On the Robustness of Forward and Inverse Artificial Neural Networks for the Simulation of Ethylene/1‐Butene Copolymerization

Abstract

AbstractForward and inverse artificial neural network (ANN) models are used to describe ethylene/1‐butene copolymerization with a model catalyst having two site types. The forward ANN predicts number and weight average molecular weights, average comonomer content, and polymer yield as a function of a set of polymerization conditions, while the inverse model estimates polymerization conditions needed to produce copolymers with desired microstructures. The forward model is found to be robust and resilient to random noise introduced into the datasets. The inverse model, however, leads to multiple solutions (several polymerization conditions can produce polymers with similar microstructures) and is sensitive to random noise in the data. Although the polymerization conditions estimated from inverse ANN are different from the model data, the estimated polymerization conditions are found to provide similar microstructures even with the random noise.