  This paper studies a dynamic real-time optimization in the context of
model-based time-optimal operation of batch processes under parametric model
mismatch. In order to tackle the model-mismatch issue, a receding-horizon
policy is usually followed with frequent re-optimization. The main problem
addressed in this study is the high computational burden that is usually
required by such schemes. We propose an approach that uses parameterized
conditions of optimality in the adaptive predictive-control fashion. The
uncertainty in the model predictions is treated explicitly using reachable sets
that are projected into the optimality conditions. Adaptation of model
parameters is performed online using set-membership estimation. A class of
batch membrane separation processes is in the scope of the presented
applications, where the benefits of the presented approach are outlined.
