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

doi:10.1039/d6ra01011c

January 1, 2026RSC AdvancesOpen Access

Machine learning-based prediction of biomass pyrolysis kinetics: integrating mechanistic modeling and compositional features

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Key Points

This research aims to predict the kinetic triplet of biomass pyrolysis using machine learning and mechanistic modeling.
Utilized machine learning to analyze descriptors related to sapodilla-leaves pyrolysis.
Applied Coats–Redfern thermogravimetric analysis (TGA) for kinetic and thermodynamic parameter fitting.
Successfully predicted the kinetic triplet of pyrolysis using machine learning techniques.
Identified stage-specific kinetic and thermodynamic parameters through the mechanistic interpretation of the data.

Implication

Study predicts pyrolysis kinetics in biomass using machine learning, indicating valuable insights for bioenergy applications.

Abstract

ML predicted the kinetic triplet of sapodilla-leaves pyrolysis from descriptors, while Coats–Redfern TGA fitting provided stage-specific kinetic and thermodynamic parameters for mechanistic interpretation.

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Cite This Study

Asif et al. (2026) studied this question.

synapsesocial.com/papers/6a1295e248a0ea1665672360 https://doi.org/10.1039/d6ra01011c

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Machine learning-based prediction of biomass pyrolysis kinetics: integrating mechanistic modeling and compositional features

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine learning-based prediction of biomass pyrolysis kinetics: integrating mechanistic modeling and compositional features

Key Points

Implication

Abstract

Discussion

Cite This Study

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