Treatment of wastewater containing ciprofloxacin using the hybrid treatment approach based on acoustic cavitation
Bibliographic record
Abstract
Abstract The present work investigates the treatment of wastewater containing an antibiotic, ciprofloxacin (CIP), using acoustic cavitation (AC) coupled with other advanced oxidation processes (AOPs). For the laboratory scale degradation study, operating parameters like drug concentration, operating pH, and initial temperature were initially optimized and then used in hybrid approach of AC in combination with AOPs. At 15 m/L initial drug concentration, neutral pH of 7, and ambient temperature of 30°C, optimum CIP degradation of 13.66% was obtained within 120 min of operation. The intensification of the AC‐based approach was subsequently achieved using other oxidants like H 2 O 2 , potassium persulphate (KPS), and O 3 . The combination of AC with H 2 O 2 and KPS resulted in 44.30% and 35.41% CIP degradation, respectively, while AC combined with ozone resulted in almost complete degradation of CIP within 90 min of treatment with a maximum cavitational yield of 4.761 × 10 −6 mg/J. The cost estimation for the optimized treatment approaches revealed that AC combined with ozone is the best process for degradation with the least operational cost of 8.7 Rs/L (105 US $/m 3 ). Complete degradation of CIP with AC + O 3 based approach at much lower costs opens a window for implementation in the pharmaceutical industries.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".