MODELING AND CONTROL OF MULTIVARIABLE DISTILLATION COLUMN USING MODEL PREDICTIVE CONTROL USING UNISIM - Abdul Wahid; Richi Adi (SINERGI, 2016)
Distillation columns are widely used in chemical industry as unit operation and required advance process control because it has multi input multi output (MIMO) or multi-variable system, which is hard to be controlled. Model predictive control (MPC) is one of alternative controller developed for MIMO system due to loops interaction to be controlled. This study aimed to obtain dynamic model of process control on a distillation column using MPC, and to get the optimum performance of MPC controller. Process control in distillation columns performed by simulating the dynamic models of distillation columns by UNISIM R390.1 software. The optimization process was carried out by tuning the MPC controller parameters such as sampling time (Ts = 1 – 240 s), prediction horizon (P = 1-400), and the control horizon (M=1-400). The comparison between the performance of MPC and PI controller is presented and Integral Absolut Error (IAE) was used as comparison parameter. The results indicate that the performance of MPC was better than PI controller for set point change 0.95 to 0.94 on distillate product composition using a modified model 1 with IAE 0.0584 for MPC controller and 0.0782 for PI controller.