Chemical Engineering August 2014 - 53

Block column: energy loss profile (stage-energy loss)
35
30
25
20
15
10
5
Design 1
Design 2
Figure 8. Exergy
loss profiles for
different design
options. Note that
Design 1 has reduced
exergy loss
at the feed stage
when compared to
Design 2
can be identified. Integrated economics
can further enable consideration
of capital and operating
costs with respect to each design
alternative. Dynamic modeling
provides additional opportunities
to identify operating strategies
that will incrementally improve
column operations.
Beyond the areas that have been
discussed in this article, new innovations
are around the corner, in
terms of detailed modeling of columns
and more convenient columndynamics
tools.
■
Edited by Gerald Ondrey
References
1. Demirel, Y., Sustainable Operations for Distillation
Columns, Chem. Eng. Process Tech.,
vol. 1, p. 1,005, 2013.
0.05 0.1
0.15 0.2 0.25 0.3 0.35 0.4 0.45
Energy loss, MW
2. Samant, K., I. Sinclair, and G. Keady. Integrated
Thermal and Hydraulic Analysis of
Distillation Columns, Proceedings from the
24th National Industrial Energy Technology
Conference, Houston, April 2002.
mentioned earlier, when the reflux
ratio is reduced in order to decrease
loads on condensers and the reboiler
and save energy, the number
of stages needs to be increased to
ensure adequate separation, which
will lead to increased capital costs.
In order to make quicker, more optimal
decisions, engineers can employ
software that can estimate the
capital and operating costs of the
process, (using built-in rigorous asbuilt
estimating models and sizing
and mapping algorithms that operate
against the process parameters)
at a high enough level of accuracy
to establish the tradeoffs between
several proposed improvements, or
between implementing the design
modifications or not. A common example
of using economic software in
the decision process would be to decide
whether to add a side reboiler
or condenser, and in that context to
compare the capital costs associated
with new equipment to the reductions
in operating costs.
Operability and profitability
There are a variety of changes
and upsets that occur in process
operations. In order to better control
column operations and both
achieve the desired product mix
while minimizing energy, dynamic
simulation is an excellent tool. The
complexity of columns means that
they are inherently not at steady
state and can usually achieve better
operations when the dynamics
are well understood.
Some steady-state process simulators
incorporate integrated dynamics-modeling
environments, such
that a steady-state process model
can be used to quickly develop a
dynamic model. Dynamic modeling
of columns typically achieves better
control strategies, better control of
products, and optimization of energy
use [4].
Concluding remarks
Columns often are operated below
optimum operating conditions due
to their complexity and the imperative
to keep them within safe
operating limits. The design, troubleshooting
and debottlenecking of
columns presents many opportunities
to achieve improved control
over product yields and reduction
in energy consumption.
When the process model is used
in conjunction with integrated
pinch analysis, opportunities for
energy savings can be examined
and optimal process arrangements
3. Dhole, V. R., and Linnhoff, B., Distillation
Column Targets, Computers Chem. Eng., 17,
pp. 549-560, 1993.
4. Brownrigg, N. and Others, Jump Start: Using
Aspen HYSYS Dynamics with Columns,
AspenTech, 2013.
Authors
Irina Rumyantseva is a
member of engineering product
marketing at Aspen Technology,
Inc. (200 Wheeler
Road, Burlington, MA 01803;
Phone: +1-781-221-6400;
Fax: +1-781-221-6410). With
a background in chemical
engineering, she helps to
advise customers from the
oil, gas and petrochemical
industries on how to meet
operational challenges through AspenTech's
aspenONE Engineering solutions, including
Aspen HYSYS and its associated features and
benefits. Rumyantseva graduated from the Jacobs
School of Engineering at the University of
California, San Diego.
Ron Beck is the engineering
product marketing director
for Aspen Technology,
Inc. (same address as above),
and covers the aspenONE
engineering suite. He has
worked for AspenTech for
five years and is the marketing
manager for aspenONE
Engineering. Beck spent
10 years in a research and
development organization
commercializing fluidized-bed technologies, enhanced
oil recovery methods and environmental
technology. He has 20 years of experience
in the development, adoption and marketing
of software solutions for engineering and plant
management. Beck has been involved with the
development of integrated solutions for several
global chemical enterprises. At AspenTech, he
has also been involved with AspenTech's economic
evaluation products. Beck is a graduate
of Princeton University.
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