Catalyst Deactivation and Reactor Fouling during Hydrogenation of Conjugated Cyclic Olefins over a Commercial Ni–Mo–S/γ-Al<sub>2</sub>O<sub>3</sub>Catalyst
Bibliographic record
Abstract
The dimerization of conjugated cyclic olefins during hydrogenation at low temperatures (≤250 °C) on a spent commercial Ni–Mo–S/γ-Al 2 O 3 catalyst is reported. Hydrogenation of 4-methylstyrene versus α-methylstyrene showed that the methyl group attached to the vinyl group of α-methylstyrene decreased the dimer yield as a result of steric hindrance, while the yield of hydrogenated products remained high. The addition of 20 wt % cyclohexene to 4-methylstyrene and reaction at a lower temperature (200 versus 250 °C) decreased the 4-methylstyrene hydrogenation rate. An increased concentration of 4-methylstyrene and a lower reaction temperature increased dimer and gum yields. The data indicate that dimers are precursors to gum formation and that catalyst deactivation is linked to gum formation that results in an increased carbon content and a decreased Brunauer–Emmett–Teller surface area of the used catalyst. Furthermore, an increase in pressure drop across the fixed-bed reactor with time on stream (TOS) observed with 4-methylstyrene as the reactant but not with α-methylstyrene is consistent with cumulative gum deposition in the catalyst bed. The pressure drop is well-described by the Ergun equation, assuming that gum deposition reduces bed voidage with TOS.
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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".