Automatic Extraction of Knowledge Rules from Process Operational Data

Abstract

An alternative technique for developing intelligent process monitoring and control systems is expert systems (ESs). ESs use logic rules to carry out heuristic reasoning. Knowledge used to reach a conclusion is transparent because ESs have explanation capabilities. In this respect, ESs are superior to neural networks because the knowledge embedded in neural networks is opaque. It has long been recognised that a critical issue in developing knowledge based ESs is the bottleneck of knowledge acquisition. Traditionally knowledge acquisition has been dependent on consulting domain experts. A disadvantage of this approach is that experts are usually better in collecting and archiving cases than in expressing the experience and cases explicitly into production rules. There are more difficulties in compiling knowledge about process operations due to the large number of interacting variables.