Kinetic Study and Thermal Decomposition Behavior of Lignite Coal
Bibliographic record
Abstract
A thermogravimetric analyzer was employed to investigate the thermal behavior and extract the kinetic parameters of Canadian lignite coal. The pyrolysis experiments were conducted in temperatures ranging from 298 K to 1173 K under inert atmosphere utilizing six different heating rates of 1, 6, 9, 12, 15, and 18 K min −1 , respectively. There are different techniques for analyzing the kinetics of solid-state reactions that can generally be classified into two categories: model-fitting and model-free methods. Historically, model-fitting methods are broadly used in solid-state kinetics and show an excellent fit to the experimental data but produce uncertain kinetic parameters especially for nonisothermal conditions. In this work, different model-free techniques such as the Kissinger method and the isoconversional methods of Ozawa, Kissinger-Akahira-Sunose, and Friedman are employed and compared in order to analyze nonisothermal kinetic data and investigate thermal behavior of a lignite coal. Experimental results showed that the activation energy values obtained by the isoconversional methods were in good agreement, but Friedman method was considered to be the best among the model-free methods to evaluate kinetic parameters for solid-state reactions. These results can provide useful information to predict kinetic model of coal pyrolysis and optimization of the process conditions.
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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 | 0.001 | 0.001 |
| 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".