Fault diagnosis is destined to hold central importance in maintaining a chemical industry after installation of Advanced Process Control system (APCS). It has been estimated that US- based petrochemical industry could save up to $10 billion annually if abnormal process behavior could be detected, diagnosed and appropriately dealt with [Nimmo et al, 1995]. Further, it is highlighted [Vedam, 1999] that the same industry loses over $20 billion annually due to inappropriate reaction to abnormal behavior.
The purpose of this research is to establish and maintain safety of processes through automation and analysis methodologies. Because modern chemical plants are large and complex, early and accurate fault detection and diagnosis is imperative. Effective application of these methods can reduce product rejection rates, limit downtime, and help to attain stringent safety requirements. The central goal of abnormal situation management is to document all possible normal modes of a plant operation and detect deviations from normal behavior. Fault detection and diagnosis have gained a central importance in the chemical process industries over the past decade. This presents a need for fault diagnosis systems which use limited information of the process dynamics and accurately detect, isolate, and identify faults.