Dynamic model of an alkaline electrolyzer based an artificial neural networks
Bibliographic record
Abstract
This paper presents an alkaline electrolyzer (AE) modelling based on artificial neural networks (ANN). Artificial neural networks can be applied to develop models for predicting the performance of complex and nonlinear systems. An alkaline electrolyzer behavior was modeled with success using a Multilayer Perceptron Network (MLP). The dynamic model which is used has been trained by using a Levenberg-Marquardt back propagation algorithm to learn the relationships that govern the electrolyzer and then predict its behavior without any physical equations. The absorbed electric current and the operating temperature were used as input vector of the neural networks which allows to predict the cell voltage behavior. The performance of this predictive neural network model is carried out using Matlab/Simulink software. Simulation results show that this predictive model estimated accurately the electrolyzer's cell voltage with the tracking errors within ± 0.01 V, which is less than ± 0.44 %.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".