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 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".