Removal of Sulfur Compounds from Industrial Emission Using Activated Carbon Derived from Petroleum Coke
Bibliographic record
Abstract
Activated carbon (AC) materials are porous structures generated by activation of either pyrolyzed plant or coke materials through physical or chemical means. While being widely used in industry for water, air, and product purification, ACs also may be suitable for the removal of pollutants from flue gas or sulfur compounds from natural gas fuels before combustion, provided the processes/materials are economic. ACs derived from petroleum coke (petcoke) that is often stranded and considered a low-quality byproduct are relatively inexpensive. To date, the pure component adsorption and selectivities for AC from petcoke have not been reported and compared to other reported ACs for practical application with flue gas, sour gas, or acid gas purification. Here we show that an AC from petcoke displays both high-selectivity and capacity toward SO 2 and H 2 S. Single component volumetric adsorption experiments show adsorption as high as 554 mg g –1 for SO 2 at p = 0.56 bar and 256 mg g –1 for H 2 S at p = 1 bar ( T = 25 °C). This SO 2 uptake is 66% higher than the previous highest SO 2 uptake on an AC and 39 times as selective toward SO 2 versus N 2 . These results suggest that AC from petcoke is an excellent material for recovering sulfur compounds from industrial flue gas or raw fuel, with the benefit of making use of a petroleum solid waste.
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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".