50. Hodge DB, Karim MN, Schell DJ et al (2009) Model-based fed-batch for high-solids enzymatic
cellulose hydrolysis. Appl Biochem Biotechnol 152:88–107. https://doi.org/10.1007/s12010008-8217-0
51. Bück A, Casciatori FP, Thoméo JC et al (2015) Model-based control of enzyme yield in solidstate fermentation. Proc Eng 102:362–371. https://doi.org/10.1016/j.proeng.2015.01.163
52. Luttmann R, Munack A, Thoma M (1985) Mathematical modelling, parameter identification
and adaptive control of single cell protein processes in tower loop bioreactors. In: Fiechter A,
Aiba S, Bungoy HR et al (eds) Agricultural feedstock and waste treatment and engineering, vol
32. Springer, Berlin, pp 95–205
53. Witte VC, Munack A, Märkl H (1996) Mathematische Modellierung und adaptive
Prozeßsteuerung der Kultivierung von Cyathus striatus. Zugl.: Hamburg-Harburg, Techn.
Univ., Arbeitsbereich Regelungstechnik und Systemdynamik [i.e. Arbeitsbereich
Regelungstechnik] und Arbeitsbereich Bioprozess- und Bioverfahrenstechnik, Diss., 1996,
Als Ms. gedr. Fortschritt-Berichte/VDI Reihe 17, Biotechnik, vol 144. VDI-Verl., Düsseldorf
54. Patle DS, Ahmad Z, Rangaiah GP (2014) Operator training simulators in the chemical industry:
review, issues, and future directions. Rev Chem Eng 30. https://doi.org/10.1515/revce-20130027
55. Cameron D, Clausen C, Morton W (2002) Dynamic simulators for operator training. In:
Braunschweig B, Gani R (eds) Software architectures and tools for computer aided process
engineering, vol 11, 1st edn. Elsevier, Amsterdam, pp 393–431
56. Pörtner R, Platas-Barradas O, Gradkowski J et al (2011) “BioProzessTrainer” as training tool
for design of experiments. BMC Proc 5(Suppl 8):P62. https://doi.org/10.1186/1753-6561-5-S8P62
57. Gerlach I, Hass VC, Brüning S et al (2013) Virtual bioreactor cultivation for operator training
and simulation: application to ethanol and protein production. J Chem Technol Biotechnol
88:2159–2168. https://doi.org/10.1002/jctb.4079
58. Reinig G, Winter P, Linge V et al (1998) Training simulators: engineering and use. Chem Eng
Technol 21:711–716. https://doi.org/10.1002/(SICI)1521-4125(199809)21:9<711:AIDCEAT711>3.0.CO;2-H
59. Ahmad AL, Low EM, Abd Shukor SR (2010) Safety improvement and operational enhancement via dynamic process simulator: a review. Chem Prod Process Model 5. https://doi.org/10.
2202/1934-2659.1502
60. González Hernández Y, Jáuregui Haza UJ, Albasi C et al (2014) Development of a submerged
membrane bioreactor simulator: a useful tool for teaching its functioning. Educ Chem Eng 9:
e32–e41. https://doi.org/10.1016/j.ece.2014.03.001
61. Gerlach I, Brüning S, Gustavsson R et al (2014) Operator training in recombinant protein
production using a structured simulator model. J Biotechnol 177:53–59. https://doi.org/10.
1016/j.jbiotec.2014.02.022
62. Ahmad Z, Patle DS, Rangaiah GP (2016) Operator training simulator for biodiesel synthesis
from waste cooking oil. Process Saf Environ Prot 99:55–68. https://doi.org/10.1016/j.psep.
2015.10.002
63. Balaton MG, Nagy L, Szeifert F (2013) Operator training simulator process model implementation of a batch processing unit in a packaged simulation software. Comput Chem Eng
48:335–344. https://doi.org/10.1016/j.compchemeng.2012.09.005
64. Gerlach I, Mandenius C-F, Hass VC (2015) Operator training simulation for integrating
cultivation and homogenisation in protein production. Biotechnol Rep (Amst) 6:91–99.
https://doi.org/10.1016/j.btre.2015.03.002
65. Hass VC, Kuhnen F, Schoop K-M (2005) An environment for the development of operator
training systems (OTS) from chemical engineering models, vol 20. Elsevier, Amsterdam, pp
289–293. https://doi.org/10.1016/S1570-7946(05)80170-1
66. Brüning S, Gerlach I, Pörtner R et al (2017) Modeling suspension cultures of microbial and
mammalian cells with an adaptable six-compartment model. Chem Eng Technol 40:956–966.
