Pyrolysis kinetics and activation thermodynamic parameters of exhausted coffee residue and coffee husk using thermogravimetric analysis
Bibliographic record
Abstract
Abstract Exhausted coffee residue (ECR) and coffee husk (CH) are potential feedstock for energy production through thermochemical and biochemical conversion processes. Kinetic study of ECR and CH is essential for the design and optimization of different thermochemical conversion processes. In this study, four different iso‐conversional methods were employed in the estimation of the activation energy (E A ) and pre‐exponential factor (A). The methods used includes Flynn‐Wall‐Ozawa (FWO), Kissinger‐Akahira‐Sunose (KAS), Kissinger's method, and the Friedman method. Data from the thermogravimetric/derivative thermogravimetric analysis (TGA/DTG) at varying heating rates of 5‐20°C/min in an inert environment were used in this study. It was observed that the heating rate influences the pyrolysis parameters such as peak temperature, maximum degradation rate and initial decomposition temperature. The activation energy for ECR using the FWO method was in the range of 62.3‐102.4 kJ · mol −1 . Likewise, the KAS and Friedman methods yielded activation energy between 51.3‐93.3 kJ · mol −1 and 10.6‐122.7 kJ · mol −1 , respectively. In addition, the activation energy calculated for CH using FWO, KAS, and Friedman methods were shown to range from 39.1‐140.6 kJ · mol −1 , 27.7‐131.6 kJ · mol −1 , and 24.9‐111.2 kJ · mol −1 , respectively.
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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 | 0.001 | 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".