Enhancing predictive accuracy in alkaline water electrolysis: A machine learning approach to the effects of trans‐diaphragm fluid flow using experimental data
Bibliographic record
Abstract
Abstract Enhancing the efficiency of alkaline water electrolysis is critical for large‐scale green hydrogen production, yet accurately predicting hydrogen‐in‐oxygen concentrations remains a significant challenge due to the complex nonlinear interactions between electrochemical and fluid dynamic parameters. This study employs machine learning to improve the predictive accuracy of hydrogen‐in‐oxygen levels under varying trans‐diaphragm fluid flow conditions, addressing a gap in existing modelling approaches that rely primarily on theoretical or empirical methods. Five artificial neural network models were developed using experimental data from a 0.6 m single‐stack electrolyzer operating with an electrolyte. The models were trained and tested on 132 experimental data points, with 75% allocated for training and 25% for testing. The number of neurons in the hidden layer of the network models developed with a single hidden layer and the TanSig activation function was optimized by analyzing the performance of different network models. The models achieved exceptional predictive accuracy, with mean squared errors below 1.47E‐02, correlation coefficients exceeding 0.989, and margin of deviation within ±0.82% across all test cases. These findings confirm the capability of machine learning‐based predictive modelling to enhance electrolysis optimization, reduce experimental costs, and support the scalable deployment of green hydrogen production. The novel integration of machine learning in trans‐diaphragm fluid flow analysis advances predictive modelling beyond conventional techniques, offering a robust approach for industrial‐scale electrolysis system enhancement. This study primarily aims to develop accurate predictive models for hydrogen‐in‐oxygen concentrations under varying trans‐diaphragm flow conditions, addressing a critical gap in monitoring and controlling alkaline water electrolysis systems.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".