Study of sawdust pyrolysis and its devolatilisation kinetics
Bibliographic record
Abstract
Abstract Pyrolysis of sawdust was studied using a thermogravimetric analyser (TGA) to understand the devolatilisation process and to obtain its global kinetic parameters. The influences of particle size, initial weight of the sample and heating rate on the devolatilisation of sawdust particles have been studied. Results from proximate analysis show that smaller particle size has more ash content compared to larger particle size. The TG and derivative TG curve for variation in particle size and initial weight of the sample showed significant difference in the third stage of the pyrolysis. In addition, the pyrolysis of sawdust differed significantly for variation in heating rate. As the heating rates increased, the char yield also increased. The devolatilisation kinetics was studied considering different stages of pyrolysis. The kinetic parameters for thermal devolatilisation of the sawdust were determined through a nonlinear optimisation method of two independent parallel nth‐order reaction models. The kinetic parameters such as activation energy, frequency factor and order of the reaction for the two stages considered in the model were: E2 = 79.53 (kJ/mol), E3 = 60.71 (kJ/mol); k02 = 1.90 × 106 (1/min), k03 = 1.01 × 103 (1/min); n2 = 0.91, n3 = 1.78, respectively. The results show good agreement between the proposed model and the experimental data of the sawdust pyrolysis.
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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".