High strength bioethanol wastewater inoculated with single‐strain or binary consortium feeding air‐cathode microbial fuel cells
Bibliographic record
Abstract
Bioethanol industries generate a large volume of high strength wastewater called vinasse. This study compares the performance of microbial fuel cells (MFCs), inoculated with a single‐strain or a binary consortium, to produce bioelectricity while simultaneously reducing the COD levels and removing the vinasse color. The MFCs achieved the power density of 124.0 ±0.2 mW/m2 when inoculated with a strain of Shewanella oneidensis (MFC/So) and 178.0 ±0.8 mW/m2 after inoculation with a strain of Clostridium butyricum (MFC/Cb). However, a better performance in power density was reached by the MFC inoculated with both cultures (MFC/So‐Cb), which produced 205.0 ±0.5 mW/m2. Additionally, the results showed a decrease in the initial COD (36,440 mg/L), as well as a decolorization. Thus, the MFC/So, MFC/Cb, and MFC/So‐Cb caused a reduction in COD by 41.8 ± 2.4%, 20.1 ± 4.4%, and 53.8 ± 5.9%, respectively, and in color by 62 ± 4%, 70 ± 4%, and 58 ± 5%, respectively. These findings demonstrate that high power density can be produced using raw vinasse as a substrate in air‐cathode MFCs. However, the coulombic efficiency was low indicating that the COD conversion into electricity must be improved. Novelty: This work combines fermentative and metal‐reducing bacteria, instead of mixed cultures with unknown microorganisms, to break down complex organics in the vinasse, simultaneously to electricity generation. No studies have been found concerning Shewanella oneidensis and Clostridium butyricum strains for harvesting bioelectricity from bioethanol wastewater. Second, different from early studies, this work uses the real high strength wastewater, without dilution or any pre‐treatment, before MFC process. Third, here we consider the total biomass at the end process to calculate the COD removal rate. Finally, we employed electronic load, never used previously, in MFC polarization measurements. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 380–386, 2019
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".