Interplay between the Physical Properties of Athabasca Bitumen + Diluent Mixtures and Coke Deposition on a Commercial Hydroprocessing Catalyst
Bibliographic record
Abstract
Bulk and nanoscale physical properties of mixtures comprising Athabasca bitumen, its subfractions, and diluents such as n -dodecane, n -decane, and 1-methylnaphthalene affect coke deposition on a commercial, nanoporous, hydrotreating catalyst (NiMo/γ-Al 2 O 3 ). The interplay among properties at diverse length scales is complex and coking outcomes can appear counterintuitive. For example, in this work we show that dilution of Athabasca bitumen with n -dodecane (a poor physical solvent) reduces coke deposition on catalyst pellets vis-à-vis dilution with 1-methylnaphthalene (a good physical solvent), whereas we show a counter example for Athabasca vacuum residue + n -decane and 1-methylnaphthalene mixtures at the same temperature. Here, we reconcile such findings and link them to mixture properties at the macroscopic scale (the number, nature, and composition of phases present), the nanoscale (asphaltene nanoaggregation within phases) and the molecular scale (hydrogen solubility by phase). We also show that dilution of these feedstocks with n -dodecane and 1-methylnaphthalene enhances vanadium deposition selectivity in a commercial catalyst relative to the feeds. Results such as these underscore the need for the explicit incorporation of physical phenomena in the development of coke deposition models.
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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".