Optimization of Winery Wastewater Co-treatment at Municipal Wastewater Treatment Plants
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; both teacher heads agree on what is shown here.
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".