| Titre : | Design of dynamic experiments in modeling for optimization of batch processes (2009) |
| Auteurs : | Ernesto C. Martinez, Auteur ; Mariano D. Cristaldi, Auteur ; Ricardo J. Grau, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Industrial & engineering chemistry research (Vol. 48 N° 7, Avril 2009) |
| Article en page(s) : | pp. 3453–3465 |
| Note générale : | Chemical engineering |
| Langues : | Anglais |
| Tags : | 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 |
