Removal of heavy metals from mine water using a hybrid electrocoagulation-ceramic membrane filtration process
Bibliographic record
Abstract
Mining operations must account for increasingly stringent wastewater discharge limits for various contaminants, such as heavy metals and suspended solids. In the treatment of metal-bearing wastes, electrochemical processes, including electrocoagulation (EC), are gaining popularity. At large scales, EC processes can be hindered by considerable amounts of flocculants and by sedimentation tank sizes required to properly operate. This work sought to address these concerns by evaluating EC in mine water treatment using a pilot system coupled with ceramic membrane microfiltration (MF) and ultrafiltration (UF). The use of MF and UF after EC allows for the removal of suspended solids without needing a flocculation-sedimentation process. EC evaluation was performed using field samples of mine water from a North American concentrator. Under optimal conditions, EC removed over 95 % of Cr, Co, Cu, Fe, Mn, Ni, Pb, Ti and Zn from the mine water. Effluent from EC was used as feed in MF and UF processes with ceramic membranes. The tested membranes rejected 100 % of suspended solids from the EC effluent. Permeate from the membranes was free of solids and possessed turbidities of 0.05–0.15 NTU. Filtrate from the hybrid process satisfied environmental regulations for heavy metal concentrations, offering new opportunities for reuse or discharge. The hybrid process applied in this work can thus be used in mining operations to safely discharge their water, and/or to increase their water recirculation due to environmental concerns and water shortage. • Electrocoagulation-ceramic membrane filtration process was used to treat mine water. • Electrocoagulation was capable of removing > 95 % of heavy metals from the mine water. • Permeate from ceramic MF/UF had no suspended solids and turbidity of 0.05-0.15 NTU. • Metal concentrations in permeate met discharge limits imposed on Canadian mines. • Hybrid process reduces footprint and offers new opportunities for water reuse/discharge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".