Role of ettringite formation in the stabilizationsolidification of sulphide-bearing mine waste
Bibliographic record
Abstract
Disposal of acid generating sulphidic tailings is a major environmental problem facing the Canadian as well as the international mining and mineral industries. If the deposited tailings are not treated the sulphide minerals oxidise and create an acidic environment. The leachate from such a site is rich in soluble heavy metals and has the potential to contaminate the groundwater as well as the local surface watercourses. This thesis explores an alternative treatment method via ettringite formation, which stabilises and solidifies these highly reactive tailings. Ettringite has the capacity of uptaking other metals into its structure by isomorphous substitution. Ettringite has also a negative surface charge, which makes it a good adsorbent for positively charged cations. Furthermore, the high pH solution required for ettringite formation will cause the precipitation of heavy metals from the solution. Therefore, ettringite in a lime-remediated sulphate rich acid mine drainage system could serve as a physical and chemical stabilization agent, which could also reduce the remediation costs over other commercially available stabilization technology. In order to assess the feasibility of forming ettringite in sulphide rich tailings, a thermodynamic model was used to investigate the optimal geochemical parameters. The model predicts that ettringite is stable over a wide range of compositions and pH. Results presented in this thesis show that the sulphate content of the tailings samples could limit the precipitation of ettringite, and monosulphate could be the end-precipitate. Long-term leachability and durability analysis showed that ettringite can be formed as a stable mineral in lime, fly ash, and aluminum rich sulphidic tailings samples. Ettringite formation reduced the leachability of the heavy metals and improved the geotechnical characteristics of the treated samples. Freeze and thaw analyses showed that formation of ettringite produce a low permeability and high strength tailings sample capable of withstanding harsh environmental fluctuations.
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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".