Grade transition using dynamic neural networks for an industrial high-pressure ethylene–vinyl acetate (EVA) copolymerization process

In this paper, estimating of melt index (MI) in an industrial ethylene and vinyl acetate (EVA) copolymerization process will be studied. Three products with their melt indexes ranging from 2.49 to 167.21 are dynamically estimated by an artificial neural networks (ANN) model based on available plant measurements. With this dynamic estimator, a simple MI controller with only proportional-integral mode can be established for the purpose of grade transition by suitably adjusting the chain modifier feed rate. Simulation results demonstrate that significant reduction in the grade transition time can be gained in comparison with the base strategy by step-changing of the operating recipe. A simple model updating algorithm is also proposed for the adjustment of the predicted MI using infrequent lab measurement to handle plant-model mismatches.