Towards intelligent process supervision: Industrial penicillin fermentation case study
Due to the their biological nature and inherent variability bioprocesses place significant demands on even the most advanced supervision approaches. Sophisticated supervision software, such as G2 from Gensym (a real-time knowledge based system) is already finding wide industrial bioprocess application. However, in order to overcome the severe challenges posed a straightforward 'coding' of operators and engineers knowledge is not sufficient. What is required is a methodology which attempts to maximise the information available for supervision purposes. This paper describes the development of an application in which feature extraction and data based methodologies are integrated with sophisticated physiological models to considerably enhance a rule-based supervisory system. Together within the G2 real-time knowledge based system framework they offer the potential for bioprocess operational improvement.
artificial neural networks supervisory control pattern recognition empirical modelling, red-batch fermentation