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‐Al2O3 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 H2/oil ratio = 600 m3 H2/m3 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".