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Modeling the degradation of polyamide-12 by biocompatible fuels using artificial neural networks

In the first stage of this work, the degradation temperature of polyamide-12 was investigated using biocompatible fuels as a reference with different temperatures by thermogravimetric analysis (TGA) test. Those fuels were containing 20% and 85% ethanol as well as ethanol-free. In the second stage, the multilayer perceptron (MLP) neural network and radial basis function neural network (RBF) were designed to predict the degradation temperature of polyamide-12. Fuel temperature, ethanol percentage, and the time of placing samples in the fuel were selected as input and polymer degradation temperature was defined as network output. The results obtained from the modeling were compared with the results obtained from the test TGA. The results obtained from neural networks MLP and RBF showed slight differences with the experimental results that can be used as an efficient and low-cost tool to predict the degradation temperature of polymers.