Iterative operating policy optimization strategy for bioreactors

MARIANO CRISTALDI; ERNESTO C. MARTINEZ; RICARDO GRAU

Mar del Plata

Congreso; VI Congreso Argentino de Ingeniería Química; 2010

Asociación Argentina de Ingenieros Químicos

The fast development and fierce competence for being the first in launching biotech product have gone waking up biotechnological companies. The importance of accounting with an optimization and scaling up methodology as a quick and safe way of improving bioreactors production appear as a very need at industrial scenario. Here, an iterative algorithm for operating policy of batch processes is proposed. Convergence analysis of iterative identification-optimization schemes is a key issue in modeling for optimization of batch processes. In this work, it is formally shown that for convergence is sufficient to guarantee that parametric uncertainty is increasingly reduced on a run-to-run basis. The integration of global sensivity analysis with confidence interval boostrapping in the design of a convergent algorithm for model-based policy iteration is proposed. A simple bioprocess is used to exemplify run-to-run improvement.