Dynamic optimization in planning under process model uncertainty

Abstract: "Planning for multi-product batch plants involves decisions for facility design, product scheduling, inventory control etc. These are usually addressed for recipe based processes with fixed stage processing times. There is significant benefit in incorporating detailed dynamic process models while planning, and addressing the overall problem simultaneously (Bhatia and Biegler [1]). It thus becomes desirable to have sufficiently accurate process models, or develop methods that deal with process model uncertainty. This work addresses dynamic processing and planning decisions simultaneously for multi product batch plants, now with uncertainty in process parameters. Instances of uncertain parameters are used to construct planning scenarios that are addressed through a multiperiod planning formulation. Dynamic processing under uncertainty is addressed via a closed loop state feedback based correction strategy. Control parameters in the closed loop implementation of processing decisions are then determined in an open loop manner, and treated as invariant design variables in the multiperiod problem formulation."