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

Alberton, 2010, Sequential experimental design based on multiobjective optimization procedures, Chem. Eng. Sci., 65, 5482, 10.1016/j.ces.2010.07.010 Balsa-Canto, 2008, Computing optimal dynamic experiments for model calibration in predictive microbiology, J. Food Process Eng., 32, 186, 10.1111/j.1745-4530.2007.00147.x Balsa-Canto, 2001, Dynamic optimization of chemical and biochemical processes using restricted second-order information, Comput. Chem. Eng., 25, 539, 10.1016/S0098-1354(01)00633-0 Bauer, 2000, Numerical methods for optimum experimental design in DAE systems, J. Comput. Appl. Math., 120, 1, 10.1016/S0377-0427(00)00300-9 Biegler, 1984, Solution of dynamic optimization problems by successive quadratic programming and orthogonal collocation, Comput. Chem. Eng., 8, 243, 10.1016/0098-1354(84)87012-X Biegler, 2007, An overview of simultaneous strategies for dynamic optimization, Chem. Eng. Process.: Process Intensification, 46, 1043, 10.1016/j.cep.2006.06.021 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. Das, 1997, A closer look at drawbacks of minimizing weighted sums of objectives for Pareto set generation in multicriteria optimization problems, Struct. Optim., 14, 63, 10.1007/BF01197559 Das, 1998, Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems, SIAM J. Optim., 8, 631, 10.1137/S1052623496307510 Deb, 2002, A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 181, 10.1109/4235.996017 Donckels, 2010, An ideal point method for the design of compromise experiments to simultaneously estimate the parameters of several rival models, Chem. Eng. Sci., 65, 1705, 10.1016/j.ces.2009.11.009 Fisher, 1935 Franceschini, 2008, Model-based design of experiments for parameter precision: state of the art, Chem. Eng. Sci., 63, 4846, 10.1016/j.ces.2007.11.034 Franceschini, 2008, Novel anticorrelation criteria for model-based experiment design: theory and formulations, AIChE J., 54, 1009, 10.1002/aic.11429 Haimes, 1971, On a bicriterion formulation of the problems of integrated system identification and system optimization, IEEE Trans. Syst. Man Cybernetics, SMC-1, 296, 10.1109/TSMC.1971.4308298 Houska, 2011, ACADO toolkit—an open-source framework for automatic control and dynamic optimization, Optimal Control Appl. Methods, 32, 298, 10.1002/oca.939 Körkel, 2004, Numerical methods for optimal control problems in design of robust optimal experiments for nonlinear dynamic processes, Optim. Methods Software J., 19, 327, 10.1080/10556780410001683078 Kud, 2010, 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., 65, 4194, 10.1016/j.ces.2010.04.025 Leineweber, 2003, An efficient multiple shooting based reduced SQP strategy for large-scale dynamic process optimization. Part I. Theoretical aspects, Comput. Chem. Eng., 27, 157, 10.1016/S0098-1354(02)00158-8 Ljung, 1999 Logist, 2010, Fast Pareto set generation for nonlinear optimal control problems with multiple objectives, Struct. Multidisciplinary Optim., 42, 591, 10.1007/s00158-010-0506-x Logist, 2010, Efficient multiple objective optimal control of dynamic systems with integer controls, J. Process Control, 20, 810, 10.1016/j.ces.2011.06.018 Logist, 2011, Robust multi-objective optimal control of uncertain (bio)chemical processes, Chem. Eng. Sci., 66, 4670, 10.1016/j.jprocont.2010.04.009 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. Logist, 2012, Novel insights for multi-objective optimisation in engineering using normal boundary intersection and (enhanced) normalised normal constraint, Struct. Multidisciplinary Optim., 45, 417, 10.1007/s00158-011-0698-8 Messac, 2003, The normalized normal constraint method for generating the Pareto frontier, Struct. Multidisciplinary Optim., 25, 86, 10.1007/s00158-002-0276-1 Miettinen, 1999 Pukelsheim, 1993 Rangaiah, 2009 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〉. 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. Sanchis, 2008, A new perspective on multiobjective optimization by enhanced normalized normal constraint method, Struct. Multidisciplinary Optim., 36, 537, 10.1007/s00158-007-0185-4 Sargent, 1978, The development of an efficient optimal control package, 158 Van Derlinden, 2010, Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature, J. Theor. Biol., 264, 347, 10.1016/j.jtbi.2010.01.003 Vassiliadis, 1994, Solution of a class of multistage dynamic optimization problems. 1. Problems without path constraints, Ind. Eng. Chem. Res., 33, 2111, 10.1021/ie00033a014 Vassiliadis, 1994, Solution of a class of multistage dynamic optimization problems. 2. Problems with path constraints, Ind. Eng. Chem. Res., 33, 2123, 10.1021/ie00033a015 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. Versyck, 1999, Feed rate optimization for bed-batch bioreactors: from optimal process performance to optimal parameter estimation, Chem. Eng. Commun., 172, 107, 10.1080/00986449908912766 Walter, 1997