Munack / Schönert Computer Applications in Biotechnology

1. Auflage 2014
ISBN: 978-1-4832-9690-6
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 367 Seiten, Web PDF

Reihe: IFAC Postprint Volume

The 6th Computer Applications in Biotechnology (CAB6) conference was a continuation of 2 series of events: the IFAC symposia on Modelling and Control of Biotechnical Processes and the International Conferences on Computer Applications in Fermentation Technology. This conference provided the opportunity for both sides, leading researchers and industrial practitioners, in this interdisciplinary field to exchange new ideas and technology; concepts and solutions. This postprint volume contains all those papers which were presented at the conference.

Autoren/Hrsg.

Munack, A.

Schönert, K.

Inhaltsverzeichnis

1;Front Cover;1
2;Computer Applications in Biotechnology;2
3;Copyright Page
;3
4;6th INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS IN BIOTECHNOLOGY - CAB 6;4
5;Table of Contents;6
6;PART I: ADVANCED MEASURING AND BIOPROCESS MONITORING TECHNIQUES ;12
6.1;CHAPTER 1. STATISTICAL DATA-PROCESSING FROM ON-LINE LASER TUBIDIMETER AND AUTOFEEDING OF NATURAL COMPLEX NUTRIENTS IN SEMI-BATCH CULTURES;12
6.1.1;1. INTRODUCTION;12
6.1.2;2. STATISTICAL PROCESSING OF DATA;12
6.1.3;3. MATHEMATICAL CONSIDERATIONS;14
6.1.4;4. APPLICATIONS TO HIGH-CELL-DENSITY FED-BATCH CULTURES ON NATURAL COMPLEX MEDIA;15
6.1.5;5. CONCLUSIONS;16
6.1.6;6. REFERENCES;16
6.2;CHAPTER 2. MODEL-AIDED ON-LINE GLUCOSE MONITORING FOR COMPUTER-CONTROLLED HIGH CELL DENSITY FERMENTATIONS ;17
6.2.1;1. INTRODUCTION;17
6.2.2;2. MATERIALS AND METHODS;17
6.2.3;3. RESULTS;18
6.2.4;4. CONCLUSION;20
6.2.5;5. ACKNOWLEDGEMENTS;20
6.2.6;6. REFERENCES;20
6.2.7;7. NOMENCLATURE;21
6.3;CHAPTER 3. DEPROTECTION MONITORING IN SPPS: CONDUCTIMETRIC VERSUS SPECTROPHOTOMETRIC TECHNIQUES ;23
6.3.1;1. INTRODUCTION;23
6.3.2;2. MATERIALS AND METHODOLOGY;24
6.3.3;3. APPARATUS;24
6.3.4;4. RESULTS AND DISCUSSION;24
6.3.5;5. CONCLUSIONS;26
6.3.6;ABBREVIATIONS;27
6.3.7;REFERENCES;27
6.4;CHAPTER 4. PROJECTIVE REFERENCE EVALUATION - THE RELIABLE EVALUATION TECHNIQUE FOR FLOW INJECTION ANALYSIS SIGNALS ;28
6.4.1;1. INTRODUCTION;28
6.4.2;2. MATERIAL AND METHODS;29
6.4.3;3. RESULTS AND DISCUSSION;30
6.4.4;4. CONCLUSION;33
6.4.5;REFERENCES;34
6.5;CHAPTER 5. FEEDBACK CONTROL OF A RECOMBINANT FED-BATCH FERMENTATION USING ON-LINE HPLC MEASUREMENTS ;35
6.5.1;INTRODUCTION;35
6.5.2;MATERIALS AND METHODS;36
6.5.3;CLOSED-LOOP EXPERIMENT;36
6.5.4;OPTIMIZATION STRATEGY;37
6.5.5;SIMULATION EXPERIMENTS;37
6.5.6;EXPERIMENTAL RESULTS;37
6.5.7;DISCUSSION;38
6.5.8;CONCLUSIONS;38
6.5.9;Acknowledgements;38
6.5.10;References;38
6.6;CHAPTER 6. REAL-TIME RECURSIVE PARAMETER ESTIMATION FOR FAULT DETECTION IN FLOW-INJECTION ANALYSIS SYSTEMS ;40
6.6.1;1. INTRODUCTION;40
6.6.2;2. THEORY;41
6.6.3;3. EXPERIMENT AND POSSIBLE FAULTS;43
6.6.4;4. APPLICATION OF THE FAULT DETECTION SYSTEM;43
6.6.5;5. CONCLUSION AND DISCUSSION;45
6.6.6;ACKNOWLEDGEMENT;45
