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Record W4412184632 · doi:10.1002/ese3.70214

Conversion of Waste to Sustainable Transport Fuel via Fischer–Tropsch Synthesis: Process Modeling and Life Cycle Analysis

2025· article· en· W4412184632 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnergy Science & Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of AlbertaGreenField Specialty Alcolhols (Canada)Greenfield Research (Canada)
Fundersnot available
KeywordsEnvironmental scienceFischer–Tropsch processCombustionWaste managementHydrothermal liquefactionBiofuelJet fuelChemistryEngineeringCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT A lifecycle analysis was performed on a process for distributed forest residue collection and conversion into biocrude, followed by biocrude transport to a central facility and indirect liquefaction using Fischer–Tropsch synthesis with refining to sustainable aviation fuel (SAF). It was found that the carbon intensity (CI) of the process for conversion of forest residue to biocrude in satellite facilities was 7.1 g CO 2 e MJ −1 , for biocrude to SAF conversion at the central facility was 18.9 g CO 2 e MJ −1 , and coproduction of electric power at the central facility for export to the grid was −16.2 g CO 2 e MJ −1 . The CI contribution of biocrude production was higher than the CI contribution of biocrude to SAF conversion with electric power coproduction. Overall, the CI of the process, including the contribution of SAF combustion during its use, was 11.6 g CO 2 e MJ −1 , compared to the reference value for petroleum‐derived jet fuel of 68 g CO 2 e MJ −1 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.186
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it