CO<sub>2</sub> Gasification of Sugarcane Bagasse Char: Consideration of Pyrolysis Temperature, Silicon and Aluminum Contents, and Potassium Addition for Recirculation of Char
Bibliographic record
Abstract
In sugarcane bagasse gasification, char recirculation to the gasifier improves the syngas quality and process efficiency. To determine the effect of char properties on the reaction kinetics, in this work, the pregasification pyrolysis temperature, particle size, and catalyst (potassium) loading were varied. Char samples were prepared at 750–900 °C via pyrolysis and gasified isothermally in a thermogravimetric analysis unit at 850 °C with CO 2, and gasification data were modeled using the random pore and extended random pore models. Increasing pyrolysis temperatures did not affect the char morphology and surface composition but did reduce the surface area, as determined by N 2 adsorption, decreasing initial gasification rates, and the overall fitted rate constants. Reduction of the particle size via ball milling decreased the time required for complete conversion and changed the shape of the rate versus conversion curves from monotonically decreasing to concave down. The char sample prepared via pyrolysis at 900 °C was an exception, having a maximum rate at ∼10% conversion without ball milling. After ball milling of the char sample prepared at 750 °C, there was an accumulation of ash (Al and Si) on the surface of the particles and a reduction in the surface area, consistent with the ash blocking pores—the porosity in these samples increased during the initial stages (up to ∼20% conversion) of gasification. The gasification behavior was generally well modeled by the extended random pore model. Although the addition of KOH (K/Al mass ratio ∼ 0.2–1.25) enhanced the gasification rates, too much K—from the addition of KOH or after 90% conversion—created mass-transfer limitations resulting in lower gasification 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".