Validation of Nonlinear PID Controllers on a Lab-Scale Batch Reactor

With wide acceptance of batch processes for polymer production, this study aims to model the temperature dynamics of a batch polymerization reactor using Hammerstein and neural network approaches. And to design nonlinear PID controllers in combination to the models to control the temperature of exothermic reactions happening inside the reactor. A temperature trajectory is used as reference signal which is designed based on reactor safety constraints for the reaction of choice Acrylamide Polymerization. The Hammerstein Model based nonlinear PID (HNPID) makes use of polynomial structures to approximated the inverse of the model, so that a higher order model can be used that provides better accuracy. On the other hand, the neural network based PID (NNPID) uses an optimization approach to tune the PID gains. With Batch Reactor as system of interest, both the controllers are validated in simulation to account for energy consumption by each of them. It is noted HNPID consumes less power than NNPID with better tracking hence a perfect candidate for polymer production in realtime.