Institute of Chemical, Environmental and Bioscience Engineering, Technische Universität Wien, Vienna, Austria.
Competence Center CHASE GmbH, Linz, Austria.
Adv Biochem Eng Biotechnol. 2021;176:71-96. doi: 10.1007/10_2020_149.
Digital methods for process design, monitoring, and control can convert classical trial-and-error bioprocess development to a quantitative engineering approach. By interconnecting hardware, software, data, and humans currently untapped process optimization potential can be accessed. The key component within such a framework is a digital twin interacting with its physical process counterpart. In this chapter, we show how digital twin guided process development can be applied on an exemplary microbial cultivation process. The usage of digital twins is described along a typical process development cycle, ranging from early strain characterization to real-time control applications. Along an illustrative case study on microbial upstream bioprocessing, we emphasize that digital twins can integrate entire process development cycles if the digital twin itself and the underlying models are continuously adapted to newly available data. Therefore, the digital twin can be regarded as a powerful knowledge management tool and a decision support system for efficient process development. Its full potential can be deployed in a real-time environment where targeted control actions can further improve process performance.
数字方法可用于工艺设计、监控和控制，将传统的反复试验的生物工艺开发转变为定量工程方法。通过硬件、软件、数据和人员的互联，可以利用当前未开发的工艺优化潜力。在这样的框架中，关键组件是与物理过程相对应的数字孪生体。在本章中，我们展示了如何将数字孪生体指导的工艺开发应用于示例微生物培养过程。沿着典型的工艺开发周期描述了数字孪生体的使用，从早期的菌株表征到实时控制应用。通过一个关于微生物上游生物工艺的说明性案例研究，我们强调如果数字孪生体本身和基础模型不断适应新的可用数据，数字孪生体可以集成整个工艺开发周期。因此，数字孪生体可以被视为一种强大的知识管理工具和决策支持系统，用于高效的工艺开发。它的全部潜力可以在实时环境中部署，其中目标控制措施可以进一步提高工艺性能。