6.6.7;REFERENCES;45
7;PART II: STRATEGIES AND NEW CONCEPTS IN PROCESS DATA ANALYSIS ;46
7.1;CHAPTER 7. EXPLORATORY DIAGNOSIS OF LARGE-SCALE CHROMATOGRAPHIC PROCESSES BY PRINCIPAL COMPONENT ANALYSIS ;46
7.1.1;1. INTRODUCTION;46
7.1.2;2. MATERIALS AND METHODS;47
7.1.3;3. THE CASE STUDY APPLICATION;48
7.1.4;4. RESULTS AND DISCUSSION;48
7.1.5;5. CONCLUSIONS AND FUTURE WORK;51
7.1.6;6. ACKNOWLEDGEMENTS;51
7.1.7;6. REFERENCES;51
7.2;CHAPTER 8. PATTERN RECOGNITION FOR PHASE DETECTION IN BIOPROCESSES ;52
7.2.1;1. INTRODUCTION;52
7.2.2;2. EXAMPLE PROCESS;53
7.2.3;3- DETECTION OF TRIGGER AND DIAGNOSIS EVENTS;53
7.2.4;4. PHASE DETECTION;55
7.2.5;REFERENCES;57
7.3;CHAPTER 9. HYBRID SIMULATION OF MICROBIAL BEHAVIOR COMBINING A STATISTICAL PROCEDURE AND FUZZY IDENTIFICATION OF CULTURE PHASES ;58
7.3.1;1. INTRODUCTION;58
7.3.2;2. HYBRID SIMULATION SYSTEM;59
7.3.3;3. MATERIALS AND METHOD;61
7.3.4;4. RESULTS AND DISCUSSION;61
7.3.5;NOMENCLATURE;62
7.3.6;REFERENCES;63
7.4;CHAPTER 10. Seed Data Analysis for Production Fermenter Performance Estimation ;64
7.4.1;1. INTRODUCTION;64
7.4.2;2. BIOPROCESS DESCRIPTION;64
7.4.3;3. PATTERN RECOGNITION TECHNIQUES;65
7.4.4;4. APPLICATION OF PATTERN RECOGNITION TECHNIQUES;67
7.4.5;5. DISCUSSION AND FUTURE WORK;69
7.4.6;6. ACKNOWLEDGEMENTS;69
7.4.7;7. REFERENCES;69
7.5;CHAPTER 11. ARTIFICIAL NEURAL NETWORKS OF IMPROVED RELIABILITY FOR INDUSTRIAL PROCESS SUPERVISION ;70
7.5.1;1. INTRODUCTION;70
7.5.2;2. ANNs AND FUZZY RULE SYSTEMS;70
7.5.3;3. ESTIMATION OF ANN PERFORMANCE;73
7.5.4;4. COMPACTNESS OF THE ANN;74
7.5.5;CONCLUDING REMARKS;76
7.5.6;ACKNOWLEDGEMENTS;76
7.5.7;REFERENCES;76
7.6;CHAPTER 12 . REACTION MECHANISMS AND CELL DENSITY ESTIMATORS FOR ANIMAL CELL CULTURES ;77
7.6.1;Abstract;77
7.6.2;1 Introduction;77
7.6.3;2 First reaction network;77
7.6.4;3 First cell density estimator;78
7.6.5;4 Extended reaction network and cell density estimator;79
7.6.6;5 How many amino-acids are needed ?;80
7.6.7;6 Using lactic acid data;80
7.6.8;7 Conclusion;81
7.6.9;8 Material and methods;81
7.6.10;9 Acknowledgements;81
8;PART III: ADVANCED (INTELLIGENT) CONTROL OF BIOPROCESSES ;83
8.1;CHAPTER 13. ADVANCED SUPERVISION OF MAMMALIAN CELL CULTURES USING HYBRID PROCESS MODELS ;83
8.1.1;1. INTRODUCTION;83
8.1.2;2. PROCESS;83
8.1.3;3. MODELLING;84
8.1.4;4. RESULTS AND DISCUSSION;87
8.1.5;5. CONCLUSIONS;87
8.1.6;REFERENCES;88
8.1.7;APPENDIX;88
8.2;CHAPTER 14. CONTROL OF CONTINUOUS FERMENTATION PROCESSES BY SLIDING MODE DESIGN ;89
8.2.1;1. INTRODUCTION;89
8.2.2;2. PROCESS MODEL;90
8.2.3;3. STATIC SLIDING CONTROL;91
8.2.4;4. STATIC QUASI-SLIDING CONTROL;91
8.2.5;5. SIMULATIONS;92
8.2.6;6. CONCLUSIONS;93
8.2.7;ACKNOWLEDGEMENTS;93
8.2.8;REFERENCES;93
8.3;CHAPTER 15. DESIGN OF A CONTROL STRATEGY FOR A LIPASE PRODUCTION PROCESS ;94
8.3.1;1. INTRODUCTION;94
8.3.2;2. THE PROCESS;95
8.3.3;3. THE CONTROL STRATEGY;95
8.3.4;4. THE CONTROLLER;97
8.3.5;5. IMPLEMENTATION ON THE PROCESS;98
8.3.6;6. CONCLUSION;99
8.3.7;7. REFERENCES;99
8.4;CHAPTER 16. DEVELOPMENT OF INTELLIGENT CONTROL SYSTEMS FOR BIOREACTORS ;100
