A set of experiments has been performed in an industrial 112 m(3) fermentor in order to get a complete map of oxygen concentration and temperature distribution in the system. Five fermentations of non-Newtonian broths of two different strains, in various operating conditions, were examined. A simple model has been developed which takes into account both the mixing and the mass-transfer properties of the fermentor, and a dimensionless parameter has been identified which is sufficient to characterize the oxygen axial distribution in the reactor in any operating condition.
During the course of fermentation, online measuring procedures able to estimate the performance of the current operation are highly desired. Unfortunately, the poor mechanistic understanding of most biologic systems hampers attempts at direct online evaluation of the bioprocess, which is further complicated by the lack of appropriate online sensors and the long lag time associated with offline assays. Quite often available data lack sufficient detail to be directly used, and after a cursory evaluation are stored away. However, these historic databases of process measurements may still retain some useful information. A multivariate statistical procedure has been applied for analyzing the measurement profiles acquired during the monitoring of several fed-batch fermentations for the production of erythromycin. Multivariate principal component analysis has been used to extract information from the multivariate historic database by projecting the process variables onto a low-dimensional space defined by the principal components. Thus, each fermentation is identified by a temporal profile in the principal component plane. The projections represent monitoring charts, consistent with the concept of statistical process control, which are useful for tracking the progress of each fermentation batch and identifying anomalous behaviors (process diagnosis and fault detection).
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