Multi‐Objective Optimization of a Climate‐Responsive Green Hydrogen‐Based Multi‐Generation System with Advanced Energy Storage and Heat Recovery
Bibliographic record
Abstract
Abstract Heavy reliance on fossil fuels for power generation leads to significant energy waste, high operating costs, and substantial CO 2 emissions, highlighting the urgent need for climate‐responsive solutions, such as hydrogen‐based energy systems. This article introduces and optimizes a novel hydrogen‐based multi‐generation system that combines Compressed Air Energy Storage (CAES), a Proton Exchange Membrane Electrolyzer (PEME), and an Organic Rankine Cycle (ORC) to enhance thermodynamic performance and reduce environmental impacts. Using Response Surface Methodology (RSM) in Minitab, six system scenarios incorporating different organic fluids and oils in the ORC are evaluated under varying climatic conditions (Paris, London, San Francisco, and Dubai), representing temperate, maritime, and hot desert climates. The optimal scenario achieves an Exergy Round Trip Efficiency (ERTE) of 64.28%, a cost rate reduction of 62.5 $/h, and a CO 2 emission decrease of 56.26 kg kWh −1 . The findings suggest that strategic deployment of the proposed system in temperate climates substantially boosts system performance and reduces environmental cost. This research offers practical and theoretical advancements in sustainable hydrogen‐based power solutions, directly contributing to Sustainable Development Goals (SDG) 7 and 13 through improved energy efficiency, reduced emissions, and climate‐responsive design. Future work should explore adaptive control strategies, low‐cost materials, and assessments in extreme climates.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".