Effect of Hydrogen Purity on Hydroprocessing of Heavy Gas Oil Derived from Oil-Sands Bitumen
Bibliographic record
Abstract
The effect of hydrogen purity on hydroprocessing of heavy gas oil (HGO) was studied in a trickle bed reactor over a commercial Ni−Mo/γ-alumina catalyst. Methane was used as a diluent for the hydrogen stream, and its effect on the catalyst performance was compared to that of helium, which is inert toward the catalyst. Furthermore, a deactivation study was conducted over a period of 66 days, during which the catalyst was subjected to H 2 purity ranging from 95 to 75% (with the rest methane); no significant deterioration in the hydroprocessing activities of the catalyst was observed. Therefore, it was concluded that methane was inert toward a commercial Ni−Mo/γ-alumina catalyst. However, its presence resulted in hydrogen partial pressure reduction, which in turn led to a decrease in hydrodesulphurization (HDS), hydrodenitrogenation (HDN), hydrodearomatization (HDA) conversions. This reduction can be offset by increasing the total pressure of the system. HDS, HDN, HDA, and mild hydrocracking (MHC) conversions were studied. Also determined were cetane index, density, aniline point, diesel index, and fractional distribution of the products. The temperature and liquid hourly space velocity (LHSV) were set constant at 380 °C and 1 h −1, respectively. Pressure and gas/oil ratio ranges were 7.2−10.1 MPa and 400−1270 mL/mL, respectively. The hydrogen purity was varied in the range of 0−100 vol % (with the rest methane or helium).
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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".