Advancements and challenges of shipboard carbon capture technologies : a comparative assessment
Bibliographic record
Abstract
Carbon Capture (CC) technologies are primarily used for onshore projects, such as Shell Canada's Quest in Alberta [1], and only to a limited extent on ships. The lack of current commercial shipping applications of CC emphasises the need for further research and development to reduce maritime CO2 emissions. Green Marine, a project funded by Horizon Europe [2], intends to speed up climate neutrality in water transport by adding emission control solutions to existing fleets. This includes retrofitting guidelines, a software tool catalogue, and showcasing new technologies for carbon capture, energy saving, and reducing fuel consumption. Detailed information on the technologies developed for retrofitting purposes is available in D1.1-Engineering and Preparations for Retrofitting [3] of this project. As part of the project’s further development, this study undertakes a comparative assessment to identify the most promising CC technology for shipboard application based on a comprehensive review of pertinent articles and project reports [4]. The paper first addresses the challenges of implementing shipboard CC technology and then follows with a comparative assessment of different CC technologies to identify promising solutions based on their potential to address these challenges. The results of this overview study will help stakeholders understand the opportunities and challenges of implementing commercially established CC technologies onboard ships. Additionally, the study provides insights for selecting the most promising technologies based on preferences in terms of space, energy requirements, or cost, while addressing other challenges.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".