Potential yields and emission reductions of biojet fuels produced via hydrotreatment of biocrudes produced through direct thermochemical liquefaction
Bibliographic record
Abstract
BACKGROUND: The hydrotreatment of oleochemical/lipid feedstocks is currently the only technology that provides significant volumes (millions of litres per year) of "conventional" biojet/sustainable aviation fuels (SAF). However, if biojet fuels are to be produced in sustainably sourced volumes (billions of litres per year) at a price comparable with fossil jet fuel, biomass-derived "advanced" biojet fuels will be needed. Three direct thermochemical liquefaction technologies, fast pyrolysis, catalytic fast pyrolysis and hydrothermal liquefaction were assessed for their potential to produce "biocrudes" which were subsequently upgraded to drop-in biofuels by either dedicated hydrotreatment or co-processed hydrotreatment. RESULTS: A significant biojet fraction (between 20.8 and 36.6% of total upgraded fuel volume) was produced by all of the processes. When the fractions were assessed against general ASTM D7566 specifications they showed significant compliance, despite a lack of optimization in any of the process steps. When the life cycle analysis GHGenius model was used to assess the carbon intensity of the various products, significant emission reductions (up to 74%) could be achieved. CONCLUSIONS: It was apparent that the production of biojet fuels based on direct thermochemical liquefaction of biocrudes, followed by hydrotreating, has considerable potential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".