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λͺ¨λΈ 곡μ λΆμΌμΉ μν©μμ νν μλ¬Ό 곡μ μ λ°μ΄ν° κΈ°λ° μ΅μ νλ₯Ό μν κ°μ ν μ μλ²
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Όλ¬Έ (λ°μ¬)-- μμΈλνκ΅ λνμ : 곡과λν ννμ물곡νλΆ, 2018. 2. μ΄μ’
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νμλ€.1. Introduction 26
1.1 Background and motivation 26
1.2 Literature review 28
1.2.1 Real time optimization 28
1.2.2 Optimality loss by model-plant mismatch 32
1.2.3 Methods to overcome the model-plant mismatch 33
1.3 Major contributions of this thesis 42
1.4 Outline of this thesis 44
2. Data-driven optimization via modifier adaptation 45
2.1 Introduction 45
2.2 Satisfaction of necessary conditions of optimality 47
3. Three issues of modifier adaptation 50
3.1 Issue 1: Frequent and large disturbance 50
3.1.1 Design of feedforward decision maker using machine learning and historical disturbance data 50
3.1.2 Illustrative example 70
3.1.3 Run-to-run optimization of bioprocess 82
3.1.4 Concluding remarks 88
3.2 Issue 2: Experimental gradient estimation under noisy and multivariate condition 89
3.2.1 Importance of gradient estimation for the modifier adaptation 89
3.2.2 Motivational example: Run-to-run optimization of bioreactor 91
3.2.3 Conventional experimental gradient estimation 96
3.2.4 Regression based gradient estimation and its application to modifier adaptation 99
3.2.5 Concluding remarks 129
3.3 Issue 3: A novel structure of modifier adaptation for robustness 130
3.3.1 Feasibility and structural robustness 130
3.3.2 Proposition of new structural modifier adaptation 135
3.3.3 Illustrative example 149
3.3.4 Concluding remarks 155
4. Conclusions and future works 156
4.1 Conclusions 156
4.2 Future works 157Docto