The effect of acidic pH and presence of metals as parameters in establishing a sulfidogenic process in anaerobic reactor
Bibliographic record
Abstract
The successful use of anaerobic reactors for bioremediation of acid mine drainage has been shown in systems with neutral pH. However, the choice of an efficient and suitable process for such wastewater must consider the capability of operating at acidic pH and in the presence of metals. This work studies the performance of an anaerobic batch reactor, under conditions of varying initial pH for its efficiencies in sulfate removal and metal precipitation from synthetic acid mine drainage. The chemical oxygen demand/sulfate (COD/SO4(2-)) ratio used was 1.00, with ethanol chosen as the only energy and carbon source. The initial pH of the synthetic drainage was progressively set from 7.0 to 4.0 to make it as close as possible to that of real acid mine drainage. Metals were also added starting with iron, zinc, and finally copper. The effectiveness of sulfate and COD removal from the synthetic acid mine drainage increased as the initial pH was reduced. The sulfate removal increased from 38.5 ± 3.7% to 52.2 ± 3%, while the removal of organic matter started at 91.7 ± 2.4% and ended at 99 ± 1%. These results indicate that the sulfate reducing bacteria (SRB) community adapted to lower pH values. The metal removal observed was 88 ± 7% for iron, 98.0 ± 0.5% for zinc and 99 ± 1% for copper. At this stage, an increase in the sulfate removal was observed, which reaches up to 82.2 ± 5.8%. The kinetic parameters for sulfate removal were 0.22 ± 0.04 h(-1) with Fe, 0.26 ± 0.04 h(-1) with Fe and Zn and 0.44 ± 0.04 h(-1) with Fe, Zn, and Cu.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".