Optical In Situ Monitoring of Acid Mine Drainage Remediation: Laboratory and Model Investigation
Bibliographic record
Abstract
Abstract A permeable reactive barrier (PRB) can be deployed to remediate acid mine drainage. The performance of a PRB material under different boundary conditions (pH, flow velocity, and sulfate concentration) was investigated in a series of column experiments applying in‐situ optical sensing methods for pH and oxygen detection. The reactive material consisted of organic components (compost, wood, and coconut shell) mixed with calcium carbonate and fine gravel. The input concentrations were around 1000 mg/L for iron and 3000 mg/L for sulfate, and the pH value was 6.2. The remediation efficiency of iron was 14.6% and of sulfate 15.2%, but was expected to scale up when moving to a field‐site PRB with greater thickness. The iron and sulfate removal was influenced by decreasing the flow velocity and increasing the sulfate input concentration and the pH value. In an experiment with low pH boundary conditions (pH = 2.2), acidity was neutralized in the PRB by calcium carbonate during an experiment duration of 47 days. The modeling program MIN3P was used to create a simulation of the laboratory experiments. This helps to design parameters, for example, the residence time in the PRB, which is necessary for close to 100% remediation efficiency. This study shows the application of optical oxygen and pH monitoring in PRBs. In this context, they can be used to monitor the stability of a PRB for the remediation of acid mine drainage (AMD).
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".