Pyrolysis of Medium-Density Fiberboard: Optimized Search for Kinetics Scheme and Parameters via a Genetic Algorithm Driven by Kissinger’s Method

The pyrolysis kinetics of charring materials plays an important role in understanding material combustions especially for construction materials with complex degradation chemistry. Thermogravimetric analysis (TGA) is frequently used to study the heterogeneous kinetics of solid fuels; however, there is no agreed method to determine the pyrolysis scheme and kinetic parameters for charring polymers with multiple components and competing reaction pathways. This study develops a new technique to estimate the possible numbers of species and sub-reactions in pyrolysis by analyzing the second derivatives of thermogravimetry (DDTG) curves. The pyrolysis of a medium-density fiberboard (MDF) in nitrogen is studied in detail, and the DDTG curves are used to locate the temperature of the peak mass-loss rate for each sub-reaction. Then, on the basis of the TG data under multiple heating rates, Kissinger's method is used to quickly find the possible range of values of the kinetic parameters (A and E). These ranges are used to accelerate the optimization of the inverse problem using a genetic algorithm (GA) for the kinetic and stoichiometric parameters. The proposed method and kinetic scheme found are shown to match the experimental data and are able to predict accurately results at different heating rates better than Kissinger's method. Moreover, the search method (K–K method) is highly efficient, faster than the regular GA search alone. Modeling results show that, as the TG data available increase, the interdependence among kinetic parameters becomes weak and the accuracy of the first-order model declines. Furthermore, conducting TG experiment under multiple heating rates is found to be crucial in obtaining good kinetic parameters.