% IMPORTANT: The following is UTF-8 encoded. This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.
@PHDTHESIS{Burre:844880,
author = {Burre, Jannik},
othercontributors = {Mitsos, Alexander and Martin, Mariano Martin},
title = {{O}ptimal design of power-to-x processes},
volume = {25},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2022-04275},
series = {Aachener Verfahrenstechnik series AVT.SVT - Process systems
engineering},
pages = {1 Online-Ressource : Illustrationen},
year = {2022},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2022},
abstract = {The increasing share of renewable energy sources in the
electricity grid causes curtailments, which prevent
exploiting the full environmental and economic potential of
renewable electricity. Power-to-X processes can utilize this
electricity to produce certain products that would have been
otherwise produced from fossil-based sources. To benefit the
most, these Power-to-X processes need to be optimized for a
maximum resource-efficiency. We demonstrate that the sole
replacement of raw materials for industrial process concepts
is not expedient. We therefore develop optimization-based
methods to identify sustainable process concepts and support
their optimal design. These methods are applied to the
production of dimethoxymethane (referred to as DMM or
OME1)—a promising synthetic fuel candidate and
intermediate for the production of longer-chain oxymethylene
ethers (OME3-5). To analyze DMM and OME3-5 production using
established process concepts, we implement process models
with detailed thermodynamic models from the open literature.
Even by considering their maximum potential for heat
integration, these process concepts have been found to be
much less efficient than those for the production of other
synthetic fuel candidates. Therefore, fundamentally new
processes need to be designed. Emerging Power-to-X processes
are usually on a very different stage of development. To
enable a fair comparison and support process design, we
develop a methodology that incorporates optimization-based
methods on different hierarchy levels. The methodology
allows a systematic way to design and evaluate each
candidate regarding three key performance indicators:
production costs, exergy efficiency, and global warming
impact. Applied to five reaction pathways for DMM
production, we identified the direct reduction of CO2 to be
the most suitable one for sustainable DMM production at its
current state. For a successful implementation, detailed
process models are necessary. As the complicated form of
such models often cause difficulties for deterministic
optimization, we develop a hybrid process model for
reductive DMM production incorporating Gaussian processes
and artificial neural networks. For solving the resulting
nonconvex program, we use a reduced-space formulation and a
hybrid between the McCormick and the auxiliaryvariable
method implemented in our deterministic global solver
MAiNGO. Only with these measures on both the modeling and
algorithm level, convergence was possible. As Power-to-X
design problems often contain discrete decisions, we analyze
different problem formulations regarding their suitability
for global superstructure optimization and applied the most
suitable one to the design problem for reductive DMM
production. For mixed-integer nonlinear programming problems
containing nonconvex functions, we identified such
formulations as particularly promising that reduce the
number of optimizationvariables. Although they introduce
nonconvex terms, corresponding relaxations remain comparably
tight for our example problems. However, a large library
with benchmark problems of different complexity would be
necessary to derive generally valid statements. The
application of optimization-based methods to DMM production
has demonstrated great potential. However, also limitations
and further improvement potential was identified—for both
the methods and DMM production as a Power-to-X process.},
cin = {416710},
ddc = {620},
cid = {$I:(DE-82)416710_20140620$},
pnm = {Verbundvorhaben P2X: Erforschung, Validierung und
Implementierung von 'Power-to-X' Konzepten - Teilvorhaben Z0
(03SFK2Z0) / BMBF-03SF0566P0 - Verbundvorhaben NAMOSYN
(BMBF-03SF0566P0)},
pid = {G:(BMBF)03SFK2Z0 / G:(DE-82)BMBF-03SF0566P0},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2022-04275},
url = {https://publications.rwth-aachen.de/record/844880},
}
guest ::