Research Aide- ESIA- Obadiah, Emo-7.30.26

Argonne National Laboratory•Lemont, IL

4d•Onsite

About The Position

Co-processing bio-derived feedstocks in conventional petroleum refineries can diversify feedstock sources and enable production of drop-in fuels while leveraging existing refinery infrastructure. Robust life-cycle analysis (LCA) in this area requires careful definition of system boundaries, appropriate treatment of biogenic carbon, and consistent accounting of upstream and refinery-stage energy use and emissions. Previous Argonne co-processing LCAs relied on detailed linear-programming (LP) refinery modelling with process-level inputs/outputs. To avoid resource-intensive modeling that depends on comprehensive refinery data, this project will focus on developing and validating streamlined approaches. This will support evaluation of a broader range of co-processing conditions, including different feedstocks, insertion points, refinery configurations, and product slates.

Requirements

Currently enrolled in undergraduate or graduate studies at an accredited institution.
Graduated from an accredited institution within the past 3 months; or actively enrolled in a graduate program at an accredited institution.
Must be 18 years or older at the time the appointment begins.
Must possess a cumulative GPA of 3.0 on a 4.0 scale.
Must pass a screening drug test.
Must complete a satisfactory background check.

Responsibilities

Review prior analyses and relevant literature, building a foundation on co-processing concepts and LCA methodologies.
Help identify key factors that influence LCA results for co-processed fuels, including feedstock characteristics, refinery insertion points, refinery configuration and operating conditions, product slates, and methodological choices.
Conduct analyses to evaluate a broad range of co-processing pathway options.
Summarize findings in internal documents or memos.
Share progress through discussions and/or brief presentations with Argonne project team.
Document outstanding issues and caveats to help identify when simplified methods may be appropriate and where more detailed analysis is warranted.