8.4.1;1. INTRODUCTION;100
8.4.2;2. MEASUREMENT, STATE ESTIMATION AND CONTROL OF BIOREACTORS;100
8.4.3;3. KNOWLEDGE-BASED EXPERT SYSTEMS;101
8.4.4;4. FUZZY CONTROL;101
8.4.5;5. NEURO CONTROL;102
8.4.6;6. NEURO-FUZZY CONTROL;102
8.4.7;7. FUZZY NEURAL NETWORK;103
8.4.8;8.GENETIC ALGORITHM;104
8.4.9;9. DISCUSSION AND CONCLUSION;104
8.4.10;REFERENCES;105
8.5;CHAPTER 17. NEURAL MODELS IN PREDICTIVE CONTROL ;106
8.5.1;1. INTRODUCTION;106
8.5.2;2. THEORY;107
8.5.3;3. EXPERIMENTAL SETUP;108
8.5.4;4. RESULTS;109
8.5.5;5 CONCLUSION;110
8.5.6;REFERENCES;110
8.6;CHAPTER 18. MODELLING AND CONTROL OF A FED-BATCH FERMENTATION PROCESS USING NEURAL NETWORKS AND ITERATIVE LEARNING METHOD ;112
8.6.1;1. INTRODUCTION;112
8.6.2;2. MODEL-BASED ITERATIVE LEARNING;112
8.6.3;3. MODELLING OF A FED BATCH FERMENTATION PROCESS;114
8.6.4;4. LEARNING CONTROL OF THE FERMENTATION PROCESS;116
8.6.5;5. CONCLUSION;117
8.6.6;ACKNOWLEDGEMENTS;117
8.6.7;REFERENCES;117
8.7;CHAPTER 19. DYNAMICS AND CONTROL OF THE FED-BATCH PRODUCTION OF POLY-/3-HYDROXYBUTYRATE ;118
8.7.1;1. INTRODUCTION;118
8.7.2;2. DYNAMICAL MODEL;118
8.7.3;3. SELECTION OF FEEDING STRATEGIES;119
8.7.4;4. DESIGN OF CONTROLLERS;119
8.7.5;5. EXPERIMENTAL RESULTS AND DISCUSSION;121
8.7.6;6. CONCLUSIONS;123
8.7.7;7. REFERENCES;123
8.8;CHAPTER 20. DVANCED CONTROL METHOD BASED ON THE PATTERN RECOGNIZE AND NEURAL NETWORK FOR BIOPROCESS ;124
8.8.1;1. INTRODUCTION;124
8.8.2;2. STREPTOMYCIN FERMENTATION TECHNOLOGY;125
8.8.3;3. OPTIMALIZE THE STREPTOMYCIN FERMENTATION PROCESS;125
8.8.4;4. PREDICTION;127
8.8.5;5. CONTROL STRATEGY;127
8.8.6;6. RESULT AND DISCUSSION;128
8.8.7;7. ACKNOWLEDGEMENT;128
8.8.8;REFERENCES;128
9;PART IV: MODELLING OF CELL METABOLISM AND ITS REGULATION ;129
9.1;CHAPTER 21. ON-LINE PHYSIOLOGICAL STATE RECOGNITION AND PARAMETER ESTIMATION IN THE METABOLIC REACTION MODEL USING ERROR VECTORS ;129
9.1.1;1. INTRODUCTION;129
9.1.2;2. ON-LINE STATE RECOGNITION IN A YEAST FED-BATCH CULTURE;130
9.1.3;3. ON-LINE ESTIMATION USING A METABOLIC REACTION MODEL;132
9.1.4;REFERENCES;134
9.2;CHAPTER 22. MODELLING OF SHORT TERM CRABTREE-EFFECT IN BAKERS YEAST ;135
9.2.1;1. DESIGN OF THE MODEL;135
9.2.2;2. COMPARISON BETWEEN MODEL PREDICTIONS AND EXPERIMENTAL OBSERVATIONS;138
9.2.3;2. CONCLUSION;140
9.2.4;3. REFERENCES;140
9.3;CHAPTER 23. IN VIVO STATIONARY FLUX DETERMINATION USING 13C NMR ISOTOPE LABELLING EXPERIMENTS ;141
9.3.1;1. INTRODUCTION;141
9.3.2;2. EXPERIMENTAL SETUP;142
9.3.3;3. A SIMPLE EXAMPLE;142
9.3.4;4. GENERAL FLUX MODEL;143
9.3.5;5. COMPUTER AIDED DATA ANALYSIS;145
9.3.6;7. CONCLUSION;145
9.3.7;8. REFERENCES;146
9.4;CHAPTER 24. A KINETIC MODEL FOR THE PENICILLIN BIOSYNTHETIC PATHWAY IN PENICILLIUM CHRYSOGENUM ;147
9.4.1;1. INTRODUCTION;147
9.4.2;2. KINETIC MODEL;147
9.4.3;3. MATERIAL AND METHODS;149
9.4.4;4. RESULTS;149
9.4.5;5. DISCUSSION;150
9.4.6;6. NOMENCLATURE;151
9.4.7;7. REFERENCES;152
10;PART V: PROGRESS IN PROCESS MODELLING AND IDENTIFICATION ;153
