Sub- and Supercritical Water Gasification of Rice Husk: Parametric Optimization Using the I-Optimality Criterion
Bibliographic record
Abstract
In this study, rice husk biomass was gasified under sub- and supercritical water conditions in an autoclave reactor. The effect of temperature (350-500 °C), residence time (30-120 min), and feed concentration (3-10 wt %) was experimentally studied using the response surface methodology in relation to the yield of gasification products. The quadratic models have been suggested for both responses. Based on the models, the quantitative relationship between various operational conditions and the responses will reliably forecast the experimental outcomes. The findings revealed that higher temperatures, longer residence times, and lower feed concentrations favored high gas yields. The lowest tar yield obtained was 2.98 wt %, while the highest gasification efficiency and gas volume attained were 64.27% and 423 mL/g, respectively. The ANOVA test showed that the order of the effects of the factors on all responses except gravimetric tar yield follows temperature > feed concentration > residence time. The gravimetric tar yield followed a different trend: temperature > residence time > feed concentration. The results revealed that SCW gasification could provide an effective mechanism for transforming the energy content of RH into a substantial fuel product.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".