Challenges and avenues for acid mine drainage treatment, beneficiation, and valorisation in circular economy: A review
Bibliographic record
Abstract
Mining activities are notorious for their environmental impact, with acid mine drainage (AMD) being among the most significant issues. Specifically, AMD has recently been a topical issue of prime concern, primarily due to the magnitude of its environmental, ecotoxicological, and socioeconomic impacts. AMD originates from both active and abandoned mines (primarily gold and coal) and is encountered in Canada, China, Russia, South Africa, USA, and other countries with strong mining industry. Owing to its acidity, AMD contains elevated levels of dissolved (toxic) metals, metalloids, rare-earth elements, radionuclides, and sulfates. Practical and cost-effective solutions to prevent its formation are still pending, while for its treatment active (driven by frequent input of chemicals and energy) or passive (based on oxidation/reduction) technologies are typically employed with the first being more efficient in contaminants removal, however, at the expense of process complexity, cost, and materials and energy consumption. More recently, and under the circular economy concept, hybrid (combination of active and passive technologies) and particularly integrated (sequential or stepwise treatment) systems have been explored for AMD beneficiation and valorisation. These systems are costly to install and operate but are cleaner production systems since they can effectively prevent pollution and can be used for closed-loop and sustainable AMD management (e.g., zero liquid discharge (ZLD) systems). Herein, the body of knowledge on AMD treatment, beneficiation (metals/minerals recovery), valorisation (water reclamation), and life cycle assessment (LCA) is comprehensively reviewed and discussed, with focus placed on circular economy. Future research directions to introduce reuse, recycle, and resource recovery paradigms in wastewater treatment and to inspire innovation in valorising this toxic and hazardous effluent are also provided. Overall, AMD beneficiation and valorisation appears promising since the reclaimed water and the recovered minerals/metals could offset treatment costs and environmental impacts. However, the main challenges include high-cost, complexity, co-contamination in the recovered minerals, and the generation of a higly heterogeneous and mineralised sludge.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".