Optimizing sustainable energy systems: A comparative study of geothermal-powered desalination for green hydrogen production
Bibliographic record
Abstract
The synergy between hydrogen and water is crucial in moving towards a sustainable energy future. This study explores the integration of geothermal energy with desalination and hydrogen production systems to address water and clean energy demands. Two configurations, one using multi-effect distillation (MED) and the other reverse osmosis (RO), were designed and compared. Both configurations utilized geothermal energy, with MED directly using geothermal heat and RO converting geothermal energy into electricity to power desalination. The systems are evaluated based on various performance indicators, including net power output, desalinated water production, hydrogen production, exergy efficiency, and levelized costs. Multi-objective optimization using an artificial neural network (ANN) and genetic algorithm (GA) was conducted to identify optimal operational conditions. Results highlighted that the RO-based system demonstrated higher water production efficiency, achieving a broader range of optimal solutions and lower levelized costs of water (LCOW) and hydrogen production, while the MED-based system offered economic advantages under specific conditions. A case study focused on Canada illustrated the potential benefits of these systems in supporting hydrogen-powered vehicles and residential water needs, emphasizing the significant impact of using high-quality desalinated water to enhance the longevity and efficiency of proton exchange membrane electrolyzers (PEME). This research provides valuable insights into the optimal use of geothermal energy for sustainable water and hydrogen production. • Geothermal energy is used for both desalination and hydrogen production, promoting sustainability. • The study compares Multi-Effect Distillation (MED) and Reverse Osmosis (RO) for optimal geothermal-based desalination. • Multi-objective optimization using ANN and GA identifies the best operational conditions for efficiency and cost reduction. • The study highlights RO's cost efficiency in desalination by evaluating the levelized costs of water and hydrogen. • A case study in Canada shows geothermal energy's potential to support hydrogen-powered vehicles and water systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".