Compressional dewatering of flocculated mineral suspensions
Bibliographic record
Abstract
The recovery of water for reuse in minerals processing is an important issue in the reduction of water use in mining, as is the dewatering of tailings to reduce the propensity of “wet” tailings dams. It is common in these dewatering operations to use thickeners whereby polymeric flocculants are combined with the dilute feed to the thickener to aid sedimentation and the subsequent consolidation of particulate materials. Thickeners are the work horses of water recovery and tailings dewatering, and although the basics are well understood, there is still no ability to reliably predict the throughput and underflow density from a thickener based on traditional sedimentation tests and flux analysis. The reasons for the discrepancy between full scale operations and predictions based on laboratory tests are numerous, including difficulties in mimicking the flocculation conditions experienced in the field and the shear conditions in the thickener. The latter effects are little understood and hard to quantify and include effects due to flocculated aggregate densification, complimentary shear and compressional effects and aggregate break‐up. A set of test protocols to quantify these effects and their contributions to compressional dewatering in a thickening environment has been developed. The data and analysis show that aggregate densification is a dominant contributor to the difference between observed and predicted behaviour based on laboratory tests. Also of interest is the shear rate, concentration, and time dependence of the aggregate densification process. A basic model of the process has been developed along with network yielding tests conducted using rheometry.
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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 | 0.001 |
| 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".