Greenhouse gas emissions from industry and transportation contribute significantly to climate change and its associated adverse impacts on the environment and economy. With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO2) has received increasing attention; however, a first-principles model for electrochemical CO2 reduction has not been fully developed because of the complexity of its reaction mechanism. At this point, data driven methods and machine learning (ML) can be used to model the process. At UCLA, we have constructed an experimental rotating cylinder electrode cell (RCE) setup to develop a deeper understanding of the process, and design scale-up strategies. The experimental equipment is digitalized on a computer interface using Smart Manufacturing principles, legacy sensors (such as Gas Chromatogram (GC)) are automated, and voluminous steady state and dynamic data sets are generated. Leveraging these datasets and data analytics, machine learning and hybrid models are built using machine learning methods, such as Support Vector Machines (SVR), and artificial neural networks (ANN) and recurrent neural networks (RNN), that are capable of capturing nonlinearities and time dependencies. These models are used to optimize the most profitable setpoints and are used in single-input single-output (SISO) and multi-input multi-output (MIMO) feedback control schemes using multiple proportional integral (PI) controllers and model predictive control. This study proposes approaches for experimental implementation such as incorporating delayed GC feedback into control loops, training dynamic ML models with dead times, data variability and noise, and linearizing the RNN models using Koopman operators for fast real-time optimization.

The electrochemical CO2 reduction process has potential to use other chemical processes as CO2 source while sustainably producing other useful chemicals. In addition to the RCE setup, an experimental electrically-heated steam methane reforming (SMR) setup for hydrogen production from natural gas is digitalized, modeled, and controlled. A lumped parameter approach is used for fast calculations, and an extended Luenberger observer is used to compensate for missing and delayed sensor feedback. Finally, a model predictive controller using the estimation scheme is experimentally implemented to control hydrogen flow rates by manipulating the current, showing much faster response compared to PI control.