Semester
Fall
Date of Graduation
2024
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Mechanical and Aerospace Engineering
Committee Chair
David Mebane
Committee Member
Fernando Lima
Committee Member
Xueyan Song
Abstract
Electrolysis systems are critical to several societal applications, particularly energy storage and conversion. Developing these systems requires a detailed knowledge of the chemistry and thermodynamics of the materials used in the electrolysis cell. This work focuses on using embedded scientific machine learning as an efficient way to build an interpretable model for the reaction and transport kinetics in the LSCF electrode, whose performance directly influences the electrolysis system’s performance. The models developed in this study are trained using the publicly available machine learning package, FoKL-GP. This package incorporates a robust Gibbs sampler that employs a forward variable selection process to construct Karhunen–Lo`eve decomposed Gaussian Process (GP) functions in an automated manner. The model training process uses experimental data from open literature and scientifically derived models to obtain GP functions that accurately represent vital parameters such as the equilibrium constant, rate constant, and diffusivity in LSCF. The bulk defect equilibrium model is derived by interpreting the half-reaction at the LSCF electrode and computed using thermogravimetric measurements. Once quantified, a model for the equilibrium constant is built as a function of the site fraction of oxygen vacancies in LSCF and the temperature. Using data from electrical conductivity relaxation (ECR) experiments, GP functions for the rate constant and diffusivity are built as a function of the site fraction of oxygen vacancies in LSCF and the temperature. The data-driven models for the LSCF electrode can be paired with models for the other components in the overall cell to optimize the electrolysis system. Embedded scientific machine learning is an advancement in the design of chemical processes, offering precise and predictive insights crucial for optimizing performance and efficiency. By harnessing datasets from both experimental and theoretical sources, these models provide a solid foundation for informed decision-making. This approach deepens our understanding of complex chemical interactions and fosters innovation by identifying optimal conditions, reducing trial-and-error experimentation, and paving the way for more sustainable and cost-effective solutions.
Recommended Citation
Campbell, Ferron, "Data-Driven Modeling of Oxygen Kinetics in La0.6Sr0.4Co0.2Fe0.8O3−δ (LSCF) for High-Temperature Reduction of CO2 in an Electrolysis Cell" (2024). Graduate Theses, Dissertations, and Problem Reports. 12630.
https://researchrepository.wvu.edu/etd/12630
Included in
Data Science Commons, Energy Systems Commons, Materials Chemistry Commons, Other Materials Science and Engineering Commons, Statistical Models Commons, Thermodynamics Commons, Transport Phenomena Commons
