Sustainable aviation fuel: Impact of alkene concentration on jet fuel thermal oxidative test (JFTOT)
Bibliographic record
Abstract
Abstract Of the processes that are approved to produce synthetic kerosene for use in jet fuel, about half produce olefinic kerosene that is hydrotreated. The alkene concentration in synthetic kerosene is indirectly regulated through the thermal oxidative stability specification. Perceptions about the deleterious influence of alkenes on thermal oxidative stability suggest that olefinic kerosene must be deeply hydrogenated. The extent of olefin saturation required has economic implications. To evaluate what an acceptable alkene concentration in synthetic kerosene is, the impact of alkene concentration on the outcome of the jet fuel thermal oxidative stability test (JFTOT) performed at 325°C in accordance with the ASTM D3241 standard test method was experimentally evaluated. Model synthetic kerosene mixtures to which different concentrations of alkenes (1‐decene, α‐methylstyrene, indene) were added, as well as control samples were studied. In the concentration range investigated, up to 10 wt% 1‐decene, 5 wt% α‐methylstyrene, and 2 wt% indene did not lead to increased fouling in the JFTOT. Fouling passed through a minimum value with increasing alkene concentration and alkene concentration on its own was a poor predictor of thermal oxidative stability. Analysis of the kerosene collected after passing through the JFTOT found measurable changes in density and refractive index. Dissolved oxygen reacting during thermal oxidative stability testing was accounted for mostly in oxygen‐containing products in the kerosene boiling range, which indicated that the heavier products were mainly hydrocarbon in nature. In addition to initiation by autoxidation, the investigation also pointed to the existence of a second thermally initiated fouling pathway that does not require the presence of oxygen.
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.
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 | 0.001 |
| 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".