Process-aware data-driven modelling and model predictive control of bioreactor for the production of monoclonal antibodies

Abstract

This manuscript addresses the problem of controlling a bioreactor to maximize the production of a desired product while respecting the constraints imposed by the nature of the bio-process. The approach is demonstrated by first building a data-driven model and then formulating a model predictive controller (MPC) with the results illustrated by implementing a detailed monoclonal antibody production model (the test bed) created by Sartorius Inc. In particular, a recently developed data-driven modelling approach using an adaptation of subspace identification techniques is utilized that enables the incorporation of known physical relationships in the data-driven model development (constrained subspace model identification), making the data-driven model process aware. The resultant controller implementation demonstrates a significant improvement in production compared to the existing proportional integral (PI) controller strategy used in the monoclonal antibody production. Simulation results also demonstrate the superiority of the process-aware or constrained subspace MPC compared to traditional subspace MPC. Finally, the robustness of the controller design is illustrated via the implementation of a model developed using data from a test bed with a different set of parameters, thus showing the ability of the controller design to maintain good performance in the event of changes such as a different cell line or feed characteristics.