INTERNAL MODEL CONTROL OF CUMENE PROCESS USING ANALYTICAL RULES AND EVOLUTIONARY COMPUTATION

Cumene is a precursor for producing many organic chemicals and is thinner in paints and lacquers. Its production process involves one of the large-scale manufacturing processes with complex kinetics. Different classical control strategies have been implemented and compared in this process for the cumene reactor. As a system with large degrees of freedom, a novel approach for extracting the state space model from the COMSOL Multiphysics implementation of the system is adopted here. Internal Modern Control (IMC) based PI and PID controllers are derived for the system. The system is reduced to the FOPDT and SOPDT model structure to derive the controller setting using Skogestad half rules. The integral time is modified for excellent set point tracking and faster disturbance rejection. From the analysis, it can be stated that the PI controller suits more for this specific process. The particle swarm optimization (PSO) algorithm, an evolutionary computation technique, is also used to tune the PI settings. The PI controllers with IMC, Zeigler Nichols, and PSO tuning are compared, and it can be concluded that the PSO PI controller settles at 45 s without any oscillations and settles down faster for the disturbance of magnitude 0.5 applied at t = 800 s. However, it is computationally intensive compared to other controller strategies.