Renewable hydrogen from seafood shell waste for long-term energy storage on islands
Bibliographic record
Abstract
This study explores the potential of renewable seafood shell waste for sustainable energy conversion and long-term storage, particularly for isolated communities. Despite its rich chitin and protein composition, seafood shell waste is often neglected. The research evaluates and compares three advanced gasification technologies: biomass gasification, plasma gasification, and chemical looping, to convert seafood shell waste into syngas and H 2 . The study uses validated Aspen Plus models to optimize feedstock blending ratios and operational parameters. Results show that feedstocks high in lobster and shrimp shells yield higher H 2 outputs and improved syngas quality compared to clam-dominated blends. For instance, biomass gasification at 1200 °C yielded approximately 500 kg/h of H 2 from pure lobster or shrimp feeds, while plasma gasification at 4500 °C achieved yields near 730 kg/h. Plasma gasification, when integrated with fuel cell conversion and heat recovery systems, can generate over 10,000 kWh during a 6-hour peak period, enough to power over 1100 single-detached homes. Its levelized cost of hydrogen (LCOH) varies from $5.72-$8.37/kg H 2 , making it less expensive than chemical looping and biomass gasification. Plasma gasification also has the lowest global warming potential (GWP) at 6 kg CO 2 e/kg H 2 . Combining plasma gasification with carbon capture and storage may reduce GWP to 0.3 kg CO 2 e/kg H 2 and can be further explored. These findings underscore the technical and economic viability of converting seafood shell renewable waste into H 2 , advancing sustainable energy transitions, and supporting net-zero goals.
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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 | 0.000 |
| 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".