Determination of Distributed Activation Energy Model Kinetic Parameters Using Simulated Annealing Optimization Method for Nonisothermal Pyrolysis of Lignin
Bibliographic record
Abstract
The distributed activation energy model (DAEM) has been shown to be more descriptive of the pyrolysis reaction than other applicable models. In this study, the temperature dependency of the preexponential factor has been included in the DAEM. The model equation has been solved using Simpson’s 1/3 rule, and the kinetic parameters were determined using an optimization method. Simulated annealing method has been used to determine the DAEM kinetic parameters for the nonisothermal pyrolysis of lignin using thermogravimetric analysis (TGA) data. The nonisothermal pyrolysis of lignin was conducted at three different heating rates of 5, 10, and 15 °C/min under nitrogen atmosphere. Predicted results from the optimum kinetic parameters have been compared with the experimental data. The DAEM equation predicts the experimental data very well for different heating rates.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".