New Directions in Bioprocess Modeling and Control

Maximizing Process Analytical Technology Benefits

Gregory K. McMillan, Christopher Stuart, Rehman Fazeem, Zachary T. Sample, Timothy Schieffer

• Publisher: Wiley
• Year: 2021
• Print ISBN:
  9781643311043
• Edition: 2
• eBook ISBN:
  9781643311050
• Topic: Technology & Engineering
• Subtopic: Automation in Engineering
• Index: Technology & Engineering

New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits, 2nd Edition

This book provides practical, comprehensive knowledge on how to use the advances in analytical measurements and basic and advanced control to improve batch profiles and endpoint consistency. The consequential integration of measurements, models, and controls into a digital twin with recently developed blocks to provide profiles and predict endpoints enables:

• Developing dynamic models from trend charts that are used for experiment design, diagnosing the sources of limitations and inconsistences, and developing and testing solutions 500 times real time without interfering with existing plant operation.
• Comprehensive views of basic process control with the possibilities of model predictive control to control and optimize batch profiles by non-intrusive development and confirmation.
• The ability to determine how to maximize the performance of bioreactors, which are often the bottleneck and the key to product quality and consistency, without tests or trials in the actual plant.

The resulting improvements in batch cycle time and consistency can be designed, tested, quantified, and confirmed; and operators can be trained independently of actual plant operation. The benefit from the elimination of a bad batch—particularly for new biologics—is potentially worth ten or more million dollars.

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Contents
• Acknowledgments
• Preface
• About the Authors
• Chapter 1: Opportunities
• Chapter 2: Dynamics
• Chapter 3: Basic Control
• Chapter 4: Model-Predictive Control
• Chapter 5: Digital Twin
• Chapter 6: First-Principle Models
• Chapter 7: Analytical Technologies
• Chapter 8: Data Analytics
• Chapter 9: Models to Improve Operator, Automation, and Process Performance
• Appendix A: Automation System Performance Top 10 Concepts
• Appendix B: Bioprocess Biology
• Appendix C: Enhanced PID Controller for Wireless and Analyzer Applications
• Appendix D: Modern Myths
• Appendix E: Enzyme Inactivity Decreased by Controlling the pH with a Family of Bézier Curves
• Appendix F: First-Principle Process Relationships
• Appendix G: Gas Pressure Dynamics
• Appendix H: Charge Balance to Model pH
• Appendix I: Interactive to Noninteractive Time Constant Conversion
• Appendix J: Jacket and Coil Temperature Control
• Appendix K: PID Forms and Conversion of Tuning Settings
• Appendix L: Liquid Mixing Dynamics
• Appendix M: Mammalian Bioreactor Model
• Appendix N: Debottlenecking Using Sensitivity Analysis
• Bibliography
• Index