Use of a combined method of photo-electrocoagulation-oxidation for simultaneous removal of Ni, CN, Zn, and Cu from synthesized mine wastewater
Bibliographic record
Abstract
• More than 92% pollutants were removed by photoelectrocoagulation-oxidation method. • The simultaneous production of hydroxyl and superoxide radicals increases the efficiency of pollutant removal. • The stainless steel electrode had a higher efficiency than the aluminum electrode. A diverse range of heavy metals and toxic elements are present in the wastewater from mineral processing. A novel approach for eliminating contaminants from wastewater involves electrocoagulation (EC), which is occasionally employed in conjunction with additional techniques such as advanced oxidation to enhance the overall efficacy of pollutant removal. Therefore, in this study, for the simultaneous elimination of Ni, CN, Zn, and Cu, the combined method of EC-UV along with the oxidizing agents of hydrogen peroxide (HP) and ozone (O) were used. Based on the findings of this study, the utilization of integration of photo-electrocoagulation and oxidation methods together yielded the highest removal rates for Ni, CN, Zn, and Cu, with removal efficiencies reaching 100%, 98.8%, 95.2%, and 95.8%, respectively. The highest simultaneous elimination of the target pollutants was attained when stainless steel and iron were employed as anode electrodes, while aluminum and graphite served as cathodes. This optimal removal was achieved under conditions including the addition of 4 mg/L hydrogen peroxide, an ozone flow rate of 3 L/min, current density of 15 mA/cm², pH = 10, process duration of 10 min, and an electrode spacing of 5 cm in batch mode. The relatively high efficacy in removing pollutants was attributed to the simultaneous generation of hydroxyl radicals and superoxide, alongside the formation of coagulants produced through electrocoagulation. According to the promising results of this research, the integration of the advanced oxidation process with electrocoagulation demonstrates high efficacy in eliminating various contaminants, including both organic and inorganic pollutants, from wastewater.
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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 | 0.002 |
| 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 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".