Optimal experiment design for dynamic bioprocesses: A multi-objective approach

Chemical Engineering Science

Volumn 78, Issue , 2012, Pages 82-97

  Telen, D ^a   Logist, F ^a   Van Derlinden, E ^a   Tack, I ^a   Van Impe, J ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Bioprocess; Criterion selection; Dynamic optimisation; Multi objective optimisation; Optimal experiment design; Parameter estimation

Indexed keywords

BIOPROCESSES; COST-INTENSIVE; CRITERION SELECTION; DYNAMIC PROCESS; ENZYME PRODUCTION; FED-BATCH BIOREACTORS; INDISPENSABLE TOOLS; LOTKA-VOLTERRA; MULTI OBJECTIVE; OBJECTIVE FUNCTIONS; OPTIMAL EXPERIMENT DESIGN; OPTIMAL EXPERIMENT DESIGN FOR PARAMETER ESTIMATION; OPTIMISATIONS; PARAMETER ESTIMATE;

EXPERIMENTS; FISHER INFORMATION MATRIX; MULTIOBJECTIVE OPTIMIZATION; PARAMETER ESTIMATION;

DESIGN;

EID: 84861725475     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2012.05.002     Document Type: Article

References (39)

1. Alberton A.L., Schwaab M., Biscaia E.C., Pinto J.C. Sequential experimental design based on multiobjective optimization procedures. Chem. Eng. Sci. 2010, 65:5482-5494.
2. Balsa-Canto E., Alonso A., Banga J. Computing optimal dynamic experiments for model calibration in predictive microbiology. J. Food Process Eng. 2008, 32(2):186-206.
3. Balsa-Canto E., Banga J., Alonso A., Vassiliadis V. Dynamic optimization of chemical and biochemical processes using restricted second-order information. Comput. Chem. Eng. 2001, 25(4-6):539-546.
4. Bauer I., Bock G., Korkel S., Schloder J. Numerical methods for optimum experimental design in DAE systems. J. Comput. Appl. Math. 2000, 120:1-25.
5. Biegler L. Solution of dynamic optimization problems by successive quadratic programming and orthogonal collocation. Comput. Chem. Eng. 1984, 8:243-248.
6. Biegler L. An overview of simultaneous strategies for dynamic optimization. Chem. Eng. Process.: Process Intensification 2007, 46:1043-1053.
7. Bock, H., Plitt, K., 1984. A multiple shooting algorithm for direct solution of optimal control problems. In: Proceedings of the 9th IFAC World Congress. Pergamon Press, Budapest.
8. Das I., Dennis J. A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems. Struct. Optim. 1997, 14:63-69.
9. Das I., Dennis J. Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J. Optim. 1998, 8:631-657.
10. Deb K., Pratap A., Agarwal S., Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 2002, 6:181-197.
11. Donckels B., De Pauw D., Vanrolleghem P., De Baets B. An ideal point method for the design of compromise experiments to simultaneously estimate the parameters of several rival models. Chem. Eng. Sci. 2010, 65:1705-1719.
12. Fisher R. The Design of Experiments 1935, Oliver & Boyd.
13. Franceschini G., Macchietto S. Model-based design of experiments for parameter precision: state of the art. Chem. Eng. Sci. 2008, 63:4846-4872.
14. Franceschini G., Macchietto S. Novel anticorrelation criteria for model-based experiment design: theory and formulations. AIChE J. 2008, 54:1009-1024.
15. Haimes Y., Lasdon L., Wismer D. On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Trans. Syst. Man Cybernetics 1971, SMC-1:296-297.
16. Houska B., Ferreau H., Diehl M. ACADO toolkit-an open-source framework for automatic control and dynamic optimization. Optimal Control Appl. Methods 2011, 32:298-312.
17. Körkel S., Kostina E., Bock H., Schlöder J. Numerical methods for optimal control problems in design of robust optimal experiments for nonlinear dynamic processes. Optim. Methods Software J. 2004, 19(3-4):327-338.
18. Kud A., Körkel S., Maixner S. A cubic equation of state based on saturated vapour modeling and the application of model-based design of experiments for its validation. Chem. Eng. Sci. 2010, 65:4194-4207.
