successfully developed utilising the Digital Twin. An ideal process operation not
only requires well-designed and tuned controllers but also well-trained plant operators. This can be achieved using OTSs that may be considered as “early-stage
Digital Twins”. From the further development of OTSs, educational Digital Twins
have emerged, which are characterised by the following features:
1. High fidelity representation of biological, physico-chemical and chemical
kinetics.
2. Detailed technical simulation of the reactor environment including peripheral
equipment.
3. Realistic investigation of various control strategies.
4. Accelerated and resource-saving simulation (digital experimentation and
training).
As new advanced bioprocessing plants are put into operation worldwide, the
challenge of covering the need for suitably qualified operators to run these plants will
increase. Educational Digital Twins are an effective tool to meet this challenge. In
the future, simple and cost-effective educational Digital Twin development tools are
required to adequately handle the additional complexities present in bioprocesses.
Acknowledgements The authors would like to thank C. Fittkau and S. Dreßler for their excellent
laboratory work at Furtwangen University. We gratefully appreciate that parts of the presented work
have been funded by the German Federal Ministry of Education and Research, Innovation Alliance
prot P.S.I. (FKZ: 031B0405C).
References
1. Grieves M (2016) Origins of the digital twin concept: working paper
2. Glaessgen E, Stargel D (2012) The digital twin paradigm for future NASA and U.S. Air Force
Vehicles: 22267B. https://doi.org/10.2514/6.2012-1818
3. El Saddik A (2018) Digital twins: the convergence of multimedia technologies. IEEE MultiMedia 25:87–92. https://doi.org/10.1109/MMUL.2018.023121167
4. He R, Chen G, Dong C et al (2019) Data-driven digital twin technology for optimized control in
process systems. ISA Trans 95:221–234. https://doi.org/10.1016/j.isatra.2019.05.011
5. Zobel-Roos S, Schmidt A, Mestmäcker F et al (2019) Accelerating biologics manufacturing by
modeling or: is approval under the QbD and PAT approaches demanded by authorities
acceptable without a digital-twin? PRO 7:94. https://doi.org/10.3390/pr7020094
6. Zhang C, Ji W (2019) Digital twin-driven carbon emission prediction and low-carbon control of
intelligent manufacturing job-shop. Proc CIRP 83:624–629. https://doi.org/10.1016/j.procir.
2019.04.095
7. Dudley T, de Villiers P, Bouwer W et al (2008) The operator training simulator system for the
pebble bed modular reactor (PBMR) plant. Nucl Eng Des 238:2908–2915. https://doi.org/10.
1016/j.nucengdes.2007.12.028
8. Appl C, Fittkau C, Moser A et al (2019) Adaptive, model-based control of Saccharomyces
cerevisiae fed-batch cultivations. In: AIDIC SERVIZI SRL (ed) Book of abstracts: bridging
science with technology, pp 1504–1505
90
C. Appl et al.
only requires well-designed and tuned controllers but also well-trained plant operators. This can be achieved using OTSs that may be considered as “early-stage
Digital Twins”. From the further development of OTSs, educational Digital Twins
have emerged, which are characterised by the following features:
1. High fidelity representation of biological, physico-chemical and chemical
kinetics.
2. Detailed technical simulation of the reactor environment including peripheral
equipment.
3. Realistic investigation of various control strategies.
4. Accelerated and resource-saving simulation (digital experimentation and
training).
As new advanced bioprocessing plants are put into operation worldwide, the
challenge of covering the need for suitably qualified operators to run these plants will
increase. Educational Digital Twins are an effective tool to meet this challenge. In
the future, simple and cost-effective educational Digital Twin development tools are
required to adequately handle the additional complexities present in bioprocesses.
Acknowledgements The authors would like to thank C. Fittkau and S. Dreßler for their excellent
laboratory work at Furtwangen University. We gratefully appreciate that parts of the presented work
have been funded by the German Federal Ministry of Education and Research, Innovation Alliance
prot P.S.I. (FKZ: 031B0405C).
References
1. Grieves M (2016) Origins of the digital twin concept: working paper
2. Glaessgen E, Stargel D (2012) The digital twin paradigm for future NASA and U.S. Air Force
Vehicles: 22267B. https://doi.org/10.2514/6.2012-1818
3. El Saddik A (2018) Digital twins: the convergence of multimedia technologies. IEEE MultiMedia 25:87–92. https://doi.org/10.1109/MMUL.2018.023121167
4. He R, Chen G, Dong C et al (2019) Data-driven digital twin technology for optimized control in
process systems. ISA Trans 95:221–234. https://doi.org/10.1016/j.isatra.2019.05.011
5. Zobel-Roos S, Schmidt A, Mestmäcker F et al (2019) Accelerating biologics manufacturing by
modeling or: is approval under the QbD and PAT approaches demanded by authorities
acceptable without a digital-twin? PRO 7:94. https://doi.org/10.3390/pr7020094
6. Zhang C, Ji W (2019) Digital twin-driven carbon emission prediction and low-carbon control of
intelligent manufacturing job-shop. Proc CIRP 83:624–629. https://doi.org/10.1016/j.procir.
2019.04.095
7. Dudley T, de Villiers P, Bouwer W et al (2008) The operator training simulator system for the
pebble bed modular reactor (PBMR) plant. Nucl Eng Des 238:2908–2915. https://doi.org/10.
1016/j.nucengdes.2007.12.028
8. Appl C, Fittkau C, Moser A et al (2019) Adaptive, model-based control of Saccharomyces
cerevisiae fed-batch cultivations. In: AIDIC SERVIZI SRL (ed) Book of abstracts: bridging
science with technology, pp 1504–1505
90
C. Appl et al.