KINETIC AND THERMAL DECOMPOSITION STUDIES OF RIGID POLYURETHANE FOAMS MODELED BY ARTIFICIAL NEURAL NETWORKS

Kinetic models of solid thermal decomposition are traditionally used for individual fit of isothermal experimental data. However, this methodology presents unacceptable errors in some regions of the data. To solve this problem, a neural network was 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 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 and D2 models described the thermal decomposition kinetic process for all temperatures for both foams with smaller residual error. However, the network, combining the kinetic models, presented residual errors on average 102 times lower compared to these individual models. The determined activation energy is 12.44 kJ mol-1 higher for the loaded foam. This result corroborates the use of this material as flame retardant, even with the presence of a small amount of charge in its structure.