Kinetic Modeling of the Devolatilization of Pulverized Coal, Poplar Wood, and Their Blends in a Thermogravimetric Analyzer and a Flat Flame Reactor
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Devolatilization kinetics of coal, poplar wood, and blends containing 10 and 20 wt % of biomass were characterized. Measurements were carried out under inert atmosphere with heating rates between 10 K min –1 and ∼10 6 K s –1 using a thermogravimetric analyzer (TGA) and a flat flame reactor (FFR). Measured data were simulated using the chemical percolation devolatilization (CPD) model and a global kinetic scheme based on two competitive reactions integrating a refined differential reaction model. The CPD model failed to simulate TGA results but reproduced FFR data relatively well. As for the global model, selecting kinetic parameters from the literature turned out to lead to unsuitable predictions. Fitted values of the activation energies E a, i, pre-exponential factors A i, mass stoichiometric coefficients Y i, and the reaction model factor n were therefore inferred using a genetic algorithm-based optimization procedure, leading to obtain an excellent agreement between simulated and measured data. The assessed E a,i values were found to be lower for wood than for coal, which is consistent with the higher energy required to break the strong C–C bonds holding the highly cross-linked aromatic structures of coal. Besides, blending coal with 20 wt % of wood induced a decrease of E a, i values, which went from 99.79 to 86.1 kJ mol –1 and from 186.72 to 171.57 kJ mol –1 for the first and second reactions prevailing at low and high temperatures, respectively. Finally, the fact that the activation energy of the first devolatilization reaction was found to be lower with the blend containing 20% of wood than for wood illustrated the probable existence of synergies, as also exemplified by the characteristic devolatilization times for blended samples, which were found to be relatively similar to and even lower than that of wood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".