Electrocoagulation (EC) for the Removal of Silica from Mining ARD Water Treatment
Bibliographic record
Abstract
During the nanofiltration (NF) and reverse osmosis (RO) process, ions in the reject stream becomes increasingly concentrated. NF/RO concentrates from the treatment of mining acid rock drainage (ARD) water contain high levels various impurities—depending on the geochemistry of the waste rock and the treatment processes applied. Presence of cross-linked fine colloidal particles, especially silica, at certain levels in NF/RO concentrate streams can cause membrane fouling creating significant operational and water management challenges. Electrocoagulation (EC) is a mature technology for many types of industrial waste water treatments. The technology can remove metal ions, colloidal suspensions, fine particles, and soluble inorganic pollutants from aqueous solution by introducing highly charged polymeric metal hydroxide species. In this paper, EC is explored as a treatment option for concentrated ARD water with focus in silica removal. Fe metal plates were employed as sacrificial electrodes. The experiments systematically investigate the influence of pH, dissolved oxygen (DO), current density (CD), and duration on the formation of green rusts (GRs) and silica removal. Batch tests have proved the concept that EC is a viable approach for silica removal from 80% RO reject water. Silica can be reduced from ~14 ppm down to ~3.0 ppm under 25mA/cm2 current density for 60 minute or 12.5 mA/cm2 for 120mins. Such level of silica removal can alleviate membrane fouling during subsequent NF/RO treatment. The DO level in solution has an important influence on pH and water chemistry, governs ferrous or ferric or ferrous/ferric mixed GR generation, and impacts the removal of silica in RO rejects from ARD water. EC with ferrous green rust formation under lowest DO level is best for silica removal.
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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 | 0.000 | 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 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".