Assessment of Membrane and Electrochemical Technologies for the Treatment of a Selenium-Bearing Mine water: Technology Performance and Effect on Toxicity and Bioaccumulation
Bibliographic record
Abstract
Abstract Nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), and electrocoagulation (EC), were all tested at the bench scale for removing selenium (Se) from mine water. All of these technologies reduced the concentration of total Se from 216 µg/L (i.e. 120.1 µg/L of selenate; 59.1 µg/L of selenite, and 0.6 µg/L methyl-selenic acid) in the raw mine water to about 2 µg/L or less in the treated water, equivalent to more than 99% removal. Electrodialysis was found to be the most effective, removing more than 99.5% of the Se. The untreated mine water was toxic to algae. In contrast, RO and NF reduced the toxicity of the mine water, allowing algae to grow between 15,000 to 25,000 cells/mL, while ED and EC did not allow algal growth, likely due to complete removal of essential minerals (ED) or the presence of other contaminants (EC), such as copper. The Se speciation did not change as a result of membrane filtration; however, selenite in the effluent was almost fully transformed to selenate in the brines from the ED and EC treatment processes. The effluent treated by NF and EC generated seleno-cyanate at 0.37 and 1.01 µg/L, respectively. Further testing is recommended at the pilot-scale with the same mine water as well as different mine water.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".