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Kinetic models of solid thermal decomposition are traditionally used forindividual fit of isothermal decomposition experimental data. However, thismethodology can provide unacceptable errors in some cases. To solve thisproblem, a neural network (MLP) was developed and adopted in this work. Theimplemented algorithm uses the rate constants as predetermined weights betweenthe input and intermediate layer and kinetic models as activation functions ofneurons in the hidden layer. The contribution of each model in the overall fitof the experimental data is calculated as the weights between the intermediateand output layer. In this way, the phenomenon is better described as a sum ofkinetic processes. Two rigid polyurethane foam samples: loaded with Al2O3 andno inorganic filler were used in this work. The R3 model described the thermaldecomposition kinetic process for all temperatures for both foams with smallerresidual error. However, the network residual errors are on average 102 timeslower compared to this individual kinetic model. This improved methodologyallows detailed study of physical processes and therefore a more accuratedetermination of kinetic parameters such as the activation energy and frequencyfactor.

Kinetic study of rigid polyurethane foams thermal decomposition by artificial neural network