Fermentation analysis by clustering

To supervise, stabilize and optimize antibiotic fermentations in the industrial scale expert systems are presently worked out. For the knowledge acquisition various classifiers are tested using a set of 27 nourseothricin fermentation runs. Two methods are applied: optimal clustering by help of minimum variance criterion and hierarchical clustering by help of dendrograms. The fermentations are classified with respect to the specific material costs as well as the product formation kinetics.List of Symbols a kg/m³ initial value of linearized product kinetics - b kg/(m³ · h) slope of linearized product kinetics - B binary variable (value 0 or 1) - C DM/kg specific costs - d distance - m number of samples - p kg/m³ product concentration - pO₂ % dissolved oxygen concentration - t h fermentation time - T h initial time of linearized product kinetics - n number of fermentation runs