10.1;CHAPTER 25. COMPARING DIFFERENT MODELLING TECHNIQUES FOR THE ESCHERICHIA COU FERMENTATION PROCESS ;153
10.1.1;1. INTRODUCTION;153
10.1.2;2. LINEAR MODELS;153
10.1.3;3. NON-LINEAR MODELS;154
10.1.4;4. RESULTS;156
10.1.5;5. CONCLUSION;158
10.1.6;ACKNOWLEDGEMENTS;158
10.1.7;REFERENCES;158
10.2;CHAPTER 26. DYNAMICAL MODELLING OF A BIOLOGICAL DETOXICATION PROCESS IN A FIXED BED REACTOR ;159
10.2.1;1. INTRODUCTION;159
10.2.2;2. MODELLING ASSUMPTIONS;159
10.2.3;3. DYNAMICAL MODEL;160
10.2.4;4. SIMULATION RESULT;162
10.2.5;5. CONCLUSIONS;164
10.2.6;References;164
10.3;CHAPTER 27. MATHEMATICAL MODELS FOR GROWTH AND PRODUCTION OF SINGLE PELLETS AND PELLET POPULATIONS ;165
10.3.1;1. Introduction;165
10.3.2;2. Growth and production of a single pellet;165
10.3.3;3. Reduced model of single pellet growth;166
10.3.4;4. Population balance;167
10.3.5;5. Conclusion;169
10.3.6;6. REFERENCES;169
10.4;CHAPTER 28. MODELLING OF STIRRED TANK BIOREACTORS ;170
10.4.1;1. APPLICATION EXAMPLES;172
10.4.2;2. CONCLUSIONS;175
10.4.3;REFERENCES;175
10.5;CHAPTER 29. BIOFUEL CELL UTILIZING Saccharomyces cerevisiae - MODELLING of the PROCESS ;176
10.5.1;1. INTRODUCTION;176
10.5.2;2. MATERIALS AND METHODS;177
10.5.3;3. RESULTS AND DISCUSSION;178
10.5.4;4. MODELLING AND SIMULATION OF THE BIOFUEL CELL PROCESS;179
10.5.5;5. CONCLUSION;181
10.5.6;REFERENCES;181
10.6;CHAPTER 30. SIMULATION AND PROFIT ESTIMATION FOR BAKER'S YEAST CONTINUOUS PRODUCTION ;182
10.6.1;1. INTRODUCTION;182
10.6.2;2. DESCRIPTION OF THE PROCESS;182
10.6.3;3. PROCESS MODEL;183
10.6.4;4. ESTABLISHMENT OF PROFIT FUNCTION;184
10.6.5;5. RESULTS AND DISCUSSION;185
10.6.6;6. CONCLUSION;187
10.6.7;REFERENCES;187
10.7;CHAPTER 31. IMPROVEMENTS IN THE ON-LINE PARAMETER IDENTIFICATION OF BIOPROCESSES ;188
10.7.1;1. INTRODUCTION;188
10.7.2;2. EXEMPLARY MODEL;188
10.7.3;3. THE FISHER INFORMATION MATRIX;189
10.7.4;4. THE IDENTIFICATION METHODS;190
10.7.5;5. RESULTS AND DISCUSSION;191
10.7.6;REFERENCES;193
10.8;CHAPTER 32. NEURAL NETWORKS IN ESTIMATION AND CONTROL OF ANTIBODY PRODUCTION USING HYBRIDOMA CELLS IN FED-BATCH CULTURES ;194
10.8.1;INTRODUCTION;194
10.8.2;NEURAL NETWORKS IN BIOTECHNOLOGY;195
10.8.3;MATERIALS AND METHODS;195
10.8.4;TRAINING OF NEURAL NETWORKS;196
10.8.5;DISCUSSIONS AND CONCLUSIONS;196
10.8.6;ACKNOWLEDGMENTS;197
10.8.7;REFERENCES;197
11;PART VI: FUTURE TRENDS IN COMPUTER APPLICATIONS IN BIOTECH ;200
11.1;CHAPTER 33. TEXTURE CHARACTERIZATION OF COLONIES ON SOLID SUBSTRATE ;200
11.1.1;1. INTRODUCTION;200
11.1.2;2. MATERIALS AND METHODS;200
11.1.3;3. TEXTURE CHARACTERIZATION;202
11.1.4;4. RESULTS;203
11.1.5;5. CONCLUSIONS;204
11.1.6;REFERENCES;205
11.2;CHAPTER 34. PATTERN RECOGNITION METHODS FOR FERMENTATION DATABASE MINING ;206
11.2.1;1. INTRODUCTION;206
11.2.2;2. METHODOLOGY: WAVELET DECOMPOSITION;206
11.2.3;3. TREND EXTRACTION: TRIANGLES;207
11.2.4;3. DECISION TREE;208
11.2.5;4. APPLICATIONS;208
11.2.6;5. CONCLUSION;209
11.2.7;REFERENCES;209
11.3;CHAPTER 35. A SYSTEMATIC APPROACH TO STRUCTURED BIOLOGICAL MODELS ;210
11.3.1;1. Introduction;210
