Bibliographic record
Abstract
Fischer-Tropsch Synthesis (FTS) has been used on a commercial scale for more than eighty years. It was initially developed for strategic reasons because it offered a source of transportation fuels that was independent from crude oil. Unlike crude, Fischer-Tropsch synthetic crude is rich in olefins and oxygenates, while being sulphur and nitrogen free. Consequently, the catalysis involved in refining it is significantly different and only a few catalysts have been developed for the purpose. Until now, an account of this topic has been missing from the literature, despite mounting interest in the technology. This is the first book to provide a review and analysis of the literature (journal and patent) on the catalysis needed to refine syncrude to transportation fuels. It specifically highlights the impact of oxygenates and how oxygenates affect selectivity and deactivation. This aspect is also related to the refining of biomass derived liquids. Topics covered include: dimerisation / oligomerisation, isomerisation / hydroisomerisation, catalytic cracking / hydrocracking and hydrogenation, catalytic reforming, aromatic alkylation, etherification, dehydration, and some oxygenate and wax specific conversions.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".