Iron removal in highly contaminated acid mine drainage using passive biochemical reactors
Bibliographic record
Abstract
Passive biochemical reactors (PBRs) are a viable alternative to neutralization plants for the treatment of acid mine drainage (AMD) because they require lower investment costs and use residual materials. However, high iron (Fe) concentrations (≥0.5 g/L) in AMD are challenging for their long-term efficiency. Sorption and precipitation are the main Fe removal mechanisms, but the relative importance of each is mostly unknown. In this study, locally available natural materials (organic and inorganic) were characterized and tested for their performance in Fe removal from highly contaminated AMD (pH 3.5, 4 g/L of Fe, and 9 g/L of sulfate). Iron retention capacity of the materials was then evaluated and the efficiency of eight mixtures of materials was compared through 40-day laboratory batch tests. All batch-type PBRs increased the pH up to 6.5 and decreased dissolved metals concentrations, including Fe, up to 99%. Results showed that organic residual materials (manures, municipal wastewater sludge, and compost) were the best substrates for Fe removal.These findings allowed for the selection of three reactive mixtures with distinct characteristics (mixture #1 - 30% organic wastes; mixture #4 - 50% calcite; and mixture #7 - 50% sand) to be further evaluated in column type PBRs.
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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".