Hybrid Process Combining Electrocoagulation, Electroreduction, and Ozonation Processes for the Treatment of Grey Wastewater in Batch Mode
Bibliographic record
Abstract
The present study investigates the electrocoagulation-electroreduction (EC-ER) and ozonation process (ECRO process) for the treatment of grey wastewater (GWW) loaded with organic and inorganic matter, oil and greases (O&G), and total suspensions solids (TSS). Several factors, such as electrode materials, current density, electrolysis time, initial pH, wastewater conductivity, and ozone dosage were investigated. High treatment efficiency of GWW was recorded while applying the EC-ER technique followed by the ozonation process. The best performance for GWW treatment by the EC-ER process was obtained using aluminum and graphite electrodes operated at current density of 0.9 A/dm2, during 90 min of electrolysis time and at pH around 10 whereas the ozonation treatment of GWW was found to be more effective at pH 8 and at 9.2 g/h of ozone dosage. Under these optimal conditions, combining the electrochemical (EC-ER) and ozonation processes enhanced the removal of organic and inorganic contaminants from GWW. The ECRO process reduced total chemical oxygen demand (CODT) by 91.31±1.09%, total organic carbon (TOC) by 84.59±1.71%, soluble chemical oxygen demand (CODs) by 90.17±0.26%, and dissolved organic carbon (DOC) by 82.11±2.19%. Besides, the removal efficiency of biological organic demand (BOD), O&G, and total phosphorous (PT) reached 92.61±0.24%, 90.40±0.31%, and 86.66±0.00%, respectively.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".