Trickle‐bed scrubbing of flue gas SO<sub>2</sub> using non‐aqueous solvents
Bibliographic record
Abstract
Abstract Experiments were carried out on the performance of several ketone solvents for the scrubbing of dilute SO 2 from a gas stream and its conversion to sulfuric acid in a trickle‐bed reactor packed with activated carbon. Using a bench‐scale trickle bed packed with a structured packing based on Sulzer static mixers coated with Centaur TM activated carbon and a Teflon binder, measurements of SO 2 removal, conversion to acid and catalyst productivity showed that all were considerably greater than levels achieved with water flushing. The combination of Teflon‐coated Centaur activated carbon with a non‐aqueous solvent as the flushing agent provided from 10 to 100 times higher catalyst productivity than those obtained with water alone and other activated carbons. Also, the productivities obtained by this combination were up to 40 times higher than the productivity of typical vanadium pentoxide catalyst operating at 350°C to 400°C.
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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 | 0.000 |
| 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) | 0.001 | 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".