Addressing Freshwater Scarcity and Hydrogen Production: Offshore Wind and Reverse Osmosis Synergies
Bibliographic record
Abstract
Abstract The transition from fossil fuels to renewable energy sources is imperative to mitigate climate change and achieve sustainable development goals (SGDs). Hydrogen, as a clean energy carrier, holds great potential for decarbonizing various sectors, yet its production remains predominantly reliant on fossil fuels. This study explores a novel approach to sustainable hydrogen production by integrating offshore wind energy with reverse osmosis (RO) desalination technology. The proposed configuration harnesses offshore wind power to energize both a RO desalination system and water electrolysis unit. Initially, the wind energy powers the RO desalination process, purifying seawater, and then desalinated water is directed to water electrolysis system for generating green hydrogen directly from seawater. The resulting renewable hydrogen holds potential for diverse applications, including marine industries, and can be transported onshore as needed. The RO system is configured to treat 20 kg s−1 of seawater with a salinity of 35 000 ppm, aiming for a high recovery ratio and reduced freshwater salinity. A pressure exchanger (PX) is integrated to recover energy from high‐pressure brine stream and transfer it to the low‐pressure feed water, thus reducing the overall energy consumption of the RO process. The concentrated brine extracted from RO desalination is proposed to be utilized for the production of sodium hydroxide that can further pretreat incoming seawater and enhance the effectiveness of the filtration process by mitigating membrane fouling. This pressure exchanger increases the energy efficiency of the RO system from 63.1% to 64.0% and exergetic efficiency from 13.9% to 18.2% increasing the overall first and second law efficiencies to 37.9% and 33.5%. By leveraging offshore wind power to drive RO desalination systems, this research not only addresses freshwater scarcity but also facilitates green hydrogen generation, contributing to the advancement of renewable energy solutions and fostering environmental sustainability.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".