Hot syngas cleanup for pilot two-stage fluidized bed steam-oxygen biomass gasification plant
Bibliographic record
Abstract
• A 30 kg/h pilot-scale biomass gasification was designed and built for renewable natural gas production • Hot syngas cleanup is conducted for pilot fluidized bed biomass gasification plant • An iron-based bauxite residue catalyst is developed and tested for tar cracking • Tar content in real syngas decreased from 2.6–27.7 to 0.10–0.65 g/Nm 3 Biomass gasification as a renewable energy technology has been a widely explored research and development area. The efficient and economic removal of harmful components, particularly tars, in raw syngas from the biomass gasifier is still a major challenge. In this study, a novel two-stage fluidized bed pilot-scale gasifier has been developed to enhance the steam-oxygen biomass gasification to generate low-tar syngas; while, a prototype hot syngas cleanup system has been designed, built and tested to further reduce the tar content and purify the syngas from the biomass gasifier for downstream applications. The results showed that the tar removal efficiency by a catalytic tar cracker using an iron-based bauxite residue derived catalyst prepared in-house can reach 82.8–98.0% at reaction temperatures of 678–801°C, and 90.6–98.0% at 784–801°C, respectively. Furthermore, the tar content of the cleaned syngas can be as low as 0.10–0.65 g/Nm 3 when the raw syngas tar content is 2.59–27.71 g/Nm 3 . In the case of syngas composition, H 2 content ranged from 32.7% to 48.0%, CH 4 from 2.8% to 4.8%, CO from 26.3% to 35.7%, and CO 2 from 18.4% to 33.9%. The H 2 /CO molar ratio varies from 1.0 to 1.8, requiring the application of the water–gas shift reaction to increase the H 2 /CO ratio to 3 for downstream methanation to produce renewable natural gas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".