Screening activated carbons produced from recycled petroleum coke for acid gas separation
Bibliographic record
Abstract
Activated carbons derived from petroleum coke (petcoke) have the potential to (a) help reduce sulfur dioxide emissions through desulfurization, (b) help reduce carbon dioxide and (c) utilize a common waste product. Herein we present results for the selective adsorption of H2S and CO2 from a synthetic sour gas mixture using 7 activated carbons, four derived from petcoke and three obtained commercially. The petcoke activated with sodium hydroxide (P_Na) showed an H2S/CH4 selectivity up to SH₂S/CH₄ = 152 in temperature swing adsorption experiments. The H2S/CH4 selectivity was observed to be inversely proportional to the BET apparent surface area and directly proportional to the oxygen content of the activated carbons. H2S/CH4 and H2S/CO2 selectivity for P_Na was found to increase with increasing temperature. The P_Na activated carbon maintained a high H2S selectivity (SH₂S/CH₄ > 50 and SH₂S/CO₂ > 20) after regeneration at temperatures of T = 423 – 723 K. Pure component CH4, CO2, and H2S adsorption isotherms at T = 288.15 K, 298.15 K and 308.15 K were collected and used to estimate the multi-component adsorption. The results of these studies indicate that the petcoke activated carbons are viable materials for separating H2S and CO2 from sour natural gas streams or biogas.
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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 | 0.001 | 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".