Conversion of waste to sustainable aviation fuel via Fischer–Tropsch synthesis: Front‐end design decisions
Bibliographic record
Abstract
Abstract Front‐end design decisions for a process to produce sustainable aviation turbine fuel from waste materials were presented. The design employs distributed conversion of wastes to oils, which are then transported to a central facility for gasification, syngas cleaning, Fischer–Tropsch synthesis and refining, that is, a spoke‐and‐hub approach. Different aspects of the front‐end design, that is, the steps up to syngas cleaning, were evaluated. The evaluation employed a combination of case studies, calculations, experimental investigations, and literature review. The supply of sustainable aviation fuel (SAF) as a 50:50 mixture of waste‐derived and petroleum‐derived kerosene to meet the demand of an international airport (Pearson, Toronto) was employed as case study. The amount of raw material required made it impractical to make use of only one type of waste. Using the same set of assumptions, it was shown that in terms of cumulative transport distance required, a spoke‐and‐hub approach was twice as efficient as centralized processing only. Technologies for decentralized production of oils were assessed, and oils produced by pyrolysis and hydrothermal liquefaction (HTL) in pilot‐scale and larger facilities were procured and characterized. These oils were within the broader compositional space of pyrolysis oils and HTL oils reported in laboratory studies. The oil compositions were employed to study the impact of oil composition on entrained flow gasification. Thermodynamic equilibrium calculations of pyrolysis and HTL oil entrained flow gasification resulted in H2/CO ratios of syngas and O2 consumption rates in a narrow range, despite the diversity of feeds. At the same time, to produce an equal molar amount of syngas (H2 + CO), less HTL oil than pyrolysis oil was required as feed. Gas cleaning technologies were reviewed to ascertain types of contaminants anticipated after gasification, their removal effectiveness, and Fischer–Tropsch catalyst poisoning potential. Raw syngas cleaning requirements were comparable to that from coal gasification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".