Applications of Polynomial Chaos Expansions in optimization and control of bioreactors based on dynamic metabolic flux balance models

Abstract

This work proposes model based approaches for on-line or off-line economic optimization of batch reactors in the presence of model error (uncertainty). Polynomial Chaos Expansions are used as an effective and computationally efficient tool to propagate the error in model parameters into the optimizations’ cost functions. The computational efficiency of the proposed uncertainty propagation approach is essential for the implementation of the on-line approach that takes into account feedback corrections. The role of feedback, as applied in the on-line formulation, is proved to be instrumental for reducing conservatism of the optimization results. The proposed approaches can serve to design recipes for maximizing productivity in batch, fed-batch or perfusion operation of bioreactors.