Multi-Scenario Robust Online Optimization and Control of Fed-Batch Systems via Dynamic Model-Based Scenario Selection

The manuscript proposes a novel robust methodology for the model-based online optimization/optimal control of fed-batch systems, which consists of two different interacting layers executed asynchronously. The first iteratively computes robust control actions online via multi-scenario stochastic optimization while the second iteratively re-estimates the optimal scenario map after every single/every certain number of control action/actions. The novelty of the approach is twofold: (I) the scenario map is optimally computed/updated based on probabilistic information on the process model uncertainty as well as the sensitivity of the controlled system to the uncertain parameters; and (II) the scenario set is dynamically re-estimated, thus accounting for the effect of disturbances and changes in the operating conditions of the target process. The proposed approach is applied to a fed-batch Williams-Otto process and compared to an existing multi-scenario optimization/control algorithm as well as a non-robust optimization/control strategy to draw conclusions about which method is more effective. (C) 2016 American Institute of Chemical Engineers