Role of Mixing Energy in the Flocculation of Mature Fine Tailings
Bibliographic record
Abstract
High-molecular-weight polymer flocculants are used for accelerated dewatering of mature fine tailings (MFTs) to reclaim the land occupied by containment ponds. Two anionic polymers were used to flocculate an MFT sample containing 98% by weight of fines smaller than 44 μm in diameter, with 85% of this fraction being clay minerals. The polymer solutions and polymer-treated MFT followed the Herschel-Bulkley and Bingham equations of state, respectively. The coupling of shear thinning and structural breakdown of the flocculated MFT gave rise to cavern formation during mixing. The mixing energy input for a series of MFT flocculation tests, in which other conditions were held constant, was proportional to the mixing time. The mixing tools used for the flocculation process were a Rushton turbine (RT), a pitched-blade turbine (PBT), a vane, and hydrofoil impellers. The flocculation outcome was evaluated on the basis of the amount of water released and the capillary suction test time (CST). The CST of the treated MFT was inversely proportional to its water release volume in settling columns. There was a clear peak in the rate of water release and a minimum in the CST as a function of mixing time, clearly showing that there is an optimal mixing energy that corresponds to the most rapid dewatering of flocculated MFT.
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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) | 0.000 | 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".