Computational fluid dynamics investigation of bitumen residues in oil sands tailings transport in an industrial horizontal pipe
Bibliographic record
Abstract
Pipeline transport is commonly used in the oil sand industry to convey crushed oil sand ores and tailings. Bitumen residues in the oil sand tailings can be a threat to the environment that separating them from tailings before disposal is crucial. However, low bitumen concentration in the tailing slurry and the complex transport characteristics of the four-phase mixture make the process difficult. This study establishes an Eulerian–Eulerian (E–E) computational fluid dynamics model for an industrial-scale oil sand tailings pipeline. A comprehensive sensitivity analysis was conducted on the selection of carrier-solid and solid-bitumen drag models. The combination of small and large particle sizes (i.e., 75 and 700 μm) and bitumen droplet size (i.e., 400 μm) provided good agreement with field data in velocity profiles and pressure drop. The validated model was subsequently extended to investigate the influence of the secondary phase (i.e., bitumen droplets and bubbles) on flow characteristics in a tailing pipeline. The investigation covered a range of bitumen droplet size (100–400 μm), bitumen fraction (0.0025–0.1), bubble size (5–1000 μm), and bubble fraction (0.0025–0.3) and their influences on the velocity, solids, and bitumen distribution are revealed. For an optimum bubble size of 500 μm, a maximum recovery of 59% from the top 50% and 83% from the top 75% of the pipe cross section was obtained. The present study demonstrates the preferential distribution of bitumen and provides valuable insight into bitumen recovery from an industrial-scale tailing pipeline.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".