Stable Performance of Microbial Fuel Cell Technology Treating Winery Wastewater Irrespective of Seasonal Variations
Bibliographic record
Abstract
The seasonality of wastewater production is a challenge for biological treatment systems due to the seasonal variation of wastewater volume and contaminant concentration. This study assessed the performance of a microbial fuel cell (MFC) during changes in feed from winery wastewater (vintage season) to dog food (idle season). The 100-mL lab-scale MFCs exhibited slightly different output power performance (410 versus 290 mW/m3) with chemical oxygen demand (COD) removal of 84%±10% and 88%±1% with winery wastewater and dog food, respectively. COD removal occurred prior to a decline in voltage production. The COD removal and total electrical energy recovered per cycle were both linearly proportional to the initial COD of each batch when the COD was increased from 1,000 to 10,000 mg/L. An increase in COD increased the duration of power output but not the maximum output power. The energy recovery per kilogram COD improved with increasing COD concentration in the wastewater up to 0.042 kW·h/kg COD removed. This study demonstrated the efficacy of MFCs to treat agricultural wastewater and how dog food could be used as an ideal alternative feed to maintain effective reactor performance during the idle season of a seasonal agricultural wastewater system.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".