Towards a Circular Economy in the Mining Industry: Possible Solutions for Water Recovery through Advanced Mineral Tailings Dewatering
Bibliographic record
Abstract
The mining industry is confronted with substantial challenges in achieving environmental sustainability, particularly regarding water usage, waste management, and dam safety. The increasing global demand for minerals has led to increased mining activities, resulting in significant environmental consequences. By 2025, an estimated 19 billion tons of solid tailings are projected to accumulate worldwide, exacerbating concerns over their management. Tailings storage facilities represent the largest water sinks within mining operations. The mismanagement of water content in tailings can compromise their stability, leading to potential dam failures and environmental catastrophes. In response to these pressing challenges, the mining industry is increasingly turning to innovative solutions such as tailings dewatering and water reuse/recycling strategies to promote sustainable development. This review paper aims to (I) redefine the role of mine tailings and explore their physical, chemical, and mineralogical characteristics; (II) discuss environmental concerns associated with conventional disposal methods; (III) explore recent advancements in dewatering techniques, assessing their potential for water recovery, technical and economic constraints, and sustainability considerations; (IV) and present challenges encountered in water treatment and recycling within the mining industry, highlighting areas for future research and potential obstacles in maximizing the value of mine tailings while minimizing their environmental impact.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".