https://doi.org/10.1002/ceat.201600639
Digital Twins for Bioprocess Control Strategy Development and Realisation
93
cellulose hydrolysis. Appl Biochem Biotechnol 152:88–107. https://doi.org/10.1007/s12010008-8217-0
51. Bück A, Casciatori FP, Thoméo JC et al (2015) Model-based control of enzyme yield in solidstate fermentation. Proc Eng 102:362–371. https://doi.org/10.1016/j.proeng.2015.01.163
52. Luttmann R, Munack A, Thoma M (1985) Mathematical modelling, parameter identification
and adaptive control of single cell protein processes in tower loop bioreactors. In: Fiechter A,
Aiba S, Bungoy HR et al (eds) Agricultural feedstock and waste treatment and engineering, vol
32. Springer, Berlin, pp 95–205
53. Witte VC, Munack A, Märkl H (1996) Mathematische Modellierung und adaptive
Prozeßsteuerung der Kultivierung von Cyathus striatus. Zugl.: Hamburg-Harburg, Techn.
Univ., Arbeitsbereich Regelungstechnik und Systemdynamik [i.e. Arbeitsbereich
Regelungstechnik] und Arbeitsbereich Bioprozess- und Bioverfahrenstechnik, Diss., 1996,
Als Ms. gedr. Fortschritt-Berichte/VDI Reihe 17, Biotechnik, vol 144. VDI-Verl., Düsseldorf
54. Patle DS, Ahmad Z, Rangaiah GP (2014) Operator training simulators in the chemical industry:
review, issues, and future directions. Rev Chem Eng 30. https://doi.org/10.1515/revce-20130027
55. Cameron D, Clausen C, Morton W (2002) Dynamic simulators for operator training. In:
Braunschweig B, Gani R (eds) Software architectures and tools for computer aided process
engineering, vol 11, 1st edn. Elsevier, Amsterdam, pp 393–431
56. Pörtner R, Platas-Barradas O, Gradkowski J et al (2011) “BioProzessTrainer” as training tool
for design of experiments. BMC Proc 5(Suppl 8):P62. https://doi.org/10.1186/1753-6561-5-S8P62
57. Gerlach I, Hass VC, Brüning S et al (2013) Virtual bioreactor cultivation for operator training
and simulation: application to ethanol and protein production. J Chem Technol Biotechnol
88:2159–2168. https://doi.org/10.1002/jctb.4079
58. Reinig G, Winter P, Linge V et al (1998) Training simulators: engineering and use. Chem Eng
Technol 21:711–716. https://doi.org/10.1002/(SICI)1521-4125(199809)21:9<711:AIDCEAT711>3.0.CO;2-H
59. Ahmad AL, Low EM, Abd Shukor SR (2010) Safety improvement and operational enhancement via dynamic process simulator: a review. Chem Prod Process Model 5. https://doi.org/10.
2202/1934-2659.1502
60. González Hernández Y, Jáuregui Haza UJ, Albasi C et al (2014) Development of a submerged
membrane bioreactor simulator: a useful tool for teaching its functioning. Educ Chem Eng 9:
e32–e41. https://doi.org/10.1016/j.ece.2014.03.001
61. Gerlach I, Brüning S, Gustavsson R et al (2014) Operator training in recombinant protein
production using a structured simulator model. J Biotechnol 177:53–59. https://doi.org/10.
1016/j.jbiotec.2014.02.022
62. Ahmad Z, Patle DS, Rangaiah GP (2016) Operator training simulator for biodiesel synthesis
from waste cooking oil. Process Saf Environ Prot 99:55–68. https://doi.org/10.1016/j.psep.
2015.10.002
63. Balaton MG, Nagy L, Szeifert F (2013) Operator training simulator process model implementation of a batch processing unit in a packaged simulation software. Comput Chem Eng
48:335–344. https://doi.org/10.1016/j.compchemeng.2012.09.005
64. Gerlach I, Mandenius C-F, Hass VC (2015) Operator training simulation for integrating
cultivation and homogenisation in protein production. Biotechnol Rep (Amst) 6:91–99.
https://doi.org/10.1016/j.btre.2015.03.002
65. Hass VC, Kuhnen F, Schoop K-M (2005) An environment for the development of operator
training systems (OTS) from chemical engineering models, vol 20. Elsevier, Amsterdam, pp
289–293. https://doi.org/10.1016/S1570-7946(05)80170-1
66. Brüning S, Gerlach I, Pörtner R et al (2017) Modeling suspension cultures of microbial and
mammalian cells with an adaptable six-compartment model. Chem Eng Technol 40:956–966.
https://doi.org/10.1002/ceat.201600639
Digital Twins for Bioprocess Control Strategy Development and Realisation
93