Effects of Hydrogen Partial Pressure on Hydrotreating of Heavy Gas Oil Derived from Oil-Sands Bitumen: Experimental and Kinetics
Bibliographic record
Abstract
The effect of hydrogen partial pressure (H 2 pp) on hydrotreating conversions, feed vaporization, H 2 dissolution, and H 2 consumption was studied in a micro trickle-bed reactor, using a commercial NiMo/γ-Al 2 O 3 catalyst. Heavy gas oil (HGO) from Athabasca bitumen was used as feed. The H 2 pp level was set inside the reactor by means of manipulating other operating variables, namely, H 2 purity, pressure, gas/oil ratio, liquid hourly space velocity (LHSV), and temperature. Their ranges were as follows: 75−100 vol % (with the rest methane), 7−11 MPa, 400−1200, 0.65−2 h −1, and 360−400 °C, respectively. HYSYS was used to determine the inlet and outlet H 2 pp. The results show that hydrodenitrogenation (HDN) and hydrodearomatization (HDA) are significantly more affected by H 2 pp than hydrodesulfurization (HDS), with HDN being the most affected. Moreover, it was observed that H 2 dissolution and H 2 consumption increase with increasing H 2 pp. No clear trend was observed for the effect of H 2 pp on feed vaporization. Kinetic studies of HDS, HDN, and HDA were performed using the power law model, multi-parameter model, and Langmuir−Hinshelwood-type (L−H) model, and the prediction abilities of the resultant models were tested. It was determined that, while the multi-parameter model yielded better prediction, the L−H model had an advantage in that it took a lesser number of experimental data to determine its parameters. The prediction ability of the power law was not tested because it excludes many operating variables.
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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".