Modeling and Simulations of NOx and SO2 Seawater Scrubbing in Packed-Bed Columns for Marine Applications
Bibliographic record
Abstract
Seawater scrubbing of nitrogen oxides and sulfur oxide from marine emissions was simulated in packed-bed columns exposed to static inclination and heaving/oscillating motions. Fourth generation random packings (Raschig super-Rings) while providing much smaller pressure drop than traditional Pall-Rings ensure comparable absorption efficiency for the pollutants. Complete removal of SO2 was predicted over the tested pressure range with absorption efficiency indifferent to scrubber inclination or heaving/oscillating motions. In contrast, NOx and CO2 absorptions are negatively impacted for inclined seawater scrubbers. Removal efficiency is not lowered significantly owing to larger scrubber pressure and because diffusion of N2O4 into the liquid phase is associated with a rapid pseudo first-order reaction. The asymmetrical oscillating motion of the scrubber degrades the removal performance which exhibits wavy patterns close to the steady-state solution of the average inclination angle. NO and CO2 absorption performance waves are moving toward a steady-state solution of vertical scrubber when the asymmetry of the two inclined positions of the scrubber downgrades. Symmetric oscillation and heaving motion led to performance disturbance waves around a steady-state solution of the vertical scrubber which are determined by the parameters of angular/heaving motion.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".