Abstract
As bioprocess industries continually strive to improve productivity, enhanced supervision has been seen as one means of reducing process variability and hence increasing productivity. Sophisticated supervision software, such as G2, a real-lime knowledge based system from Gensym, is already finding wide industrial bioprocess application. However, due to their biological nature and inherent variability, bioprocesses place significant demands on even the most advanced supervision approaches. 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. In this paper several issues regarding the construction of an Intelligent Supervisory System have been considered. Feature extraction and data based methodologies arc integrated with sophisticated physiological models to considerably enhance a rule-based system. Together within the G2 real-time knowledge based system framework, they offer the potential for bioprocess operational improvement.
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