Hybrid Process Combining Electrocoagulation, Electroreduction, and Ozonation Processes for the Treatment of Grey Wastewater in Batch Mode
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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