Thermogravimetric analysis and kinetic modeling of the co-pyrolysis of a bituminous coal and poplar wood
Bibliographic record
Abstract
The co-pyrolysis of coal and biomass has proven to be a promising route to produce liquid and gaseous fuels as well as specific value-added chemicals while contributing to mitigating CO2 emissions. The interactions between the co-processed feedstocks, however, need to be elucidated to support the development of such a thermochemical conversion process. In this context, the present work covers the kinetic analysis of the co-pyrolysis of a bituminous coal with poplar wood. In this research, biomass was blended with coal at two different mass ratios (10% (mass) and 20% (mass)). Thermogravimetric analyses were carried out with pure and blended samples at four heating rates (5, 10, 15 and 30 °C·min−1). A direct comparison of experimental and theoretical results (based on a simple additivity rule) failed to yield a clear-cut conclusion regarding the existence of synergistic effects. Kinetic analyses have therefore been achieved using two model-free methods (the Ozawa–Flynn–Wall and Kissinger–Akahira–Sunose models) to estimate the rate constant parameters related to the pyrolysis process. A significant decrease of the activation energy has thus been observed when adding wood to coal (activation energies associated with the blend containing 20% (mass) of biomass being even lower than those estimated for pure wood at low conversion degrees). This trend was attributed to the possible presence of synergies whose related mechanisms are discussed. The rate constant parameters derived by means of the two tested models were finally used to simulate the evolution of the conversion degree of each sample as a function of the temperature, thus leading to a satisfying agreement between measured and simulated data.
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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 | 0.001 |
| 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) | 0.001 | 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".