Catalytic Gasification of Sawdust Derived from Various Biomass
Bibliographic record
Abstract
A systematic study is conducted for the steam gasification of biomass materials (cellulose, Cedar, and Aspen) using temperature-programmed gasification (TPG) and constant-temperature gasification (CTG) methods in order to produce H 2 -rich gas. The performance of catalyst (CaO) was also studied by varying the catalyst loading from 0 to 8.9 wt % during TPG and CTG processes. The TPG and CTG experiments showed that the use of CaO as a catalyst reduced the maximum gasification temperature by ∼150 °C. Also, the rate of H 2 and cumulative H 2 productions were increased with the impregnation of CaO in cellulose, Cedar, and Aspen during TPG and CTG processes. In TPG, the rate of production of H 2 was increased from 0.21 to 0.38 cm 3 (STP)/min/(0.04 g of sample) when 5.5 wt % CaO was impregnated in cellulose. Higher CaO loading of 8.9 wt % did not improve H 2 production. In CTG, the rate of H 2 production and cumulative production of H 2 increased from 0.18 to 0.31 cm 3 (STP)/min and from 11 to 14 cm 3 (STP)/(0.04 g of sample) when 5.5 wt % CaO was impregnated in cellulose. The rate of production and cumulative production of H 2 from Cedar and Aspen were significantly higher than those from cellulose for catalytic as well as for noncatalytic TPG and CTG processes. Total fuel yield, H 2, and carbon yields were also significantly increased with the impregnation of CaO in cellulose, Cedar, and Aspen.
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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".