Effect of waste rock particle size on acid mine drainage generation: Practical implications for reactive transport modeling
Bibliographic record
Abstract
Mine waste rock poses significant environmental challenges. Evaluating management and reclamation options is particularly complex because of the wide particle size distribution, the non-uniform distribution of acid-generating and buffering minerals, and the variable contribution of the different particle size fractions to acid mine drainage (AMD) generation. Reactive transport simulations can be useful to complement and overcome the limitations of laboratory and field experiments. However, predicting field-scale and long-term geochemical behavior of waste rock requires a better understanding of numerical parameters scale-up. In this study, three waste rocks, with different mineral composition and particle size distribution, were separated into different fractions and tested in the laboratory. Kinetic tests were used to calibrate numerical models and adjust minerals' effective kinetic rate constants to match measured pH and metal concentrations. Calibrated reactive transport simulations were able to reproduce accurately the effect of particle size on pH and sulfate and calcium production rates. Experimental and numerical results confirmed that waste rock oxidation and neutralization rates tended to decrease with increasing particle sizes. Several models were tested and the weighted geometric mean of the effective kinetic rate constants as a function of the proportion of each fraction provided the most accurate estimation of the whole specimen kinetic rate constants. A novel approach to predict waste rock geochemical behavior from a single laboratory test also showed promising results. Overall, these results should contribute to improving the extrapolation of laboratory kinetic test results to field predictions.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".