Impact of preflocculation on scroll decanter centrifuge separation performance
Bibliographic record
Abstract
Abstract Industrial scroll decanter centrifuge (SDC) separation of the solids in fluid fine tailings (FFT) which have particles 10 μm and smaller in size require flocculant addition. Accordingly, the mechanism in the centrifuge is flocculation of the fine solid particles followed by sedimentation. Thus, feeding preflocculated material obviates the flocculation step inside the SDC resulting in improved process efficiency by reducing the power consumption and increasing the throughput capacity of each SDC. This work evaluated the separation performance of the SDC using inline static mixers to preflocculate oil sands FFT containing solids, 90 mass% of which are < 9 μm in size. The minimum G‐forces required to achieve the industry benchmarks of < 1 mass% solids in the centrate water and > 97 % fines capture rate were 750 G without preflocculation and 400 G with preflocculation. Since power consumption by the SDC, as expected, was directly proportional to the G‐force, the savings in power due to preflocculation amounted to 47 %. Preflocculation increased SDC throughput capacity by up to 50 %. Operating at lower G‐force has the additional benefits of protecting the high‐capital‐cost SDC machine and reducing ambient noise levels.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".