Microbial Fuel Cell Application for Carbonaceous and Enhanced Biological Nutrient Remediation with Cathodic Nitrate Reduction
Bibliographic record
Abstract
Conventional methods of carbon and nutrient remediation suffer from problems like high costs, complexity and lack of scalability, hence a sustainable alternative is sought. The Microbial Fuel Cell (MFC) could be such alternative; however, MFCs have historically been used for electrical energy generation and not for nutrient remediation. Furthermore, most MFCs were historically operated on carbonaceous wastewater only, hence incapable of denitrification. I investigated simultaneous carbon and nutrient remediation by bench-scale batch experimenting with Cathodic Nitrate Reducing MFC, as alternative to conventional methods, using Calgary, Alberta’s Bonnybrook Treatment Plant’s wastewater. I demonstrated non-impediment of MFC’s electrical capability by nutrient amendment, adding wastewater only and then nutrients to two MFCs. One MFC had oxygenated cathode, the second, nitrate-reducing anoxic cathode. Average voltage for nutrient-amended oxygenated MFCs runs at 280.7 mV exceeded 79.55 mV for non-nutrient-amended. Average voltage for nitrate cathode MFC’s nutrient-amended runs at 89 mV exceeded 41.3 mV for non-nutrient amended runs. However, beyond 200mg/L nitrates, electrical performance declines. I further experimented in two stages and used student-t analysis and hypothesis testing to prove kinematic rates of MFC’s denitrification was superior to a conventional denitrification control chamber. In the first stage, I added nutrients, but no methanol to the control: and the MFC’s denitrification rate at 0.82 mg/L/h exceeded control’s rate of 0.61 mg/L/h, with 90% confidence. In second stage, with methanol in control chamber: the MFC denitrification rate of 0.97 mg/L/h still outperformed control rate of 0.86 mg/L/h, with 75% confidence. Finally, I eliminated nitrites from MFC and control, but added nitrates. Subsequent nitrite detection experiments yielded positive result, further confirming the MFC’s denitrifying capability. I detected a nitrite/nitrate ratio of 0.24 in MFC, which was lower than the control’s nitrite/nitrate ratio of 0.29. I observed nitrite level may be boosted in MFC by adding methanol. Methanol addition apparently increased nitrite/nitrate ratio in the MFC to 0.61, exceeding control’s ratio of 0.32. This thesis demonstrates the MFC’s viability for carbon and nutrient remediation and may provide basis for further research on generating nitrites for methods dependent on nitrites, such as the Anammox process.
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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 | 0.001 | 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".