Neural Network Modeling of Direct Ammonia Alkaline Anion Exchange Membrane Fuel Cell
Bibliographic record
Abstract
Recently, there has been a growing interest in green ammonia as a feasible hydrogen-based alternative for clean energy generation. Ammonia presents various advantages such as simplified transportation and storage, higher density, and reduced flammability. Since ammonia can be used to directly generate electricity through a Direct Ammonia alkaline anion exchange membrane Fuel Cell (DAFC), an adequate model of DAFC is essential for operational studies and controller design. This paper proposes a novel neural network (NN) model for DAFC. The proposed NN model utilizes the input-output relationships of the DAFC to capture system dynamics, resulting in a rapid and highly accurate model. Traditional physics-based modeling of complex systems involving electrochemical reactions is arduous, time-consuming, and computationally intensive. Once trained and validated, this NN model is utilized to analyze the dynamic behavior of the DAFC. The research demonstrates that the NN-based approach is adept at predicting output parameters. The ability to develop precise data-driven models using solely data from validated physics-based models is demonstrated by the presented DAFC NN model, thus minimizing the need for extensive experimentation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".