11.3.2;2. Metabolism of bacterial cells;211
11.3.3;3. Metabolic network;211
11.3.4;4. The hierarchically structured regulatory network;212
11.3.5;5. Conclusion;214
11.3.6;Acknowledgements;214
11.3.7;6. REFERENCES;214
12;PART VII: ADVANCED MEASURING AND BIOPROCESS MONITORING TECHNIQUES ;216
12.1;CHAPTER 36. SUBSTRATE CONSUMPTION RATE - NEW CONCEPT OF MEASURING AND MONITORING IN THE ACTIVATED SLUDGE PROCESS ;216
12.1.1;1. INTRODUCTION;216
12.1.2;2. DYNAMIC MODEL OF THE CONSIDERED SYSTEM;217
12.1.3;3. SUBSTRATE CONSUMPTION RATE - THEORETICAL APPROACH;217
12.1.4;4. APPROXIMATION OF THE SUBSTRATE CONSUMPTION RATE;217
12.1.5;5. SIMULATION STUDIES AND CONCLUDING REMARKS;218
12.1.6;REFERENCES;219
12.2;CHAPTER 37. BIOMASS ESTIMATION USING NEURAL NETWORKS AND THE EXTENDED KALMAN FILTER ;220
12.2.1;1. INTRODUCTION;220
12.2.2;2. FERMENTATION EXPERIMENTS;220
12.2.3;3. MATHEMATICAL METHODS;221
12.2.4;4. RESULTS AND DISCUSSION;222
12.2.5;4. CONCLUSIONS;223
12.2.6;REFERENCES;223
12.3;CHAPTER 38. FLOW CYTOMETRIC MONITORING OF BACTERIAL CELL STATES UNDER GROWTH LIMITING CONDITIONS;224
12.3.1;1. INTRODUCTION;224
12.3.2;2. MATERIALS AND METHODS;225
12.3.3;3. RESULTS;225
12.3.4;4. DISCUSSION AND CONCLUSION;226
12.3.5;REFERENCES;226
12.4;CHAPTER 39. STUDY OF SACCHAROMYCES CEREVISIAE YEAST CELLS BY FIELD FLOW FRACTIONATION AND IMAGE ANALYSIS ;228
12.4.1;I. INTRODUCTION;228
12.4.2;2. MATERIALS AND METHODS;229
12.4.3;3. RESULTS;229
12.4.4;4. CONCLUSION;230
12.4.5;REFERENCES;231
12.5;CHAPTER 40. NEW HARDWARE AND SOFTWARE CONCEPTS FOR FULLY AUTOMATED ANALYSIS AND DATA PROCESSING ;232
12.5.1;1. INTRODUCTION;232
12.5.2;2. UNIT OPERATION CONCEPT FOR INSTRUMENTAL ANALYSIS;233
12.5.3;3. EMBEDING OF ROBOTIC AIDED INSTRUMENTATION IN A COMMUNICATION NETWORK (figure 3);233
12.5.4;4. REALIZATION OF THE CONCEPT;233
12.5.5;5. APPLICATION EXAMPLES;234
12.5.6;REFERENCES;236
12.6;CHAPTER 41. DEVELOPMENT OF ENZYMATIC AUTOANALYZERS FOR MONITORING BIOTECHNOLOGICAL PROCESSES ;237
12.6.1;1. INTRODUCTION;237
12.6.2;2. DEVELOPMENT OF STABILIZED ENZYME MEMBRANES AND REAGENTS;237
12.6.3;3. UNIVERSAL LABORATORY ANALYZER OF AMPEROMETRIC TYPE;238
12.6.4;4. INDUSTRIAL AUTOANALYZERS OF AMPEROMETRIC TYPE FOR CONTINUOUS MONITORING;238
12.6.5;5. UNIVERSAL AUTOANALYZERS OF POTENTIOMETRIC TYPE;239
12.6.6;6. RESULTS OF TESTING AUTO ANALYZERS AND DISCUSSSION;240
12.6.7;7. CONCLUSION;240
12.6.8;REFERENCES;240
13;PART VIII: STRATEGIES AND NEW CONCEPTS IN PROCESS DATA ANALYSIS ;241
13.1;CHAPTER 42. INTERACTIVE EVALUATION OF NMR SPECTRA FROM IN VIVO ISOTOPE LABELLING EXPERIMENTS ;241
13.1.1;1. INTRODUCTION;241
13.1.2;2. GENERAL SPECTRUM MODEL;242
13.1.3;3. SPECTRAL DECONVOLUTION;243
13.1.4;4. INTERACTIVE DATA ANALYSIS;244
13.1.5;5. CONCLUSION;244
13.1.6;6. REFERENCES;244
13.2;CHAPTER 43. THE ON-LINE COMPARISON OF A MEASURED VS CALCULATED OXYGEN MASS TRANSFER COEFFICIENT - A USEFUL PARAMETER FOR FAULT DIAGNOSIS ;245
13.2.1;1. INTRODUCTION;245
13.2.2;2. BASIC PRINCIPLES;245
13.2.3;3. EXPERIMENTAL RESULTS;247
13.2.4;4. DISCUSSION;248
