Real time optimization of systems with fast and slow dynamics using a lookahead strategy

Abstract

Systems with fast and slow dynamics give rise to objectives in different time scales which may not be aligned. The existing dynamic optimal control methods might become computationally infeasible due to the fine discretization required to capture the fast dynamics. On the other hand, a real time optimization (RTO) method based on steady-state models, which is computationally efficient, can greedily drive the plant towards optimal operation. The drawback of the RTO approach is that it may yield actions that only focus on near future goals and the objectives involving the slower dynamics are neglected. In this paper, we propose to extend RTO with a lookahead strategy by introducing a predictor to capture the effect of changing the current controls on the long-term objective. In this way, we introduce the long-term objectives in RTO while maintaining its computational efficiency and not losing focus of short-term objectives. The proposed approach is demonstrated in a simulation study from offshore petroleum production, that compares the proposed method with both an "industry-standard" RTO method, and a full fledged dynamic optimization method that takes both slow and fast dynamics into account. The proposed methodology performs almost as well as the dynamic optimization method while maintaining a low computational effort.