Biodegradation of Phenol in Synthetic Wastewater Using a Fixed Bed Reactor With up Flow Sludge Blanket Filtration (FUSBF)
Bibliographic record
Abstract
In the present study, the removal of phenol from synthetic wastewater was evaluated in a fixed bed reactor with up flow sludge blanket filtration (FUSBF) in comparison with a typical USBF system. At hydraulic retention time (HRT) of 24 hours and solid retention time (SRT) of 20 day, the effect of initial concentration of phenol (260-1020 mg/L) on phenol and chemical oxygen demand (COD) removal efficiency (%) was investigated in both systems. The effect of the presence of fixed bed was determined throughout the operational period. The results showed that the FUSBF system had a better ability than the typical USBF system in terms of phenol and COD removal. The average phenol and COD removal at phenol concentration of 312 mg/L was 97.52% and 92.82% for the FUSBF system and 92.80% and 82.18% for the typical USBF system, respectively. At HRT of 24 h and organic loading rate (OLR) of 30 g/m(-3).h(-1), the amount of phenol removal was 82.1%. At OLR of 30 g/m(-3).h(-1), role of fixed bed in phenol and COD removal was 25.01% and 29.3%, respectively, overall, the FUSBF system has a higher efficiency and ability than that of typical USBF and can be used for the purification of industrial wastewater containing refractory organic compounds such as phenol.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".