Pyrolysis characteristics of low‐rank coals based on double‐gaussian distributed activation energy model
Bibliographic record
Abstract
Pyrolysis is an important technology in the utilization of low‐rank coals (LRCs) and is a prerequisite stage for other conversion methods. This study aimed to develop an understanding of the pyrolysis process of LRCs, especially the relationship between the pyrolysis kinetics and the internal chemical structure. The chemical structure parameters of all of the samples were obtained using the Fourier transform infrared spectroscopy (FTIR) method. Thermogravimetric (TG) experiments were conducted at different heating rates (5, 10, and 20 K/min), and the experimental results were fitted using the distributed activation energy model (DAEM). DAEM based on double‐Gaussian distribution (2G‐DAEM) exhibited an acceptable fit to the experimental data in this study. An analysis of the chemical structure parameters of the four types of coals and the kinetic model obtained by fitting indicated that the difference in the chemical structure parameters among the coal samples could effectively explain the difference in the pyrolysis process and the activation energy distribution. The primary pyrolysis stage of the coal samples, including the thermal hysteresis effect caused by the increase in the heating rate, was accurately described by the 2G‐DAEM in this study.
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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 | 0.001 |
| 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 | 0.001 |
| 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".