The effect of preconditioning of tailings prior to inline flocculation and deposition
Bibliographic record
Abstract
Improving the dewatering characteristics of high solids tailings streams, by the addition of high molecular weight anionic polyacrylamides is known to be operationally challenging. This is particularly true in applications where a secondary flocculation occurs after primary treatment and thickening of a tailings stream – for example, underflow from a thickener or the dredging and re-treatment of unconsolidated material from a tailings dam. Previous experience has shown that high dosages of polymer are often required to increase the initial water release from the tailings on deposition and improve the longer-term consolidation of the deposit. This paper investigates the effect of preconditioning high solids tailings through the use of shear prior to flocculation with the aim of both reducing the overall polymer dose and improving the dewatering performance of the deposit. Data presented includes the effect of preconditioning on slurry rheology and initial water release of the polymer treated material. The work showed that in some circumstances, improvements may be achieved by applying an optimised level of pre-shear, but this is dependent upon the type and properties of the tailings. This study was undertaken on tailings slurries, from different mineral types, that have varying levels of clay and overall solids content.
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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 | 0.001 |
| 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".