Estimation of impurity and fouling in batch polymerisation reactors through the application of neural networks

Abstract

The estimation of the amount of reactive impurities and the level of fouling in a batch polymerisation reactor is of strategic importance to the polymerisation industry. It is essential that the level of impurities and reactor fouling are known (estimated) in order to be able to develop robust and reliable monitoring and control strategies. This paper describes two approaches based upon stacked neural network representations. In the first approach, an inverse neural network model of the polymer process is constructed and the initial reaction conditions are predicted. The amount of impurities and reactor wall fouling are then calculated by comparing the predicted values with the nominal initial conditions. In the second approach, a neural network is used to model the dynamic behaviour of the polymer process. The predicted trajectories are then compared with the on-line measurements of conversion and coolant temperatures. The techniques are compared on a first-principles-based simulation of a pilot scale batch methyl methacrylate (MMA) polymerisation reactor.