Hazard studies provide a systematic methodology for identi?cation, evaluation and mitigation of potential process hazards which can cause severe human, environmental and economic losses. Different methods are practiced at various stages during the plant life cycle(Bragatto, 2007). Most methods require considerable time and resources. Consequentially research has been stimulated to develop computer aided tools to assist in and even aiming at automating hazard analysis. Venkatasubramanian, Zhao, and Viswanathan (2000) reviewed development of knowledge based systems for automating HAZOP analysis. Venkatasubramanian and Vaidhyanathan (1994) describe a knowledge based framework, which addresses the issues of representation of process speci?c and generic knowledge about chemicals in the automation of HAZOP studies.
The knowledge, which is generic and hence applicable to a wide variety of ?ow sheets, is called process general knowledge, while the remaining knowledge is considered speci?c to a particular process, and is called process speci?c knowledge. Bragatto, Monti, Giannini, and Ansaldi (2007) build upon the notions introduced above and exploits process knowledge with the aim to develop tools for the experts to reveal potential hazards, rooted in a function based taxonomy for equipment and instrumentation (Crawley, 2000).
Thus their system includes a dictionary, the plant information database, a reasoning and analysis engine and a knowledge repository. The dictionary permits linking the terminology used in hazard analysis with that used in a particular application area. In their system objects are categorized according to a hierarchically organized function based taxonomy, which can be mapped to the corresponding standard STEP data de?nitions (STEP, 1994). Thus each item is classi?ed in terms of super-function, function, and type and with an associated set of functional parameters. However these methods relate the concept of function directly to a physical implementation and therefore they limit the possibility to abstract from one layer in a goal-purpose hierarchy to another (Crawley, 2003). A similar approach is taken by Zhao, Cui, Zhao, Qiu, and Chen (2009) in developing the PetroHAZOP systemwhich uses case based reasoning. This systemis based on the OntoCAPE ontology which attempts to cover the areas of unit operations, plant equipment and thermophysical properties.
However areas as control and operations knowledge, which are essential for HAZOP are only indirectly covered to the extent they are represented within the available cases. Consequently it is desirable to develop a HAZOP methodology based upon a model which can encompass the purpose and functional structure of the plant, including the operational goals. Such a functional model should represent the system using means-end concepts, where a system is described using goals and purposes in one dimension and whole-part concepts in another dimension (Friedman, 2003). Such a functional modeling approach lends itself directly for implementation into a computer aided reasoning tool to assist in HAZOP studies with cause and consequence determination.
Thereby a tool can be developed which supports a traditional HAZOP meeting based upon a common framework in functional modeling with foundation in action theories and cognitive science. Such functional modeling can provide a systematic ...