Reactive-transport modeling of hydrogeochemical weathering processes in mine waste rock across a wide spatiotemporal scale range
Bibliographic record
Abstract
The mining industry globally produces millions of tons of waste rock every year. The weathering of exposed metal(loid)-rich waste rock can produce poor-quality effluent, and mine sites therefore need to establish water-quality management strategies that predict and mitigate environmental impacts. Technical frameworks to support drainage quality predictions and industrial waste-rock management typically combine classic static and kinetic testing procedures, field-scale experiments and sometimes geochemical equilibrium- and reactive-transport models. However, predictions of waste rock weathering and drainage processes remain challenging on relevant spatiotemporal scales, due to site-specificity in waste rock and local weathering conditions, unresolved heterogeneity in large waste-rock systems and the intricate (non-linear) coupling between chemical kinetics and mass- and heat transfer processes. We synthesized long-term (>10 yr) hydrogeochemical field data from a multiscale experimental research program at the Antamina mine, Peru. At Antamina, various waste-rock materials have been extensively hydraulically, physically and geochemically characterized and weathered at different spatiotemporal scales. This data set provides a unique opportunity to quantitatively assess the mechanisms that affect drainage from different waste-rock types under field conditions. Monitoring of weathering rates in humidity cell tests (~1 kg), column experiments (~170 kg), field barrel kinetic tests (~350 kg), and mesoscale experimental piles (~6,500,000 kg) revealed that normalized mass loadings from different waste-rock types systematically decreased with increasing experimental scale. We developed a process-based reactive-transport framework to reproduce the recorded waste-rock drainage trends from the various field experiments. For each of the experiments, 1-D reactive-transport models were constructed in MIN3P-HPC, all including the same formulations for, e.g., transient unsaturated flow, advective-diffusive transport of aqueous species, gas diffusion, gas-liquid partitioning and equilibrium or kinetic mineral dissolution and precipitation reactions. The models were exclusively parameterized with measured field hydrostatics (e.g., tracer testing, volumetric water contents; van Genuchten parameters), analyzed physicochemical bulk waste-rock properties (e.g., bulk geochemistry, mineral content, particle size), or adopted literature values (e.g., kinetic rate laws and constants). At all experimental scales, the recorded drainage quality evolution could be successfully reproduced with the consistent suite of field-parameterized physical transport processes and kinetic rate laws. A comparison of fitted effective rate coefficients reveals that reduced weathering rates at increasing scales mostly originate from decreasing specific mineral surface areas (particle sizes increase with experimental scale) and possibly by surface passivation, although the effects of flow bypassing and channeling are not yet fully investigated. This work demonstrates that with efforts focused on the identification and parameterization of the relevant physicochemical processes, effective yet process-based models can be developed from readily available bulk waste-rock parameters to predict and upscale mine waste rock weathering and drainage quality trends across laboratory-to-practice-relevant scale ranges.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".