Dewatering of Mine Tailings Slurries Using Superabsorbent Polymers (SAPs) Reclaimed from Industrial Reject of Baby Diapers: A Preliminary Study
Bibliographic record
Abstract
Traditional deposition of tailings slurry in a tailings storage facility (TSF) can create risks of dike failure. In order to minimize these risks and slurry spillage, the surface deposition technique of densified tailings (DT) through dewatering of the slurry has emerged. The DT technique has the potential to maximize water reuse, improve the shear strength of surface tailings, and reduce the ecological footprint of TSF. The consistency of DT covers a continuum ranging from thickened state, to paste state, to dry state. Despite its efficiency and economic feasibility, DT densification using thickeners sometimes proves unable to achieve the design target solids mass concentration (Cw%). Hence, the use of superabsorbent polymers (SAPs) seems to represent a promising alternative, owing to their higher water absorbent capacity. In this paper, superabsorbent polymers (SAPs) reclaimed from industrial reject of baby diapers (Na-polyacrylates) are explored as a promising alternative to mine tailings slurries dewatering. To this end, laboratory-scale dewatering tests have been performed using two grades of Na-polyacrylate SAPs (grade 1 SAP = SAG-A06P coarse-grained, and grade 2 = SAG-M01P-100 medium-grained) for the tailings slurries densification. A higher water absorbency (or swelling capacity) was observed using the coarser SAPs (SAG-A06P) compared to the finer SAPs (SAG-M01P-100). The preliminary results showed that a SAP volume dosage in the range 10–13 kg of SAP/m3 of slurry allowed achieving a final solids mass concentration (Cw%_final) ≥ 70%, despite the occurrence of gel-blocking phenomenon.
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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".