Integrated design and control using a dynamic inversely controlled process model

Abstract

The profitability of chemical processes depends on their design and control. In practice, however, the design and control are often done in sequence, leaving little room for improving the control performance when the process design is already fixed. Many commentators have suggested design and control should be integrated. The difficulty associated with the integrated approach, however, is that the number of alternative designs increases with the size of the process. This article proposes an optimization framework using an inversely controlled process model. The combinatorial complexities associated with the controllers are disentangled from the formulation, but the process and its control structure are still designed simultaneously. This article also considers the effect of uncertainties on the optimal design. The proposed optimization framework is benchmarked on a case study from the literature using stochastic mixed integer dynamic programming. The results demonstrate the advantages of the proposed optimization framework.

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