Effect of Aromatics on Deep Hydrodesulfurization of Dibenzothiophene and 4,6-Dimethyldibenzothiophene over NiMo/Al<sub>2</sub>O<sub>3</sub> Catalyst
Bibliographic record
Abstract
The influence of different aromatics on the deep hydrodesulfurization (HDS) of dibenzothiophene (DBT) and 4,6-dimethyldibenzothiophene (4,6-DMDBT) over a commercial NiMo/γ-Al 2 O 3 hydrotreating catalyst was investigated in a fixed-bed multiphase microreactor under designed conditions. Both the sulfur-containing compounds and the aromatic compounds in feeds and hydrotreated products were identified and quantified by GC−AED. Catalyst deactivation was observed, and a simple model for it was established. The kinetic behavior of these model compounds was studied with an assumption of pseudo-first-order reaction kinetics. The rate constants and the corresponding activation energies were determined, and the heat of adsorption for each compound on the catalyst surface was computed by density functional theory using the Material Studio software. The HDS rate for 4,6-DMDBT was much lower than that for DBT, which is attributed to the steric hindrance of the methyl groups at the 4 and 6 positions. The apparent activation energy of 4,6-DMDBT was higher than that of DBT under the same conditions. TThe HDS reaction rate significantly decreased with an increase of the content of aromatics in the feed. Aromatics with 2 or more rings were found to have stronger retardant effect on HDS than monoaromatics. This adverse effect was more pronounced for 4,6-DMDBT than for DBT. The competitive adsorption between the sulfur compounds and aromatics on the catalyst surface was the main reason for the decreased HDS efficiency as qualitatively verified by the heat of adsorption data.
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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".