Characterization of Banana Peels Wastes as Potential Slow Pyrolysis Feedstock
Bibliographic record
Abstract
Uganda is the world’s second largest producer and consumer of banana after India. This has resulted into vast quantities of banana wastes, including the leaves, pseudostem, stalks, rejected and rotten fruits and the fruit peels. This study focuses on the characterization of banana peels to yield banana peels vinegar (BPV), tar and biochar as value added products that can be useful to farmers. Dried banana peels were characterized via proximate, ultimate, lignocellulosic, thermogravimetric (TG), and calorific value analyses. The obtained results showed that the volatile matter and fixed carbon contents were 88.02% and 2.70% while carbon, nitrogen and sulphur were 35.65%, 1.94% and 20.75 ppm respectively. The hemicellulose, cellulose and lignin contents were 41.38%, 9.90% and 8.90% while the higher and lower heating values were 16.15 MJ/kg and 14.80 MJ/kg. The maximum devolatilization rate in the banana peel biomass occurred in the temperatures range of 450–550oC which was taken as the slow pyrolysis regime temperature. The high levels of fixed carbon, volatile matter and ash contents were strong indicators that banana wastes are adequate feedstock for pyrolysis work to yield bio-infrastructure products. Similarly, the lignin, cellulose and hemicellulose fractions had significant correlation between the biomass heating values and the eventual chemical compounds present BPV and biochar. The characterization properties of the banana peels are akin to the leaves and pseudostem and thus are suitable for pyrolysis process.
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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 | 0.001 | 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".