New directions in bioprocess modeling and control : maximizing process analytical technology benefits

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Title:

Personal Author:

Boudreau, Michael A.

Publication Information:

Research Triangle Park, NC : ISA, 2007

Physical Description:

xii, 338 p. : ill. ; 26 cm.

ISBN:

9781556179051

Subject Term:

Biochemical engineering

Added Author:

McMillan, Gregory K. 1946-

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010178134	TP248.3 B684 2007	Open Access Book	Book	Searching... Unknown
Searching... SPACE Pulau Pinang Library	30000003499211	TP248.3 B684 2007	Open Access Book	Book	Searching... Unknown

If you are a process design, quality control, information systems, or auto-mation 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's ""Guidance for Industry PAT - A Framework for Innovative Pharmaceutical Development, Manufacturing and Quality Assurance.

Acknowledgments	p. vii
About the Authors	p. ix
Preface	p. xi
Chapter 1 Opportunities	p. 3
1-1 Introduction	p. 3
1-2 Analysis of Variability	p. 6
1-3 Transfer of Variability	p. 16
1-4 Online Indication of Performance	p. 24
1-5 Optimizing Performance	p. 27
1-6 Process Analytical Technology (PAT)	p. 28
References	p. 31
Chapter 2 Process Dynamics	p. 35
2-1 Introduction	p. 35
2-2 Performance Limits	p. 36
2-3 Self-Regulating Processes	p. 47
2-4 Integrating Processes	p. 51
References	p. 54
Chapter 3 Basic Feedback Control	p. 57
3-1 Introduction	p. 57
3-2 PID Modes, Structure, and Form	p. 60
3-3 PID Tuning	p. 71
3-4 Adaptive Control	p. 87
3-5 Set-Point Response Optimization	p. 91
References	p. 96
Chapter 4 Model Predictive Control	p. 99
4-1 Introduction	p. 99
4-2 Capabilities and Limitations	p. 100
4-3 Multiple Manipulated Variables	p. 109
4-4 Optimization	p. 116
References	p. 127
Chapter 5 Virtual Plant	p. 131
5-1 Introduction	p. 131
5-2 Key Features	p. 132
5-3 Spectrum of Uses	p. 138
5-4 Implementation	p. 141
References	p. 147
Chapter 6 First-Principle Models	p. 151
6-1 Introduction	p. 151
6-2 Our Location on the Model Landscape	p. 152
6-3 Mass, Energy, and Component Balances	p. 153
6-4 Heat of Reaction	p. 158
6-5 Charge Balance	p. 159
6-6 Parameters and Their Engineering Units	p. 162
6-7 Kinetics	p. 167
6-8 Mass Transfer	p. 180
6-9 Simulated Batch Profiles	p. 185
References	p. 188
Chapter 7 Neural Network Industrial Process Applications	p. 193
7-1 Introduction	p. 193
7-2 Types of Networks and Uses	p. 198
7-3 Training a Neural Network	p. 200
7-4 Timing Is Everything	p. 203
7-5 Network Generalization: More Isn't Always Better	p. 206
7-6 Network Development: Just How Do You Go about Developing a Network?	p. 208
7-7 Neural Network Example One	p. 211
7-8 Neural Network Example Two	p. 217
7-9 Designing Neural Network Control Systems	p. 233
7-10 Discussion and Future Direction	p. 235
7-11 Neural Network Point-Counterpoint	p. 239
References	p. 242
Chapter 8 Multivariate Statistical Process Control	p. 247
8-1 Introduction	p. 247
8-2 PCA Background	p. 249
8-3 Multiway PCA	p. 265
8-4 Model-based PCA (MB-PCA)	p. 272
8-5 Fault Detection	p. 276
References	p. 282
Appendix A Definition of Terms	p. 289
Appendix B Condition Number	p. 301
Appendix C Unification of Controller Tuning Relationships	p. 305
Appendix D Modern Myths	p. 317
Appendix E Enzyme Inactivity Decreased by Controlling the pH with a family of Bezier Curves [1]	p. 321
Index	p. 333

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