Optimizing the switching operation in monoclonal antibody production:
  Economic MPC and reinforcement learning
Sandra A. Obiri Song Bo Bernard T. Agyeman Benjamin Decardi-Nelson Jinfeng Liu
Monoclonal antibodies (mAbs) have emerged as indispensable assets in
medicine, and are currently at the forefront of biopharmaceutical product
development. However, the growing market demand and the substantial doses
required for mAb clinical treatments necessitate significant progress in its
large-scale production. Most of the processes for industrial mAb production
rely on batch operations, which result in significant downtime. The shift
towards a fully continuous and integrated manufacturing process holds the
potential to boost product yield and quality, while eliminating the extra
expenses associated with storing intermediate products. The integrated
continuous mAb production process can be divided into the upstream and
downstream processes. One crucial aspect that ensures the continuity of the
integrated process is the switching of the capture columns, which are typically
chromatography columns operated in a fed-batch manner downstream. Due to the
discrete nature of the switching operation, advanced process control algorithms
such as economic MPC (EMPC) are computationally difficult to implement. This is
because an integer nonlinear program (INLP) needs to be solved online at each
sampling time. This paper introduces two computationally-efficient approaches
for EMPC implementation, namely, a sigmoid function approximation approach and
a rectified linear unit (ReLU) approximation approach. It also explores the
application of deep reinforcement learning (DRL). These three methods are
compared to the traditional switching approach which is based on a 1% product
breakthrough rule and which involves no optimization.