Adv Biochem Eng Biotechnol (2021) 177: 29–62
https://doi.org/10.1007/10_2020_136
© Springer Nature Switzerland AG 2020
Published online: 15 August 2020
Digital Twins and Their Role
in Model-Assisted Design of Experiments
Kim B. Kuchemüller, Ralf Pörtner, and Johannes Möller
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2 Design of Experiments Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.1 Screening Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.2 Optimization Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.3 Examples and Challenges of Conventional DoE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3 Model-Assisted Design of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.1 Digital Twins in Model-Assisted Design of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2 Recommendations on the Selection of Designs for mDoE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4 Case Study: mDoE for Medium Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.1 Mathematical Process Model . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . 47
4.2 Selection of Experimental Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.3 Simulation of Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.4 Evaluation of Planned Design . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 51
4.5 Comparison to Experimentally Performed Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.6 Further Development of the Digital Twin in Process Development Workflow . . . . . . 55
5 Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Abstract Rising demands for biopharmaceuticals and the need to reduce
manufacturing costs increase the pressure to develop productive and efficient
bioprocesses. Among others, a major hurdle during process development and optimization studies is the huge experimental effort in conventional design of experiments (DoE) methods. As being an explorative approach, DoE requires extensive
expert knowledge about the investigated factors and their boundary values and often
leads to multiple rounds of time-consuming and costly experiments. The combination of DoE with a virtual representation of the bioprocess, called digital twin, in
K. B. Kuchemüller, R. Pörtner, and J. Möller (*)
Institute of Bioprocess and Biosystems Engineering, Hamburg University of Technology,
Hamburg, Germany
e-mail: johannes.moeller@tuhh.de