Digital Twins: Applications to the Design and Optimization of Bioprocesses (Advances in Biochemical Engineering/Biotechnology, 177)

Preface
Contents
Potential of Integrating Model-Based Design of Experiments Approaches and Process Analytical Technologies for Bioprocess Scale...
	1 Status of Bioprocess Scale-Down: The Need for a Model-Based Design
	2 The Digital Twin in Bioprocess Development
	3 Inhomogeneities in Industrial-Scale Bioreactors and Their Influence on the Biological System
	4 Framework for Bioprocess Scale-Down Studies
		4.1 Characterization of the Large Scale
			4.1.1 Monitoring of the Cellular State Across Different Scales
			4.1.2 Combination of CFD Approaches with Mechanistic Models (Euler-Lagrange) to Describe the Large Scale
		4.2 Execution of Scale-Down Experiments
			4.2.1 Combination of Scale-Down Experiments with Model-Based Approaches
				Model-Based Design of Scale-Down Experiments
				Model-Based Interpretation of Scale-Down Data
			4.2.2 High-Throughput Execution of Scale-Down Experiments in Parallel Cultivation Systems
	5 General Conclusions and Perspectives
	References
Digital Twins and Their Role in Model-Assisted Design of Experiments
	1 Introduction
	2 Design of Experiments Methods
		2.1 Screening Designs
			2.1.1 Full Factorial Designs
			2.1.2 Reduced Full Factorial Designs
		2.2 Optimization Designs
			2.2.1 Central Composite Designs
			2.2.2 Box-Behnken Designs
			2.2.3 Optimal Designs
			2.2.4 Space-Filling Designs
		2.3 Examples and Challenges of Conventional DoE
	3 Model-Assisted Design of Experiments
		3.1 Digital Twins in Model-Assisted Design of Experiments
			3.1.1 Mathematical Model Structures
		3.2 Recommendations on the Selection of Designs for mDoE
	4 Case Study: mDoE for Medium Optimization
		4.1 Mathematical Process Model
			4.1.1 Batch Process Model as Digital Twin
			4.1.2 Adaption of Model Parameters
		4.2 Selection of Experimental Design
		4.3 Simulation of Experiments
		4.4 Evaluation of Planned Design
		4.5 Comparison to Experimentally Performed Design
		4.6 Further Development of the Digital Twin in Process Development Workflow
	5 Conclusion and Outlook
	References
Digital Twins for Bioprocess Control Strategy Development and Realisation
	1 Introduction
	2 Advanced Bioprocess Control Development, Realisation and Optimisation Using Digital Twins
		2.1 General Approach
		2.2 Design of Digital Twins as Control Strategy Development Tools
			2.2.1 Software Tools for the Design of Digital Twins
		2.3 Control Strategies for Bioprocesses
			2.3.1 Advanced and Model-Based Control Strategies
			2.3.2 Open-Loop-Feedback-Optimal (OLFO) Control Strategy
		2.4 Digital Twin Based Development, Realisation and Optimisation of Control Strategies for Bioprocesses
	3 Digital Twins as Training and Educational Tools
	4 Case Study
		4.1 Digital Twin ``SSF-BC-Simulator´´
			4.1.1 Parameterisation of the Digital Twin ``SSF-BC-Simulator´´
			4.1.2 Digital Twin ``SSF-BC-Simulator´´ for the Development of Control Strategies
		4.2 Digital Twin Based Development of Control Strategies for the Cultivation of S. cerevisiae
			4.2.1 Experimental Setup
			4.2.2 Development of Respiratory Quotient (RQ) Feedback Control for the Cultivation of S. cerevisiae
			4.2.3 Development of Open-Loop-Feedback-Optimal (OLFO) Control for the Cultivation of S. cerevisiae
			4.2.4 Case Study Discussion
	5 Conclusion and Future Perspectives
	References
The Kalman Filter for the Supervision of Cultivation Processes
	1 Introduction
	2 Kalman Filtering Theory and Its Non-linear Extensions
		2.1 The Kalman Filter
		2.2 Continuous-Discrete Extended Kalman Filter
		2.3 Other Non-linear Extensions of the Kalman Filter
	3 Application of Kalman Filters in Bioprocess Monitoring
