Treatment of Actual Winery Wastewater by Fenton-like Process: Optimization to Improve Organic Removal, Reduce Inorganic Sludge Production and Enhance Co-Treatment at Municipal Wastewater Treatment Facilities
Bibliographic record
Abstract
Despite many wineries being equipped with onsite wastewater treatment, winery wastewater (WWW) co-treatment at municipal wastewater treatment plants (WWTPs) remains a common practice in wine-making regions. The complex and highly variable nature of WWW can result in negative impacts on WWTP operations, highlighting a need for improved co-treatment methods. In this paper, the feasibility of using the Fenton-like process to pre-treat WWW to enhance co-treatment at municipal WWTPs is assessed. First-stage pre-treatment of the WWW, in the form of dilution and settling or aerobic biological treatment, is used prior to the Fenton-like process. A three-factor BBD experimental design is used to identify optimal reaction time and initial H2O2 and Fe3+ concentrations. Chemical oxygen demand (COD) and total organic carbon (TOC) removal rates are not able to accurately reflect the extent of reaction. Additional trials identified solubilization of particulate COD and TOC, as well as samples handling requirements prior to analysis, as factors affecting the apparent COD and TOC removal rates. Inert suspended solids (ISS) generated during the sample handling process are found to be the response variable best suited to quantifying the extent of the Fenton-like reaction. Maximum ISS generation is observed at initial H2O2 and Fe3+ concentrations of 4000 mg/L and 325 mg/L, however, results suggest that optimal concentrations exceed these values. The impact of adding pre-treated WWW, with and without Fenton-like treatment, to municipal WWTPs’ primary clarifiers and aerobic bioreactors is also assessed via bench-scale trials. Challenges associated with co-treating WWW are found to remain despite the pre-treatment alternatives investigated, including negative impacts on simulated primary and secondary effluent quality. The Fenton-like AOP provides limited opportunity to optimize or enhance co-treatment at municipal WWTPs.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| 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".