Recursive data‐based prediction and control of batch product quality

Abstract

AbstractIn typical batch and semibatch processes, process/feedstock disturbances occur frequently and on‐line measurements of product quality variables are not available. As a result, most batch processes have not been able to achieve tight quality control. Empirical, data‐driven approaches are very attractive for dealing with this problem because of the difficulties associated with developing accurate process models from first principles. An approach for recursive on‐line quality prediction was developed around data‐based model structures. Techniques designed to incorporate the predictive models into on‐line monitoring and control of batch product quality were also examined. The proposed control approach can be viewed as shrinking‐horizon model‐predictive control based on empirical models. The effectiveness of the proposed prediction and control methods are illustrated by using an industrially relevant simulated polymerization example.