Jitendra Kadam, B. Srinivasan, D. Bonvin, Wolfgang Marquardt:
Optimal grade transition in industrial polymerization processes via NCO tracking
AIChE Journal, 2007, 53(3), 627 - 639
In industrial polymerization processes, several grades of polymer are frequently produced in the same plant by changing the operating conditions. Transitions between the different grades are rather slow and result in the production of a considerable amount of on-specification polymer. Grade transition improvement is viewed here as a dynamic optimization problem, for which numerous approaches exist. Open-loop implementation of the input profiles obtained from numerical optimization of a nominal process model is typically insufficient due to the presence of uncertainty in the form of model mismatch and process disturbances. This paper considers a novel measurement-based approach that consists of tracking the Necessary Conditions of Optimality (NCO tracking) using a solution model and measurements. The solution model consists of state-event-triggered controllers sequenced according to the structure of the nominal optimal solution computed on-line. The solution model is generated by dissecting the nominal input profiles into arcs and switching times that are then assigned to the various parts (active state and endpoint constraints, sensitivities) of the NCO. These input elements are then adapted on-line using appropriate measurements. In this contribution, a simulated application of NCO tracking to an industrial polymerization process for implementing optimal grade transitions is considered. A solution model is generated from the nominal optimal solution, and a control superstructure is considered to handle the possible activation of nominally-inactive constraints. Simple PI-type controllers are used to implement the solution model. For di erent uncertainty scenarios, simulation of the NCO approach shows that considerable reduction in transition time is possible, while still guaranteeing feasible operation. The control strategy that enforces (near) optimality under uncertainty is quite simple and robust for implementation in industry.
Dynamic real-time optimization, necessary conditions of optimality, optimizing control, constraint tracking, grade change, polymerization.