Extremum-seeking control with anticipative action of microbial fuel cell's power
Bibliographic record
Abstract
During the last decade, the microbial fuel cell (MFC) has been considered as a promising solution to produce renewable energy while reducing the excessive consumption of electrical energy in wastewater treatment centers. One of the problems facing the use of an MFC as a battery is the fact that its internal resistance varies with various external sources of disturbances causing a variation in its optimal point of operation. In this case the use of a real-time optimization method is necessary for the battery to work at its optimum. Extremum seeking control (ESC) can be applied to optimize the system. However, in the case where important external disturbances cause rapid changes in the optimal operating point, ESC converges to a point other than the desired optimum because of its very slow convergence rate. In this paper, a method which judiciously combines an ESC routine with a neural-network based anticipatory action is proposed. The anticipatory action takes into account a measurable external disturbance, namely the inlet substrate concentration. Simulation results show that the proposed scheme leads to an improvement of the convergence rate towards the desired optimum.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".