In this paper, we develop a dynamic real-time optimization (RTO) strategy for an integrated plant. The steady-state assumption in conventional RTOs severely limits frequency of optimization and precludes the use of dynamic degrees of freedom available in the plant (e.g. storage capacities), resulting in suboptimal economic performances. Other approaches that attempt to synchronize the frequency of the plant-level optimization to that of the local-unit-level model predictive controllers can be sensitive to local disturbances and model uncertainty in high-frequency parts of plant dynamics. We reason that a logical middle ground is to perform a dynamic optimization but at a rate significantly lower than the model predictive controllers in order to keep the modeling and computational requirements at a reasonable level for the optimization. We discuss the obtaining of a reduced-order model for a chosen optimization frequency and the interfacing of the real-time optimizer with unit controllers. Two examples are given to compare the various approaches. (C) 2004 Elsevier Ltd. All rights reserved.