A reliable modifier-adaptation strategy for real-time optimization

In model-based real-time optimization, plant-model mismatch can be handled by applying bias- and gradient-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 several optimization variables. In this paper we propose a new real-time optimization scheme that explores the inherent smoothness of the plant mapping to enable a reliable optimization. The idea here is to combine the quadratic approximation approach used in derivative-free optimization techniques with the iterative gradient-modification optimization scheme. The convergence of the scheme is analyzed. Simulation studies for the optimization of a ten-variable synthetic example and a reactor benchmark problem with considerable plant-model mismatch show its promising performance.