A new nonlinear optimization control
strategy is developed for
multivariable control of an ill-conditioned, high-purity distillation
column. A high-gain directional effect resulting from the ill-conditioned
nature of the system causes difficulty in controllability and requires
a higher performance control system. The developed optimal controller
applies a minimization of energy consumption as the optimal objective
function to treat the ill-conditioning effect, while wavelet neural
network input/output linearizing constraints force the outputs to
reach the desired set points. In this paper, ethylene dichloride purification
is used as a case study. The process dynamics are evaluated based
on relevant thermodynamic properties in Aspen Plus Dynamics and are
controlled by the proposed controller in the MATLAB/Simulink platform.
Control performances are investigated in this cosimulation environment
for set point tracking and regulatory problems. The simulation results
demonstrate that robust tracking is attained, while compensation of
the input disturbances is effectively improved compared with a model
predictive controller.