A dynamic optimization framework for integration of design, control and scheduling of multi-product chemical processes under disturbance and uncertainty

Abstract

A novel dynamic optimization framework is presented for integration of design, control, and scheduling for multi-product processes in the presence of disturbances and parameter uncertainty. This framework proposes an iterative algorithm that decomposes the overall problem into flexibility and feasibility analyses. The flexibility problem is solved under a critical (worst-case) set of disturbance and uncertainty realizations, whereas the feasibility problem evaluates the dynamic feasibility of each realization, and updates the critical set accordingly. The algorithm terminates when a robust solution is found, which is feasible under all identified scenarios. To account for the importance of grade transitions in multiproduct processes, the proposed framework integrates scheduling into the dynamic model by the use of flexible finite elements. This framework is applied to a multi-product continuous stirred-tank reactor (CSTR) system subject to disturbance and parameter uncertainty. The proposed method is shown to return robust solutions that are of higher quality than the traditional sequential method. The results indicate that scheduling decisions are affected by design and control decisions, thus motivating the need for integration of these three aspects.