Inhibition and Deactivation of Hydrodenitrogenation (HDN) Catalysts by Narrow-Boiling Fractions of Athabasca Coker Gas Oil
Bibliographic record
Abstract
The hydrodenitrogenation (HDN) of quinoline in the presence of narrow-boiling fractions of Athabasca bitumen coker gas oil was studied over a commercial NiMo/γ-Al 2 O 3 hydrotreatment catalyst. The study was conducted to determine whether trends in HDN activity with increasing boiling point were the result of the increased molecular weight of the Athabasca coker gas oil (i.e., changes in hydrocarbon structure) or due to the nitrogen species contained in the feedstocks. In each boiling-point range, the components in the gas oils demonstrated, to a varying degree, both reversible inhibition and deactivation of catalyst activity. The low-boiling gas oil fraction (bp. 343−393 °C) was more inhibitory and deactivated the catalyst to a greater degree than intermediate- (bp. 433−483 °C) or high-boiling (bp. 524 °C+) fractions for the HDN of quinoline. Nitrogen speciation analysis suggested that alkyl-carbazoles and tetrahydrobenzocarbazoles were the primary species responsible for the higher inhibition and deactivation observed in the lightest fraction. In addition, the HDN activity of the narrow-boiling fractions varied with hydrogen partial pressure and sulfur concentration, although these effects were independent of molecular weight. This study suggests that, although Athabasca coker gas oils have higher concentrations of polyaromatics, compared to conventional distillates, non-nitrogen-containing species are insignificant in inhibiting catalyst activity, in comparison to the organonitrogen compounds. Consequently, the resistance of the Athabasca coker gas oils to HDN can be attributed to the organonitrogen compounds particularly, alkyl-carbazoles and tetrahydrobenzocarbazoles rather than the aromaticity of the gas oils.
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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".