Bibliographic record
Abstract
Carbon sources, such as coal, natural gas, biomass and waste, can be converted into transportation fuels by combining appropriate gasification, Fischer–Tropsch and refining technologies. Efficient refining of the Fischer–Tropsch synthesis derived syncrude requires a different approach to refinery design than commonly applied to crude oil refinery design. The design of refineries to optimise the production of on-specification motor-gasoline, jet fuel and diesel fuel respectively from both high temperature Fischer–Tropsch (HTFT) syncrude and low temperature Fischer–Tropsch (LTFT) are considered. Refinery designs are presented for the production of motor-gasoline and jet fuel with better than 50% yield (better than 70% selectivity on transportation fuel), without resorting to very complex designs. Only diesel fuel refining presented a problem, since the production of on-specification EN590:2004 diesel fuel is limited by a Fischer–Tropsch specific cetane-density-yield trade-off. The compound classes that are required to produce diesel fuel in high yield that meet both minimum cetane number and minimum density requirements are not abundant in Fischer–Tropsch syncrude. Refinery designs for diesel fuel production was limited to a yield of less than 25% EN 590 : 2004 compliant diesel fuel. This yield restriction does not apply when diesel fuel specifications do not have a minimum density requirement.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".