Developing cyber-physical system and digital twin for smart manufacturing: Methodology and case study of continuous clarification

Abstract

Biopharmaceutical production has recently begun transitioning toward the construction of highly advanced, digitalised production facilities, as per Industry Revolution 4.0 or smart manufacturing. Clarification is the bridging step in biotherapeutics continuous production, and this study proposes a Digital twin (DT) and a cyber-physical system (CPS) for the Cadence™ Acoustic Wave Separator (CAS), that uses the novel acoustic wave separation (AWS) technology for clarification of Chinese hamster ovary (CHO) cells. While the CPS - DT serves as the core, a real-time model based on the empirical and mechanistic relationship has been implemented in tandem to present a highly reliable hybrid model so as to improve the adaptability of the model. The framework employs a distributed control system (DCS), the heart of the CPS architecture, that updates the parameters of the physical system in real-time, thereby simplifying process control. To evaluate the hybrid-model predictive control strategy in real time, three case studies were conducted, which involved introducing abrupt turbidity pulses of high, low, and multi values. The sudden deviation in the turbidity of the feed sample was controlled by adjusting the acoustic power of the chamber (the control variable). The use of the proposed controller resulted in a 5 % mean deviation from the setpoint in the first chamber, while a > 33 % mean deviation resulted in the absence of the controller. The study's outcome demonstrates the high effectiveness of hybrid model-based CPS - DT control in consistently achieving high cell separation efficiency (> 90 %) and integrating the unit operation in a continuous processing train to establish a smart manufacturing prototype for the biopharmaceutical industry.