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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
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