Wm. Chan et Cao. Nascimento, USE OF NEURAL NETWORKS FOR MODELING OF OLEFIN POLYMERIZATION IN HIGH-PRESSURE TUBULAR REACTORS, Journal of applied polymer science, 53(10), 1994, pp. 1277-1289
Neural network computing is one of the fastest growing fields of artif
icial intelligence due to its ability to ''learn'' nonlinear relations
hips. This article presents the approach of back propagation neural ne
tworks for modeling of free radical polymerization in high pressure tu
bular reactors. Industrial data were used to train the network for pre
diction of the temperature profile along the reactor, as well as polym
er properties such as density, melt flow index, and molecular weight a
verages. Comparisons were made between the neural network and mechanis
tic model predictions published in the literature. Results showed the
promising capability of a neural network as an alternative approach to
model polymeric systems. (C) 1994 John Wiley & Sons, Inc.