Machine learning-based prediction of biomass pyrolysis kinetics: integrating mechanistic modeling and compositional features
Muhammad Asif, Luqman Hakeem, Chengxi Yao, Hira, Rimsha Bibi, Muhammad Bilal, Hassan Zeb · 2026
Read Machine learning-based prediction of biomass pyrolysis kinetics: integrating mechanistic modeling and compositional features by Muhammad Asif, Luqman Hakeem, Chengxi Yao, Hira, Rimsha Bibi, Muhammad Bilal, Hassan Zeb (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13202439.txt.
Description
Muhammad Asif and colleagues offer a two thousand twenty-six study predicting how plant waste breaks down under heat, using sapodilla leaves as the test material. Burning without oxygen can turn biomass into fuels and chemicals. Knowing its speed and energy demands guides reactor design.
Heating experiments at several rates were fitted with classic kinetic methods. Energy needs changed with temperature and reaction style. Heat calculations showed the process absorbs energy and is not spontaneous. A machine learning framework then predicted the same kinetic values from composition features. The pairing of mechanism models and learning aims at faster feedstock screening.
Catalog record
| CREATOR | Muhammad Asif, Luqman Hakeem, Chengxi Yao, Hira, Rimsha Bibi, Muhammad Bilal, Hassan Zeb |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |