Influencing factors on the efficiency of tailings slurry thickening with sodium polyacrylate superabsorbent polymers
Bibliographic record
Abstract
This study proposes an alternative method for dewatering mine tailings slurries using superabsorbent polymers (SAPs), leveraging their exceptional water absorption and retention properties. The factors influencing the efficiency of this dewatering process are systematically evaluated and discussed. Experimental investigations were conducted using two types of SAPs (SAP1 and SAP2, differentiated by varying concentrations of the same adsorbed Na cation) to assess their dewatering potential on four distinct mine tailings slurries under two addition modes (direct and indirect). The initial solid mass concentrations (Cw_initial) tested were 40% and 50%, with SAP dosages (DvSAP) ranging from 6 to 29 kg dry SAP per cubic meter of tailings slurry. The findings indicate a negligible difference in absorbency between SAP1 and SAP2. Furthermore, final solid mass concentrations (Cw_final) of 70–82% were achieved with SAP dosages between 10 and 29 kg/m³. However, the efficiency of the SAP-mediated dewatering process was influenced by several factors, including DvSAP, the mineralogical and physical characteristics of the tailings’ slurry, the initial solid mass concentration (Cw_initial), the porewater chemistry and geochemistry, the SAP residence time (RT), and the addition mode (direct or indirect).
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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) | 0.001 | 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".