Assessing Risk in Chemical Plant with Pattern Matching

This paper discusses potential application of fuzzy set theory, more specifically, pattern matching, in assessing risk in chemical plants. Risk factors have been evaluated using linguistic representations of the quantity of the hazardous substance involved, its frequency of interaction with the environment, severity of its impact and the uncertainty involved in its detection in advance. For each linguistic value there is a corresponding membership function ranging over a universe of discourse. The risk scenario created by a hazard/hazardous situation having highest degree of featural value is taken as the known pattern. Each sample pattern of hazard/hazardous situation with their known featural values is then matched with the known pattern. The concept of multifeature pattern matching based on fuzzy logic is used to derive the rank ordering of process hazards. In multifeature pattern recognition/matching, a sample pattern is compared to a number of known data patterns or a known pattern is compared to a number of sample data patterns. The process assesses which known pattern resembles most closely data sample using Wang's approaching degree method. A methodology has been developed and the same has been exemplified by presenting a case example with a limited number of hazards.