Machine Learning-Based Operational Modeling of an Electrochemical Reactor: Handling Data Variability for Experimental Data

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, due to the complexity of its reaction mechanism and the largely unknown electron-transfer pathways, the development of a first-principles-based operational model of an electrocatalytic CO2 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. Additionally, 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. The insights obtained from the FNN model are applied to propose specific modifications to the empirical, first-principles model (EFP model) to improve its prediction capability and to propose optimal set points for future experiments based on the FNN predictions of operating cost and profit. The FNN model is also used as the system model to perform relative gain array analysis to determine controllability for multi-input-multi-output control schemes