Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learning

Abstract

Monoclonal antibodies (mAbs), crucial in medicine and biopharmaceuticals, require optimized large-scale production to meet high clinical dosage demands. Most of the processes for industrial mAb production rely on fed-batch operations, resulting in significant downtime. Transitioning to fully continuous and integrated processes holds the potential to boost product yield, enhance quality, and reduce storage costs for 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.