Optimization of Bioenergy Generation via Coupled Thermo-Hydro-Electrochemical Modeling of Two-Chamber Microbial Fuel Cells
Bibliographic record
Abstract
Microbial fuel cells (MFCs) have drawn increasing attention as a sustainable approach for simultaneous wastewater remediation and energy production. However, their efficiency remains constrained by challenges in cathodic reaction kinetics, electrode performance, and system control. In this study, a three-dimensional multiphysics model of a dual-chamber MFC was developed using COMSOL, coupling electrochemical reactions, fluid dynamics, and ion transport. Experimental data validated the model, with a maximum deviation of 3.15% at low to moderate current densities. The simulation highlights that electric potential gradients and ionic currents are predominantly distributed near the electrode surfaces, while central regions exhibit higher current density. Moderate acetate flow rates (0.015–0.020 m/s) enhance substrate delivery but may destabilize the biofilm if increased excessively. Elevated feed concentrations (3.5–5.0 mol/m3) improve power density, although oversupply risks microbial imbalance and substrate accumulation. Temperature variation between 303 and 323 K has a limited effect on power density but supports enzymatic reactions and electron transfer efficiency. A sensitivity assessment ranks substrate concentration and flow rate as the most influential parameters, while temperature plays a secondary role. Based on these insights, a hierarchical optimization strategy is proposed—first optimizing substrate availability and residence time, then improving hydrodynamic conditions, and finally regulating temperature within a suitable range. This work provides theoretical support for advancing MFC design and operation in sustainable water-energy systems.
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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 | 0.001 |
| 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 | 0.001 | 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".