19. Leineweber D., Bauer I., Bock H., Schlöder J. An efficient multiple shooting based reduced SQP strategy for large-scale dynamic process optimization. Part I. Theoretical aspects. Comput. Chem. Eng. 2003, 27:157-166.
20. Ljung L. System Identification: Theory for the User 1999, Prentice Hall.
21. Logist F., Houska B., Diehl M., Van Impe J. Fast Pareto set generation for nonlinear optimal control problems with multiple objectives. Struct. Multidisciplinary Optim. 2010, 42:591-603.
22. Logist F., Sager S., Kirches C., Van Impe J.F. Efficient multiple objective optimal control of dynamic systems with integer controls. J. Process Control 2010, 20(7):810-822.
23. Logist F., Houska B., Diehl M., Van Impe J. Robust multi-objective optimal control of uncertain (bio)chemical processes. Chem. Eng. Sci. 2011, 66:4670-4682.
24. Logist, F., Telen, D., Van Derlinden, E., Van Impe, J., 2011b. Multi-objective optimisation approach to optimal experiment design using ACADO toolkit. In: Proceedings of the 21st European Symposium on Computer Aided Process Engineering. Elsevier. pp. 462-466.
25. Logist F., Van Impe J. Novel insights for multi-objective optimisation in engineering using normal boundary intersection and (enhanced) normalised normal constraint. Struct. Multidisciplinary Optim. 2012, 45:417-431.
26. Messac A., Ismail-Yahaya A., Mattson C. The normalized normal constraint method for generating the Pareto frontier. Struct. Multidisciplinary Optim. 2003, 25:86-98.
27. Miettinen K. Nonlinear Multiobjective Optimization 1999, Kluwer Academic Publishers, Boston.
28. Pukelsheim F. Optimal Design of Experiments 1993, John Wiley & Sons, Inc., New York.
29. Rangaiah G.P. Multi-Objective Optimization-Techniques and Applications in Chemical Engineering 2009, World Scientific Publishing, Singapore.
30. Sager, S., 2012. Sampling decisions in optimum experimental design in the light of Pontryagin's maximum principle. SIAM J. Control Optim., submitted for publication. http://www.optimization-online.org/DB_HTML/2011/05/3037.html.
31. Sager, S., Bock, H.G., Diehl, M., Reinelt, G., Schlöder, J.P., 2009. Numerical methods for optimal control with binary control functions applied to a Lotka-Volterra type fishing problem. In: Seeger, A. (Ed.), Recent Advances in Optimization. Lectures Notes in Economics and Mathematical Systems, vol. 563. Springer, Heidelberg. pp. 269-289.
32. Sanchis J., Marinez M., Blasco X., Salcedo J. A new perspective on multiobjective optimization by enhanced normalized normal constraint method. Struct. Multidisciplinary Optim. 2008, 36:537-546.
33. Sargent R., Sullivan G. The development of an efficient optimal control package. Proceedings of the 8th IFIP Conference on Optimization Techniques 1978, 158-168. Springer-Verlag, Heidelberg. J. Stoer (Ed.).
34. Van Derlinden E., Bernaerts K., Van Impe J. Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature. J. Theor. Biol. 2010, 264:347-355.
35. Vassiliadis V., Sargent R., Pantelides C. Solution of a class of multistage dynamic optimization problems. 1. Problems without path constraints. Ind. Eng. Chem. Res. 1994, 33:2111-2122.
36. Vassiliadis V., Sargent R., Pantelides C. Solution of a class of multistage dynamic optimization problems. 2. Problems with path constraints. Ind. Eng. Chem. Res. 1994, 33:2123-2133.
37. Versyck, K., Van Impe, J. 1998. Trade-offs in design of fed-batch experiments for optimal estimation of biokinetic parameters. In: Proceedings of the 1998 IEEE International Conference on Control Applications, September. Trieste, Italy.
38. Versyck K., Van Impe J. Feed rate optimization for bed-batch bioreactors: from optimal process performance to optimal parameter estimation. Chem. Eng. Commun. 1999, 172:107-124.
39. Walter E., Pronzato L. Identification of Parametric Models from Experimental Data 1997, Springer, Paris.