Extremum-seeking control of a microbial fuel cell power using adaptive excitation
Bibliographic record
Abstract
Microbial fuel cell (MFC) is a novel bio-renewable energy source, whose maximum produced power is, like most other renewable energy sources (photovoltaic panels, wind turbines, etc.) highly dependent on external disturbances. Hence, an appropriate real-time optimization method should be used so that the MFC always operates at its optimum. Extremum-seeking control (ESC) can be applied to optimize this type of system. However, when the optimal operating point of the MFC varies very fast due to external disturbances, the slow convergence of the ESC induces a lack of precision in the tracking of the optimal power point. In this paper, it is proposed to use adaptive excitation signal amplitude in the ESC scheme to improve the precision of the tracking. The amplitude adaptation is performed using a neural-network (NN) model which estimates in real-time the error between the optimal and the actual point of operation of the MFC based on the external disturbance measurements. The improved ESC performance in terms of convergence speed and precision will be demonstrated at the level of simulation in the case of a comparative study between classic ESC and ESC with adaptive excitation applied to an MFC model subject to variations of a measurable external disturbance, namely, the inlet substrate concentration.
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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) | 0.000 | 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 | 0.001 | 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".