Determination of Thermal Process Kinetics by Artificial Neural Networks: A Review Considering Isothermal and Non-Isothermal TG and DSC Data

Abstract

The integration of artificial neural networks (ANNs) with thermal analysis techniques, such as thermogravimetry (TG) and differential scanning calorimetry (DSC), presents great potential to accurately determine kinetics of thermal processes and thermal behavior. This review examines the application of ANNs, including Multilayer Perceptron (MLP) and Hopfield Neural networks (HNN), to analyze both isothermal and non-isothermal TG and DSC data. These networks provide robust tools for predicting thermal behavior, determining kinetic parameters, and modeling complex reaction mechanisms, significantly improving accuracy when compared to traditional methods. The review highlights advances in ANN-based methodologies, including the ability to integrate multiple kinetic models and manage noisy experimental data, thereby offering new insights into thermal decomposition and reaction kinetics. Moreover, the use of HNN in distributed activation energy models (DAEM) is explored, indicating their potential in understanding heterogeneous systems. This study emphasizes the transformative potential of ANNs for improving kinetic studies, suggesting future directions to explore machine learning in material characterization and thermal process optimization.

Keywords:
artificial neural networks; thermal analysis; thermogravimetry; differential scanning calorimetry; kinetics; machine learning