Critical Quality Predictive Control of Fed-Batch Mammalian Cell Bioreactors

Abstract

Abstract A model predictive control (MPC) formulation for a mammalian cell fed-batch bioreactor processes is developed. A nonlinear fundamental model for the bioreactor is used to generate a database of historical runs comprising of the measurement variables and the manipulated input feed flow rate to the bioreactor. The database is used with subspace identification methods to develop a state-space model of the process. The identified model is used to design various MPC formulations with different objective criteria, including the conventional trajectory-tracking objective function and a novel terminal objective for maximizing the product yield at completion of a run. Case studies involving the simulated bioreactor process demonstrate the efficacy of the MPC algorithms subject to unknown disturbances, random variations in the inlet feed glucose and glutamine concentrations, and measurement noise. Compared to the traditional proportional-integral control algorithm, the trajectory-tracking predictive control algorithm is able to better track the reference glucose concentration set-point with an improvement of 5.1% in the tracking error. The critical quality attribute predictive control algorithm designed to maximize the product yield results in a 3.9% increase in the product concentration at the completion of the run.