Digital Twins for Bioprocess Control Strategy Development and Realisation.

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. Development, realisation and optimisation of control strategies utilising Digital Twins.

[1]  Brian Glennon,et al.  Glucose concentration control of a fed-batch mammalian cell bioprocess using a nonlinear model predictive controller , 2014 .

[2]  Carl-Fredrik Mandenius,et al.  Conceptual Design of an Operator Training Simulator for a Bio-Ethanol Plant , 2015 .

[3]  M. Chidambaram,et al.  Fuzzy logic control of a fed-batch fermentor , 1993 .

[4]  Jochen Strube,et al.  Accelerating Biologics Manufacturing by Modeling or: Is Approval under the QbD and PAT Approaches Demanded by Authorities Acceptable Without a Digital-Twin? , 2019, Processes.

[5]  A. Blesgen.  Entwicklung und Einsatz eines interaktiven Biogas-Echtzeit-Simulators , 2009 .

[6]  Jürgen Pannek,et al.  Nonlinear Model Predictive Control : Theory and Algorithms. 2nd Edition , 2017 .

[7]  J. García,et al.  CONTROL OF A FEDBATCH BIOPROCESS USING NONLINEAR MODEL PREDICTIVE CONTROL , 2006 .

[8]  W. Morton,et al.  Chapter 5.3 - Dynamic Simulators for Operator Training , 2002 .

[9]  Carl-Fredrik Mandenius,et al.  Virtual bioreactor cultivation for operator training and simulation: application to ethanol and protein production , 2013 .

[10]  A. Munack,et al.  Fed-Batch-Kultivierung tierischer Zellen : eine Herausforderung zur adaptiven, modellbasierten Steuerung , 2003 .

[11]  Gunter Reinig,et al.  Training Simulators: Engineering and Use , 1998 .

[12]  Volker C. Hass,et al.  16. Operator Training Simulators for Bioreactors , 2016 .

[13]  L. Grüne,et al.  Nonlinear Model Predictive Control : Theory and Algorithms. 2nd Edition , 2011 .

[14]  Joseph S. Alford,et al.  Bioprocess control: Advances and challenges , 2006, Comput. Chem. Eng..

[15]  Volker C. Hass,et al.  Efficient Biogas Production through Process Simulation , 2010 .

[16]  Claire Albasi,et al.  Development of a Submerged Membrane Bioreactor simulator: a useful tool for teaching its functioning , 2014 .

[17]  Zainal Arifin Ahmad,et al.  Operator training simulator for biodiesel synthesis from waste cooking oil , 2016 .

[18]  Volker C. Hass,et al.  Operator training simulation for integrating cultivation and homogenisation in protein production , 2015, Biotechnology reports.

[19]  Andreas Bück,et al.  Model-based Control of Enzyme Yield in Solid-state Fermentation☆ , 2015 .

[20]  Volker C. Hass,et al.  Operator training simulators for biorefineries: current position and future directions , 2018 .

[21]  Robert Gustavsson,et al.  Operator training in recombinant protein production using a structured simulator model. , 2014, Journal of biotechnology.

[22]  Chaoyang Zhang,et al.  Digital twin-driven carbon emission prediction and low-carbon control of intelligent manufacturing job-shop , 2019, Procedia CIRP.

[23]  Gade Pandu Rangaiah,et al.  Operator training simulators in the chemical industry: review, issues, and future directions , 2014 .

[24]  Ferenc Szeifert,et al.  Operator training simulator process model implementation of a batch processing unit in a packaged simulation software , 2013, Comput. Chem. Eng..

[25]  Florian Kuhnen,et al.  An environment for the development of operator training systems (OTS) from chemical engineering models , 2005 .

[26]  C. Mandenius,et al.  Modeling Suspension Cultures of Microbial and Mammalian Cells with an Adaptable Six‐Compartment Model , 2017 .

[27]  Robert Luh,et al.  The Operator Training Simulator System for the Pebble Bed Modular Reactor (PBMR) Plant , 2008 .

[28]  P Lane,et al.  Improvement of a mammalian cell culture process by adaptive, model-based dialysis fed-batch cultivation and suppression of apoptosis , 2003, Bioprocess and biosystems engineering.

[29]  Y. Shastri,et al.  Optimal control of enzymatic hydrolysis of lignocellulosic biomass , 2016, Resource-Efficient Technologies.

[30]  A.L. Ahmad,et al.  Safety Improvement and Operational Enhancement via Dynamic Process Simulator: A Review , 2010 .

[31]  Oscar Platas-Barradas,et al.  “BioProzessTrainer” as training tool for design of experiments , 2011, BMC proceedings.

[32]  M. N. Karim,et al.  Model-Based Fed-Batch for High-Solids Enzymatic Cellulose Hydrolysis , 2009, Applied biochemistry and biotechnology.

[33]  Volker C. Hass,et al.  Towards the Development of a Training Simulator for Biorefineries , 2012 .

[34]  M. Thoma,et al.  Mathematical modelling, parameter identification and adaptive control of single cell protein processes in tower loop bioreactors , 1985 .

[36]  Abdulmotaleb El Saddik,et al.  Digital Twins: The Convergence of Multimedia Technologies , 2018 .

[37]  Shufeng Sun,et al.  Data-driven digital twin technology for optimized control in process systems. , 2019, ISA transactions.

[38]  Axel Munack,et al.  Adaptive, Model‐Based Control by the Open‐Loop‐Feedback‐Optimal (OLFO) Controller for the Effective Fed‐Batch Cultivation of Hybridoma Cells , 2002, Biotechnology progress.

[39]  Hamed Moradi,et al.  Nonlinear multivariable control and performance analysis of an air-handling unit , 2011 .

[40]  Edward H. Glaessgen,et al.  The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles , 2012 .

[41]  Liang Chang,et al.  Nonlinear model predictive control of fed-batch fermentations using dynamic flux balance models , 2016 .