Thermogravimetric Analysis Integrated with Mathematical Methods and Artificial Neural Networks for Optimal Kinetic Modeling of Biomass Pyrolysis: A Review

Abstract

This review emphasized the role of mathematical models and correlations in thermogravimetric analysis to evaluate the thermal stability of various materials, including biomass, polymers, recycled plastics, and solid fuels of carbon and biomass material. Numerous thermogravimetric analysis kinetic models are driven, and they are broadly divided into model-free and model-based categories. Integral models have proven to be more effective for fitting, particularly for materials with wide decomposition temperature ranges in biomass material and mixed recycled plastic waste. The nth order model showed superior predictive accuracy compared with the first-order model, particularly for solid biomass, highlighting the significance of model selection. Traditional thermogravimetric analysis mathematical models are limited in accounting for mass loss as a function of all effective variables. In contrast, artificial neural networks (ANNs) efficiently represent and incorporate these variables, marking a significant advancement in predicting thermogravimetric analysis kinetics. ANNs provide powerful tools for managing complex analysis data, enabling robust predictions and deeper insights into material thermal behavior.