Evaluation of Measurement-Based Optimization Schemes for Batch Distillation

Abstract

The standard approach to deal with uncertainty in dynamic optimization is to take a conservative stand. Measurement-based optimization schemes allow reducing this conservatism by using measurements to compensate for the uncertainty. On the example of the productivity optimization of a batch distillation column with a terminal quality constraint, various model-based and model-free optimization schemes are compared. They all use measurements to update the input from batch-to-batch or within a batch. A novel mid-course correction scheme for satisfying the terminal constraint is proposed.

Run-to-Run Adaptation of a Semiadiabatic Policy for the Optimization of an Industrial Batch Polymerization Process

“…Finally, a trajectory-following approach could be investigated for which an off-line-determined trajectory is followed using available on-line measurements. 15,16 This approach can bring the reactor to its active terminal constraint on temperature in a single batch and, in addition, is able to reject within-run disturbances via feedback.…”

“…Also, it is proposed that these techniques be applied to a variety of recipes and different reactor scales that are currently used such as 3-, 8-, and 12-ton reactors. Finally, a trajectory-following approach could be investigated for which an off-line-determined trajectory is followed using available on-line measurements. , This approach can bring the reactor to its active terminal constraint on temperature in a single batch and, in addition, is able to reject within-run disturbances via feedback.…”