Data-Driven Modeling and Model Predictive Control of Monoclonal Antibody Process

Abstract

This thesis focuses on data-driven modeling and model predictive control for a monoclonal antibody process. The process uses live cell cultures such as Chinese Hamster Ovary (CHO) cells and thus needs special consideration. With the trend in the industry to move towards perfusion processes which are continuous allowing better productivity, advanced process control would play a vital role. Model Predictive Control (MPC) requires a suitable model to optimally control the process. First principles models for such live cell processes are complex and unsuitable for direct use in MPC. In this work, we focus on data-driven modeling given its suitability for use in control. The data-driven model is eventually also incorporated with some first principles knowledge for better performance and robustness. Data-driven modelling requires some input perturbation and data generation methods such as Pseudo-Random Binary Sequence (PRBS) inputs. By themselves, these methods are unsuitable for live cells as they can shock the system. To account for this, a suitable, intensified design of experiments (DOE) approach is used to perturb the data frequently enough to build a model of reasonable accuracy without significantly impacting live cells. This method is general enough to be used to identify appropriate input perturbation and data generation for many bioprocesses, and for our particular process of study it identified an input perturbation frequency of once per three days. The data-driven model is used in a model predictive control (MPC) scheme which respects biological constraints and can meet desired objectives. Further improvement in model robustness are made possible by incorporating first principles knowledge into the data-driven model. The methods are demonstrated on an advanced simulator developed by Sartorius with significant improvement over current industry standard.