INTEC   05402
INSTITUTO DE DESARROLLO TECNOLOGICO PARA LA INDUSTRIA QUIMICA
Unidad Ejecutora - UE
artículos
Título:
“Design of Dynamic Experiments in Modeling for Optimization of Batch Processes”
Autor/es:
ERNESTO C. MARTINEZ; MARIANO D. CRISTALDI; RICARDO J. A. GRAU
Revista:
Industrial and Engineering Chemistry Research
Editorial:
ACS Publications
Referencias:
Año: 2009 vol. 48 p. 3453 - 3465
ISSN:
0888-5885
Resumen:
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.-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.