Hydrogen-rich syngas production from sewage sludge and hydrochars via catalytic gasification with SrO
Bibliographic record
Abstract
This study investigates the production of high-quality syngas from sewage sludge and its hydrochars through gasification and catalytic gasification processes. The following types of sludge were analyzed: digested (MS1) and non-digested (MS2) municipal sludge from Silesian wastewater treatment plants (WWTPs) and two industrial sludge hydrochars (HC_IS1, HC_IS2). Hydrochars (HC_MS1, HC_MS2, HC_IS1, HC_IS2) were derived via hydrothermal carbonization at 200 °C and 2 h. Gasification and catalytic gasification processes with SrO were conducted under a CO 2 atmosphere at 850 °C. Gas chromatography was employed to identify syngas components, including H 2 , CH 4 , CO 2 , and CO. The results demonstrated that the presence of a catalyst significantly increased hydrogen (H 2 ) yield, particularly during the heating stage. For instance, hydrogen increased from 4 % to 21.7 % for HC_MS1, although with HC_MS2 no hydrogen was found until the addition of a catalyst, SrO, which increased hydrogen to 7.6 %. The findings confirm the potential of gasification for syngas production from sewage sludge and hydrochars. • Hydrochar is potential feedstock for hydrogen production via gasification. • H 2 -rich syngas is produced from sewage sludge and hydrochars via gasification. • Catalytic gasification with SrO in CO 2 atmosphere at 850 °C boosted H 2 yield. • Presence of SrO significantly increased H 2 yield.
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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) | 0.000 | 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".