Data Driven Modeling and Model Predictive Control of Bioreactor for Production of Monoclonal Antibodies

Abstract

This manuscript focuses on data driven modeling and control of an industrial bioreactor used by Sartorius to grow cells to produce monoclonal antibodies, demonstrated using a high fidelity simulation test bed. The contribution of this paper is the development of a subspace model based model predictive controller (MPC) for the bioreactor with constraints in place to manage the delicate cell health and growth. Subspace identification is first utilized for developing a linear model, and utilized, along with a state observer, to formulate and implement the Model Predictive Controller. Three implementations are shown, the first which simply tracks a desired trajectory of the viable cell density while maximizing the total product, the second maximizing the total product, and finally a formulation to enable trajectory tracking of titer. In each case the MPC is able to successfully operate the bioreactor and show improvements compared to the existing proportional-integral controller. The success of the MPC implementation on the simulation test bed paves the way for implementation on the bioreactor, as well as the development much more ambitious MPC designs.