Nested modifier-adaptation for RTO in the Otto Williams Reactor

This paper deals with the problem of uncertainty management in real time optimization (RTO). It proposes a new architecture in the modifier-adaptation methodology, reformulating the algorithm as a nested optimization problem with two layers. Using this approach, it is possible to find a point that satisfies the KKT conditions of a process using an inaccurate model, but unlike the original modifier method, with no need to estimate the experimental gradients of the process. The proposed method has been tested in the Otto Williams Reactor considering structural mismatches and perfect and noisy measurements. The results are compared with the previous modifier adaptation methodology using dual control optimization showing that the method finds a KKT point of the process with the advantage that no experimental gradient information is required and with less sensitivity to process noise.