Contents

1 Introduction 1

1.1 Fermentation Processes 1

1.2 Fed-Batch Fermentation Processes by Conventional Methods . . 4

1.3 Artificial Intelligence for Optimal Fermentation Control 7

1.4 Why is Artificial Intelligence Attractive for Fermentation Control 12

1.5 Why is Experimental Investigation Important for Fermentation Study 14

1.6 Contributions of the Book 14

1.7 Book Organization 14

2 Optimization of Fed-batch Culture 17

2.1 Introduction 17

2.2 Proposed Model and Problem Formulation 18

2.3 Genetic Algorithm 19

2.4 Optimization using Genetic Algorithms based on the Process Model 20

2.5 Numerical Results 21

2.6 Conclusions 27

3 On-line Identification and Optimization 29

3.1 Introduction 29

3.2 Fed-batch Model and Problem Formulation 30

3.3 Methodology Proposed 31

3.4 Numerical Results 32

3.5 Summary 40

4 On-line Softsensor Development 41

4.1 Introduction 41

4.2 Softsensor Structure Determination and Implementation 42

VIII Contents

4.3 Experimental Verification 49

4.4 Conclusions 56

5 Optimization based on Neural Models 57

5.1 Introduction 57

5.2 The Industry Baker's Yeast Fed-batch Bioreactor 58

5.3 Development of Dynamic Neural Network Model 58

5.4 Biomass Predictions using the Neural Model 62

5.5 Optimization of Feed Rate Profiles 66

5.6 Summary 70

6 Experimental Validation of Neural Models 71

6.1 Introduction 71

6.2 Dynamic Models 72

6.3 Experimental Procedure 74

6.4 Model Identification 80

6.5 Conclusions 89

7 Designing and Implementing Optimal Control 91

7.1 Definition of an Optimal Feed Rate Profile 91

7.2 Formulation of the Optimization Problem 94

7.3 Optimization Procedure 95

7.4 Optimization Results and Discussion 97

7.5 Conclusions 108

8 Conclusions 109

8.1 General Conclusions 109

8.2 Suggestions for Future Research 110

A A Model of Fed-batch Culture of Hybridoma Cells 111

B An Industrial Baker's Yeast Fermentation Model 113

References 117

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