Supervision of fed-batch fermentations

Process faults may be detected on-line using existing measurements based up on modelling that is entirely data driven. A multivariate statistical model is developed and used for fault diagnosis of an industrial fed-batch ferme ntation process. Data from several (25) batches are used to develop a model for cultivation behaviour. This model is validated against 13 data sets an d demonstrated to explain a significant amount of variation in the data. Th e multivariate model may directly be used for process monitoring. With this method faults are detected in real time and the responsible measurements a re directly identified. The fault detection and identification is enabled t hrough inspection of a few simple plots. Thus, the presented methodology al lows the process operator to actively monitor data from several cultivation s simultaneously. (C) 1999 Elsevier Science S.A. All rights reserved.