A comprehensive review of recent advances in membrane innovations for efficient heavy metal removal from mine effluents
Bibliographic record
Abstract
The growing global challenge of water scarcity, intensified by industrialization and population growth has heightened the need for effective wastewater management in industries, including the mining sector. Mining operations discharge substantial volumes of wastewater laden with toxic metal such as copper, iron, cobalt, lead and mercury which poses significant environmental as well as human health risk. Efficient wastewater treatment is crucial to mitigate these effects. While technological advancements have improved mine effluents treatment, there remains a need for advanced methods that enable not only removal of the toxic metals but also recovery of resources such as valuable metals and water. Due to its high efficiency, selectivity and low environmental footprint, membrane technology has gained attention especially in the treatment of various mine effluent. Though fouling is a major challenge in its implementation. The review gives an updated overview on the membrane technology in mining effluent treatment, examining the performance of various membranes (pressure driven membrane, thermal and concentration) in removal of metals and recycle of valuable resources from mine effluents such Acid Mine Drainage (AMD) and other mine effluents. It also examines innovative approaches such as pre-treatment processes, hybrid membrane system as well as the use nanocomposites polymeric membrane . Furthermore, the recent advances in membrane modification techniques such as chemical vapour deposition , sol-gel process, lithography, Atomic layer deposition , layer by layer and electrospinning have been discussed. Studies show that >95 % separation efficiency,> 85 % water recovery and >90 % metal recovery for hybrid membrane processes and chemical precipitation . The recovered metals show high purity of >99 %. Studies indicate that standalone membrane system have limitations in recovery of metals but hybrid systems (membrane coupled with other complementary methods) can achieve better results. This review identifies future direction for advancing membrane technology in sustainable mine wastewater management for improved environmental as well as mine operations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".