Process modelling for intelligent control

Abstract

This contribution considers the problem of knowledge representation in intelligent control systems: the kind of knowledge that is required and how this may be encapsulated within an application framework. It is argued that rule-base techniques need to be supplemented by other methods in order to express certain pertinent aspects of the domain. After a brief discussion of qualitative modelling techniques, attention is focused upon numerical models and a relatively new qualitative modelling approach, Qualitative Transfer Functions. The effectiveness of the latter model forms as repositories of deep knowledge are evaluated by application to a simulated distillation plant. The results indicate that although numerical models can provide accurate input-output representations, the integrated framework offered by Qualitative Transfer Functions is capable of carrying more information relevant to decision-making systems.