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@PHDTHESIS{Heelmann:988602,
author = {Heßelmann, Matthias},
othercontributors = {Wessling, Matthias and Burdyny, Thomas},
title = {{M}athematical process modeling and optimization of
electrochemical {CO}$_{2}$ reduction from micro- to
meter-scale},
volume = {45},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2024-06303},
series = {Aachener Verfahrenstechnik series - AVT.CVT - chemical
process engineering},
pages = {1 Online-Ressource : Illustrationen},
year = {2024},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2024},
abstract = {As most experimental characterization methods cannot
completely resolve local mass transport and reaction
phenomena in the electrode micro-environment, this thesis
focuses on the mathematical modeling of CO2 electrolysis to
give profound insights and derive optimization potentials
for steering productivity and selectivity. However, to fully
exploit the potential of CO2 electrolysis, research has to
look beyond the electrolyzer and holistically assess the
process integration with up- and downstream processing.
Therefore, a multi-scale modeling approach is presented in
this work that aims to decipher current bottlenecks of CO2
electrolysis on multiple length scales using different
modeling techniques. The micro-environment near a planar
plate electrode for electrochemical CO2 conversion to CO was
rigorously modeled by accounting for the size of dissolved
species in the electrolyte. The results from this study
highlight the importance of enhancing hydrodynamics at the
electrode and modulating the electrolyte concentration to
improve reactant transport and reduce the cathodic
overpotential. Due to mass transport limitations at planar
plate electrodes, more advanced electrode geometries, i.e.,
gas diffusion electrodes, have been investigated within this
work. The simulations of the multi-phase transport in gas
diffusion electrodes reveal that increasing the electrolyte
concentration and flow rate and the gas flow rate helps to
overcome ionic conductivity and mass transport limitations.
To assess the process on an industrially relevant length
scale, a machine learning-based approach was introduced that
links multiple surrogate models trained from simulation data
of the gas diffusion electrode continuum model to simulate a
pilot-scale two-dimensional electrolyzer. Finally, a
holistic process optimization was carried out to assess the
profitability of the process. The optimization highlights
the need for reducing the energy demand and improving the
selectivity of the electrochemical CO2 reduction. Moreover,
the often discussed CO2 pumping effect in CO2 electrolysis
turns out to be a cost saver rather than a cost killer. The
results from this thesis show that CO2 electrolysis can
become a viable option in the quest for sustainable
production chains when controlling the investigated process
parameters and optimizing the process from a holistic
perspective.},
cin = {416110},
ddc = {620},
cid = {$I:(DE-82)416110_20140620$},
pnm = {DFG project 441926934 - NFDI4Cat – NFDI für
Wissenschaften mit Bezug zur Katalyse (441926934)},
pid = {G:(GEPRIS)441926934},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2024-06303},
url = {https://publications.rwth-aachen.de/record/988602},
}
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