Batch distillation control improvement by novel model predictive control

Abstract

Novel model predictive control (MPC) based on a sequence of reduced-order models is developed for a ternary batch distillation operated in an optimal reflux policy. A Kalman filter (KF) estimator is employed for estimating process outputs in the presence of disturbance and measurement noise. To handle highly nonlinearity and non-stationary by nature of the batch, a series of local models are developed around different parts of the reference profiles, and further reduced their orders individually to attain only observable and controllable state-contributions. The control performance of MPC based on reduced-order models incorporating with KF has been compared with conventional MPC (based on simple model) incorporating with an extended Kalman filter (EKF). Simulation results demonstrate that the proposed control strategy gives good control performance even in a presence of external disturbance and measurement noise. The main advantage of using the reduced-order models is small computation effort and sampling frequency requirement. In addition, the knowledge of thermodynamic is not necessarily required and augmented states can be easily initialized which is applicable in real situation.