FERMENTATION FAULT MANAGEMENT AND ONLINE STATE ESTIMATION USING METHODS OF CLUSTER-ANALYSIS

The results of the cluster analysis of fermentation data are used for the supervision and on-line state estimation. The results of the class ification are presented as the average over all fermentation runs belo nging to the class as well as the standard deviation. With the help of the class information the on-line fermentation is associated with the best suiting class. Faults in the data such as spikes or total failur e of the sensors are detected as the class information automatically s upplies tolerance regions for the measurements. In case of a fault a r eliable extrapolation for the time of the fault can be calculated. The approach is implemented in the real-time expert system tool G2 and is applied to data of the carbon dioxide evolution rate (CER) of an indu strial antibiotic fermentation process.