Low‐tar, highly burnable gas production from the gasification of pelletized palm empty fruit bunch
Bibliographic record
Abstract
Abstract Gasification is an attractive method to convert lignocellulosic biomass into a combustible gas mixture for electricity and power generation. To control the tar concentration in the produced gas to be within the allowable limit of downstream applications, it is important for a gasification system to be integrated with a tar removal process. In this study, an integrated gasification system consisting of a downdraft gasifier and a secondary catalytic tar‐cracking reactor was designed and tested for the gasification of pelletized oil palm empty fruit bunch. To further purify the producer gas, the system was also integrated with a cyclone, a water scrubber, and a carbon‐bed filter. Biomass was fed at a rate of 5 kg/h, while the air equivalence ratio (ER) and the gasification temperature were set at 0.1 and 800°C, respectively. In total, 5 kg of the specially developed low‐cost Fe/activated carbons (AC) catalyst was used in the hot gas catalytic tar‐cracking reactor. Results indicate that our integrated gasification system was able to produce a clean burnable gas with a lower heating value (LHV) of 9.05 MJ/Nm 3 , carbon conversion efficiency (CCE) of 79.4%, cold gas efficiency (CGE) of 89.9%, and H 2 and CH 4 concentrations of 29.5% and 10.3%, respectively. The final outlet gas was found to only contain 32.5 mg/Nm 3 of tar, thus making it suitable for internal combustion engine (ICE) application.
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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".