Impact of Asphaltene-Rich Aggregate Size on Coke Deposition on a Commercial Hydroprocessing Catalyst
Bibliographic record
Abstract
Diverse coke deposition mechanisms and models, all supported by experimental data, have been proposed for catalytic hydrogenation processes related to heavy oil and bitumen refining. In this contribution, nanofiltration is used to partition Athabasca bitumen and Maya crude oil so that the potential impact of asphaltene-rich nano-aggregate size on coke deposition on a commercial NiMo/γ-Al 2 O 3 hydrotreating catalyst could be investigated without introducing artifacts related to the use of solvents. Crude samples were filtered at 473 K through 5, 10, 20, and 50 nm ceramic filters. The feeds, permeates, and retentates are characterized in detail in a companion contribution (Zhao and Shaw, Energy Fuels 2007, 21 (5), 2795−2804). Batch coking experiments were conducted at 653 K for 2 h at a 30:1 feed to catalyst ratio (weight basis), with feed stocks, permeates, retentates, and control samples. Surface area, pore volume, and pore size distribution of coked catalysts were measured. The results were found to be consistent with partial filling of catalyst pores for all cases. Pore mouth plugging was not observed. Elemental analyses performed on cross sections of coked catalyst pellets show that coke deposition within pellets was diffusion limited for all cases evaluated while vanadium deposition, arising primarily from the maltene fraction, was not diffusion limited. Asphaltene-rich nano-aggregate size and maltene composition are shown to play secondary roles in coke deposition within catalyst pellets.
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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".