Abstract :
Supervisory control of fed-batch fermentation processes is a difficult problem because the process is inherently non-linear, operated
at unsteady state, and often poorly modeled. This problem is exacerbated in laboratory and pilot-plant applications due to
lack of historical data from similarly operated batches. In this paper, we propose an approach for the supervisory control of fedbatch
fermentation during process development. Transitions in the process are explicitly modeled and characterized using simple
multivariate rules. Online data is then used to identify the occurrence of transitions and thus track the process across different
phases. Supervisory control actions such as sequence control, phase-specific regulatory controllers and alarm settings are then
subsequently executed based on this knowledge of the current phase. One key advantage of this approach is that it does not require
detailed process knowledge or extensive process data and is thus well suited for application in process development. This approach
has been implemented as an online expert system, called iProphet, and successfully tested on two laboratory-scale process development
case studies—a bacterial (Escherichia coli) fermentation and laboratory-scale yeast (Pichia pastoris) fermentation. The
approach and results from the two case studies are presented.
Keywords :
Fermentation , Expert system , Process monitoring , pharmaceuticals