Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)
DOI: https://doi.org/10.29363/nanoge.matsusfall.2024.145
Publication date: 28th August 2024
To become more CO2 neutral, Europe’s energy supply system and its chemical industry are getting more and more electrified. H2 and products from CO2 are envisioned as a suitable long-term energy storage option. They can be produced with high efficiencies by electrolysis. Yet, electrolysis processes account for only a small fraction of all chemical production processes. Over the next few decades, we expect to see a systematic and large-scale ramp-up of electrolysis for the production of various e-Fuels and chemicals.
A requirement for the establishing competitive electrolysis processes is a thorough, quantitative understanding and optimization of catalyst and electrode processes. This includes the identification of experimentally validated electrolysis models and, in particular, the model-based analysis and optimization of the electrodes. To date, there are few continuum-level models that include adequate reaction kinetics to describe electrochemical performance and product selectivity.
This presentation will show how suitable kinetic models and model parameters can be determined for a wide range of electrosynthesis processes: PEM water electrolysis [1-3], CO2 reduction in aqueous [4], and organic electrolyte [5]. In some of these applications, electrochemical reaction kinetics play a major role, whereas others suffer from slow sorption processes carbonation reactions in the electrolyte, gas/liquid phase equilibria or slow transport processes.
Dynamic measurements, such as cyclic voltammetry and chronoamperometry/potentiometry, are well reproduced by the models, and the underlying processes that cause a characteristic dynamic response are revealed. Crucially, the models are then used to identify the performance-limiting processes among the various reaction and transport processes, and measures are proposed to improve the performance of the electrodes. The kinetic models are essential tools and building blocks for the model-based design and optimization and condition diagnosis of electrolysis cells.
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