No abstract

Electrochemical reduction of carbon dioxide (CO2) has received increasing attention with the recent rise in awareness of climate change and the increase in electricity supply from clean energy sources. However, because of the complexity of its reaction mechanism and the largely unknown electron transfer pathways, the development of a first-principles-based operational model of a CO2 electrocatalytic reactor is still in its infancy. This work proposes a methodology to develop a feed-forward neural network (FNN) model to capture the input–output relationship of an experimental electrochemical reactor from experimental data that are obtained from easy-to-implement sensors. This FNN model is computationally efficient and can be used in real-time to determine energy-optimal reactor operating conditions. To further account for the uncertainty of the experimental data, the maximum likelihood estimation (MLE) method is adopted to construct a statistical neural network, which is demonstrated to be able to address a usual overfitting problem that occurs in the standard FNN model. In addition, by comparing the neural network with an empirical first-principles-based model, it is demonstrated that the neural network model achieves improved prediction accuracy with respect to experimentally determined input–output operating conditions. Finally, the insights obtained from the FNN model and the limitations identified of the empirical, first-principles model (EFP model) are used to propose specific modifications to the EFP model to improve its prediction capability.