Microbial Electrochemical Systems for Enhanced Wastewater Treatment and Energy Recovery
Bibliographic record
Abstract
Technologies like bio-electrochemical systems (BESs) can play a significant role in simultaneously treating waste materials and energy recovery. In this research, three kinds of BESs, including single-chamber (SCMEC) and dual-chamber (DCMEC) microbial electrolysis cells and microbial fuel cells (MFC), have been examined as wastewater treatment pathways in urban areas. The mathematical modeling of the mentioned technologies is carried out, and the models are implemented using the Python programming language. The results show that the hydrogen production rate in SCMEC is less than in DCMEC because of hydrogenotrophic bacteria activities, which are reported as about 0.86 m3 and 0.56 m3 of H2 gas per m3 of wastewater, respectively. Also, the model analysis shows that applied potential and anode surface area directly affect hydrogen production rates SCMEC and DCMEC. The calculated electric energy output was 0.033 kWh per m3 of wastewater for the MFC. Two real case studies in Montreal have been considered to investigate the potential of using the mentioned systems on an urban scale. The first district, including residential buildings, has a total daily wastewater generation of 3,000 m3/day, and 75 m3/day is assumed for the second one with a smaller non-residential building project. The model is estimated that 141 kg and 230 kg of hydrogen can be generated via SMEC and DMEC, respectively, and through the second scenario, 2.5 kWh energy can be extracted from wastewater via MFC. In the next stages, the results shown by considering the recovered energy via BESs, WWTPs consume energy to treat the same amount of wastewater compared with BESs. Also, DCMEC and SCMEC have been compared with water electrolysis (WE) technologies for hydrogen production. So, the comparisons expose that for generating one kg of hydrogen gas, DMEC is more efficient than SMEC and WE. In addition, the feasibility of using the generated hydrogen and power via microbial systems as fuel in green cars has been investigated as a final step.
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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 | 0.000 | 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".