Holistic process control framework for smart biomanufacturing: A deep learning driven approach

Abstract

This article introduces a cyber-physical system comprising four interactive modules for enabling smart manufacturing in biotherapeutic production. These modules deal with process control, process prognosis, process diagnosis, and act as an external communication platform. An acoustic wave separator, a novel CHO cell clarification device, has been used as a case study with the objective of optimizing and regulating cell clarification process. A deep neural network (DNN)-based model has been proposed for implementing rapid control of bioprocess operations in case of detected anomalies or deviations in real-time. The use of the proposed holistic controller resulted in a 5% mean deviation from the setpoint in the first chamber, with more than 30% mean deviation resulting in the absence of the controller. The intricate layers of the automation framework facilitate real-time monitoring of the critical process parameters at 10-second intervals and ensure robust control action for deviations/anomalies, thus enabling smart manufacturing for biopharmaceutical production.