Effectiveness of a hybrid process combining electro-coagulation and electro-oxidation for the treatment of domestic wastewaters using response surface methodology
Bibliographic record
Abstract
The performance of a two-stage process combining electro-coagulation (EC) and electro-oxidation (EO) was studied for the treatment of domestic wastewater (DWW) loaded with organic matter. The process was firstly evaluated in terms of its capability of simultaneously producing an oxidant and a coagulant agents using aluminum (Al) (or iron (Fe)) as bipolar and sacrificial electrodes, whereas graphite (Gr) electrodes were used as monopolar electrodes. Relatively high concentrations of chlorine (9.6 mg/min A) and Al (20-40 mg Al/L) or Fe (40-60 mg Fe/L) were produced. Subsequently, the factorial and central composite design methodologies were successively employed to define the optimal operating conditions for total chemical oxygen demand (COD) removal from DWW. Current intensity and treatment time were found to be very meaningful for chemical oxygen demand removal. The effect of these two main factors was around 90% on the investigated response, whereas the type of sacrificial electrode and the other interaction effects represent only 10%. The treatment using aluminum electrode and a current intensity imposed of 0.7 A during 39 min was found to be the optimal conditions in terms of cost/effectiveness. Under these conditions, 78% of COD removal can be obtained for a total cost of 0.78 US $/m(3).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".