Abstract
A method for online hazard aversion and fault diagnosis in chemical processes is developed. The method uses a directed graph model of process operation and control. Fault trees developed from the directed graphs are combined with real-time data to provide online diagnosis for hazard aversion and fault detection. Both hardwired control and manual control are modeled. A single control loop illustrates the modeling technique and the diagnosis method. The method provides an advance alert to process problems and an identification of the problems' causes, based on the available real-time data and prior rates of equipment malfunctions and process disturbances.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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