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TY - THES AU - Heßelmann, Matthias TI - Mathematical process modeling and optimization of electrochemical CO<sub>2</sub> reduction from micro- to meter-scale VL - 45 PB - Rheinisch-Westfälische Technische Hochschule Aachen VL - Dissertation CY - Aachen M1 - RWTH-2024-06303 T2 - Aachener Verfahrenstechnik series - AVT.CVT - chemical process engineering SP - 1 Online-Ressource : Illustrationen PY - 2024 N1 - Veröffentlicht auf dem Publikationsserver der RWTH Aachen University N1 - Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2024 AB - 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. LB - PUB:(DE-HGF)11 ; PUB:(DE-HGF)3 DO - DOI:10.18154/RWTH-2024-06303 UR - https://publications.rwth-aachen.de/record/988602 ER -