Reduction of sulphur gas emissions using activated carbon
Bibliographic record
Abstract
Sulphur dioxide, as one of the gases responsible for acid rain, is produced mainly by coal-fired power stations. Shifting to fuels with a lower carbon-to-hydrogen ratio, such as natural gas, is generally considered as an effective method to reduce greenhouse gas emissions and to drop the concentration of sulphur dioxide in the atmosphere. Elimination of hydrogen sulphide from natural gas is necessary for its corrosive effects and production of sulphur dioxide in combustion process. Activated carbon can convert this compound to elemental sulphur and water in an oxidation reaction. In this study, activated carbon produced from luscar char was used for this reaction. The effects of porous characteristics and surface chemistry on the performance of catalyst were studied using diffuse reflectance infrared Fourier transform spectroscopy, nitrogen adsorption and temperature programmed desorption methods. It is shown that oxidation of activated carbon by nitric acid increased surface oxygen groups on activated carbon, which can be thermally desorbed at the next step. This product has more active sites in comparison to original activated carbon and shows a better performance in oxidation reaction. Applying impregnating agents, especially potassium iodide reduces the production of sulphur dioxide in the reaction as an undesirable product.
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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) | 0.000 | 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) | 0.001 | 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".