Advanced Instrumentation, Data Interpretation, and Control of Biotechnological Processes

474 Pages·1998·15.673 MB·English

by  S. Isaacs, H. Søeberg (auth.), Jan F. M. Van Impe, Peter A. Vanrolleghem, Dirk M. Iserentant (eds.)

Advanced Instrumentation, Data Interpretation, and
Control of Biotechnological Processes
Advanced Instrumentation,
Data Interpretation, and
Control of Biotechnological
Processes
Edited by
Jan F.M. Van Impe
Katholieke Universiteit Leuven,
Department of Food and Microbial Technology,
Leuven, Belgium
Peter A. V anrolleghem
Universiteit Gent,
Department of Applied Mathematics, Biometrics and Process Control,
Gent, Belgium
and
Dirk M. Iserentant
Katholieke Universiteit Leuven,
Department of Food and Microbial Technology,
Leuven, Belgium
Springer-Science+Business Media, B.V.
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 978-90-481-4954-4 ISBN 978-94-015-9111-9 (eBook)
DOI 10.1007/978-94-015-9111-9
Printedon acid-free paper
All Rights Reserved
© 1998 Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers in 1998.
Softcoverreprint of the hardcover Ist edition 1998
No part of the material protected by this copyright notice may be reproduced or
utilized in any form or by any means, electronic or mechanical,
including photocopying, recording or by any information storage and
retrieval system, without written permission from the copyright owner.
To my parents
J.V.I.
Vll
Contents
I Hardware Sensors 1
1 Flow Injection Analysis for On-line Monitoring of a Waste-
water Treatment Plant 3
S. Isaacs and H. S¢eberg
1.1 Introduction . . . . . . . . . . . . 4
1.2 What is Flow Injection Analysis? 5
1.2.1 Basic principles . . . . 5
1.2.2 Advantages of FIA . . . . 7
1.2.3 Some design principles . . 8
1.3 FIA for Activated Sludge Process Monitoring 10
1.3.1 Sample acquisition, separation, selection and transport 13
1.3.2 Sample injection 18
1.3.3 Reagent transport 18
1.3.4 Reaction . . . . . . 21
1.3.5 Detection ..... 21
1.3.6 Standard calibration 22
1.3.7 Data analysis .... 25
1.3.8 Automatic and manual trouble prevention . 26
1.3.9 The analyzers .......... . 27
1.3.10 The problem of carry-over ... . 33
1.3.11 Implementation and an example 35
1.4 Bibliography 38
1.5 Appendix ·. ............... . 39
2 On-line Measurement of Viable Biomass 41
A.J.C. Spierings
2.1 Basic Theory of Biomass Measurement ....... . 42
2.1.1 Capacitance, conductance and electric fields 42
2.1.2 Frequency of an electric field ........ . 46
Vlll CONTENTS
2.1.3 Effect of dead cells, non-biomass solids and immiscible
liquids, bubbles and cell type . . . . . . . . . . . 53
2.1.4 Practical methods of monitoring cellular biomass . . . 59
2.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 64
2.2.1 Biomass monitor reading versus consistency measure-
ment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
2.2.2 Biomass monitor reading versus OD and RQ measure-
ments ... 65
2.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 66
3 Membrane Inlet Mass Spectrometry for the Characteriza-
tion and Monitoring of Biotechnological Processes 67
F.R. Lauritsen
3.1 Introduction . . . . . . . . . 68
3.2 Mass Spectrometry . . . . . 69
3.2.1 Mass spectrometers 70
3.2.2 Ionization of volatile compounds 73
3.2.3 Tandem mass spectrometry . 77
3.3 Membrane Inlet Mass Spectrometry . . 81
3.3.1 Theory ............. . 82
3.3.2 Practical use of silicone membranes for the measure
ment of volatile organic compounds . . . . . 86
3.3.3 Membrane inlet design . . . . . . . . . . . . . . 88
3.4 Applications of Membrane In:let Mass Spectrometry 91
3.4.1 Microbial degradation of chlorinated aliphatic com-
pounds ..................... . 91
3.4.2 Fermentation of Penicillium chrysogenum . . . 94
3.4.3 Identification of metabolites in microbial media 96
3.5 Bibliography . . . . . . . . . . . . . . . . . . . . . . . 101
4 Flow Cytometry 105
P. Breeuwer
4.1 Introduction . 106
4.2 The Flow Cytometer 106
4.3 Applications . . . . . 110
4.3.1 Assessment of cell viability and vitality 110
4.3.2 Measurement of intracellular pH 116
4.4 Conclusion 117
4.5 Bibliography 118
CONTENTS ~
5 Microcalorimetric Characterization of Bacterial Inocula 121
H. Vandenhove
5.1 Introduction . . . . . . . . . . . . . . . . 122
5.2 A View on Microcalorimetry . . . . . . 123
5.2.1 Main types of microcalorimeters 124
5.2.2 Principal arrangements of microcalorimeters . 125
