Calculations of activation energy and frequency factors for corn leafs pyrolysis using excel solver: new concept
Bibliographic record
Abstract
Abstract Thermal degradations of biomass corn leaves were studied for kinetic modeling. Thermogravimetric-differential analyzer runs at 5, 10, 20, and 30 °C min−1 heating rates were employed. Apparent activation energy and frequency factor values were calculated for first-order kinetics using several procedures. The procedure of Coats and Redfern showed 28.89 to 31.78 kJ mol−1 apparent activation energy and 15.5 to 157.12 min−1 frequency factor, respectively. Calculation of the apparent activation energy and frequency factor using Kissinger–Akahira–Sunose procedure gave 229.9–364.2 kJ/mol and 8.567 × 1023 and 1.13 × 1031 (min−1), respectively as the conversion increased from 0.1 to 0.9. The newly introduced excel solver procedure indicates a distribution activation energy over the entire range of conversion. For first-order reaction kinetics, the calculated apparent activation energy magnitudes ranged between 5.0 kJ mol−1 with frequency factor equals to 0.239 and 196.2 kJ mol−1 with frequency factor 2.89 × 1012 in the studied range. The low or high magnitudes of the calculated activation energy are not associated with a particular value of the conversion. The calculated apparent activation energies are related to the direct solution of the simultaneous equations that constitute the basis of the excel solver.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".