Stabilization of sulphidic mine tailings for prevention of metal release and acid drainage using cementitious materials: a review
Bibliographic record
Abstract
Environmental contamination produced by acid generating mines currently represents the largest environmental liability of the mining industry and necessitates the development of novel techniques for its mitigation. This paper reviews existing literature on stabilization of sulphidic mine tailings and prevention of acid mine drainage (AMD). It is shown that stabilization using ordinary portland cement in combination with pozzolanic and cementitious materials could be a viable option. However, variation of mine waste constituents and their interactions with different binders thwart the formulation of a generalized recipe for stabilization and further necessitate research to explore the optimal waste-binder proportions of the stabilized system components for the particular mine tailings under consideration. The demonstrated effective utilization of industrial by-products (fly ash, slag, cement kiln dust, etc.) in the preparation of modified cementitious materials for stabilization of sulphidic mining waste reinforces further interest in this area, not only to cope with acid mine drainage, but also to utilize abundant discarded industrial by-products for various beneficial considerations. This paper critically examines various mine tailings stabilization techniques in the literature, identifies the fundamental mechanisms controlling their performance and the intrinsic parameters of stabilization systems, along with the tailings-binder interaction mechanisms and performance assessment tools for stabilized tailings. Key words: stabilization, mine tailings, acid mine drainage, metallic and metalloid elements, leaching, portland cement, pozzolanic binders.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".