Hydrodenitrogenation and Hydrodesulfurization of Heavy Gas Oil Using NiMo/Al<sub>2</sub>O<sub>3</sub> Catalyst Containing Boron: Experimental and Kinetic Studies
Bibliographic record
Abstract
In this work, a systematic study has been conducted to optimize the process conditions and to evaluate kinetic parameters for hydrodenitrogenation (HDN) and hydrodesulfurization (HDS) of heavy gas oil derived from Athabasca bitumen using NiMo/Al 2 O 3 catalysts containing boron (B). In the catalyst, the concentrations of boron were varied from 0 to 1.7 wt %. Experiments were performed in a trickle-bed reactor at the temperatures, pressures, and liquid hourly space velocities (LHSVs) of 340−420 °C, 6.1−10.2 MPa, and 0.5−2 h -1, respectively. H 2 flow rate and catalyst weight were maintained constant at 50 mL/min and 4 g, respectively, in all cases. Statistical analysis of all experimental data was carried out using ANOVA to optimize the process conditions for HDN and HDS reactions. Kinetic studies for HDN and HDS reactions were studied within the temperature range of 340−400 °C using a power law model as well as the Langmuir−Hinshelwood model. The power law model showed that HDN of heavy gas oil follows first-order kinetics while the HDS process follows 1.5-order kinetics. The activation energies for HDN and HDS reactions from power law and Langmuir−Hinshelwood models were 75 and 87 kJ/mol and 110 and 159 kJ/mol, respectively.
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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".