Integrating Fuzzy AHP-TOPSIS for Material Selection in Green Hydrogen and Ammonia Production
Bibliographic record
Abstract
The transition to sustainable energy sources necessitates the efficient production of green hydrogen and ammonia, with advanced material selection playing a pivotal role in this process.This study employs a Fuzzy Analytic Hierarchy Process (Fuzzy AHP) and the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to provide a structured approach to material evaluation.Prioritization results emphasize Scalability and Efficiency as the most critical criteria, reflecting Morocco's strategic focus on optimizing production and expanding renewable energy infrastructure.Comparisons with recent literature underscore the evolving priorities in material selection, reinforcing the significance of eco-friendly and scalable technologies.The findings identify Innovative nanostructured platinum-free catalyst as the optimal choice, excelling in Scalability and Environmental Impact, which aligns with advancements in hydrogen reactor scale-up strategies.Meanwhile, Nickel-based alloy demonstrates superior efficiency and durability but faces scalability challenges, mirroring concerns in industrial deployment.These results provide theoretical advancements in decisionmaking methodologies and practical insights for energy planners and investors.They highlight the balance between technical performance and sustainability goals, offering a robust framework for material selection that supports large-scale green hydrogen and ammonia production while aligning with global decarbonization efforts.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".