Thermal decomposition of agricultural and food residues: Comparison of kinetic models
Bibliographic record
Abstract
Agricultural and food residues are sustainable biofuels and their utilization in combustion, carbonization, and gasification reduces greenhouse gas emissions. In this work, the thermal decomposition of corn cobs, rice husks, vine prunings, and palm kernel shells was studied in thermogravimetric tests to elaborate reliable pyrolysis kinetics, which are basic parameters for fixed beds and grate reactors. The adequacy and predictability of different models (single first order reaction model, n‐order, distributed activation energy, and a structural model based on the decomposition of cellulose, hemicellulose, and lignin) were quantified. The single first order reaction model had low computational cost but poor accuracy (mean discrepancy 5–8 %, maximum discrepancy > 30 %), while the accuracy increased when a further parameter (reaction order) or a distribution in the exponential factor was introduced. The best accuracy (mean discrepancy 1–2 %, maximum discrepancy 5 %) was found for the structural model, with common kinetic parameters of the chemical components.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".