Robust model-based iterative feedback optimization of steady state plant operations

We present a complementary approach to real-time optimization (RTO) for max imizing the operating profit of an existing chemical plant without requirin g a model-updating procedure, which is cumbersome and which may not necessa rily improve the model. The proposed optimization methodology is based on a n analogy between steady-state operation periods in process operation and i terations in numerical optimization. This analogy is also used by optimizat ion-based run-to-run (RtR) control for batch processes. The process measure ments are utilized to correct the gradient information used in optimization computations, resulting in better operating conditions. The plant, operati on is an integral part of the optimization, and this necessitates certain m odifications 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.