Comparison of real‐time methods for maximizing power output in microbial fuel cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Microbial fuel cells (MFCs) constitute a novel power generation technology that converts organic waste to electrical energy using microbially catalyzed electrochemical reactions. Since the power output of MFCs changes considerably with varying operating conditions, the online optimization of electrical load (i.e., external resistance) is extremely important for maintaining a stable MFC performance. The application of several real‐time optimization methods is presented, such as the perturbation and observation method, the gradient method, and the recently proposed multiunit method, for maximizing power output of MFCs by varying the external resistance. Experiments were carried out in two similar MFCs fed with acetate. Variations in substrate concentration and temperature were introduced to study the performance of each optimization method in the face of disturbances unknown to the algorithms. Experimental results were used to discuss advantages and limitations of each optimization method. © 2010 American Institute of Chemical Engineers AIChE J, 2010
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 it