Experimental and Kinetic Studies of Aromatic Hydrogenation, Hydrodesulfurization, and Hydrodenitrogenation of Light Gas Oils Derived from Athabasca Bitumen
Bibliographic record
Abstract
In this work, a systematic experimental and kinetic study of hydroprocessing of light gas oils (LGOs) such as vacuum LGO (VLGO), atmospheric LGO (ALGO), and hydrotreated LGO (HLGO) using NiW/Al 2 O 3 and commercial NiMo/Al 2 O 3 catalysts has been conducted. Experiments were performed by varying temperature from 340 to 390 °C, at a constant pressure and liquid hourly space velocity of 11.0 MPa and 0.6 h -1, respectively. H 2 /feed ratio was maintained at 550 mL/mL throughout the experiments. Appreciable hydrogenation of aromatics (AHYD) was achieved by the NiW/Al 2 O 3 catalyst at low temperatures and at high severities of hydrotreating. However, the hydrogenation activity of NiMo/Al 2 O 3 was superior to that of the NiW/Al 2 O 3 catalyst. For hydrodesulfurization (HDS) and hydrodenitrogenation (HDN) activities, higher conversions of 95−98.8 and 96−99 wt %, respectively, were attained for the commercial NiMo/Al 2 O 3 catalyst throughout the temperature range studied. Simulated distillation of the feed showed that VLGO contained the most complex and heaviest compounds followed by HLGO and ALGO. Diesel selectivity in both ALGO and HLGO increased with hydrotreating temperature, but in the case of VLGO, it decreased with temperature. Kinetics studies showed that dearomatization of the HLGO feed was the most difficult, followed by ALGO and then the VLGO. Kinetics of ALGO and VLGO were best described by a pseudo-first-order reaction mechanism while the 1.3 power law kinetics worked well with HLGO.
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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".