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.

单克隆抗体的持续生产是一个重要的研究领域。本文介绍了两种计算高效的经济模型预测控制（EMPC）实现方法，即S型函数逼近方法和修正线性单元（ReLU）逼近方法，同时探讨了深度强化学习方法的应用。这三种方法与传统的切换方法进行了比较，传统方法基于1％产品突破规则，不涉及优化。

单克隆抗体生产中的开关操作优化：经济模型预测控制与强化学习