RULE-BASED VERSUS PROBABILISTIC APPROACHES TO THE DIAGNOSIS OF FAULTSIN WASTE-WATER TREATMENT PROCESSES

The need for computer-based diagnostic tools in wastewater management is outlined. Rule-based and probabilistic approaches to the developmen t of diagnostic expert systems are critically reviewed, and it is demo nstrated that the rule-based approach has serious limitations which ma ke it unsuitable for diagnostic tasks under conditions of uncertainty. It is shown that Bayesian belief networks (BBNs), a probabilistic app roach, has none of these limitations and is well-suited to diagnosis u nder 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 BB N for the diagnosis of faults in a wastewater treatment plant. It is c oncluded that BBNs are far superior to rule-based systems in their abi lity to diagnose faults in complex systems like wastewater treatment p rocesses, whose behaviour is inherently uncertain.