13.2.5;ACKNOWLEDGMENTS;248
13.2.6;REFERENCES;248
13.2.7;SYMBOLS;248
14;PART IX: PROGRESS IN PROCESS MODELLING AND IDENTIFICATION ;250
14.1;CHAPTER 44. AN EVOLUTIONARY ALGORITHM FOR INITIAL STATE AND PARAMETER ESTIMATION IN COMPLEX BIOCHEMICAL MODELS ;250
14.1.1;1. INTRODUCTION;250
14.1.2;2. PARAMETER ESTIMATION FOR BIOCHEMICAL MODELS;250
14.1.3;3. RESULTS AND DISCUSSION;252
14.1.4;4. CONCLUSIONS;253
14.1.5;5. ACKNOWLEDGMENTS;253
14.1.6;6. REFERENCES;253
14.2;CHAPTER 45. Maximum VIrginiamycin Production Strategy and Its Realization in batch Cultivation of Streptomyces virginiae by Autoregulator Addition ;254
14.2.1;INTRODUCTION;254
14.2.2;MATERIALS AND METHODS;255
14.2.3;RESULTS AND DISCUSSION;255
14.2.4;REFERENCES;257
14.3;CHAPTER 46. IDENTIFIABILITY OF BIOKINETIC MODELS IN AN ACTIVATED SLUDGE PROCESS ;258
14.3.1;1. INTRODUCTION;258
14.3.2;2. MATHEMATICAL MODELS;258
14.3.3;3. STRUCTURAL IDENTIFIABILITY;259
14.3.4;4. OPTIMAL EXPERIMENTAL DESIGN;259
14.3.5;5. EXPERIMENTAL VALIDATION OF OED/PE;260
14.3.6;References;261
14.4;CHAPTER 47. SIMULATION OF PROFIT OPTIMIZATION FOR INDUSTRIAL BAKERS YEAST CONTINUOUS FERMENTATION ;262
14.4.1;1. INTRODUCTION;262
14.4.2;2. ON-LINE IDENTIFICATION OF THE MODEL SYSTEM;263
14.4.3;3. SIMULATION OF PROFIT OPTIMIZATION;264
14.4.4;4. DISCUSSION AND CONCLUSION;265
14.4.5;REFERENCES;265
14.5;CHAPTER 48. PERFORMANCE OF INHIBITION FUNCTIONS WITH TOTAL INHIBITION CONCENTRATION ;266
14.5.1;1 Introduction;266
14.5.2;2 Extension of Haldane and Aiba models;267
14.5.3;3 Results and discussion;267
14.5.4;4 Parameter sensitivity analysis;268
14.5.5;5 Conclusions;269
14.5.6;References;269
14.6;CHAPTER 49. MODEL-BASED ESTIMATION OF REACTION RATES IN STIRRED TANK BIOREACTORS ;270
14.6.1;1. THE ESTIMATION PROBLEM;270
14.6.2;2. THE OBSERVER-BASED ESTIMATOR;271
14.6.3;3. STABILITY ANALYSIS;271
14.6.4;4. TUNING AND DYNAMICS OF CONVERGENCE;272
14.6.5;5. PROCEDURE IMPLEMENTATION;273
14.6.6;6. A CASE-STUDY;273
14.6.7;7. RESULTS AND CONCLUSIONS;273
14.6.8;REFERENCES;274
14.7;CHAPTER 50. EDUCATION OF BEER BREWING PHYSIOLOGY AND TECHNOLOGY USING PSI SIMULATION LANGUAGE ;276
14.7.1;1. INTRODUCTION;276
14.7.2;2. PROPERTIES OF PSI LANGUAGE;276
14.7.3;3. TECHNOLOGICAL MODEL OF BEER BREWING;277
14.7.4;4. PHYSIOLOGICAL MODEL OF BEER BREWING;278
14.7.5;5. CONCLUSIONS;279
14.7.6;REFERENCES;279
15;PART X: ADVANCED (INTELLIGENT) CONTROL OF BIOPROCESSES ;280
15.1;CHAPTER 51. SIMULATION SUPPORT FOR OPERATOR CONTROL OF THE WASTEWATER TREATMENT PLANT ;280
15.1.1;INTRODUCTION;280
15.1.2;CONSIDERED PROCESS AND MATHEMATICAL MODEL;281
15.1.3;ON-LINE AND STAND-ALONE SIMULATORS;281
15.1.4;CONCLUDING REMARKS;283
15.1.5;REFERENCES;283
15.2;CHAPTER 52. SLIDING ADAPTIVE CONTROL OF BIOTECHNOLOGICAL PROCESSES ;284
15.2.1;1 INTRODUCTION;284
15.2.2;2 STATEMENT OF THE CONTROL OBJECTIVE;284
15.2.3;3 SLIDING ADAPTIVE REGULATION DESIGN;285
15.2.4;4 SIMULATION RESULTS;286
15.2.5;REFERENCES;287
15.3;CHAPTER 53. BIOREACTOR MODELING AND CONTROL BY PRINCIPAL COMPONENT BASED NEURAL NETWORKS ;288
