Variation of lignocellulosic biomass structure from torrefaction: A critical review
Bibliographic record
Abstract
In recent years, torrefaction, a kind of biomass thermal pretreatment technology, has received a great deal of attention due to its effective upgrading performance of biomass. Recent studies have also suggested that the quality of syngas and bio-oil (or biocrude) from the pyrolysis, gasification, and liquefaction of torrefied biomass can be effectively improved. Torrefaction changes the structure of the biomass, the degree of this change depends strongly on the severity of the torrefaction process. To have a better understanding of the impact that torrefaction has on the biomass structure, this study aims to provide a comprehensive and in-depth review of recent advances on this topic. Particular attention is paid to biomass structure analysis through thermogravimetric analysis, scanning electron microscopy, Fourier transform infrared analysis, Brunauer-Emmett-Teller, X-ray photoelectron spectroscopy, and nuclear magnetic resonance. From these analyses, the thermal degradation characteristics of hemicelluloses, cellulose, lignin, and other components in biomass can be recognized. In addition to the elaboration of biomass structure variation from torrefaction, future challenges and perspectives are also underlined. The insights provided in this review are conducive to the further applications of biomass torrefaction for sustainable biofuel production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".