5.2.3 Thermodynamic considerations . . . . . . . . 126
5.2.4 Applications of microcalorimetry . . . . . . . 129
5.3 Survival of Pseudomonas fluorescens Inocula: Influence of the
Physiological Growth Stage . . . . . . . . . . . 131
5.3.1 Microcalorimetry and bacterial growth . . . . 132
5.3.2 Survival in soil . . . . . . . . . . . . . . . . . 136
5.4 Microcalorimetry and Bacterial Growth Phenomena 142
5.4.1 Materials and methods . 142
5.4.2 Results and discussion 143
5.4.3 Conclusions 148
5.5 Bibliography 149
II Model based Control 159
6 On-line Dz:tta Acquisition 161
P.A. Willems and J.P. Ottoy
6.1 Introduction ..... 162
6.2 Measuring Principles 163
6.2.1 Introduction 163
6.2.2 Low level electrical measurements 164
6.2.3 Pulses . . . . 167
6.3 Signal Conditioning 168
6.4 Data Conversion . . 169
6.4.1 Types of signals 169
6.4.2 Sample and hold 169
6.4.3 A/D conversion . 170
6.4.4 D j A conversion . 174
6.5 Data Transmission . . . 176
6.5.1 Introduction 176
6.5.2 Analog data transmission 178
6.5.3 Digital data transmission 179
6.5.4 Pulse trains . . . . . . . 183
6.5.5 Internal data transport 184
6.6 Controlling Devices ...... . 185
X CONTENTS
6.6.1 Computers .. . 185
6.6.2 PLC ...... . 186
6.6.3 Microcontrollers 186
6.7 Actuator Interfacing 187
6.8 Galvanic Isolation 188
6.9 Bibliography 190
7 Data Processing for System Identification 191
P. Van Overschee
7.1 Introduction ............. . 191
7.2 Trend Determination and Correction 193
7.3 Peak Shaving . . . . . . . . 197
7.4 Estimation of Time Delays 199
7.5 Filtering . . . . . 203
7.6 About Linearity . 205
7. 7 Conclusions . 208
7.8 Bibliography 209
8 Error Diagnosis and Data Reconciliation Using Linear Con-
servation Relations 211
C. Hellinga, B. Romein, K.Ch.A.M. Luyben and J.J. Heijnen
8.1 Introduction . . . . . . . . . 212
8.2 Measurement Inaccuracies .. 212
8.2.1 Stochastic variations 212
8.2.2 Systematic deviations 213
8.2.3 Derived values .. 213
8.3 Introduction to Gross Error Detection and Data Reconciliation214
8.3.1 Gross error detection ................... 214
8.3.2 Data reconciliation . . . . . . . . . . . . . . . . . . . . 216
8.4 Gross Error Detection and Data Reconciliation Using One
Conservation Relation . . . . 217
8.4.1 Gross error detection . . . . . . . . . . . . . 217
8.4.2 Data reconciliation . . . . . . . . . . . . . . 219
8.4.3 Gross error detection: a statistical criterion 221
8.5 Gross Error Detection and Data Reconciliation Using Multi-
ple Conservation Relations . . . . . . . . . . 221
8.5.1 Formulation of the equations . . . . . 221
8.5.2 Classification of the conversion rates . 223
8.5.3 The general equations for gross error detection and
data reconciliation 224
8.6 Gross Error Diagnosis . . . . 228
CONTENTS
XI
8.6.1 Measurement errors . . 229
8.6.2 System definition errors 231
8. 7 Increasing the Test Sensitivity . 232
8.8 Detecting Errors in the Primary Measurements 233
8.9 Discussion and Conclusions . . . . . . . . . . . 234
8.10 Appendix A: Elementary Error Propagation Rules 236
8.11 Appendix B: Effect of the Number of Samples on the Esti-
mated Accuracy of a Stochastic Variable . . . . . . . . . . . . 237
8.12 Appendix C: Macrobal- a Computer Program for Data Rec-
onciliation and Gross Error Detection 239
8.13 Nomenclature 242
8.14 Bibliography 243
9 General Concepts of Bioprocess Modeling 245
G.C. Vansteenkiste
9.1 General Introduction .. 245
9.2 Simulation Scenario 245
9.3 Modeling Methodologies 247
9.4 Biotechnological Processes as ill-defined Systems 248
9.5 Needs in Advanced Simulation of ill-defined Systems 249
9.6 Perspectives of the Simulation Tool ......... . 250
10 Bioprocess Model Identification 251
P.A. Vanrolleghem and D. Dochain
10.1 Introduction . . . . . . . . . . . 252
10.1.1 Mathematical models 253
10.1.2 The model building exercise . 254
10.1.3 Current research areas w.r.t. bioprocess models 257
10.2 Case Study . . . . . . . . . 258
10.2.1 Process . . . . . . . . . . 258
10.2.2 Candidate model set . . . 259
10.3 Structure Characterization (SC) 261
10.3.1 A priori SC .. 262
10.3.2 A posteriori SC . . . . . . 264
10.4 Parameter Estimation ..... . 270
10.4.1 Theoretical identifiability 271
10.4.2 Practical identifiability . 280
10.5 Experimental Design ...... . 288
10.5.1 Introduction ...... . 288
10.5.2 Optimal experimental design for structure characteri-
zation ........................... 291