15.3.1;1. INTRODUCTION;288
15.3.2;2. MODEL OF BAKER'S YEAST PRODUCTION;290
15.3.3;3. CONCLUSIONS;291
15.3.4;REFERENCES;291
15.4;CHAPTER 54. DEVELOPMENT OF A MYSTAT WITH ONLINE-SIMULATION METHODS ;292
15.4.1;1. Introduction;292
15.4.2;2. The Mystat-Concept;292
15.4.3;3. A Mystat-Application;294
15.4.4;4. Decentralized Bioreactor Automation;294
15.4.5;5. Online-Simulation;294
15.4.6;6. The Reaction Model of HCDC;295
15.4.7;7. Online-Simulation of HCDC;296
15.4.8;8. Conclusion;296
15.4.9;9. References;296
15.4.10;10. Mathematical Symbols;296
15.4.11;11. Acknowledgement;297
15.5;CHAPTER 55. STOCK AND CONCENTRATION DYNAMICS OF ACTIVATED SLUDGE PROCESSES ;298
15.5.1;1. INTRODUCTION;298
15.5.2;2. MAIN RESULTS;298
15.5.3;3. SIMULATION AND EXPERIMENTAL RESULTS;299
15.5.4;4. DISCUSSION;300
15.5.5;5. CONCLUSION;301
15.5.6;REFERENCES;301
15.5.7;APPENDIX VERIFICATION OF THE MODEL;301
15.6;CHAPTER 56. STRATEGIES FOR OPTIMAL DISSOLVED OXYGEN (DO) CONTROL ;303
15.6.1;1. INTRODUCTION;303
15.6.2;2. MATERIALS AND METHODS;303
15.6.3;3. RESULTS AND DISCUSSION;305
15.6.4;REFERENCES;306
15.7;CHAPTER 57. COMPUTER AIDED DESIGN OF LINEARIZING CONTROL FOR BIOPROCESSES ;307
15.7.1;1. INTRODUCTION;307
15.7.2;2. LINEARIZING CONTROL;307
15.7.3;3. LINEARIZING CONTROL OF BIOPROCESSES;308
15.7.4;4. BIOCONTROL : A SOFTWARE FOR AUTOMATIC DESIGN OF LINEARIZING CONTROL FOR BIOPROCESSES;309
15.7.5;5. CONCLUSION;311
15.7.6;6. REFERENCES;311
15.8;CHAPTER 58. B10X++ - NEW RESULTS AND CONCEPTIONS CONCERNING THE INTELLIGENT CONTROL OF BIOTECHNOLOGICAL PROCESSES ;312
15.8.1;1. INTRODUCTION;312
15.8.2;2. PROCESS OPTIMIZATION USING BioX;312
15.8.3;3. THE EXTENDED SYSTEM;314
15.8.4;4. CONCLUSIONS;315
15.8.5;5. REFERENCES;315
15.9;CHAPTER 59. FUZZY CONTROL OF THE SUBSTRATE CONCENTRATION IN A BIOREACTOR ;316
15.9.1;1 INTRODUCTION;316
15.9.2;2 DESCRIPTION OF THE PROCESS;316
15.9.3;3 APPLICATION OF FUZZY CONTROL;317
15.9.4;4 CONCLUSIONS;319
15.9.5;REFERENCES;319
15.10;CHAPTER 60. EXPERT SYSTEM WITH FRAME DATA FOR INFERENCE OF SUITABLE CULTURE CONDITION ;320
15.10.1;INTRODUCTION;320
15.10.2;STRUCTURE OF EXPERT SYSTEM;320
15.10.3;CULTURE EXPERIMENTS;322
15.10.4;REFERENCES;323
15.11;CHAPTER 61. NEURAL NETWORKS IN LYSINE FERMENTATION ;324
15.11.1;1. INTRODUCTION;324
15.11.2;2. NEURAL NETWORK PROGRAM;324
15.11.3;3. STATE ESTIMATION;325
15.11.4;4. MULTI-STEP AHEAD PREDICTION;327
15.11.5;ACKNOWLEDGEMENT;327
15.11.6;REFERENCES;327
15.12;CHAPTER 62. IDENTIFICATION AND CONTROL DURING FERMENTATION START-UP - A METHOD TO AVOID SYNCHRONOUS GROWTH IN CONTINOUOUS CULTURES ;328
15.12.1;1. INTRODUCTION;328
15.12.2;2. EXPERIMENTAL PROCEDURE;328
15.12.3;3. RESULTS AND DISCUSSION;329
15.12.4;4. CONCLUSIONS.;331
15.12.5;REFERENCES;331
15.13;CHAPTER 63. MODELING AND CONTROL OF A CONTINUOUS BIOREACTOR WITH CROSS-FLOW FILTRATION ;332
15.13.1;1. INTRODUCTION;332
15.13.2;2. DYNAMIC MODEL OF PROCESS AND ITS CHARACTERISTICS;332
15.13.3;3. CONTROLLABILITY ANALYSIS AND CONTROL STRUCTURE SELECTION;334
15.13.4;4. CONCLUSIONS;336
