Techno economic analysis of electrolytic hydrogen production by alkaline and PEM electrolysers using MCDM methods
Bibliographic record
Abstract
Hydrogen, a crucial clean and renewable energy source, addresses pressing challenges of energy security and environmental pollution. Water electrolysis for hydrogen production is a promising approach to satisfy the growing demand for sustainable energy. This study uniquely performs a comprehensive techno-economic analysis of hydrogen production using both Alkaline and Proton Exchange Membrane (PEM) electrolyzers, a first in the field to evaluate their performance comprehensively with advanced Multi-Criteria Decision-Making (MCDM) techniques. Leveraging TOPSIS, WASPAS interval methods, and the Best Worst Method (BWM) with fuzzy logic, this research introduces a novel evaluation framework that incorporates a wide-ranging set of factors, including environmental, technical, technological, economic, and social aspects, divided into 30 sub-criteria. These insights offer a comprehensive understanding of each electrolyser's strengths and weaknesses, helping stakeholders make informed decisions about cost reduction in hydrogen production technologies. This has not been done before. Although cost results favour Alkaline electrolysers, PEM electrolysers are attractive for specific applications where their benefits justify the higher initial cost, choosing between Alkaline and PEM electrolysers dependent on a given hydrogen production project's requirements.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".