Skip to main content
Open Access Publications from the University of California


UCLA Electronic Theses and Dissertations bannerUCLA

Machine Learning-Based Ethylene Concentration Estimation, Real-Time Optimization, and Feedback Control of an Experimental Electrochemical Reactor


With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO\textsubscript{2}) has received increasing attention. However, a first-principles model for electrochemical CO\textsubscript{2} reduction has not been fully developed because of the complexity of its reaction mechanism. Moreover, the electrochemical CO\textsubscript{2} reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments. Thus, machine learning (ML) techniques are employed, which demonstrated the ability to capture the dynamic behavior of a chemical process from data. We propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor. This model is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer. Lastly, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View