Approximate robust optimization of time-periodic stationary states with application to biochemical processes

In this paper we present techniques to optimize periodic stationary states of processes that depend on uncertain parameters. We start with an introduction to approximate robust counterpart formulations and specialize on systems with many uncertain parameters but only a small number of inequality constraints. The presented approximate robust programming formulation has an interesting application for stable time-periodic systems where the steady state is affected by uncertainties. In order to demonstrate this, we apply our techniques to a fermentation process optimal in a periodic operation. We discuss this optimal periodic solution and robustify it with respect to unknown model parameters.