摘要：AbstractFor a large class of real-time optimization (RTO) schemes where online experimental gradients are evaluated for convergence to the plant optimum, the input signals are sufficiently excited in the noisy environment. Furthermore, the evaluations are typically persistent even if convergence is attained, for handling varying operating conditions caused by disturbances. The unsettled operation around the optimum leads to oscillations and extra economic loss. In this paper, we propose a strategy that establishes the suspending and activating conditions for RTO schemes. The conditions are developed based on process monitoring methods, which can in a passive way detect operating condition changes. Using the conditions, the RTO implementation is allowed to be suspended upon convergence and further restarted to approach the new optimum when the operating condition changes. The Williams-Otto reactor is studied to show the usefulness of the new idea.
