CO<sub>2</sub> biomass fluidized gasification: Thermodynamics and reactivity studies
Bibliographic record
Abstract
Abstract This study reports biomass gasification in a fluidized CREC Riser Simulator. Steam‐CO2 and steam‐inert gas were used as gasifier agents. Three biomass feedstocks were evaluated in terms of gasification performance, based on carbon conversion, product molar fraction, and H2/CO ratios. Results showed that gasification bed temperature influences syngas yields, as well as tar formation. This is the case regardless of the gasifier agent used. It was also shown that steam‐CO2 gasification significantly reduces tar formation while improving carbon conversion and increasing H2 and CO yields. Experimentally‐observed product molar fractions were compared with thermodynamic equilibrium model results. This thermodynamic equilibrium model accounts for biomass elemental composition, bed temperature, and gasifying agents. It was proven that for steam‐CO2 gasification, the thermodynamic equilibrium model predictions are close to the experimental results obtained in the fluidized CREC Riser Simulator. It was also demonstrated that steam‐carbon dioxide gasification leads to a zero CO2 gain, and therefore, a negligible carbon footprint.
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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".