15.13.5;REFERENCES;336
15.13.6;APPENDIX;336
16;PART XI: MODELLING OF CELL METABOLISM AND ITS REGULATION ;337
16.1;CHAPTER 64. SIMULATION OF CELL CYCLE DEPENDENT GROWTH AND METABOLISM IN ANIMAL CELL FERMENTATION ;337
16.1.1;1. INTRODUCTION;337
16.1.2;2. MODELLING;337
16.1.3;3. SIMULATIONS;338
16.1.4;4. RESULTS AND DISCUSSION;339
16.1.5;5. CONCLUSIONS;340
16.1.6;6. REFERENCES;340
16.2;CHAPTER 65. AN ARTIFICIAL INTELLIGENCE FRAMEWORK OF GENERIC CELL MODELLING ;341
16.2.1;1. INTRODUCTION;341
16.2.2;2. MATHEMATICAL DESCRIPTION OF CELLULAR PROCESSES;342
16.2.3;3.FRAME-BASED KNOWLEDGE REPRESENTATION;342
16.2.4;4. CONSTRUCTION OF THE HYBRID MODEL;342
16.2.5;5. SIMULATION EXAMPLES;343
16.2.6;6. DISCUSSION AND CONCLUSION;344
16.2.7;REFERENCES;344
16.3;CHAPTER 66. STRUCTURED DYNAMIC MODELLING OF VIRAL INFECTION IN CELL CULTURE. ;345
16.3.1;1. INTRODUCTION;345
16.3.2;2. MODELLED SYSTEM;345
16.3.3;3. MODEL STRUCTURE;346
16.3.4;4. MODEL APPLICATION;346
16.3.5;5. CONCLUSIONS;348
16.3.6;REFERENCES;348
16.4;CHAPTER 67. A TRANSFER-FUNCTION REPRESENTATION FOR FLUX RESPONSES OF A CONTROLLED METABOLIC PATHWAY TO INFLUX AND EFFLUX RATES ;349
16.4.1;1. INTRODUCTION;349
16.4.2;2. A BASIC MODEL OF CONTROLLED METABOLIC PATHWAY AND RATE EQUATION;349
16.4.3;3. A TRANSFER-FUNCTION REPRESENTATION FOR THE FLUX RESPONSES OF THE PATHWAY;350
16.4.4;4. CONCLUSIONS;352
16.4.5;REFERENCES;352
16.5;CHAPTER 68. PROTEOLYTIC ACTIVITY OF HYBRIDOMA CULTURES - A SIMULATION STUDY ;353
16.5.1;INTRODUCTION;353
16.5.2;MATERIALS AND METHODS;353
16.5.3;RESULTS;354
16.5.4;DISCUSSION;356
16.5.5;REFERENCES;357
17;PART XII: FUTURE TRENDS IN COMPUTER APPLICATIONS IN BIOTECHNOLOGY ;358
17.1;CHAPTER 69. OPTIMISATION OF BIOTECHNICAL PROCESSES UTILISING INTEGRATED METHODS FOR EXPERIMENTAL AND SAMPLING DESIGN ;358
17.1.1;1. INTRODUCTION;358
17.1.2;2. EXPERIMENTAL DESIGN METHODS APPLIED TO PROCESS OPTIMISATION;358
17.1.3;3. CONCLUSIONS;361
17.1.4;REFERENCES;361
17.2;CHAPTER 70. APPLICATION OF SYMBOLIC COMPUTATION METHODS FOR STABILITY ANALYSIS OF MICROBIAL MIXED POPULATIONS ;362
17.2.1;DYNAMICS OF A CHEMOSTAT IN WHICH ORGANISMS COMPETE FOR A COMMON SUBSTRATE;363
17.2.2;STABILITY ANALYSIS OF SYNTROPHIC INTERACTION IN A TWO SPECIES MIXED POPULATION DEGRADING SULFANILIC ACID;364
17.2.3;SUMMARY;365
17.2.4;REFERENCES;365
17.2.5;APPENDIX;365
17.3;CHAPTER 71. BEST-KIT; Development of Biochemical Engineering System Analyzing Tool-KIT (Kyushu Institute of Technology) ;367
17.3.1;1. Introduction;367
17.3.2;2 Design of Biosiniulalor;368
17.3.3;3 Scheme Editing Module;368
17.3.4;4 Automatic Derivation of Differential Equations;368
17.3.5;5 Visualization of dynamic response;370
17.3.6;REFERENCES;370
17.4;CHAPTER 72. OBJECT ORIENTED DESIGN OF AN ON LINE DATA MANAGER FOR HYBRID BIOPROCESS CONTROL ;371
17.4.1;1. INTRODUCTION;371
17.4.2;2. TIME SERIES IN BIOPROCESSES;372
17.4.3;3. SYSTEM ARCHITECTURE;373
17.4.4;4. IMPLEMENTATION DETAILS;374
17.4.5;5. CONCLUSION;374
17.4.6;6. REFERENCES;374
18;AUTHOR INDEX;375
19;KEYWORD INDEX;377