Reactive Transport Modeling of Column Experiments for the Remediation of Acid Mine Drainage
Bibliographic record
Abstract
Reactive transport modeling was used to evaluate the performance of two similar column experiments. The experiments were designed to simulate the treatment of acid mine drainage through microbially mediated sulfate reduction and subsequent sulfide mineral precipitation by means of an organic carbon permeable reactive barrier. Principal reactions considered in the simulations include microbially mediated reduction of sulfate by organic matter, mineral dissolution/precipitation reactions, and aqueous complexation/hydrolysis reactions. Simulations of column 1, which contained composted leaf mulch, wood chips, sawdust, and sewage sludge as an organic carbon source, accurately predicted sulfate concentrations in the column effluent throughout the duration of the experiment using a single fixed rate constant for sulfate reduction of 6.9 x 10(-9) mol L(-1) s(-1). Using the same reduction rate for column 2, which contained only composted leaf mulch and sawdust as an organic carbon source, sulfate concentrations at the column outlet were overpredicted at late times, suggesting that sulfate reduction rates increased over the duration of the column experiment and that microbial growth kinetics may have played an important role. These modeling results suggest that the reactivity of the organic carbon treatment material with respect to sulfate reduction does not significantly decrease over the duration of the 14-month experiments. The ability of the columns to remove ferrous iron appears to be strongly influenced by the precipitation of siderite, which is enhanced by the dissolution of calcite. The simulations indicate that while calcite was available in the column, up to 0.02 mol L(-1) of ferrous iron was removed from solution as siderite and mackinawite. Later in the experiments after approximately 300 d, when calcite was depleted from the columns, mackinawite became the predominant iron sink. The ability of the column to remove ferrous iron as mackinawite was estimated to be approximately 0.005 mol L(-1) for column 1. As the precipitation of mackinawite is sulfide limited at later times, the amount of iron removed will ultimately depend on the reactivity of the organic mixture and the amount of sulfate reduced.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".