A Comparative Study on the Carbonization of Chitin and Chitosan: Thermo-Kinetics, Thermodynamics and Artificial Neural Network Modeling

The carbonization of chitin and chitosan presents a sustainable approach to producing nitrogen-doped carbon materials for various applications, making kinetic and thermodynamic analyses crucial for assessing their viability. Meanwhile, artificial neural network (ANN)-driven modeling not only enhances the precision of thermo-kinetic and thermodynamic estimations but also facilitates the optimization of carbonization conditions, thereby advancing the development of high-performance carbon materials. In this work, we aim to develop an ANN model to estimate weight loss as a function of temperature and heating rate during the carbonization of chitin and chitosan. The experimental average activation energies of chitosan and chitin, determined by various iso-conversional methods, were found to be 128.1–152.2 kJ/mol and 157.2–160.0 kJ/mol, respectively. The best-performing ANN architectures—NN4 for chitin (R2 = 0.9995) and NN1 for chitosan (R2 = 0.9997)—swiftly predicted activation energy values with commendable accuracy (R2 > 0.92) without necessitating repetitive experiments. Furthermore, the estimation of thermodynamic parameters provided both a theoretical foundation and practical insights into the carbonization process of these biological macromolecules, while morpho-structural changes in the resulting chars were systematically examined across different carbonization temperatures. The results underscore the adaptability and effectiveness of ANN in analyzing the carbonization of biological macromolecules, establishing it as a reliable tool for thermochemical conversion studies.