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

作者：Rossi Francesco^*; Reklaitis Gintaras; Manenti Flavio; Buzzi Ferraris Guido

来源：AIChE Journal, 2016, 62(9): 3264-3284.

DOI：10.1002/aic.15346

• process control
• optimization
• stochastic programming
• robust optimal control

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.

• 出版日期2016-9