Settling Properties of Aggregates in Paraffinic Froth Treatment
Bibliographic record
Abstract
This paper presents results of studies on structural parameters and settling rates of aggregates formed during bench- and pilot-scale paraffinic froth treatment (PFT) at about 80 °C, during extraction of bitumen from the oil sands. The structures of individual aggregates were investigated using light microscopy, and their composition was estimated on the basis of image analysis. In addition, the settling velocities and dimensions of the aggregates were measured. It was found that the energy input during mixing of solvent and froth (proportional to mixing speed and duration) during PFT influenced the composition and settling velocity of the aggregates. Greater energy input resulted in higher concentrations of mineral particles (DS) in the aggregates. In bench-scale experiments, the concentration of minerals in the aggregates increased from 10 wt % to about 65 wt % with increasing energy input. The corresponding settling velocities of the aggregates increased from about 50 mm/min to above 700 mm/min. In pilot-scale tests, the average mineral content of aggregate samples was around 70 wt %, while the settling velocity ranged from 600 to 900 mm/min. It was found that the aggregates formed at higher temperature had relatively dense, nonporous structures consisting mainly of minerals and precipitated asphaltenes, and they settled rapidly. The average density of aggregates formed during pilot-scale tests, estimated on the basis of settling tests, was reaching 2000 kg/m 3 .
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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".