Role of Liquid Concentration in Coke Yield from Model Vacuum Residue–Coke Agglomerates
Bibliographic record
Abstract
The fluid coking process is an example of an upgrading process that uses hot solids to heat and crack bitumen into more valuable products. When feed liquid is sprayed into a fluid bed of hot solids, the solids tend to agglomerate, giving simultaneous heating, reaction, and disintegration processes. The reaction of mixtures of three Athabasca vacuum residues and fluid coke particles was investigated by heating them in Curie point reactors in an induction furnace up to 530 °C. Small scale reactors with machined wells were fabricated from Curie point alloy. The yield of coke was measured as a function of the ratio of liquid to solid, heating rate, and feed type. Coke yields were insensitive to heating rates from 5 to 120 °C/s, at a constant final temperature. Bubbling was observed as the ratio of liquid feed in the mixture was raised above a threshold, depending on the reactivity of the feed used. Bubbling was observed to increase with greater heating rates, but it had little effect on ultimate coke yield. Coke yield increased with the fraction of bitumen on particles for all feed types tested. When the coke yields were normalized using the microcarbon residue content of the feeds, the results did not give a single trend when plotted with fraction of feed present in the mixtures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".