Recursive data-based prediction and control of product quality for a batch PMMA reactor

In many batch processes, frequent process/feedstock disturbances and unavailability of online measurements make tight control of product quality very difficult. Motivated by this, we present a simple data-based method in which measurements of other process variables are related to the end product quality using a historical database. The developed correlation model is used to make online predictions of the end quality, which can serve as a basis for adjusting the batch condition/time so that desired product quality may be achieved. This strategy is applied to a methyl methacrylate (MMA) polymerization process. Important end quality variables, the weight average molecular weight and the polydispersity, are predicted recursively based on the measurements of the reactor cooling rate. Subsequently, a shrinking-horizon model predictive control approach is used to manipulate the reaction temperature. The results in this study show much promise for the proposed data-based inferential control method