A nonlinear model predictive control (NMPC) is applied to a slurry polymerization stirred tank reactor for the production of high-density polyethylene. Its performance is examined to reach the required mean molecular weight and comonomer composition, together with the temperature setpoint. A complete phenomenological model including the microscale, the mesoscale and the macroscale levels was developed to represent the plant. The control algorithm comprises a neural dynamic model that uses a neural network structure with a feedforward topology. The algorithm implementation considers the optimization problem, the manipulated and controlled variables adopted and presents results for the regulatory and servo problems, including the possibility of dead time and multi-rate sampling in the controlled variables. The simulation results show the high performance of the NMPC algorithm based in a model for one-step ahead prediction only, and, at the same time, attests the strong difficulty to control polymer properties with dead time in their measurements.
Olefin Polymerization; Predictive control; neural networks
