Fig. 1: Overview of the proposed data-driven optimization framework.
From: Dynamic optimizers for complex industrial systems via direct data-driven synthesis
a The offline phase processes historical data to learn a dynamic optimization policy. b The learned optimizer operates online, using real-time data to determine optimal setpoints for supervisory control. c The conventional RTO approach uses online models, parameter/state estimation, and RTO to compute setpoints.
