Structure Optimization of Vessel Seawater Desulphurization Scrubber Based on CFD and SVM‐GA Methods
Bibliographic record
Abstract
The miniaturization of the scrubber used in vessel flue gas desulphurization is a critical issue. In this study, the structure optimization of the vessel desulphurization scrubber based on CFD (computational fluid dynamics) and the SVM‐GA (support vector machine and genetic algorithm) is performed and a miniaturized scrubber design with the same desulphurization efficiency as large scrubbers is obtained. First, the seawater SO 2 absorption process in a vessel desulphurization scrubber is investigated using the CFD‐DPM method, and the effects of the operating and structural parameters on the desulphurization efficiency η are discussed in detail. Results show that η is positively correlated to the absorption area height, H 2 , and the spray level number, N , and η first increases and then decreases with an increase in the scrubber diameter, D 1 , as well as the inlet flue angle, θ . Then, a prediction model of η considering D 1 , H 2 , θ , and N is established and validated using the SVM with the simulation data. Finally, D 1 , H 2 , θ , and N are optimized using the GA method based on the SVM model. The results show that the volume of the absorption area, V ab , can be reduced by 30 % while maintaining the same η through the use of this optimization method.
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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".