Dynamic optimization in the presence of uncertainty: From off-line nominal solution to measurement-based implementation

Abstract The problem of optimizing a dynamic system in the presence of uncertainty is typically tackled using measurements. The method most widely documented in the literature is based on repetitive optimization of an updated process model. Recently, a computationally less expensive alternative that is based on the real-time adaptation of a solution model using measurements has been proposed. The solution model, which relates elements of the input profiles to the set of active constraints and to appropriate sensitivities, has typically been derived manually from physical insight and intuition. This paper presents a systematic and automated approach to generate a solution model based on recent results in numerical optimization of dynamic systems. This concept provides the first step toward an entirely automated procedure for constrained dynamic optimization of uncertain large-scale processes.

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