Coprocessing Renewable and Waste Feedstocks: Critical Technology for Aviation Decarbonization
Bibliographic record
Abstract
Co-processing renewable feedstocks with petroleum streams represents a critical pathway for achieving aviation industry decarbonization, offering the only viable route to scale sustainable aviation fuel (SAF) production while avoiding high facility investments. This comprehensive review demonstrates that coprocessing is essential for meeting urgent climate commitments, as it leverages existing refinery infrastructure to enable immediate SAF deployment. The analysis examines integration strategies for diverse renewable feedstocks─including fats, oils, and grease, pyrolysis oils from biomass, waste plastics and scrap tire, Fischer–Tropsch wax, and hydrothermal liquefaction biocrude─highlighting how each contributes to the renewable feedstock portfolio required for large-scale low-carbon aviation fuel production. Combined with novel catalyst systems with improved contaminant tolerance, coprocessing as the foundational technology facilitates the aviation fuel transformation. This Review establishes that mixed feedstock coprocessing strategies, supported by regulatory momentum (EU ReFuelEU mandates, US tax incentives), represent the primary mechanism through which aviation can achieve net-zero emissions by 2050, making coprocessing development one of the most important priorities for future aviation fuel research.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".