Rule-based versus probabilistic approaches to the diagnosis of faults in wastewater treatment processes

Abstract

The need for computer-based diagnostic tools in wastewater management is outlined. Rule-based and probabilistic approaches to the development of diagnostic expert systems are critically reviewed, and it is demonstrated that the rule-based approach has serious limitations which make it unsuitable for diagnostic tasks under conditions of uncertainty. It is shown that Bayesian belief networks (BBNs), a probabilistic approach, has none of these limitations and is well-suited to diagnosis under uncertainty. The theory and application of BBNs are outlined and illustrated by a simple example based on a wastewater treatment plant. A brief case study is presented of the development of a full-scale BBN for the diagnosis of faults in a wastewater treatment plant. It is concluded that BBNs are far superior to rule-based systems in their ability to diagnose faults in complex systems like wastewater treatment processes, whose behaviour is inherently uncertain.