Documents disponibles écrits par cet auteur

Design of dynamic experiments in modeling for optimization of batch processes / Ernesto C. Martinez in Industrial & engineering chemistry research, Vol. 48 N° 7 (Avril 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 7 (Avril 2009) . - pp. 3453–3465 Titre : Design of dynamic experiments in modeling for optimization of batch processes Type de document : texte imprimé Auteurs : Ernesto C. Martinez, Auteur ; Mariano D. Cristaldi, Auteur ; Ricardo J. Grau, Auteur Année de publication : 2009 Article en page(s) : pp. 3453–3465 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Designing  dynamic  experiments  Hamilton−Jacobi−Bellman  optimality  equation  Model-based  policy  iteration  algorithm Résumé : Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speeding up the development of innovative products and processes. Modeling for optimization is proposed as a systematic approach to bias data gathering for iterative policy improvement through experimental design using first-principles models. Designing dynamic experiments that are optimally informative in order to reduce the uncertainty about the optimal operating conditions is addressed by integrating policy iteration based on the Hamilton−Jacobi−Bellman optimality equation with global sensitivity analysis. A conceptual framework for run-to-run convergence of a model-based policy iteration algorithm is proposed. Results obtained in the fed-batch fermentation of penicillin G are presented. The well-known Bajpai and Reuss bioreactor model validated with industrial data is used to increase on a run-to-run basis the amount of penicillin obtained by input policy optimization and selective (re)estimation of relevant model parameters. A remarkable improvement in productivity can be gain using a simple policy structure after only two modeling runs despite initial modeling uncertainty. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8000953 [article] Design of dynamic experiments in modeling for optimization of batch processes [texte imprimé] / Ernesto C. Martinez, Auteur ; Mariano D. Cristaldi, Auteur ; Ricardo J. Grau, Auteur . - 2009 . - pp. 3453–3465. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 7 (Avril 2009) . - pp. 3453–3465 Mots-clés : Designing  dynamic  experiments  Hamilton−Jacobi−Bellman  optimality  equation  Model-based  policy  iteration  algorithm Résumé : Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speeding up the development of innovative products and processes. Modeling for optimization is proposed as a systematic approach to bias data gathering for iterative policy improvement through experimental design using first-principles models. Designing dynamic experiments that are optimally informative in order to reduce the uncertainty about the optimal operating conditions is addressed by integrating policy iteration based on the Hamilton−Jacobi−Bellman optimality equation with global sensitivity analysis. A conceptual framework for run-to-run convergence of a model-based policy iteration algorithm is proposed. Results obtained in the fed-batch fermentation of penicillin G are presented. The well-known Bajpai and Reuss bioreactor model validated with industrial data is used to increase on a run-to-run basis the amount of penicillin obtained by input policy optimization and selective (re)estimation of relevant model parameters. A remarkable improvement in productivity can be gain using a simple policy structure after only two modeling runs despite initial modeling uncertainty. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8000953