1 research outputs found
Creating signed directed graph models for process plants
The identification of possible hazards in chemical plants is a very important part of the
design process. This is because of the potential danger that large chemical installations
pose to the public. One possible route for speeding up the identification of hazards in
chemical plants is to use computers to identify hazards automatically. This will facilitate
safe plant design and will avoid late design changes which can be very costly to
implement.
Previous research at Loughborough has concentrated on developing a model-based
approach and an analysis algorithm for automating hazard identification. The results
generated have demonstrated the technical feasibility of the approach. This approach
requires a knowledge-base of unit models. This library of models describes how different
plant equipment behaves in qualitative terms.
The research described in this thesis develops a method for creating and testing the
equipment models. The model library was previously achieved by an expert writing the
models in a format that could be directly used by the system described above. An
engineer unfamililar with the system would find this difficult. An alternative method
would have been to use an intermediary (a knowledge engineer) to gather information
from the engineer and convert it into the system format. This would be expensive. Both
methods would take up a lot of the engineer's time. An engineer should be able to enter
information personally in order to maintain efficiency and avoid information loss through
the intermediary. A front end interface has been built to the system which enables an
expert to enter information directly without needing to understand details of the
application system. This interface incorporates ideas from the knowledge acquisition field
in order to produce a tool that is simple to use.
Unit-based qualitative modelling can lead to incorrect or ambiguous inference. The
method developed here identifies situations where ambiguities may arise. A new modular
approach is presented to overcome this type of problem. This method also presents a
technique to verify that the models created are both complete and correct