Treatment of Textile Wastewater by a Novel Clay/TiO2/ZnO-Based Catalyst, Applying a Synergic Catalytic Ozonation–Electroflocculation Process
Bibliographic record
Abstract
This study aims to investigate the treatment of real textile wastewater using a novel bentonite clay/TiO2/ZnO-based ozonation catalyst. In this study, synergic electroflocculation/catalytic ozonation, catalytic ozonation, and ozonation processes are applied in a modified hybrid reactor. To the authors’ knowledge, this is the first application of bentonite clay/TiO2/ZnO as an ozonation catalyst for treating real textile wastewater. The four operational variables—ozone dose (0.2–0.8 mg/min), reaction time (0–120 min), DC voltage supply (5–15 V), and catalyst dose (0.5–2 g/L)—were studied for decolorization and for the removal of chemical oxygen demand (COD). The results showed that the combined process (electroflocculation + clay/TiO2/ZnO/O3) had the highest removal efficiencies for COD and color (97.86% and 97.90%, respectively) at optimum parameters of 10 DC volts. an ozone dose of 0.8 mg/min, and a catalyst dose of 2 g/L in textile wastewater. The results further revealed that the initial pH of wastewater plays an essential role in the process’s overall performance. The studied synergic process was efficient for real wastewater treatment under alkaline pH (6–9). Based on empirical work, we established that the synergic process is suitable for effectively treating textile wastewater.
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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) | 0.000 | 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 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".