ACID NEUTRALIZATION AND METAL MOBILIZATION IN OIL SANDS FROTH TREATMENT TAILINGS
Bibliographic record
Abstract
Acid generation and metal(loid) release is an emerging closure consideration for oil sands mines in northern Alberta, Canada. Froth treatment tailings (FTT) generated during bitumen extraction generally exhibit higher sulfide-mineral contents relative to other tailings streams. Recent studies have shown that pyrite oxidation can promote pore-water acidification and metal(loid) release within the unsaturated zone of FTT beach deposits. The corresponding sequence of acid neutralization reactions and their influence on metal(loid) release has not been studied. Laboratory column experiments examined acid neutralization reactions and their influence on metal(loid) mobility in samples collected from a commercial scale FTT beach deposit. Near-surface samples were collected from non-weathered, partially weathered, and highly weathered regions of this sub-aerial deposit, which provided an opportunity to examine the influence of initial weathering extent on acid neutralization and metal(loid) release. The experiments also considered non-solvent-washed (i.e., as received) and solvent-washed sample splits to assess impacts of residual hydrocarbons on these reactions. An acidic solution (i.e., 0.05 M H2SO4; pH ~1.5) solution was continuously pumped through each column and effluent samples were regularly collected for geochemical analysis. Effluent pH decreased from ~7 to 5.5 over the first 5 pore volumes (PVs) for the non-weathered and partially weathered columns. Gradual decreases in effluent pH to ~ 4.5 were observed over time, with subsequent rapid pH decreases to < 3 observed after more than 50 PVs in these columns. Effluent pH was consistently <2.0 for the highly weathered columns. We attribute these effluent pH ranges to the dissolution of Mg-bearing carbonates (pH ~6.5 to 6), Fe-bearing carbonates (pH ~5.6 to 4.5), Al hydroxides (pH ~4.5 to 4.0), and silicates (pH < ~2). These interpretations are supported by pH-dependent increases in effluent concentrations of Fe (< 1 to > 500 mg L−1), Al (<0.1 to >10 mg L−1), Si (<0.1 to >10 mg L−1), and several additional metal(loid)s (e.g., Ni, Zn, V, As) associated with FTT minerals. Cumulative mass release for metal(loids) was typically highest in the non-weathered samples, and generally higher in solvent-washed compared to non-solvent washed sample splits. These results offer important new insight into relationships between acid neutralization and metal(loid) release in FTT deposits that can inform FTT management and reclamation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".