Durene conversion in a methanol‐to‐jet process: Exploring conversion at 150–350°C over different acid catalysts
Bibliographic record
Abstract
Abstract Acid catalyzed conversion of durene by isomerization, disproportionation, and transalkylation is of interest to the development of a methanol‐to‐jet process to reduce the amount of durene in the kerosene. It was of interest to determine whether durene conversion could be performed at a temperature lower than 350°C. As secondary objective, it was of interest to determine to what extent geometric constraints imposed by the acid catalyst affected product distribution. Catalysts with a range of zeolite framework types were employed, in order of increasing pore size: FER, MFI, MOR, BEA, and FAU. All reactions were performed in a batch reactor using a 25 wt.% durene and 75 wt.% toluene feed mixture over the temperature range 150–350°C and autogenous pressure. It was found that durene was more readily converted than toluene due to its higher basicity. Conversion over zeolite catalysts was more related to acid site concentration than acid strength. In zeolites with comparable silica‐to‐alumina ratio (acid strength) the observed activity for durene conversion also increased with increasing pore size (effective diffusivity): FER < MFI < MOR < BEA < FAU. All of the zeolite catalysts were sufficiently constraining to exhibit some product shape‐selectivity. Isomerization (monomolecular reaction) was favoured over disproportionation and transalkylation (bimolecular reactions) in all catalysts. Among zeolites, narrower pores and higher silica‐to‐alumina ratios further favoured monomolecular over bimolecular reactions due to the size and site adjacency requirements of bimolecular reactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".