Optimization of Winery Wastewater Co-treatment at Municipal Wastewater Treatment Plants
Notice bibliographique
Résumé
This work presents a comprehensive assessment of the environmental impact of winery wastewater (WWW) and methods to optimize its co-treatment at municipal wastewater treatment plants (WWTPs). A comprehensive review of full-scale treatment at 53 wineries in Niagara Region, Canada finds constructed wetlands are the most common type of on-site treatment. On-site systems do not address all treatment needs, requiring a portion of the WWW to be co-treated at WWTPs. From freshwater resource impact perspective, the grey water footprint (WF) associated with treated WWW effluents is often neglected from wine-making WF assessments. However, co-treated WWW effluents from WWTPs in Niagara Region are found to exert a substantial grey WF equivalent to over 960 times their volume. While full-scale operating data indicate that anaerobic co-digestion with municipal sludges is effective (89% chemical oxygen demand (COD) removal), co-digestion capacity is limited. Bench-scale co-treatment trials confirm that WWTPs’ aerobic activated sludge systems can effectively co-treat WWW provided organic loading rates are limited. A combined Michaelis-Menten-University of Cape Town kinetic model is found to best describe the pH-inhibited oxidation by heterotrophs, and the associated specific rate of substrate consumption is 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). The feasibility of using the Fenton-like process to pre-treat WWW to enhance co-treatment is assessed. Solubilization of particulate COD and total organic carbon (TOC), and sample handling requirements prior to analysis, are identified as factors affecting their apparent removal rates. Inert suspended solids generated during sample handling is found to be the variable best suited to quantifying the extent of reaction. However, the Fenton-like process provides limited opportunity to optimize co-treatment at WWTPs. A novel pre-treatment method, the Waste Activated Sludge-High Rate (WASHR) process, is proposed to optimize the co-treatment. The WASHR process combines the contact stabilization and sequencing batch reactor processes. It utilizes waste activated sludge from the WWTP as its biomass source, allowing rapid start-up. Bench-scale trials confirm that the WASHR process, vs. direct co-treatment, can reduce COD and total suspended solids loadings to the WWTP’s liquid treatment train by more than 81% and 92%, respectively, and to the solids treatment train by more than 59% and 30%, respectively. A case study is used to confirm the economic viability and environmental sustainability of the WASHR process compared with direct co-treatment. Finally, robust correlations between easily measured parameters and key organic and nutrient parameters are developed. The correlations provide a promising, rapid and cost-effective means of characterising WWW to allow improved process control.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».