New Directions in Bioprocess Modeling and Control: Maximizing Process Analytical Technology Benefits

by Boudreau, Michael A.; McMillan, Gregory K.

• ISBN13: 9781556179051
• ISBN10: 1556179057
• Format: Paperback
• Copyright: 2006-09-19
• Publisher: Isa

Summary

Models offer benefits even before they are put on line. Based on years of experience, the authors reveal in New Directions in Bioprocess Modeling and Control 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. 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. This technology is important for maximizing benefits from analyzers and control tool investments. If you are a process design, quality control, information systems, or automation engineer in the biopharmaceutical, brewing, or bio-fuel industry, this handy resource will help you 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#xC3;#x1A;'s Guidance for Industry PAT #xC3;#x1A;- A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance. It states in the Process Analyzer section of the FDA#xC3;#x1A;'s guidance: #xC3;#x1A;"For certain applications, sensor-based measurements can provide a useful process signature that may be related to the underlying process steps or transformations. Based on the level of process understanding these signatures may also be useful for the process monitoring, control, and end point determination when these patterns or signatures relate to product and process quality.#xC3;#x1A;"

Table of Contents

Acknowledgments	p. vii
About the Authors	p. ix
Preface	p. xi
Opportunities	p. 3
Introduction	p. 3
Analysis of Variability	p. 6
Transfer of Variability	p. 16
Online Indication of Performance	p. 24
Optimizing Performance	p. 27
Process Analytical Technology (PAT)	p. 28
References	p. 31
Process Dynamics	p. 35
Introduction	p. 35
Performance Limits	p. 36
Self-Regulating Processes	p. 47
Integrating Processes	p. 51
References	p. 54
Basic Feedback Control	p. 57
Introduction	p. 57
PID Modes, Structure, and Form	p. 60
PID Tuning	p. 71
Adaptive Control	p. 87
Set-Point Response Optimization	p. 91
References	p. 96
Model Predictive Control	p. 99
Introduction	p. 99
Capabilities and Limitations	p. 100
Multiple Manipulated Variables	p. 109
Optimization	p. 116
References	p. 127
Virtual Plant	p. 131
Introduction	p. 131
Key Features	p. 132
Spectrum of Uses	p. 138
Implementation	p. 141
References	p. 147
First-Principle Models	p. 151
Introduction	p. 151
Our Location on the Model Landscape	p. 152
Mass, Energy, and Component Balances	p. 153
Heat of Reaction	p. 158
Charge Balance	p. 159
Parameters and Their Engineering Units	p. 162
Kinetics	p. 167
Mass Transfer	p. 180
Simulated Batch Profiles	p. 185
References	p. 188
Neural Network Industrial Process Applications	p. 193
Introduction	p. 193
Types of Networks and Uses	p. 198
Training a Neural Network	p. 200
Timing Is Everything	p. 203
Network Generalization: More Isn't Always Better	p. 206
Network Development: Just How Do You Go about Developing a Network?	p. 208
Neural Network Example One	p. 211
Neural Network Example Two	p. 217
Designing Neural Network Control Systems	p. 233
Discussion and Future Direction	p. 235
Neural Network Point-Counterpoint	p. 239
References	p. 242
Multivariate Statistical Process Control	p. 247
Introduction	p. 247
PCA Background	p. 249
Multiway PCA	p. 265
Model-based PCA (MB-PCA)	p. 272
Fault Detection	p. 276
References	p. 282
Definition of Terms	p. 289
Condition Number	p. 301
Unification of Controller Tuning Relationships	p. 305
Modern Myths	p. 317
Enzyme Inactivity Decreased by Controlling the pH with a family of Bezier Curves [1]	p. 321
Index	p. 333
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