Nonlinear model predictive control of an industrial polymerization process

• Maintain polymer quality requirements at specified production rates.
• Achieve process objectives that have different settling times.
• Address the computational needs of the associated dynamic optimization problem.

Nonlinear model predictive control (NMPC) is used to maintain and control polymer quality at specified production rates because the polymer quality measures have strong interacting nonlinearities with different temperatures and feed rates. Polymer quality measures that are available from the laboratory infrequently are controlled in closed-loop using a NMPC to set the temperature profile of the reactors. NMPC results in better control of polymer quality measures at different production rates as compared to using the nonlinear process model with reaction kinetics to implement offline targets for reactor temperatures.