Hybrid Process Combining Electrocoagulation and Electro-Oxidation Processes for the Treatment of Restaurant Wastewaters
Bibliographic record
Abstract
The present study investigates the electrocoagulation-electro-oxidation (EC-EO) process for the treatment of restaurant wastewater (RWW) loaded with organic and inorganic matter, oil, grease, and suspensions solids. The EC-EO process was evaluated in terms of its capability to simultaneously produce an oxidant and coagulant agents by using either iron or aluminum electrodes arranged in a bipolar configuration or graphite electrodes arranged in a monopolar configuration in the same electrolytic cell. Relatively high concentrations of active chlorine (9.6 mg/min) and aluminum (20–40 mg Al/L) or iron (40–60 mg Fe/L) were produced in situ. The best performance for RWW treatment was obtained by using aluminum and graphite plates alternated in the electrode pack and operated at current of 0.4 A during 90 min of treatment with pH adjusted to approximately 7.0. Under these conditions, more than 98% of oil and greases (O&G) were removed, whereas chemical oxygen demand (COD) and biological oxygen demand (BOD) removal reached 90% and 86%, respectively. Likewise, more than 88% of soluble phosphate was removed, and the process was effective in removing turbidity (98%) and suspended solids (98%). The EC-EO process operated under the best conditions involved a total cost of US$1.56±0.01/m3 of treated restaurant effluent. This cost includes energy and electrode consumption, chemicals, and sludge disposal.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| 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".