Statistical optimization of arsenic removal from synthetic water by electrocoagulation system and its application with real arsenic-polluted groundwater
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| 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 it