Adaptive predictive control of bioprocesses with constraintbased modeling and estimation Abstract   Control of biotechnological processes is currently recipebased with insufficient ability to handle possible uncertainties, which results in suboptimal production processes. To address this problem, modelbased optimization and control approaches can be implemented to derive optimal control strategies. However, for reliable performance of modelbased control, it is crucial to use flexible and adaptive control strategies which address biological variability while compensating for uncertainties. In this work, we present an approach for adaptive control of a bioprocess based on dynamic flux balance models. A previously developed bilevel approach for bioprocess optimization is implemented inside a model predictive control (MPC) routine. To account for model uncertainties, a moving horizon estimation algorithm is combined with the MPC in order to estimate uncertain parameters of the underlying model online for different metabolic modes. We apply this method to maximize the productivity of a target metabolite under microaerobic conditions by adapting the degree of oxygenlimitation online.

Timeminimal set point transition for nonlinear SISO systems under different constraints Abstract   Set point transition of nonlinear plants plays an important role in many applications where dynamic process management has to be considered. This transition should be rapid xe2x80x93 as operation in the new set point increases the economical benefit xe2x80x93 but in compliance with all safety regulations. We present a feedforward approach for a timeminimal set point transition of singleinput, singleoutput nonlinear plants with respect to input, state and output constraints. The conceptual idea is based on the design of an optimization problem utilizing a coordinate change of the plant and a special setup function for the output trajectory. This allows the simultaneous planning of the trajectory and determination of the corresponding control signal. The formulation of the set point transition as an optimization problem provides a flexible design with respect to the integration of inequality constraints. Additional flexibility is provided by the type of setup functions, which permit any adjustment of the output trajectory between the set points. In contrast to other works, we focus on the stationarity of the system output, which allows a faster transition compared to the requirement to reach a steady state of all states. Finally, we present an example from the field of process systems engineering to demonstrate the applicability of the proposed methodology.