Combining qualitative and quantitative reasoning to support hazard identification by computer

This thesis investigates the proposition that use must be made of quantitative information to control the reporting of hazard scenarios in automatically generated HAZOP reports. HAZOP is a successful and widely accepted technique for identification of process hazards. However, it requires an expensive commitment of time and personnel near the end of a project. Use of a HAZOP emulation tool before conventional HAZOP could speed up the examination of routine hazards, or identify deficiencies I in the design of a plant. Qualitative models of process equipment can efficiently model fault propagation in chemical plants. However, purely qualitative models lack the representational power to model many constraints in real plants, resulting in indiscriminate reporting of failure scenarios. In the AutoHAZID computer program, qualitative reasoning is used to emulate HAZOP. Signed-directed graph (SDG) models of equipment are used to build a graph model of the plant. This graph is searched to find links between faults and consequences, which are reported as hazardous scenarios associated with process variable deviations. However, factors not represented in the SDG, such as the fluids in the plant, often affect the feasibility of scenarios. Support for the qualitative model system, in the form of quantitative judgements to assess the feasibility of certain hazards, was investigated and is reported here. This thesis also describes the novel "Fluid Modelling System" (FMS) which now provides this quantitative support mechanism in AutoHAZID. The FMS allows the attachment of conditions to SDG arcs. Fault paths are validated by testing the conditions along their arcs. Infeasible scenarios are removed. In the FMS, numerical limits on process variable deviations have been used to assess the sufficiency of a given fault to cause any linked consequence. In a number of case studies, use of the FMS in AutoHAZID has improved the focus of the automatically generated HAZOP results. This thesis describes qualitative model-based methods for identifying process hazards by computer, in particular AutoHAZID. It identifies a range of problems where the purely qualitative approach is inadequate and demonstrates how such problems can be tackled by selective use of quantitative information about the plant or the fluids in it. The conclusion is that quantitative knowledge is' required to support the qualitative reasoning in hazard identification by computer.