Mathematical modelling of a semi-dry SO2 scrubber based on a Lagrangian-Eulerian approach
Bibliographic record
Abstract
Semi-dry desulfurization is an efficient means of SO2 removal from the effluent gases from electrolysis cells in aluminum smelters. These gases are at low temperature and contain low concentrations of SO2, as opposed to thermal power plants. The removal is carried out by injecting powdered alkaline sorbent, hydrated lime (solid particles), into the SO2-containing gas (gas phase) in the presence of humidity. The reaction is controlled by the adsorption of SO2 onto the surface of lime. This study involves the mathematical modelling of a lab-scale scrubber using a Lagrangian-Eulerian approach in order to analyze the desulfurization efficiency. The model was validated based on experimental data. A parametric study was carried out to investigate the effects of particle size, sorbent amount, and relative humidity (RH) on the desulfurization efficiency. The results show that the particle size is the most important parameter; as the particle size decreases, the desulfurization efficiency increases. However, using finer particles may increase the process cost. The loss in SO2 capture efficiency due to the use of coarser particle size could be compensated by increasing the relative humidity (RH) of the gas, another key parameter of the process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".