		3.1 Type of Kalman Filter
		3.2 Microorganism
		3.3 Cultivation Mode
		3.4 Bioprocess Phase
		3.5 Measurement Device
		3.6 Process Model
	4 An Extended Kalman Filter for the Monitoring of a Yeast Cultivation
		4.1 The Cultivation Process
		4.2 EKF Algorithm
		4.3 Online Ethanol Measurements
		4.4 Offline Measurements
		4.5 State Equations of the Cultivation Process
		4.6 Results
	5 Conclusion
	Appendix
	References
The Challenge of Implementing Digital Twins in Operating Value Chains
	1 Introduction
	2 Industrial Bioprocesses and Corresponding Value Chains
	3 Analysis of Operating Value Chains
		3.1 Stakeholders Analysis
		3.2 Use Case Specification
		3.3 Infrastructure Analysis
		3.4 Process Characterization
		3.5 Composition of the Big Picture
	4 Standardization and Generation of Digital Twins
		4.1 Overview of Existing Standards
		4.2 Process Standardization
		4.3 Data Standardization
		4.4 Data Sharing Standards
	5 Integration of Models and Data Sources into a DT-Compatible Platform
	6 Risk and Hurdles for a DTMS Implementation
	7 Case Studies
		7.1 Digital Twin Management: Implementation of a DTMS in an Operating Production Process
		7.2 Organic Supply Chains: Implementation of a DTMS for Vegetable and Beef Supply Chains
		7.3 Shared Digital Twins
	8 Summary and Outlook
	References
Digital Twins: A General Overview of the Biopharma Industry
	1 Introduction
	2 Technical Prerequisites and Components of Digital Twins
		2.1 Context
	3 Major Prerequisites
		3.1 Sensors
		3.2 Connectivity
		3.3 Virtual Model of Physical Asset
		3.4 Asset Framework
		3.5 Configuration Management
		3.6 Dynamic Model
		3.7 Data
		3.8 Data Modeling and Ontologies
		3.9 People
	4 Typical Lifecycle of a Twin
	5 Digital Twin: Potential Applications in Healthcare and Biopharma Industry
	6 Digital Twin: Case Study from Merck KGaA Darmstadt, Germany
		6.1 Overall Approach
		6.2 What Is the Technological Backbone?
	7 Digital Twin: Case Study from Sanofi
		7.1 Objective
		7.2 Challenges
		7.3 Digital Twin Solution
		7.4 Lessons Learnt
	8 Conclusion and Outlook
	References
Numerical Methods for the Design and Description of In Vitro Expansion Processes of Human Mesenchymal Stem Cells
	1 Introduction
	2 In Vitro Expansion Approaches: Current Situation
		2.1 Planar Approach (2D Cultures)
		2.2 Dynamic Approach (3D Cultures)
			2.2.1 Growth in Spheroids
			2.2.2 Growth on Microcarriers
	3 Computational Fluid Dynamics as a Modern Tool for Bioreactor Characterization
		3.1 Modelling Approaches
		3.2 Advanced Fluid Flow Characterization of Small-Scale Spinner Flasks: A Case Study
			3.2.1 Reactor Geometries and Model Approaches
			3.2.2 Results from Single-Phase Modelling
			3.2.3 Results from Multi-phase Modelling
				Oxygen Mass Transfer
				Microcarrier Distribution Based on a Euler-Euler Granular Approach
				Microcarrier Tracking Based on a Euler-Lagrange Approach
			3.2.4 Linking of CFD-Derived Data with Cultivation Studies
	4 Mathematical Growth Modelling of MC-Based hMSC Expansions
		4.1 Modelling Approaches
		4.2 Kinetic Growth Model for the MC-Based hMSC Expansion: A Case Study
	5 Conclusions and Outlook
	References
Euler-Lagrangian Simulations: A Proper Tool for Predicting Cellular Performance in Industrial Scale Bioreactors
	1 Introduction
	2 Embedding Cells in Microenvironmental Heterogeneities of Bioreactors
		2.1 The Core Idea of Lifeline Analysis
		2.2 How to Get Biologically Sound Readouts?
	3 Lifeline Analysis in Practice
		3.1 Eulerian Simulation Setup
		3.2 Eulerian Simulation Outputs
		3.3 Lagrangian Setup
		3.4 Lagrangian Readouts
	4 Scale-Down Examples and Methods from the Literature
	5 Advantages and Considerations
	6 Conclusion and Outlook
	References