Noncatalytic Gasification of Lignin in Supercritical Water Using a Batch Reactor for Hydrogen Production: An Experimental and Modeling Study
Bibliographic record
Abstract
In this work, Central Composite Design (CCD) methodology was first introduced to noncatalytic SCWG of lignin for experimental design, model building, and data analysis. Noncatalytic SCWG of lignin was performed in a batch reactor with the specific focus on hydrogen yield optimization. By both experimental and statistical modeling, the main effects as well as interaction effects of three parameters including temperature, pressure, and water to biomass ratio were investigated in a wide range of 399–651 °C, 23–29 MPa, 3–8, respectively. As the result, up to 651 °C higher temperature is desirable for hydrogen production; however, change of pressure from 23–29 MPa did not show significant effect on hydrogen yield. Strong interaction between temperature and water to biomass ratio was observed at temperatures higher than 525 °C, and a dramatic decrease in hydrogen yield with increase in water to biomass ratio was observed at 600 °C. According to the model, the maximum hydrogen yield can reach 1.60 mmol/g biomass when the reaction conditions are temperature = 651 °C, pressure = 25 MPa, and water to biomass ratio = 3.9.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".