The effect of torrefaction on the thermo‐kinetics of thermally processed black pine
Bibliographic record
Abstract
Abstract This paper investigates the kinetic behaviour of thermal decomposition of pre‐treated pine needles. The model‐free methods of Flynn‐Wall‐Ozawa (FWO), Kissinger‐Akahira‐Sunose (KAS), and Kissinger, are used to determine the kinetic parameters of the processed pine needles. For a comparative analysis, the kinetic characteristic of the pre‐treated pine needles is equated to the thermal decomposition of the raw pine needles. For torrefaction of the raw substrate, some changes related to the design have been made to the furnace. The raw material has been thermally pre‐treated at a temperature of 523 K for 5 minutes. The volumetric rate of nitrogen during the torrefaction process is 42 L · min −1 , while the purge flow rate of nitrogen gas is 0.2 L · min −1 for the thermogravimetric analysis. The temperature range for the thermal degradation of the torrefied pine needles is 308 K‐873 K. The activation energies determined by the FWO and the KAS methods are 157.08 kJ · mol −1 and 160.54 kJ · mol −1 , respectively, whereas it is 137 kJ · mol −1 by the Kissinger method. The activation energy computed by the FWO and the KAS schemes for the raw material is found to be 1.3% less than that of the processed pine needles. The aromaticity of the thermally processed pine needles is increased by 13.61%. Thermal immunity or stability due to the increasing fraction of lignin causes the activation energy and frequency factor of the pre‐treated pine needles to slightly elevate. The redistribution of the pyrolysis stages has been observed for the torrefied pine needle sample, the temperature scale at a constant heating rate increases slightly by 0.34% during the char formation.
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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".