Investigation of Steam Treatment on the Sorption Behavior of Rice Straw Pellets
Bibliographic record
Abstract
Agricultural residue is a readily available biomass source for making biofuels. However, complex structure along with low calorific value makes it reluctant to be utilized. This necessitates the pretreatment of biomass to improve its accessibility. In this study, rice straws were treated with saturated steam at 180–200 °C for 5–10 min and pelletized. Results indicated that the pretreatment process could effectively improve the calorific values and enhance the hydrophobicity of the rice straws. The steam explosion process changed the content of chemical components in the biomass materials. With the increase of reaction temperature and retention time, the content of cellulose, hemicelluloses and lignin declined. Comparedwith the raw materials, there was an increase in the calorific value from 16.24 MJ/kg to 17.26 MJ/kg for the steam treated straws. For moisture sorption test under the condition of 30 °C and 90% relative humidity, the equilibrium moisture content dropped from 21% for untreated straw pellets to 14.2% for the steam treated straw pellets treated at 200 °C for 10 min. Moisture adsorption rate reduced along with the increase in steam temperature and retention time. This study demonstrated that steam explosion performed an effective way to improve the characteristics of agricultural residues.
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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".