Optimal experimental design for an enzymatic biodiesel production system

IFAC-PapersOnLine

Volumn 28, Issue 8, 2015, Pages 1258-1263

  Yu, Hui ^a   Yue, Hong ^a   Halling, Peter ^a  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

Author keywords

Enzymatic biodiesel reaction system; Local sensitivity analysis (LSA); Measurement set selection; Optimal experimental design (OED); Optimal sampling strategy; Parameter estimation

Indexed keywords

BIODIESEL; CONVEX OPTIMIZATION; SENSITIVITY ANALYSIS; STATISTICS;

CONVEX OPTIMIZATION PROBLEMS; INTERIOR POINT ALGORITHM; LOCAL SENSITIVITY ANALYSIS; OPTIMAL EXPERIMENTAL DESIGNS; OPTIMAL SAMPLING; OPTIMAL SAMPLING DESIGN; PARAMETER ESTIMATION ERRORS; REACTION SYSTEM;

PARAMETER ESTIMATION;

EID: 84992494573     PISSN: None     EISSN: 24058963     Source Type: Conference Proceeding    
DOI: 10.1016/j.ifacol.2015.09.141     Document Type: Conference Paper

References (27)

1. Asprey, S. P. and Macchietto, S. (2002). Designing robust optimal dynamic experiments. Journal of Process Control, 12(4), 545-556.
2. Asyali, M. H. (2010). Design of optimal sampling times for pharmacokinetic trials via spline approximation. Turk. J. Elec. Eng. & Comp. Sci., 18(6), 1019-1030.
3. Atkinson, A. C. and Bogacka, B. (2002). Compound and other optimum designs for systems of nonlinear differential equations arising in chemical kinetics. Chemometr. Intell. Lab., 61(1), 17-33.
4. Balsa-Canto, E., Rodriguez-Fernandez, M. and Banga, J. R. (2007). Optimal design of dynamic experiments for improved estimation of kinetic parameters of thermal degradation. J. Food. Eng., 82(2), 178-188.
5. Banga, J. R., Versyck, K. J. and Van Impe, J. F. (2002). Computation of optimal identification experiments for nonlinear dynamic process models: a stochastic global optimization. Ind. Eng. Chem. Res., 41(10), 2425-2430.
6. Bauer, I., Bock, H. G., Körkel, S. and Schlöder, J. P. (2000). Numerical methods for optimum experimental design in DAE systems. J. Comput. Appl. Math., 120(1), 1-25.
7. Boyd, S. P. and Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.
8. Brun, R., Reichert, P. and Künsch, H. R. (2001). Practical identifiability analysis of large environmental simulation models. Water Resour. Res., 37(4), 1015-1030.
9. Chaloner, K. and Verdinelli, I. (1995). Bayesian experimental design: A review. Statistical Science, 10(3), 273-304.
10. Faller, D., Klingmüller, U. and Timmer, J. (2003). Simulation methods for optimal experimental design in systems biology. Simulation, 79(12), 717-725.
11. Franceschini, G. and Macchietto, S. (2008). Model-based design of experiments for parameter precision: state of the art. Chem. Eng. Sci., 63(19), 4846-4872.
12. Hagen, D. R., White, J. K. and Tidor, B. (2013). Convergence in parameters and predictions using computational experimental design. Interf. focus, 3(4).
13. He, F., Brown, M. and Yue, H. (2010). Maximin and Bayesian robust experimental design for measurement set selection in modelling biochemical regulatory systems. Int. J. Robust and Nonl. Contr., 20(9), 1059-1078.
14. Jia, J. and Yue, H. (2008). Model simplification of signal transduction pathway networks via a hybrid inference strategy. The 17th IFAC Conference. Seoul, Korea, 10307-10312.
15. Kreutz, C. and Timmer, J. (2009). Systems biology: experimental design. FEBS Journal, 276(4), 923-942.
16. Kutalik, Z., Cho, K.-H. and Wolkenhauer, O. (2004). Optimal sampling time selection for parameter estimation in dynamic pathway modeling. Biosystems, 75(1), 43-55.
17. Ljung, L. (1998). System Identification. Springer.
18. Maria, G. (2004). A review of algorithms and trends in kinetic model identification for chemical and biochemical systems. Chem. Biochem. Eng. Q., 18(3), 195-222.
19. Martinez, E. C., Cristaldi, M. D. and Grau, R. J. (2009). Design of dynamic experiments in modeling for optimization of batch processes. Ind. Eng. Chem. Res., 48(7), 3453-3465.
20. Pagendam, D. and Pollett, P. (2013). Optimal design of experimental epidemics. J. Stat. Plan. Infer., 143(3), 563-572.
21. Price, J., Hofmann, B., Silva, V. T., Nordblad, M., Woodley, J. M. and Huusom, J. K. (2014). Mechanistic modelling of biodiesel production using a liquid lipase formulation. Biotechnol. Progr.
22. Price, J. A., Nordblad, M., Woodley, J. and Huusom, J. K. (2013). Fed-batch feeding strategies for enzymatic biodiesel production. 19th World Congress of the International Federation of Automatic Control.
23. Sandink, C., Mcauley, K. and Mclellan, P. (2001). Selection of parameters for updating in on-line models. Ind. Eng. Chem. Res., 40(18), 3936-3950.
24. Sun, C. and Hahn, J. (2006). Parameter reduction for stable dynamical systems based on Hankel singular values and sensitivity analysis. Chem. Eng. Sci., 61(16), 5393-5403.
25. Yao, K. Z., Shaw, B. M., Kou, B., Mcauley, K. B. and Bacon, D. (2003). Modeling Ethylene/Butene copolymerization with multi-site catalysts: parameter estimability and experimental design. Polym. React. Eng., 11(3), 563-588.
26. Yue, H., Brown, M., He, F., Jia, J. and Kell, D. B. (2008). Sensitivity analysis and robust experimental design of a signal transduction pathway system. Int. J. Chem. Kinet., 40(11), 730-741.
27. Yue, H., Brown, M., Knowles, J., Wang, H., Broomhead, D. S. and Kell, D. B. (2006). Insights into the behaviour of systems biology models from dynamic sensitivity and identifiability analysis: a case study of an NF-κB signalling pathway. Molec. Bio., 2(12), 640-649.