Nickel and Zinc Removal from Acid Mine Drainage: Roles of Sludge Surface Area and Neutralising Agents
Bibliographic record
Abstract
During acid mine drainage (AMD) treatment by alkaline reagent neutralisation, Ni and Zn are partially removed via sorption to Fe and Al hydroxide precipitates. This research evaluated the effect of surface area of precipitates, formed by neutralisation of AMD using three alkalinity reagents (NaOH, Ca(OH)2, and CaCO3), on the sorption of Ni and Zn. The BET surface area of the precipitates formed by neutralisation of AMD with NaOH (173.7 m2 g−1) and Ca(OH)2 (168.2 m2 g−1) was an order of magnitude greater than that produced by CaCO3 neutralisation (16.7 m2 g−1). At pH 6.5, the residual Ni concentration was 0.32 and 0.41 mg L−1 for NaOH and Ca(OH)2 neutralised AMD, respectively, resulting in up to 60% lower Ni concentrations than achieved by CaCO3 neutralisation which had no effect on Ni removal. The residual Zn concentration was even more dependent on precipitate surface area for NaOH and Ca(OH)2 neutralised AMD (0.33 and 1.02 mg L−1), which was up to 85% lower than the CaCO3 neutralised AMD (2.20 mg L−1). These results suggest that the surface area of precipitated flocs and the selection of neutralising reagent critically affect the sorption of Ni and Zn during AMD neutralisation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".