Abstract:
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.
