Electrochemical reduction of CO 2 poses a vast potential to contribute to a defossilized industry. Despite tremendous developments within the field, mass transport limitations, carbonate salt formation, and electrode ...
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Electrochemical reduction of CO 2 poses a vast potential to contribute to a defossilized industry. Despite tremendous developments within the field, mass transport limitations, carbonate salt formation, and electrode degradation mechanisms still hamper the process performance. One promising approach to tweak CO 2 electrolysis beyond today's limitations is pulsed electrolysis with potential cycling between an operating and a regeneration mode. Here, we rigorously model the boundary layer at a silver electrode in pulsed operation to get profound insights into the dynamic reorganization of the electrode microenvironment. In our simulation, pulsed electrolysis leads to a significant improvement of up to six times higher CO current density and 20 times higher cathodic energy efficiency when pulsing between −1.85 and −1.05 V vs SHE compared to constant potential operation. We found that elevated reactant availability in pulsed electrolysis originates from alternating replenishment of CO 2 by diffusion and not from pH-induced carbonate and bicarbonate conversion. Moreover, pulsed electrolysis substantially promotes carbonate removal from the electrode by up to 83 % compared to constant potential operation, thus reducing the risk of salt formation. Therefore, this model lays the groundwork for an accurate simulation of the dynamic boundary layer modulation, which can provide insights into manifold electrochemical conversions.
Gaussian processes (Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models...
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Artificial neural networks (ANNs) are used in various applications for datadriven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN emb...
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The aim of the present paper is to provide (n-1)-reliability to a power grid, guaranteeing nominal operation after the failure of any one out of n present grid components. Building on previous work (Fliscounakis et al...
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The aim of the present paper is to provide (n-1)-reliability to a power grid, guaranteeing nominal operation after the failure of any one out of n present grid components. Building on previous work (Fliscounakis et al., IEEE Transactions on Power systems, 2013), a hierarchical programming problem is proposed to characterize the worst-case behavior of a power grid under a given contingency. The formulation is a mixed-integer linear generalized semi-infinite program with a max-min program embedded. The different levels correspond to the choice of preventive actions, realization of uncertainties in the power supply and demand, and the choice of corrective actions. In order to model active components of the grid, models are proposed for load balancing and the behavior of phase-shifting transformers. Since no rigorous solution approaches are published for the problem at hand, the possibility of extending generalized semi-infinite programming approaches to the present problem is discussed.
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