Microwave absorber utilization to improve grinding and particle surface structure characteristics of torrefied switchgrass particles for bioenergy applications
Bibliographic record
Abstract
Torrefaction treatment improves biomass grindability by transforming the fibrous herbaceous to a more brittle and lighter coal-like material. Microwave-assisted torrefaction is a promising technology for biomass conversion into energy, fuels, and chemicals. The study applied microwave absorbers in the torrefaction process to improve the thermochemical characteristics and grindability of switchgrass. Switchgrass in two particle sizes was torrefied in a microwave reactor with biochar added as a microwave absorber under inert conditions. After torrefaction, the geometric mean particle and size distribution and selected physical characteristics were evaluated, and the grindability of the torrefied ground and chopped with and without biochar were compared with those of untreated switchgrass. The geometric diameter results decreased, and the specific energy required for grinding torrefied switchgrass with biochar was significantly reduced with extended residence times and at a torrefaction temperature of 300 °C. After grinding, the lowest grinding energy of 32.82 kJ at 300 °C/20 min was recorded with torrefied ground switchgrass/biochar. The 10% biochar added/250 °C resulted in deep cell wall disarrangement, whereas at a torrefaction temperature of 300 °C, large surface deformation and carbonized weight fractions were observed.
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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".