Co-Treatment of Winery and Domestic Wastewaters in Municipal Wastewater Treatment Plants: Analysis of Biodegradation Kinetics and Process Performance Impacts
Bibliographic record
Abstract
Winery wastewater (WWW) handling strategies often include co-treatment at municipal wastewater treatment plants (WWTPs). Despite this, definitive information regarding oxidation kinetics and process and performance impacts due to co-treatment is lacking. A combined Michaelis–Menten–University of Cape Town kinetic model has been found to best describe the pH-inhibited aerobic biological oxidation of WWW by heterotrophs in activated sludge from four municipal WWTPs. The specific rate of substrate consumption was highest in biomass that had been exposed to WWW (57.3 mg COD/g MLVSS·h) compared to biomass that had not (20.7 mg COD/g MLVSS·h). Bench-scale aerobic co-treatment trials confirm that sorption is a key removal mechanism, with up to 98% chemical oxygen demand and 97% total organic carbon removal after 6 h of reaction time. The WWW solids are quickly incorporated into the biological floc and may improve settleability at loading rates above 75 mg WWW suspended solids/L bioreactor volume at the expense of significantly increasing the observed yield. The aerobic-activated sludge system at municipal WWTPs can effectively co-treat WWW, provided the organic loading rates are limited and the WWTP is designed to accommodate the seasonal loadings of winery wastewater. The identified mitigation measures can be used by co-treating facilities to optimize the co-treatment performance of WWW along with domestic 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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".