Nickel, cyanide, zinc, and copper removal from the effluent using photo-electrocoagulation-oxidation
Bibliographic record
Abstract
• The in-situ generation of ozone significantly improved the pollutants removal rate. • The simultaneous production of oxidizing agents caused the high removal efficiency. • The photoelectrocoagulation method increases ozone production. • The complete removal of copper and cyanide were achieved. • The stainless steel electrode had a significant role on the ozone agent generation. One emerging approach for eliminating organic and inorganic pollutants from wastewater is electrocoagulation, often coupled with traditional methods to enhance efficacy. This study investigates the simultaneous elimination of nickel (Ni), cyanide (CN), zinc (Zn), and copper (Cu) from the natural wastewater of a gold processing plant using the photo-electrocoagulation method with ozone as an oxidizing agent (ECOUV), both in continuous and batch modes, produced in situ. When performing the test in batch mode, CN, Ni, Cu, and Zn were removed at their peak of 100, 79.1, 100, and 89 %, respectively, at pH=10 and at i = 15 mA/cm 2 using graphite-aluminum cathodes and stainless-steel anodes for 60 min without injecting oxidizing agent and solely based on in-situ ozone production. During the continuous mode test, the highest removal efficiencies achieved were 100 % for CN, 73 % for Ni, 100 % for Cu, and 78.8 % for Zn, all under identical operational parameters. These results confirm that ECOUV holds promise as a feasible approach for removing pollutants from the wastewater discharged by mineral processing facilities.
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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) | 0.000 | 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".