Hydrocracking of Athabasca Vacuum Residue Using Ni-Mo-Supported Drill Cuttings
Bibliographic record
Abstract
Ni-Mo supported drill cuttings were used to catalyze the hydrocracking (HDC) of Athabasca vacuum residue (AVR) in an autoclave. Drill cuttings are a common waste product that are, depending on their origin, plentiful in acidic sites. The catalyst was prepared using the wet impregnation method. HDC was carried out at both low and high H2 pressure at 400 °C. Control thermal cracking (TC) and HDC runs with and without raw drill cuttings were performed to better examine the role of the supported drill cuttings catalyst. The quality in terms of viscosity and °API gravity, and the yield of various fractions making up the product oil were used to gauge the performance of the catalyst. Similar temperature and energy profiles between TC and HDC suggested strong overlap between the two different reactions, despite H2 presence. Nevertheless, supported drill cuttings runs at high H2 pressures promoted H2 consumption to a strong extent. Consequently, the liquid yield was the highest (~75 wt.%) and the coke yield was negligible. High temperature simulated distillation results revealed a residue conversion of ~55% for both low and high pressure HDC catalytic runs. The product oil quality with respect to viscosity and °API gravity was also found to be comparable between the low and high pressure HDC catalytic runs. Accordingly, no trade-off between liquid yield and quality was incurred at high H2 pressure. Effectively the supported drill cuttings drastically reduced coke formation, while maximizing the yield of the desired liquid product.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".