Pyrolysis Kinetics of Pre-Torrefied Woody Biomass Based on Torrefaction Severity—Experiments and Model Verification
Bibliographic record
Abstract
A kinetic model was developed for the pyrolysis of pre-torrefied lignocellulosic biomass requiring solely knowledge of the pyrolysis kinetics of raw biomass. To predict yield and differential thermogravimetry (DTG) profiles for the pyrolysis of pre-torrefied biomass specimens, the classical three-parallel first-order pyrolysis kinetic model was modified to incorporate severity factors accounting for the impact of torrefaction time and temperature on the devolatilization of hemicellulose, cellulose, and lignin biomass components. The model also included features to account for the effect of pyrolysis heating rate on pyrolysis activation energies of the biomass components both for raw and pre-torrefied substrates. Specifically, severity factor correlations between pre-torrefaction conditions and biomass component relative weights prior to pyrolysis were developed so that the kinetic model, validated at the outset for the pyrolysis of raw biomass specimens, could also be applicable for the pre-torrefied samples. Thermal decompositions of raw and pre-torrefied birch, aspen, and sawdust specimens were tested through thermogravimetric analyses under various pyrolysis heating rates and isothermal torrefaction exposure times and temperatures. Model confrontation against pyrolysis yields and DTG rates measured at varying severities for both raw and pre-torrefied woody biomass confirmed that predicted pyrolysis yields and rates were in a good agreement with experiments.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".