Thermogravimetric Analysis Integrated with Mathematical Methods and Artificial Neural Networks for Optimal Kinetic...

Area of Science:

• Materials Science
• Chemical Engineering
• Analytical Chemistry

Background:

• Thermogravimetric analysis (TGA) is crucial for evaluating material thermal stability.
• Mathematical models are widely used to interpret TGA data for materials like biomass and polymers.
• Traditional models face limitations in capturing complex thermal decomposition behaviors.

Purpose of the Study:

• To review the role of mathematical models in TGA for material thermal stability assessment.
• To compare the effectiveness of different TGA kinetic models.
• To highlight the potential of artificial neural networks (ANNs) in TGA kinetic prediction.

Main Methods:

• Review of existing literature on mathematical models for thermogravimetric analysis.
• Categorization of TGA kinetic models into model-free and model-based approaches.
• Evaluation of integral models and the nth order model for fitting and prediction.
• Introduction of artificial neural networks (ANNs) as an advanced predictive tool.

Main Results:

• Integral models show effectiveness for materials with wide decomposition ranges (e.g., biomass, recycled plastics).
• The nth order model demonstrates superior predictive accuracy over the first-order model for solid biomass.
• Traditional models struggle to account for all effective variables influencing mass loss.
• Artificial neural networks (ANNs) efficiently incorporate multiple variables for robust TGA kinetic prediction.

Conclusions:

• Model selection is critical for accurate TGA interpretation, with integral and nth order models showing promise.
• ANNs represent a significant advancement, offering powerful tools for complex TGA data analysis and deeper material insights.
• The study underscores the evolving landscape of TGA data analysis, moving towards more sophisticated predictive methodologies.