Practical data-driven modeling and robust predictive control of mammalian cell fed-batch process

Abstract

Even if the performances of bioprocesses can be significantly improved by model-based control, there often remains a tradeoff between model complexity and control robustness. This paper proposes an original data-driven strategy for fast design of dynamic bioprocess models with minimal complexity (i.e., minimal number of bioreactions). Maximum likelihood principal component analysis (MLPCA) is applied to infer the minimal reaction scheme from a 25-state mammalian cell culture database. Then, a systematic algorithm is used to provide a continuous kinetic model formulation assuming all rates to occur simultaneously, which may be far from true cell metabolic conditions sometimes presenting discontinuous metabolic switches. A robust model predictive formulation is therefore adopted to reduce the impact of model structural uncertainty on the process performances. Additional numerical results show that the proposed strategy presents excellent performances in presence of unexpected metabolic switches.