Digital Twins for Bioprocess Control Strategy Development and Realisation

被引:16
作者
Appl, Christian [1 ,2 ]
Moser, Andre [1 ]
Baganz, Frank [2 ]
Hass, Volker C. [1 ,2 ]
机构
[1] Furtwangen Univ, Fac Med & Life Sci, Villingen Schwenningen, Germany
[2] UCL, Dept Biochem Engn, London, England
来源
DIGITAL TWINS: APPLICATIONS TO THE DESIGN AND OPTIMIZATION OF BIOPROCESSES | 2021年 / 177卷
关键词
Bioprocess; Control strategy development; Digital Twin; Operator training simulator (OTS); OPERATOR TRAINING SIMULATOR; FED-BATCH CULTIVATION; MODEL-BASED CONTROL; CELLS;
D O I
10.1007/10_2020_151
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
New innovative Digital Twins can represent complex bioprocesses, including the biological, physico-chemical, and chemical reaction kinetics, as well as the mechanical and physical characteristics of the reactors and the involved peripherals. Digital Twins are an ideal tool for the rapid and cost-effective development, realisation and optimisation of control and automation strategies. They may be utilised for the development and implementation of conventional controllers (e.g. temperature, dissolved oxygen, etc.), as well as for advanced control strategies (e.g. control of substrate or metabolite concentrations, multivariable controls), and the development of complete bioprocess control. This chapter describes the requirements Digital Twins must fulfil to be used for bioprocess control strategy development, and implementation and gives an overview of research projects where Digital Twins or "early-stage" Digital Twins were used in this context. Furthermore, applications of Digital Twins for the academic education of future control and bioprocess engineers as well as for the training of future bioreactor operators will be described. Finally, a case study is presented, in which an "early-stage" Digital Twin was applied for the development of control strategies of the fed-batch cultivation of Saccharomyces cerevisiae. [GRAPHICS] .
引用
收藏
页码:63 / 94
页数:32
相关论文
共 67 条
[11]   Modeling Suspension Cultures of Microbial and Mammalian Cells with an Adaptable Six-Compartment Model [J].
Bruening, Simone ;
Gerlach, Inga ;
Poertner, Ralf ;
Mandenius, Carl-Fredrik ;
Hass, Volker C. .
CHEMICAL ENGINEERING & TECHNOLOGY, 2017, 40 (05) :956-966
[12]   Model-based control of enzyme yield in solid-state fermentation [J].
Bueck, A. ;
Casciatori, F. P. ;
Thomeo, J. C. ;
Tsotsas, E. .
NEW PARADIGM OF PARTICLE SCIENCE AND TECHNOLOGY, PROCEEDINGS OF THE 7TH WORLD CONGRESS ON PARTICLE TECHNOLOGY, 2015, 102 :362-371
[13]  
Cameron D, 2002, COMP AID CH, V11, P393
[14]   Nonlinear model predictive control of fed-batch fermentations using dynamic flux balance models [J].
Chang, Liang ;
Liu, Xinggao ;
Henson, Michael A. .
JOURNAL OF PROCESS CONTROL, 2016, 42 :137-149
[15]  
CORYS, 2020, IND PLUS
[16]   Glucose concentration control of a fed-batch mammalian cell bioprocess using a nonlinear model predictive controller [J].
Craven, Stephen ;
Whelan, Jessica ;
Glennon, Brian .
JOURNAL OF PROCESS CONTROL, 2014, 24 (04) :344-357
[17]   The Operator training simulator system for the Pebble Bed Modular REactor (PBMR) plant [J].
Dudley, Trevor ;
de Villiers, Piet ;
Bouwer, Werner ;
Luh, Robert .
NUCLEAR ENGINEERING AND DESIGN, 2008, 238 (11) :2908-2915
[18]  
DuPont Industrial Biosciences, 2020, OP TRAIN SIM TRAIN S
[19]   Digital Twins The Convergence of Multimedia Technologies [J].
El Saddik, Abdulmotaleb .
IEEE MULTIMEDIA, 2018, 25 (02) :87-92
[20]  
Fenila F., 2016, RESOURCE EFFICIENT T, V2, pS96, DOI DOI 10.1016/J.REFFIT.2016.11.006