Hydrogen storage systems at ports for enhanced safety and sustainability: a review
Bibliographic record
Abstract
Abstract With the increasing demand for clean energy and the global push toward carbon neutrality, hydrogen has emerged as a promising alternative fuel. Ports are critical nodes in the hydrogen supply chain that are increasingly being utilized as long-term hydrogen storage hubs. However, integrating hydrogen storage systems into port infrastructure presents unique technical, environmental, and safety challenges. This review systematically examines current technologies used for hydrogen storage in port environments—including compressed gas, cryogenic liquid, cryo-compressed gas, ammonia, liquid organic hydrogen carriers, solid-state hydrides, and underground storage. Each technology is evaluated based on performance, infrastructure requirements, accident risks, environmental impact, and cost. The study also assesses port-specific infrastructure vulnerabilities under operational stress and climate change conditions and explores strategies for accident prevention, emergency response, and postincident recovery. A comprehensive framework is proposed to enhance the resilience and safety of hydrogen storage systems at ports. This study offers valuable insights for stakeholders and researchers by addressing technical gaps, regulatory challenges, and future directions for sustainable and safe hydrogen storage in port facilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".