  Kinetic models of solid thermal decomposition are traditionally used for
individual fit of isothermal decomposition experimental data. However, this
methodology can provide unacceptable errors in some cases. To solve this
problem, a neural network (MLP) was developed and adopted in this work. The
implemented algorithm uses the rate constants as predetermined weights between
the input and intermediate layer and kinetic models as activation functions of
neurons in the hidden layer. The contribution of each model in the overall fit
of the experimental data is calculated as the weights between the intermediate
and output layer. In this way, the phenomenon is better described as a sum of
kinetic processes. Two rigid polyurethane foam samples: loaded with Al2O3 and
no inorganic filler were used in this work. The R3 model described the thermal
decomposition kinetic process for all temperatures for both foams with smaller
residual error. However, the network residual errors are on average 102 times
lower compared to this individual kinetic model. This improved methodology
allows detailed study of physical processes and therefore a more accurate
determination of kinetic parameters such as the activation energy and frequency
factor.
