Performance analysis of three advanced controllers for polymerization batch reactor: An experimental investigation

• We designed and analyzed the performances of three advanced non-linear controllers for FRP of styrene.
• The three controllers are artificial neural network-based MPC, FLC and GMC.
• Different types of disturbances are made to test the stability of controller performance.
• The experimental studies revealed that the performance of NN-MPC is superior to that of FLC and GMC.

The performances of three advanced non-linear controllers are analyzed for the optimal set point tracking of styrene free radical polymerization (FRP) in batch reactors. The three controllers are the artificial neural network-based MPC (NN-MPC), the artificial fuzzy logic controller (FLC) as well as the generic model controller (GMC). A recently developed hybrid model (Hosen et al., 2011a. Asia-Pac. J. Chem. Eng. 6(2), 274) is utilized in the control study to design and tune the proposed controllers. The optimal minimum temperature profiles are determined using the Hamiltonian maximum principle. Different types of disturbances are introduced and applied to examine the stability of controller performance. The experimental studies revealed that the performance of the NN-MPC is superior to that of FLC and GMC.