Modeling the Reduction of Ship Exhaust Emissions through CO<sub>2</sub> Capture/Chemical Conversion and SO<sub>2</sub> Seawater Scrubbing
Bibliographic record
Abstract
Developing innovative, energy-efficient technologies to capture CO 2 from marine emissions and convert it represents an effective way to move toward a circular approach to reduce CO 2 emissions. Additionally, SO 2 removal, as a short-term interim solution for the current maritime sector, allows the use of less desulfurized/expensive fuels to meet International Maritime Organization emission standards. In this context, we investigated an integrated process of capturing CO 2 /SO 2 onboard ships and converting captured CO 2, thus initiating a process close to carbon neutrality. CO 2 absorption by monoethanolamine and SO 2 scrubbing with seawater were envisaged in packed-bed columns, whose hydrodynamics and performance were analyzed under vertical, inclined, and rolling conditions using three-dimensional (3D) Eulerian models to understand their behavior under changing ocean states. CO 2 conversion via an integrated process combining a sorption-enhanced reverse water gas shift and sorption-enhanced methanol synthesis was proposed. By including a reverse water gas shift and in situ H 2 O removal, CO and methanol yields were significantly improved.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".