Microbubble Enhanced Bitumen Separation fromTailing Slurries with High Solid Contents
Bibliographic record
Abstract
Removing bitumen from oil-sands tailings reduces greenhouse gas emission during waste disposal. However, it is technically challenging to efficiently recover bitumen residual in tailing slurries with high solid contents, due to the tiny fraction of bitumen and complicated composition in the tailings. This work focuses on microbubble-enhanced bitumen recovery in a flow of artificial tailings that consisted of 50 wt% solids, 0.2 wt% bitumen and 49.8 wt% process water. Using a laboratory\textendash scale transport pipeline loop, we show that several operation conditions had a positive impact on the efficiency of bitumen recovery, including bubble formation from intensified cavitation, the addition of gas integrated with cavitation, or increasing the temperature up to 52 C. The efficiency of bitumen recovery was even higher from the addition of CO_{2} bubbles compared to air bubbles. Under optimal conditions, more than 61 % bitumen was recovered from an extremely initial fraction of 0.2 wt% with the generation of microbubbles. This work provides guideline in finding an effective combination of operational conditions for bitumen separation from hydrotransported real tailings containing high solid content. The same approach may be also applicable for cleaning of oil contaminated slurries.
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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".