1660251906323 000137kx

from the department of "listen up, you pharma folks, we'll tell you how it's done!"

March 13, 2007
From ISA: Research Triangle Park, NC (13 March 2007) - Industry experts Gregory K. McMillan and Michael A. Boudreau have written and published a new resource on bioprocess modeling. "New Directions in Bioprocess Modeling and Control" focuses on the benefits that models offer before they are put...
From ISA: Research Triangle Park, NC (13 March 2007) - Industry experts Gregory K. McMillan and Michael A. Boudreau have written and published a new resource on bioprocess modeling.
"New Directions in Bioprocess Modeling and Control" focuses on the benefits that models offer before they are put online. Based on years of experience, the authors reveal that significant improvements can result from the process knowledge and insight that are gained when building experimental and first-principle models for process monitoring and control. According to the resource, doing modeling in the process development and early commercialization phases is advantageous because it increases process efficiency and provides ongoing opportunities for improving process control. The book explains that this technology is important for maximizing benefits from analyzers and control tool investments. The book is designed for process design, quality control, information systems, or automation engineers in the biopharmaceutical, brewing, or bio-fuel industry. The text helps professionals define, develop, and apply a virtual plant, model predictive control, first-principle models, neural networks, and multivariate statistical process control. The synergistic knowledge discovery on bench top or pilot plant scale can be ported to industrial scale processes. This learning process is consistent with the intent in the Process Analyzer and Process Control Tools sections of the FDA's Guidance for Industry PAT - A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. Here's the table of contents, so you can get a deep look at what Greg and Mike have done: TABLE OF CONTENTS Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii About the Authors . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi Chapter 1 Opportunities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1-1. Introduction. . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1-2. Analysis of Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1-3. Transfer of Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1-4. Online Indication of Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1-5. Optimizing Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1-6. Process Analytical Technology (PAT) . . . . . . . . . . . . . . . . . . . . . . . 28 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Chapter 2 Process Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2-1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2-2. Performance Limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2-3. Self-Regulating Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 2-4. Integrating Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Chapter 3 Basic Feedback Control . . . .. . . . . . . . . . . . . . . . . . . . . 57 3-1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 3-2. PID Modes, Structure, and Form . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 3-3. PID Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3-4. Adaptive Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 3-5. Set-Point Response Optimization. . . . . . . . . . . . . . . . . . . . . . . . . . . 91 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Chapter 4 Model Predictive Control. . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4-1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 4-2. Capabilities and Limitations . . . .. . . . . . . . . . . . . . . . . . . . . . 100 4-3. Multiple Manipulated Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 109 4-4. Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Chapter 5 Virtual Plant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 5-1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 5-2. Key Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 5-3. Spectrum of Uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 5-4. Implementation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Chapter 6 First-Principle Models . . . . . . . . . . . . . . . . . . .. . . . . . . . . . 151 6-1. Introduction. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . 151 6-2. Our Location on the Model Landscape . . . . . . . . . . . . . . . . . . . . . 152 6-3. Mass, Energy, and Component Balances . . . . . . . . . . . . . . . . . . . 153 6-4. Heat of Reaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 6-5. Charge Balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 6-6. Parameters and Their Engineering Units . . . . . . . . . . . . . . . . . . . 162 6-7. Kinetics . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 6-8. Mass Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180 6-9. Simulated Batch Profiles . . . . . . . . . . . . .. . . . . . . . . . . . . . 185 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 Chapter 7 Neural Network Industrial Process Applications . . . . . . . . . . . 193 7-1. Introduction. . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 7-2. Types of Networks and Uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 7-3. Training a Neural Network. .. . . . . . . . . . . . . . . . . . . . . . . . . . . 200 7-4. Timing Is Everything . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 7-5. Network Generalization: More Isn't Always Better . . . . . . . . . . 206 7-6. Network Development: Just How Do You Go About Developing a Network? . . . . . . . . 208 7-7. Neural Network Example One. . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 7-8. Neural Network Example Two . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 7-9. Designing Neural Network Control Systems. . . . . . . . . . . . . . . . 233 7-10. Discussion and Future Direction . . . . . . . . . . . . . . . . . . . . . . . . . . 235 7-11. Neural Network Point-Counterpoint . . . . . . . . . . . . . . . . . . . . . . 239 References . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . 242 Chapter 8 Multivariate Statistical Process Control . . . . . . . . . . . . . . . . . . . 247 8-1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247 8-2. PCA Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249 8-3. Multiway PCA . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 265 8-4. Model-based PCA (MB-PCA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 8-5. Fault Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 References . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . 282 Appendix A Definition of Terms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Appendix B Condition Number . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . 301 Appendix C Unification of Controller Tuning Relationships. . . . . . . . . . . . 305 Appendix D Modern Myths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317 Appendix E Enzyme Inactivity Decreased by Controlling the pH with a family of Bezier Curves [1] . . . . . . . . . . . . . . . . . 321 Index. . . . . . . . . . . . .. .. . . . . . . . . . . . . . . . . . . . . . . . . . . 333 For more information about this or any of ISA's resources, visit www.isa.org/books.

Sponsored Recommendations

Measurement instrumentation for improving hydrogen storage and transport

Hydrogen provides a decarbonization opportunity. Learn more about maximizing the potential of hydrogen.

Get Hands-On Training in Emerson's Interactive Plant Environment

Enhance the training experience and increase retention by training hands-on in Emerson's Interactive Plant Environment. Build skills here so you have them where and when it matters...

Learn About: Micro Motion™ 4700 Config I/O Coriolis Transmitter

An Advanced Transmitter that Expands Connectivity

Learn about: Micro Motion G-Series Coriolis Flow and Density Meters

The Micro Motion G-Series is designed to help you access the benefits of Coriolis technology even when available space is limited.