Comparison of Modifier Adaptation Schemes in Real-Time Optimization

In model-based real-time optimization, plant-model mismatch can be handled by applying gradient- and bias-corrections to the cost and constraint functions in an iterative optimization procedure. One of the major challenges in practice is the estimation of the plant gradients from noisy measurement data, in particular for the case of several optimization variables. In this paper we compare four modifier adaptation schemes which were proposed to handle noisy data, iterative gradient-modification optimization, dual modifier adaptation, nested modifier adaptation and modifier adaptation with quadratic approximation. Simulation studies for the optimization of the Otto-Williams reactor with plant-model mismatch are used to illustrate the performance of the different schemes.