Modelling of H<sub>2</sub> consumption and process optimization for hydrotreating of light gas oils
Bibliographic record
Abstract
Abstract Hydrogen consumption regression models were developed for the hydrotreating of various light gas oil streams derived from Canadian oil sands including virgin light gas oil (VLGO), hydrocracker light gas oil (HLGO), coker light gas oil (KLGO), and a partially hydrotreated heavy gas oil (PHTHGO) stream over commercial NiMo/ɣ‐Al 2 O 3 in a micro‐trickle bed reactor. The experiments were designed by central composite design (CCD) and covered a wide range of temperatures (353–387 °C), pressures (8.27–10.12 MPa), and liquid hourly space velocity (LHSV) (0.7–2.3 h −1 ), at H 2 /oil ratio = 600 m 3 H 2 /m 3 oil. A composite regression model comprising of all four feed streams was also developed and tested against a new batch of experimental data. The composite model compared favourably with the experimental data. In addition, the composite model fits better than similar correlations from the literature. The effects of process conditions on hydrodesulphurization (HDS), hydrodenitrogenation (HDN), and hydrodearomatization (HDA) conversions were also studied in this work. Based on the experimental data, regression models were developed for each feedstock to obtain the optimum conditions to maximize hydrotreating conversions.
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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".