Sewage sludge ashes as a primary catalyst for the abatement of tar in biomass gasification: Bubbling versus spouted‐fluidized bed configuration
Bibliographic record
Abstract
Abstract Sewage sludge (SS) ashes, rich in iron and calcium, have been tested as a primary catalyst during air gasification of commercial wood pellets in a pre‐pilot scale fluidized bed (FB) reactor. The shifting from a conventional fluidized bed to a spouted‐fluidized bed configuration has been assessed on the catalyst performance. Specifically, at constant total air inlet flow rate, two different values of the air flow rate in a central spouting nozzle have been adopted, which correspond to 20% and 37.5% of the total inlet gas flow rate. Under the conventional fluidized bed configuration (i.e., bubbling regime), SS ashes exhibit good performance in term of tar reduction (about 20% decrease compared to a bed of inert silica sand), without significantly affecting the syngas composition. Concerning the transition to the spouted‐fluid bed configuration, the gas‐solid contact efficiency is enhanced at lower air flow rates through the central nozzle, with respect to the FB regime, leading to better gasification performance in terms of tar reduction (around 40% less) and syngas quality. A slightly worse gasification performance is obtained at high values of air flow rate in the central nozzle, due to a progressive increase of gas bed bypassing. Furthermore, moving from the conventional FB configuration to the spouted‐fluid bed one dramatically boosts the elutriation rate of carbon fines as well as the attrition of catalyst particles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 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".