Combustion Reactivity of Hydrochars Derived from Sugarcane Green Harvesting Residues via Hydrothermal Carbonization
Bibliographic record
Abstract
Abstract A non-isothermal thermogravimetric analysis was performed on hydrochars produced from sugarcane green harvesting residues (GHR) to assess their combustion reactivity. The Coats–Redfern method was employed to simulate conditions, determining kinetic parameters such as activation energy, pre-exponential factor, and reaction order, and assessing the reactivity of the hydrochars. Additionally, characteristic temperatures (ignition, peak, and burning) and combustion indices were identified. The hydrochars were produced at a rate of 12 °C/min with an airflow of 100 mL/min, reaching a maximum temperature of 900 °C. Results indicate that hydrochars produced at 300 °C with H 2 O/GHR ratios of 5:1 and 10:1 exhibit significantly elevated peak decomposition temperatures (447.1 °C and 465.4 °C, respectively) compared to 324.6 °C for the raw GHR, reflecting enhanced thermal stability. These hydrochars also showed a marked reduction in combustion reactivity indices—Di decreased from 12.3 to 3.78, Sn from 49.7 to 7.58, and Cx from 13.7 to 3.64—when compared to those produced at 200 °C. Furthermore, the activation energy increased substantially at 300 °C (40.92 and 38.00 kJ/mol) relative to raw GHR (21.05 kJ/mol) and HTC at 200 °C (≈25 kJ/mol), accompanied by higher pre-exponential factors, particularly for H–300–5 (31.25 min -1 ), indicating a shift in combustion kinetics and the formation of more recalcitrant, carbon-rich structures at higher HTC severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".