Hydrogen Production and Char Formation Assessment through Supercritival Gasification of Biomass
Bibliographic record
Abstract
Abstract The massive potential in biomass gasification could satisfy the rising energy demand. A promising technology for sustainable hydrogen production is supercritical water gasification of biomass (SCWG). This study proposes a new model to assess gas yields and char formation through SCWG. To this end, a thermodynamic approach is utilized to model the reactor, assuming the equilibrium condition. The impact of catalyst on the SCWG is also involved in the new model, considering a deviation term for the Gibbs free energy of solid char. Two different feedstocks, including sunflower and corncob, are assessed toward SCWG. The newly developed model considerably improves gas yields and char formation predictions considering the experimental data. Compared to the non-modified modeling strategy, the sunflower and corncob's gas yield and char formation are improved by 85.37 and 62.52, respectively. The sensitivity results indicate that temperature and feed concentration substantially impact the gas yields and char formation, while pressure is less impactful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".