Machine Learning for Natural Gas to Electric Power System Design
Technology Description:
Pacific Northwest National Laboratory (PNNL) will apply multiple machine learning tools to develop next-generation natural gas to electric power conversion system designs. The project leverages a physics-informed machine learning tool for automated reduced order model (ROM) construction. This will significantly reduce prediction errors compared to traditional approaches. Machine learning will also leverage a superstructure-based mathematical optimization tools combined with reinforcement learning and graph network methods to explore and optimize component connections in fuel to electric power conversion systems.
Potential Impact:
DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. If successful, DIFFERENTIATE will yield the following benefits in ARPA-E mission areas:
Security:
Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.
Environment:
Use these tools to solve our most challenging energy and environmental problems by facilitating an economically-attractive transition to lower carbon-footprint energy sources and systems.
Economy:
Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.
Contact
ARPA-E Program Director:
Dr. David Tew
Project Contact:
Dr. Jie Bao
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
jie.bao@pnnl.gov
Partners
National Energy Technology Laboratory
University of Washington
Related Projects
Release Date:
04/05/2019