Robust Model-Based Iterative Feedback Optimization of Steady State Plant Operations

We present a complementary approach to real-time optimization (RTO) for maximizing the operating profit of an existing chemical plant without requiring a model-updating procedure, which is cumbersome and which may not necessarily improve the model. The proposed optimization methodology is based on an analogy between steady-state operation periods in process operation and iterations in numerical optimization. This analogy is also used by optimization-based run-to-run (RtR) control for batch processes. The process measurements are utilized to correct the gradient information used in optimization computations, resulting in better operating conditions. The plant operation is an integral part of the optimization, and this necessitates certain modifications to the optimization algorithm that we use (feasible sequential quadratic programming). The methodology is tested with a CSTR process and is shown to be robust in the presence of substantial model−plant mismatch.