Online prediction of polymer product quality in an industrial reactor using recurrent neural networks

In this paper, internally recurrent neural networks (IRNN) are used to predict a key polymer product quality variable from an industrial polymerization reactor. IRNN are selected as the modeling tools for two reasons: 1) over the wide range of operating regions required to make multiple polymer grades, the process is highly nonlinear; and 2) the finishing of the polymer product after it leaves the reactor imparts significant dynamics to the process by “mixing” effects. IRNN are shown to be very effective tools for predicting key polymer quality variables from secondary measurements taken around the reactor