An Efficient RTO Scheme for the Optimal Operation of Chemical Processes Under Uncertainty

In this contribution, an efficient Real-time Optimization (RTO) scheme for the optimal operation of chemical processes under uncertainty is proposed. This work builds on two recently published iterative robust optimization methodologies: Modifier Adaptation with Quadratic Approximation (MAWQA) and Directional Modifier Adaptation (DMA) and proposes a unified framework where the benefits of both methods are combined. As a consequence, fast convergence to the true plant optimum is achieved despite the presence of plant-model mismatch. The methodology is illustrated by simulation studies of a novel transition metal complex catalyzed process.