Uncertainty quantification analysis of electrochemical reduction of CO2

Abstract

Electrochemical reduction of CO₂ is a promising technique for converting CO₂ to value-added products. However, a lack of quantitative understanding of how design and operating conditions impact a CO₂ electrolyzer's performance is a hurdle in its optimization and scale-up. In this context, mathematical modeling and simulations can play a significant role. However, the uncertainty in the model parameters poses a significant challenge. This uncertainty propagates to the model predictions, making the model validation difficult and reducing its utility. Uncertainty quantification (UQ) is imperative in investigating the impact of various uncertainties in the model parameters on the model predictions. In this work, we develop a UQ framework for electrochemical reduction of CO₂. To this end, a one-dimensional mathematical model is developed, and the model parameters' uncertainties are quantified and propagated through the model. The model predictions of partial current densities containing uncertainty are compared with experimental data. We show that the uncertainty in the kinetic parameters is essential to consider in the UQ analysis. This uncertainty arises from human bias, polarization technique, and the catalyst preparation method. Moreover, the uncertainty in the kinetic parameters can explain the deviations between the model predictions and experimental measurements.