Removal of sulphates acidity and iron from acid mine drainage in a bench scale biochemical treatment system
Bibliographic record
Abstract
The focus of this study was to develop a simple biochemical system to treat acid mine drainage for its safe disposal. Recovery and reuse of the metals removed were not considered. A three-step process for the treatment of acid mine drainage (AMD), proposed earlier, separates sulphate reducing activity from metal precipitation units and from a pH control system. Following our earlier work on the first step (biological reactor), this paper examines the second step (i.e. chemical reactor). The objectives of this study were: (1) to determine the increase in pH and the reduction of iron in the chemical reactor for different proportions of simulated AMD, and (2) to assess the capability of the chemical reactor. A series of experiments was conducted to study the effects of addition of alkaline sulphidogenic liquor (ASL) derived from a batch sulphidogenic biological reactor (operating with activated sludge and a COD/SO4 ratio of 1.6) on the simulated AMD characteristics. At 60-minute contact time, addition of 30% ASL (pH of 7.60-7.76) to the chemical reactor with 70% AMD (pH of 1.65-2.02), increased the pH of the AMD to 6.57 and alkalinity from 0 to 485 mg l(-1) as CaCO3, respectively and precipitated about 97% of the iron present in the simulated AMD. Others have demonstrated that metals in mine drainage can be precipitated by bacterial sulphate reduction. In this study, iron, a common and major component of mine drainage was used as a surrogate for metals in general. The results indicate the feasibility of treating AMD by an engineered sulphidogenic anaerobic reactor followed by a chemical reactor and that our three-step biochemical process has important advantages over other conventional AMD treatment systems.
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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) | 0.001 | 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 | 0.001 | 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".