at the top of the column is not
Chemeca 2013, Brisbane, Australia, Setp. 29 - Oct. 2
In contrast, increasing the pressure at the top of the column
makes the methanol/ethanol separation difficult, but this
reduced the flooding factor. Therefore, increasing the pressure
at the top of the column enables the column to accommodate
higher flow rates, and increase the throughput of the column.
The only drawback of this method is the marginal increase in
the reboiler duty required to meet the ethanol specification.
Figure 3 shows the flooding factors for different column
pressures at the top. As stated previously lowering the top
pressure increases the flooding factor, whilst increasing the
pressure reduces the flooding factor. Increasing throughput at
a given top pressure changes the flooding profile of the
column, but only has a marginal effect on the flooding factors
at the top of the column.
110 kPa
It is recommended to carry out these changes in three phases.
Firstly, the fusel draw should be diverted to the bottoms and
any changes to the plant should be monitored. It is predicted
that there will be a minor gain in reboiler duty. In phase two
the feed location should be changed from the current position
to the lowest possible position, as a result the ethanol
specification at the products draw should reduces below 7ppm.
In phase three, the feed rate should be increased until the
product ethanol specification of 7 PPM is reached.
Some distillation facilities in industry cannot combine the
fusel stream with bottoms stream, as these plants are not
equipped with adequate water treatment facilities. If so it is
recommended to lower both the fusel stream and feed stream.
C. Changing the location of the product draw
134 kPa
150 kPa
F Factor
very bottom of the distillation column. It is estimated that this
scheme would allow for an increased throughput of 2%.
150 kPa High flow
Currently the product draw of refining column is located on
the 85th tray. The remaining three plates at the top of the
column are used as a pasteurization zone, for light gases that
have not been removed at the topping column. If the topping
column was performing adequately, this zone would not be
necessary. Thus, the product draw could be diverted from the
85th tray to the reflux flow, via existing piping. The reflux
flow is located three stages higher up the column. Thus, the
reflux should contain a lower ethanol fraction.
11 21 31 41 51 61 71 81
Tray #
Figure 3: Flooding factor
Based on data shown in Figure 3, the industry was advised to
operate the distillation columns at a high top pressure to
optimize for throughput, provided that the plant was not
suffering from steam shortages. This should allow for ~2%
increase in throughput.
If the plant is suffering from steam shortages it was advised to
operate the column at slightly lower pressures as this allows
for an easier separation.
B. Feed & Fusel flow location
Based on steady state simulations, it was found to be optimal
to change the feed location to the column to a lower tray. This
could be done without any additional capital expenditure, as
the columns in industry already have the necessary piping to
carry this out. It was also recommended to close the fusel
draw and combine it with bottoms draw. This change allows a
greater number of trays to carry out the methanol/ethanol
separation. This action lowers the area where ethanol
concentrates in the column, from the fusel draw location to the
Preliminary analysis of the above mentioned situation shows
that the throughput can be increased by ≈ 1% of production at
the same reboiler duty.
The change should be carried out in two phases. In phase one,
the reflux should be diverted to the product draw, this should
result in a lower ethanol ppm specification in the product. In
phase two the feed rate should be increased, until the product
ethanol specification of 7 ppm is reached.
Upon a request from industry, the simulated distillation
column was used to investigate the potential throughput
optimization, which could be achieved by relaxing the
overhead ethanol ppm specification from the current value of
7ppm to 10ppm and 50 ppm. The results are listed in Table 2.
As expected increasing the ppm specification reduced the
required R/D ratio, which in turn reduces the required reboiler
duty. Increasing the flow utilizes this excess reboiler duty and
enable for a significant throughput optimization.
The simulation was then used to check the effect these
significant changes would have on the tray flooding factors.
Chemeca 2013, Brisbane, Australia, Setp. 29 - Oct. 2
The overhead circulation rates between the three cases were
identical. This means the column traffic at the top, where the
column is most vulnerable to flooding remained fairly stable.
Table 2: Throughput optimization
Based on the above evidence the industry was advised, that
relaxing the ethanol ppm specification from 10ppm to 50ppm
would allow for a significant increase in throughput, without
significantly affecting the internal traffic and flooding factors,
despite the huge changes in external flows.
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In this paper, a steady state model was built using a
commercial simulation software HYSYS. The operating
optimal conditions were identified for a high purity distillation
column. On average these changes are expected to improve
the throughput by 5%. The model was also used to predict
changes to key internal column parameters, in the event of
relaxed ethanol specification and high throughput. For the
future work, all factors will be considered simultaneously.
Also the distillation unit as a whole (topping column and
refining column) will be taken into consideration.
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Renewable Engergy, “Distillation Column Modeling
Tools”, Washington, DC,
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NY (1992).
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M.W., “Advanced Control Unleashed: Plant Performance
Management for Optimum Benefit,” International Society
for Automation (ISA), Research Triangle Park, NC (2003).
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of internal thermally coupled distillation columns based on
nonlinear wave model”, Journal of Process Control, 21:
920-926 (2011).
[5] Biswas, P.P., Ray, S., Samanta, A.N., “Nonlinear control
of high purity distillation column under input saturation
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Chemeca 2013, Brisbane, Australia, Setp. 29 - Oct. 2
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