Data Reconciliation Using Neural Networks for the Determination of KLa

The oxygen mass transfer coefficient (K

L

a) is of paramount importance in conducting aerobic fermentation. K

L

a also serves to compare the efficiency of bioreactors and their mixing devices as well as being an important scale-up factor. In submerged fermentations, four methods are available to estimate the overall oxygen mass transfer coefficient (K

L

a): the dynamic method, the stationary method based on a previous determination of the oxygen uptake rate (Q

O2

X), the gaseous oxygen balance and the carbon dioxide balance. Each method provides a distinct estimation of the value of K

L

a. Data reconciliation can be used to obtain the most probable value of K

L

a by minimizing an objective function that includes measurement terms and oxygen conservation models, each being weighted according to their level of confidence. Another alternative, for a more rapid determination of K

L

a, is using a neural network which has been previously trained to predict K

L

a from the series of oxygen conservation models. Results obtained with this new approach show that K

L

a can be predicted rapidly and gives values that are equivalent to those obtained with the conventional data reconciliation algorithm.