Treatment of manganese in acid and neutral mine drainage using modified dolomite
Bibliographic record
Abstract
The treatment of manganese [Mn] in acid mine drainage (AMD) and neutral mine drainage (NMD) is prone to variation in performance, which depends on several factors, including pH, Mn and dissolved oxygen (DO) concentrations, catalysis by Mn-/iron [Fe]-solids, presence of ligands, and bacteria. Notwithstanding the recent technological and scientific advancements, Mn removal is notoriously difficult and still challenging because of its complex chemistry and high solubility. Precipitation of Mn(II), in AMD and NMD, occurs in the form of oxides, with prior oxidation to Mn(III) and Mn(IV), or as carbonates and sulphides. However, most treatment systems have limited efficiency in simultaneously treating Mn and other metals in AMD/NMD, necessitating secondary treatment for Mn removal. Modification of natural or residual materials was found efficient for the treatment of NMD. In this context, this study evaluated the performance of half-charred dolomite (1 h at 750C) in batch testing (solid: liquid ratio of 3 g:400 mL) for Mn treatment in AMD and NMD. Results showed that modified dolomite treated more than 98% Mn in synthetic NMD (pH 6.1-6.3, up to 1 g/L Mn), at final pH of 9.7-10.6. Similar efficiency (98%) in Mn removal was also found for synthetic AMD (pH 3.6, up to 100 mg/L Mn and 1 g/L Fe, Mn:Fe molar ratio 1:10), while Fe was completely treated, at final pH of 9.7-9.8. In addition, Mn removal was 99.5% within the first 2 h (when pH increased to 8.0), while after 4 h, the efficiency was up to 99.9% (at final pH of 9.6). DO also decreased (from 8 to 2.2 mg/L), at initial Mn concentrations of 1 g/L, and dropped (from 8 to 0.7 mg/L), when Fe concentrations increased to 1 g/L (Mn:Fe molar ratio 1:10). Based on these findings, half-charred dolomite seems a promising option for the treatment of Mn in both AMD and NMD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".