Calculation and Simulation of Thermal Degradation Kinetics by Artificial Neural Network

The thermal degradation of materials such as plastics, cartons, municipal wastes, etc. has received renewed attention due to the possibility of converting these wastes into useful energetic products or into valuable chemicals. The thermogravimetric analysis (TGA) is one of the most commonly used techniques to study the reactions of thermal decomposition of solids [1-2]. On the other hand, it has been proved in many cases that the appropriated kinetic model can explain a set of TG dynamic data with a single set of kinetic parameters regardless of the heating rate [3]. In this paper, we introduces a new approach based on artificial neural network (ANN) to study the kinetics of thermal decomposition reactions of polymeric material, using dynamic thermogravimetry analysis (TGA) at several heating rates. A multilayer neural network model was trained and tested using experimental data, allowing the proposed model a very good correlation of the weight-loss data. As an example, the same kinetic model has been applied to simultaneously fit runs performed at different heating rates.