What Affects Dewatering Performance of High Density Slurry?
Bibliographic record
Abstract
The dewatering of flocculated high density slurry presents a significant challenge to most mining industries. The new technologies to treat high density slurry require a consistent and robust flocculation method in order to enter the market of tailings management. The flocculation of high density slurry, however, due to its complexity, is always a challenge to be undertaken appropriately and to evaluate the dewatering performance correctly. This paper probes the complexity by using a torque-controlled mixing technique to demonstrate the influence of feed properties, polymer type, polymer dosage, and mixing conditions on dewatering performance. The study shows that flocculant should be dosed at the optimal range to achieve the highest dewatering performance. A full dosage responsive curve including under dosage, optimal dosage, and overdosage is critical to evaluate the dewatering performance of high density slurries and flocculants. The mixing conditions such as mixing speed, mixing time, and geometry of the mixing impeller affect the flocculation efficacy. It was found that the dewatering performance of high density slurry is sensitive to solids content, water chemistry, and clay activity. High sodicity and high clay activity in the high density slurry decreases the dewatering performance. Therefore, it is critical to evaluate flocculants across multiple feeds and dosages with replication in order to select optimal dewatering performance. Using multiple key performance indicators (KPIs) to build technical and economic criteria is also critical for polymer evaluation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".