Corynebacterium glutamicum - JUWEL - Forschungszentrum Jülich

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4.2. First Case: Fermentation Ferm2, whereas the substrate concentrations according to Ferm1 were already very low for a long time. In such a situation it can be expected that the model variance is very high for model Ferm2 and rather low for Ferm1, which might explain the suggested experiment. The fact that the experiment lasts shorter than the maximal allowed time, is caused by the bounds on the expected results, as mentioned before. This limited experiment provided a better discriminating experiment according to the used criterion, than experiments which would last longer. It is noteworthy that none of the experiments which were suggested by the other criteria, including the longer lasting experiments, would lead to a better value for the current criterion. Buzzi-Ferraris and Forzatti (BF) The designed experiment according to the BF criterion is shown in figure 4.4, illustrated by the expected concentrations according to the competing models. Again, the suggested experiment was shorter than the maximal duration. The same remarks as stated for the experiment according to the BH criterion hold here, including the fact that all other suggested experiments result in lower values for the BF criterion than the suggested experiment. The criterion was with 5.9 · 10 5 by far high enough to exceed the amount of responses (here 63), which was suggested by Buzzi-Ferraris and Forzatti as a limit. Chen and Asprey (CA) The designed experiment according to the CA criterion is shown in figure 4.5, illustrated by the expected concentrations according to the competing models. Since all measurements at different time-points are regarded to be independent, also with respect to the model variance, the criterion consists of a summation over all timepoints and can readily be shown over time as is done here in figure 4.5(c), showing that basically only the last measurement determines the criterion (98.67% of the criterion is accounted for by the final measurement point). The optimized criterion is with 15.34 too small according to the limit which was suggested in the original paper by Chen and Asprey, who suggested not to perform the experiment when the criterion would not exceed the amount of measured values, which are 39 in the suggested experiment. However, if we would only measure the state variables at the final time-point in this experiment, the criterion would be 15.15 and exceed the 3 measured values easily, which is a clear example of the problem with the limit as suggested by Chen and Asprey. Once again, the suggested experiment was shorter than the maximal duration. None of the experiments, suggested by the other design criteria, would give a better value of the CA criterion. 67
4. Simulative Comparison of Model Discriminating Design Criteria Feed [L/h] C X [g/L] C S [mM] C P [mM] 0.06 0.03 0 0 10 20 time [h] 30 40 50 25 (a) Feedrate in L/h of a 3889 mM substrate solution. 0 0 400 10 20 30 40 200 0 0 1000 10 20 30 40 500 0 0 10 20 time [h] 30 40 (b) Expected concentrations of biomass (CX), substrate (CS) and product (CP ) according to model Ferm1 with closed markers and according to Ferm2 with open markers. Figure 4.4.: Designed experiment according to the BF criterion. Initial substrate concentration: 55 mM. 68
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Lebenswissenschaften Life Sciences
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Forschungszentrum Jülich GmbH Inst
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’Therefore, mathematical modeling
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Contents 3.4. Application: L-Valine
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Contents 4
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1. Introduction need for improved m
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2. Theory In a batch process, all s
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2. Theory 1997; Goncalves et al., 2
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2. Theory For batch fermentations,
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2. Theory with Yps being the yield
Page 23 and 24: 2. Theory Inhibition Some catalytic
Page 25 and 26: 2. Theory The method of least squar
Page 27 and 28: 2. Theory evaluation can be made by
Page 29 and 30: 2. Theory We define the measured or
Page 31 and 32: 2. Theory build in the model. An ob
Page 33 and 34: 2. Theory nations, the researcher i
Page 35 and 36: 2. Theory W = J · Covθ · J T (2.
Page 37 and 38: 2. Theory arg max ξ � m� m�
Page 39 and 40: 2. Theory with all measured values
Page 41 and 42: 2. Theory which can be estimated us
Page 43 and 44: 2. Theory One option to account for
Page 45 and 46: 2. Theory θ 2 $θ 2 0 area ≅ det
Page 47 and 48: 2. Theory designed using a paramete
Page 49 and 50: 2. Theory proteome14 (Hermann et al
Page 51 and 52: 2. Theory 44
Page 53 and 54: 3. Dynamic Modeling Framework itera
Page 55 and 56: 3. Dynamic Modeling Framework 3.3.
Page 57 and 58: 3. Dynamic Modeling Framework 3.3.3
Page 59 and 60: 3. Dynamic Modeling Framework 52 du
Page 61 and 62: 3. Dynamic Modeling Framework the c
Page 63 and 64: 3. Dynamic Modeling Framework For d
Page 65 and 66: 3. Dynamic Modeling Framework subse
Page 67 and 68: 3. Dynamic Modeling Framework 60
Page 69 and 70: 4. Simulative Comparison of Model D
Page 73: 4. Simulative Comparison of Model D
Page 95 and 96: 5. L-Valine Production Process Deve
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5. L-Valine Production Process Deve
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A. Nomenclature Table A.1.: Symbols
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Table A.1.: Symbols and Abbreviatio
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Table A.1.: Symbols and Abbreviatio
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B. Ratio between OD600 and Dry Weig
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C. The Influence of the Oxygen Supp
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The measurements of amino acids and
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kiv [mM] lac [mM] 15 10 5 0 0 10 20
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OTR, CTR 0.06 0.04 0.02 0 0 10 20 3
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DO properly at a low value in dynam
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D. Dilution for Measurement of Amin
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E. Models used in the First Model D
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Model 1.6 Model 1.6 is similar to m
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Table E.1: Model Parameters of the
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Table E.2: Model Parameters of the
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161
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Model 2.2 Model 2.2 is similar to m
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Table F.1.: Model Parameters of the
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Table F.2.: Estimated Lag-Times of
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Table F.3: Model Parameters of the
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Table F.4.: Estimated Lag-Times of
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G. Alternative Off-line Optimized P
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G.2. Overall Yield of Product on Su
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Summary Fast development of product
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Zusammenfassung Es ist wichtig, die
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Acknowledgements This thesis result
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Bibliography K. Akashi, H. Shibai,
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Bibliography A. Bodalo-Santoyo, J.L
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Bibliography A.J. Cooper, J.Z. Gino
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Bibliography A.G. Fredrickson, R.D.
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Bibliography W.G. Hunter and A.M. R
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Bibliography A.C. McHardy, A. Tauch
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Bibliography E. Radmacher, A. Vaits
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Bibliography K.J. Versyck, J.E. Cla
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Corynebacterium glutamicum - JUWEL - Forschungszentrum Jülich

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