Comparing seawater desalination technologies for green hydrogen production: Reverse Osmosis and Multi-Effect Distillation
Bibliographic record
Abstract
Due to increasing global energy demands and a need to move away from non-renewable energy sources, green hydrogen is now being considered as a possible replacement for fossil-based energy sources. Especially in “hard-to-abate” sectors, such as steel and petrochemical production, and long-distance transport, where the change to renewable electricity is more difficult. Green hydrogen is produced through electrolysis of water and powered by renewable energy sources. The water quality demand for the electrolysis feedwater is high, and wa-ter of very high purity is required. Meanwhile, there are also challenges with water supply and demand in many regions of the world, but by using desalination to purify seawater for electrolysis it is possible to avoid the need to compete for groundwater with other water-demanding sectors. The most common desalination technology on commercial scale is the membrane technolo-gy Reverse Osmosis (RO), due to its relatively low energy demand and high reliability. Thermal desalination technologies such as multi-effect distillation (MED) are used less, due to high thermal energy demand. The electrolysis process does however produce waste heat, and Alfa Laval would like to utilise this heat to power their MED system HyDuo and simultaneously provide cooling to the electrolysis systems. Thus, this study aims to investigate and compare the Alfa Laval HyDuo system with RO system, to evaluate whether the HyDuo can compete with the established RO systems considering costs and process requirements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".