Usage of Digital Twins Along a Typical Process Development Cycle.

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.

Generic Workflow for the Setup of Mechanistic Process Models.

Process models, consisting of transferable and applicable knowledge, can be used for various tasks such as process development and optimization, and for predicting and controlling critical process variables. In this regard, mechanistic process models, describing the mechanism of a system with a distinct model structure and characteristic parameters, are very promising.The development of a reliable and applicable model is usually the critical step, before model simulation and application show beneficial effects. In this chapter, a workflow for the generation of mechanistic process models is presented and applied on a typical cell culture process. The workflow includes the definition of critical reactions and the identification of their kinetics. By an iterative approach different reactions and kinetics are tested and model quality is assessed, leading to a final, target-oriented model.