MERCon - 2019

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item: Conference-Abstract
Adaptive model predictive control with successive linearization for distillate composition control in batch distillation
Mendis, P; Wickramasinghe, C; Narayana, M; Bayer, C
This paper investigates the application of adaptive model predictive control (MPC) with successive linearization for the control of top product purity of a batch distillation column. Adaptive MPC with successive linearization can overcome the prediction inaccuracies associated with linearization of highly non-linear and dynamic mathematical model of a batch distillation column, with a lower computational load than nonlinear MPC. A binary mixture of methanol and water was selected to demonstrate the controller development, and its performance was investigated by varying MPC tuning parameters in the MATLAB/Simulink simulation environment. Results indicated that the choice of tuning parameters had a considerable influence on the MPC’s ability to track a constant set-point for the output. With the correct choice of tuning parameters, however, it is possible for the controller to track a constant set-point. The present approach is compared with nonlinear MPC in order to gain a quantitative understanding on accuracy and computational effort.