Combined anaerobic-aerobic and UV/H<sub>2</sub>O<sub>2</sub>processes for the treatment of synthetic slaughterhouse wastewater
Bibliographic record
Abstract
The biological treatment of a synthetic slaughterhouse wastewater (SSWW) is studied using an anaerobic baffled bioreactor (ABR) and an aerobic activated sludge (AS) at a laboratory scale in continuous mode. The total organic carbon (TOC) loading rate, the total nitrogen (TN) loading rate, and the flow rate are 0.03-1.01 g/(L.day), 0.01-0.19 g/(L.day), and 2.93-11.70 mL/min, respectively. The results reveal that combined anaerobic-aerobic processes had higher efficiency to treat SSWW than a single process. Up to 96.36% TOC, 80.53% TN, and 99.38% 5-day carbonaceous biochemical oxygen demand (CBOD5) removal from an influent concentration of 1,009 mgTOC/L, 420 mgTN/L, and 640 mgCBOD5/L at the hydraulic retention time (HRT) of 6.24 days and a flow rate of 3.75 mL/min are achieved. The UV/H2O2 process is studied to treat a secondary effluent of SSWW with TOC loadings of 65-350 mg/L. Up to 75.22% TOC and 84.38% CBOD5 removal are obtained at the HRT of 3 h with H2O2 concentration of 900 mg/L. Optimum molar ratios of 13.87 mgH2O2/mgTOCin and 4.62 mgH2O2/mgTOCin.h are also obtained. Combined anaerobic-aerobic and UV/H2O2 processes enhanced the biodegradability of the TOC, TN, and CBOD5 present in the SSWW. Up to 99.98% TOC, 82.84% TN, and 99.69% CBOD5 overall removals are obtained for an influent concentration of 1,005 mgTOC/L, 200 mgTN/L, and 640 mgCBOD5/L at the HRT of 4 days and a flow-rate of 5.90 mL/min.
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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 | 0.000 |
| 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".