Coal Tar Naphtha Refining: Phenol Alkylation with 1-Hexene and the Impact of Pyridine
Bibliographic record
Abstract
Coal tar naphtha is produced from coal carbonization, moving bed coal gasification, and thermal liquefaction of coal. The naphtha can contain up to 60% aromatics and 15% olefins, as well as nitrogen-, oxygen-, and sulfur-containing compounds. Usually only hydrotreating is considered, but when producing motor gasoline, olefin–aromatic alkylation could reduce the associated octane number loss due to olefin hydrogenation by converting olefins to alkylated phenols and aromatics. The plausibility of using acid-catalyzed alkylation with coal tar naphtha, which contains nitrogen bases, was investigated by studying a model system comprising phenol and 1-hexene in the absence and presence of pyridine. It was found that pyridine only inhibited conversion over a range of amorphous silica–alumina catalysts. The most effective catalyst was Siral 30 (30% silica, 70% alumina) and at 315 °C, 0.05 wt% pyridine caused a 35% inhibition of phenol conversion compared to conversion in the absence of pyridine. Catalyst activity could be restored by rejuvenating the catalyst with clean feed at a higher temperature. The results supported a description of phenol alkylation with olefins that took place by at least two pathways, one involving protonation of the olefin (typical for Friedel–Crafts alkylation) and one where the olefin is the nucleophile.
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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) | 0.000 | 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".