Optimisation of a polymer plant by an embedded hybrid architecture

In this paper a hybrid architecture is developed to optimise set-point changes between different polyethylene brands. This architecture combines two different AI-techniques: a recurrent neural network, which is used to model the polyethylene plant; and a simulated annealing-based optimisation algorithm. Several experiments were performed to demonstrate the capabilities of this hybrid optimisation architecture.