Digital Twins: Tools and Concepts for Smart Biomanufacturing (Advances in Biochemical Engineering/Biotechnology, 176)

Preface
Contents
Towards the Development of Digital Twins for the Bio-manufacturing Industry
	1 Introduction
		1.1 General Remarks
		1.2 The Bio-manufacturing Domain, Challenges, and Developments
	2 The Evolution of Data and Their Use in the Bio-manufacturing Industry
		2.1 Data Collection
		2.2 Data Characteristics and Challenges
		2.3 Data Processing
	3 Bioprocess Modelling
		3.1 First-Principles Models
		3.2 Surrogate Models
		3.3 Compartmental Models
		3.4 Hybrid Models
		3.5 Benchmark Simulation Models
		3.6 Control and Monitoring
	4 Tools Integration and Standardization
	5 Future Vision: Towards Smart Manufacturing and DTs in the Bio-manufacturing Industry
	References
When Is an In Silico Representation a Digital Twin? A Biopharmaceutical Industry Approach to the Digital Twin Concept
	1 Introduction
	2 Building a Digital Twin
	3 Cost Benefits of Digital Twins
		3.1 Research and Development Costs
		3.2 Direct Process Operating Costs
		3.3 Life-Cycle Quality Costs
	4 QbD, Digital Twin, and the Regulators
		4.1 How DTs Could Enable Company-Wide Cost-Effective Quality by Design
		4.2 The Time Is Now: DTs Are Expected to Render Early Adopters Extremely Competitive and to Facilitate Interaction with Regula...
	5 Theoretical Case Scenarios
		5.1 Example 1: Upstream Heterologous Antigen Production in a Bacterial Host
		5.2 Example 2: Whole Cell DT
	6 Conclusion
	References
Digitalization and Bioprocessing: Promises and Challenges
	1 Current Limitations of Bioprocess Development
	2 Digitalization Opportunities for Biotechnological Processes: Biocatalysis
	3 Digitalization Opportunities for Biotechnological Processes: Fermentation and Cell Culture
	4 Digitalization Strategies
	5 Regulatory Considerations
	6 Conclusions and Outlook
	References
Usage of Digital Twins Along a Typical Process Development Cycle
	1 Introduction
	2 Identification of Strain and Process Characteristics
	3 Model-Based Process Design
	4 Process Transfer and Model Lifecycle Management
	5 Real-Time Usage of Digital Twins
		5.1 Monitoring
		5.2 Control
	6 Conclusion
	References
Digital Seed Train Twins and Statistical Methods
	1 Introduction
	2 Need for Digitalization in Seed Trains
		2.1 Digital Twin
	3 Construction of a Digital Seed Train Twin
	4 Parameter Estimation and Inverse Uncertainty Quantification in Bioprocesses
		4.1 Frequentist Parameter Estimation
		4.2 Frequentist Inverse Uncertainty Quantification
		4.3 Bayesian Parameter Estimation Including Inverse Uncertainty Quantification
	5 Prediction, Propagation of Uncertainty, and Model Updating
		5.1 Frequentist Framework
		5.2 Bayesian Framework
		5.3 Model Validation
		5.4 Model Updating
	6 A Case Study: Integration of Prior Knowledge, Uncertainty Quantification, and Model Parameter Updating for Seed Train Predic...
		6.1 Development of a Digital Seed Train Twin
		6.2 Parameter Estimation: Quantification of Prior Knowledge
			6.2.1 Prior on Model Parameters
			6.2.2 Prior on Starting Concentrations
		6.3 Parameter Estimation: Computation of Posterior Parameter Distributions
		6.4 Prediction and Bayesian Updating
		6.5 Case Study Conclusion
	7 Conclusion and Outlook
	References
Mechanistic Mathematical Models as a Basis for Digital Twins
	1 Introduction
	2 Process Models as a Basis for Digital Twins
		2.1 Submodel Framework of the Process Model
		2.2 Submodel Framework of the Digital Twin
	3 Modelling Approach
		3.1 Model Development
		3.2 Model Requirements
	4 Model Types
		4.1 Mechanistic Models
			4.1.1 Unstructured Models
			4.1.2 Structured Models
			4.1.3 Segregated Models
			4.1.4 Multiple-Model Framework
			4.1.5 Comparison of Mechanistic Models
		4.2 Non-Mechanistic Models
			4.2.1 Modelling with Fuzzy Sets
			4.2.2 Artificial Neural Networks
		4.3 Hybrid Models
	5 Model Based Process Optimization
		5.1 Open Loop Control
		5.2 Model Predictive Control
		5.3 Adaptive Nonlinear Model Predictive Controllers
	6 Case Study: A Generalized Structured Modular Model as a Basis for Digital Twins and Process Optimization
		6.1 Structure of the Generalized Model
		6.2 Six-Compartment Model
		6.3 Extension of the Model Structure
		6.4 Adaption to Different Processes
		6.5 Compartment Model as a Basis for Process Optimization
		6.6 Basis for Digital Twins
	7 Conclusions and Future Perspectives
	References
Digital Twins in Biomanufacturing
	1 Introduction
		1.1 Digitalization and Digital Twins
		1.2 Digital Twins in Manufacturing
			1.2.1 Low Hanging Fruit
			1.2.2 Rising Complexity
		1.3 Models and Data
		1.4 Quality-by-Design (QbD) and Process Analytical Technology (PAT) for Regulated Industries, Exemplified on a Plant Extractio...
			1.4.1 Risk Assessment of Plant Material Preparation
			1.4.2 Impact Factors of Preparation on Extraction of Yew Material
			1.4.3 Control Strategy for Separations
	2 Digital Twins for Manufacturing of Botanicals
	3 Digital Twins for Manufacturing of Biologics
		3.1 Total Process Modelling
		3.2 USP Fermentation Fed-Batch and Perfusion
		3.3 Capture, LLE, Cell Separation and Clarification
		3.4 UF/DF, SPTFF for Concentration and Buffer Exchange
		3.5 Precipitation/Crystallization
		3.6 Chromatography, Membrane Adsorption
		3.7 Lyophilization
	4 Process Integration
		4.1 Process Analytical Technology (PAT) Approach: In-Line/At-Line/Off-Line Analytics Toward Real Time Release Testing (RTRT) a...
		4.2 Piloting Studies: Test Amount and Model Validation
	5 Conclusion
		5.1 Chemical Industries: i.e. Petro- and Bulk Chemicals as well as Regulated Industries - Pharmaceuticals, Botanicals and Biol...
	References