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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".