Treatment of real industrial pharmaceutical wastewater using wet peroxide oxidation
Bibliographic record
Abstract
Abstract The presence of recalcitrant organic molecules with high amounts of chemical oxygen demand, low biodegradability, and lack of effective treatment for pharmaceutical wastewater result in environmental pollution. Batch wet peroxide oxidation (WPO) experiments have been performed to study the effect of temperature (150°C‐250°C), oxidant coefficient (OC 0‐3), and reaction time (20 minutes‐60 minutes) on degradation efficiency of industrial pharmaceutical wastewater. Box‐Behnken design (BBD) with response surface methodology was used to study the effect of independent parameters on total organic carbon (TOC) removal response. The optimum temperature, oxidant coefficient, and reaction time of the process were found to be 250°C, OC 3, and 60 minutes, which resulted in TOC conversion of 57.96%. The obtained quadratic model has been able to predict the response with minimum deviation. The experimental modelling results conveyed that influence of process parameters followed the order: temperature > time > oxidant coefficient. To improve the mineralization efficiency, process parameters were changed to attain the near complete conversion (~99%) of the pharmaceutical wastewater. The qualitative analysis also showed that only a few components of pharmaceuticals were present in the treated effluent.
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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) | 0.001 | 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".