Effects of Coal Particles on Microbubble-Enhanced Bitumen Separation in the Concentrated Slurry Flow of Oil Sands Tailings
Bibliographic record
Abstract
Our study investigates the segregation of bitumen residues within the transport pipeline before disposal in the presence of coal particles in carriers and microbubbles. Coal particles decreased the bitumen recovery by 17% without the injection of microbubbles. In addition, the improvement in bitumen recovery efficiency by 6 mL of H 2 O 2 is negligible due to a small number of bubbles formed from H 2 O 2 decomposition in the flow. However, tremendous enhancement in the recovery efficiency was achieved with the simultaneous addition of coal particles and H 2 O 2 . Further increase in recovery was noted as a larger volume of H 2 O 2 was injected to form more microbubbles. Computational fluid dynamics (CFD) simulations were conducted to help understand the effects of coal particles and microbubbles. The simulation results illustrated that the introduction of coal particles caused bitumen contents to accumulate in the middle of the pipe. Furthermore, an increased volume fraction of microbubbles contributed to a higher distribution of bitumen at the top of the pipe. This study not only offers valuable insights for developing an innovative strategy to enhance the efficiency of bitumen separation in hydrotransport processes but also contributes to a deeper understanding of the intricate interactions among bubbles, bitumen, and coal particles in a slurry flow.
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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) | 0.000 | 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 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".