Robust nonlinear predictive control for a bioreactor based on a Dynamic Metabolic Flux Balance model

Abstract

A robust economic nonlinear predictive controller is proposed for batch biochemical processes. The algorithm is based on a dynamic metabolic flux modeling (DFBM) approach that has gained increasing acceptance in the pharmaceutical industry. Since DFBM models are formulated as LP (linear programming) problems, the corresponding robust economic optimal control solution results in a bi-level optimization consisting of maximizing an economic objective subject to the LP based model. To address robustness the DFBM model is represented by using a Polynomial Chaos Expansion model which allows quick propagation of the uncertainty in parameters onto the cost function to be optimized by the controller. The performance of the robust algorithm is applied to E.coli batch fermentation and it is found to be computationally efficient and superior to the nominal (non-robust) predictive controller.