(740b) MINLP Optimization of a Membrane-Absorption-Hybrid System for the Removal of CO2 From OCM Product Gas

The Oxidative Coupling of
Methane (OCM) presents a possibility for catalytically turning methane into
longer hydrocarbons, especially ethylene and ethane. Hence, it could be an
opportunity for replacing oil with methane from natural or biogas for producing
base chemicals such as polymers, ethylene oxide etc.

As
part of the Cluster of Excellence ?Unifying Concepts in Catalysis?, funded by
the German Research Foundation, a mini-plant has been built at Berlin University
of Technology (Technische Universität Berlin). The mini-plant features several
types of reactors, e.g. fixed-bed, membrane, and fluidized-bed reactors. For
the subsequent product gas separation an absorption-desorption process, two gas
separation membranes, and an adsorption-desorption unit for the removal of
carbon dioxide (CO₂) are installed. An initial introduction of a
single membrane module upstream to the absorption unit showed a decrease of the
energy required for the removal of 90% of the CO₂ contained in the
feed gas from 5.02 MJ to 2.76 MJ per captured kilogram of CO₂ [1].

Figure 1:
Simplified process flow sheet for the initial membrane-absorption-hybrid system
for the removal of CO₂ from OCM product gas [2].

Hereby, the determination
of the optimal combination and operation conditions of these reaction and
separation units only through experimental investigations has proven to be
challenging. On top of that, there are fluctuations in all mass and energy
flows, which have to be taken into regard to ensure a safe and reliable
operation. Consequently, simulative optimization under uncertainty of the
entire superstructure is proposed. As a first step, the stand-alone amine-based
absorption desorption process was modeled and fitted to mimic the actual
behavior of the installed unit in the mini-plant. As a second step the
two-stage membrane system was added upstream to the absorption unit to achieve
a decrease of the required energy duty for the CO₂ removal. The OCM
product gas is fed to a first polyimide (MATRIMID®) membrane module, which
shows a high selectivity for CO₂ compared to ethylene, ethane, and
methane. Additional CO₂ is removed in the second module, which
contains a polyethylene oxide (PEO) membrane. Given a slightly lower CO₂/C₂H₄
selectivity a part of its permeate stream is recycled and added to the OCM
product gas at the inlet. The retentate flow of the second membrane module is
then fed to the absorption desorption unit to achieve the required CO₂
removal of 90%. Initial NLP optimization studies of the proposed structure
depicted in Figure 1 [2] showed a further decrease of the energy required per
captured kilogram of CO₂ from 2.76 down to 2.57 MJ, while keeping
the ethylene loss in the hybrid system below 5%. This is mostly achieved by a
considerably lower feed gas pressure of 21.2 instead of 32.0 bar and an
increase of the CO₂ concentration after the recycle before the first
membrane. In order to optimize the system validated process models have been
developed both for the membrane models as well as the absorption process [2],
which will be adapted here.

In this contribution, the
complexity of the membrane section of the hybrid system is increased. Apart
from the two-stage combination of Matrimid® and PEO membrane modules several
other combinations with even more membrane types are of interest. A possible
superstructure with a maximum of six membranes is given in Figure 2.

Figure 2: Superstructure
showing a variety of possible connections to combine up to four membrane
modules before feeding the gas to the absorption process. The membranes can
contain either Matrimid® or PEO as permeable polymers.

Based on this
superstructure alone, 4096 different structures for the membrane section by
itself are possible. Seeing as the two-stage membrane together with the
absorption system showed a nice behavior in terms of computation time and
complexity, an MINLP formulation of the new system is done. The superstructure
shown above is added to the absorption process. The whole system is mathematically
described with the models already derived in previous studies [2] using the
web-based modeling environment MOSAIC [3] from the Berlin University of
Technology to formulate and to export the optimization problem to AMPL.
Therein, various solvers such as Couenne [4] and Bonmin [5] are employed to
solve the MINLP. To increase the numerical stability of the MINLP optimization
a method is implemented to update the starting values of inactive parts of the
superstructure.

The objective of the MINLP
optimization is the minimization of the specific energy required per kilogram
of CO₂ removed, while keeping the CO₂ removal rate at 90%
and the ethylene loss below 5%. Apart from the structural binary variables, the
decision variables consist of the membrane types, the length and operation
pressure of each membrane module, recycle flow rates as well as the main
operational variables of the absorption process, i.e., feed gas pressure,
absorbent flow rate, as well as heating duty in the desorption.

In this contribution
details on the modeling and optimization of the MINLP are presented to discuss
the benefit of more complex gas separation process structures to reduce product
loss as well as the required energy. Additionally, the experimental validation in the actual mini-plant will be discussed.

Acknowledgements: This work is part of the Cluster of Excellence ?Unifying Concepts in
Catalysis? coordinated by the Berlin University of Technology
(TechnischeUniversität Berlin). Financial support by the German Research
Foundation (Deutsche Forschungsgemeinschaft, DFG) within the framework of the
German Initiative for Excellence is gratefully acknowledged.

References

[1]       Stünkel,
S., Bittig, K., Godini, H., Jaso, S., Martini, W., Arellano-Garcia, H., and
Wozny, G. (2012) Process Development in a Miniplant Scale ? A Multilevel -
Multiscale PSE Approach for Developing an Improved Oxidative Coupling of
Methane Process, 11th International Conference on Process Systems Engineering,
Singapore.

[2]       Esche, E.,
Müller, D., Song, S., Wozny, G. (2013) Optimization of a Membrane-Absorption
System for the Removal of CO2 from OCM Product Gas, 6^th
International Conference on Process Systems Engineering (PSE ASIA 2013), accepted
for publication.

[3]       Kuntsche, S.,
Arellano-Garcia, H., and Wozny, G. (2011) MOSAIC, an environment for web-based
modeling in the documentation level, Computer Aided Chemical Engineering 29,
1140-1144 ISBN 978-0-444-54-298-4.

[4]       Belotti, P.,
Lee, J., Liberti, L., Margot, F., Wächter, A. (2009) Branching and bounds
tighteningtechniques for non-convex MINLP, Optimization Methods and Software 24
(4-5), 597 ? 634.

[5]       Bonami, P.,
Biegler, L.T., Conn, A.R., Cornuéjols, Grossmann, I.E., Laird, C.D., Lee, J.,
Lodi, a., Margo, F., Sawaya, N., Wächter, A. (2005) An algorithmic framework
for convex mixed integer nonlinear programs. Tech. rep., IBM Research Report
RC23771. To appear in Discrete Optimization.