Statistical optimization of arsenic removal from synthetic water by electrocoagulation system and its application with real arsenic-polluted groundwater
Bibliographic record
Abstract
Arsenic presence in the water has become one of the most concerning environmental problems. Electrocoagulation is a technology that offers several advantages over conventional treatments such as chemical coagulation. In the present work, an electrocoagulation system was optimized for arsenic removal at initial concentrations of 100 µg/L using response surface methodology. The effects of studied parameters were determined by a 23 factorial design, whereas treatment time had a positive effect and current intensity had a negative effect on arsenic removal efficiency. With a p-value of 0.1629 and a confidence of level 99%, the type of electrode material did not have a significant effect on arsenic removal. Efficiency over 90% was reached at optimal operating conditions of 0.2 A of current intensity, and 7 min of treatment time using iron as the electrode material. However, the time necessary to accomplish with OMS arsenic guideline of 10 µg/L increased from 7 to 30 min when real arsenic-contaminated groundwater with an initial concentration of 80.2 ± 3.24 µg/L was used. The design of a pilot-scale electrocoagulation reactor was determined with the capacity to meet the water requirement of a 6417 population community in Sonora, Mexico. To provide the 1.0 L/s required, an electrocoagulation reactor with a working volume of 1.79 m3, a total electrode effective surface of 701 m2, operating at a current intensity of 180 A and an operating cost of 0.0208 US$/day was proposed. Based on these results, electrocoagulation can be considered an efficient technology to treat arsenic-contaminated water and meet the drinking water quality standards.
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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) | 0.001 | 0.001 |
| 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".