Acknowledgments xiii Preface xv About the Authors xvii Chapter 1 Opportunities 1 1.1 Introduction 1 1.2 Analysis of Variability 4 1.3 Transfer of Variability 16 1.4 Online Indication of Performance 24 1.5 Optimizing Performance 27 1.6 PAT 28 1.7 Insights 30 1.

8 Best Practices 32 Chapter 2 Dynamics 35 2.1 Introduction 35 2.2 Performance Limits 36 2.3 Self-Regulating Processes 47 2.4 Integrating Processes 52 2.5 Wireless Devices and Analyzers 55 2.6 Best Practices 59 Chapter 3 Basic Control 61 3.1 Introduction 61 3.

2 Key Process Measurements 62 3.3 Control Valves and VFDs 64 3.4 PID Fundamentals 67 3.5 PID Form and Structure 72 3.6 PID Tuning 73 3.7 PID Options 83 3.8 PID Performance 84 3.9 Control Strategies 96 3.

10 Best Practices 100 Chapter 4 Model-Predictive Control 109 4.1 Introduction 109 4.2 Capabilities and Limitations 111 4.3 Multiple Manipulated Variables 119 4.4 Optimization 127 4.5 Mammalian Cell Bioreactor Optimization 135 4.6 MPC Best Practices 137 Chapter 5 Digital Twin 143 5.1 Introduction 143 5.

2 Key Features 145 5.3 Spectrum of Uses 151 5.4 Implementation 162 5.5 Conclusion 168 5.6 Digital Twin Best Practices 168 Chapter 6 First-Principle Models 173 6.1 Introduction 173 6.2 Modeling Challenges 174 6.3 Modeling Opportunities 175 6.

4 Modeling Breakthroughs 177 6.5 General Form of Kinetics 178 6.6 Media Ordinary Differential Equations with Speedup Factors 179 6.7 Concentration and Flow of Charges, Manipulated and Recycle Streams 182 6.8 Specific Growth Rate Equations with Bias and Gain Terms 183 6.9 Cell Death (Lysis) 186 6.10 Specific Product Formation Rate Equations with Bias and Gain Terms 187 6.11 Product Consumption and Degradation 188 6.

12 Specific By-product Formation Rate Equations with Bias and Gain Terms 188 6.13 Utilization Rates 189 6.14 OUR and Carbon Dioxide Production Rate 190 6.15 Agitation and Sparge 190 6.16 Dissolved Gas ODEs with Speedup Factors 193 6.17 Parameters and Variables 194 6.18 Best Practices 202 Chapter 7 Analytical Technologies 207 7.1 Introduction 207 7.

2 Overview 208 7.3 DO 208 7.4 Dissolved Carbon Dioxide 210 7.5 Turbidity 211 7.6 Dielectric Spectroscopy 213 7.7 Near-Infrared Spectroscopy 214 7.8 Mass Spectrometers 214 7.9 BioProfile FLEX2 215 7.

10 Liquid Chromatographs 217 7.11 Best Practices 218 Chapter 8 Data Analytics 221 8.1 Introduction 221 8.2 PCA Background 223 8.3 Multiway PCA 238 8.4 Model-Based PCA 246 8.5 Fault Detection 250 8.6 Data Analytics Best Practices 257 Chapter 9 Models to Improve Operator, Automation, and Process Performance 265 9.

1 Introduction 265 9.2 Overview 267 9.3 Bountiful Boundaries 268 9.4 Data Drives Dynamics 269 9.5 Digitizing the Plant for Process Performance 270 9.6 Titration Curve Modeling 272 9.7 Equipment Modeling 275 9.8 Sparge Modeling 277 9.

9 Kinetics Modeling 279 9.10 Instrumentation Modeling 284 9.11 Speedup 296 9.12 Performance Monitoring 297 9.13 Generation and Fitting of Profiles 298 9.14 Simplifications and Practical Solutions 300 9.15 Best Practices 302 References 304 Appendix A: Automation System Performance Top 10 Concepts 307 Appendix B: Bioprocess Biology 323 Appendix C: Enhanced PID Controller for Wireless and Analyzer Applications 335 Appendix D: Modern Myths 355 Appendix E: Enzyme Inactivity Decreased by Controlling the pH with a Family of Bézier Curves 357 Appendix F: First-Principle Process Relationships 369 Appendix G: Gas Pressure Dynamics 387 Appendix H: Charge Balance to Model pH 389 Appendix I: Interactive to Noninteractive Time Constant Conversion 399 Appendix J: Jacket and Coil Temperature Control 403 Appendix K: PID Forms and Conversion of Tuning Settings 409 Appendix L: Liquid Mixing Dynamics 417 Appendix M: Mammalian Bioreactor Model 421 Appendix N: Debottlenecking Using Sensitivity Analysis 427 Bibliography 437 Index 449.