The central topic of this seminar is modeling approaches to facilitate resource conservation and a just energy transition. Potential subtopics are an emerging technology’s potential for scaling, life-cycle assessment for measuring social and environmental impacts, uncertainty quantification, and economic modeling for the energy transition. Our goal is to create an intimate, collaborative space for students, postdocs, scientists, and PIs within the Stanford techno-economic modeling and systems modeling community. These seminars will provide an opportunity to disseminate insights from your studies, connect with fellow researchers, and strengthen bonds across the community.

This week's speaker is:

Spencer (Zhihao) Zhang, Ph.D. Candidate, Stanford University
"Life Cycle Assessment of Geologic Hydrogen Production Carbon Intensity"

Description of the work:
Geologic hydrogen—both naturally occurring and stimulated—presents a novel pathway for large-scale, low-carbon hydrogen production. This talk shows the work in progress where we integrate real-world production data from industry and research collaborators to quantify emissions and energy use in geologic hydrogen extraction. A modified Oil Production Greenhouse Gas Emissions Estimator (OPGEE) model is used to conduct a rigorous engineering-based life cycle assessment (LCA), incorporating key thermodynamic and process parameters specific to geologic hydrogen production. A detailed modeling of Pressure Swing Adsorption (PSA) for hydrogen purification is added to ensure an accurate representation of energy requirements and separation efficiencies. Beyond assessing emissions, this work identifies the key parameters that determine

Bio: Spencer (Zhihao) Zhang is a PhD candidate advised by Prof Adam Brandt in Energy Science and Engineering, focusing on assessment of the environmental impact of energy production, including oil, natural gas, and geologic hydrogen. Using engineering-based modeling and life cycle assessment (LCA), Spencer and her coworkers analyze how different production schemes affect emissions and identify hotspots for reduction opportunities. With a strong background in process engineering, supply chain analysis, and data-driven modeling, their work helps identify transition pathways to cleaner energy systems.