A Refreshing Take: Analysing Accident Scenarios through Causal Network Topology Metrics

PresentationAccident causation investigation and even more hazard scenario identification are troubled by the complexity of interactions between three elements in a process facility: People, Plant and Procedures. Interactions are of various nature, such as physical change and information transfer, all influencing the process. To facilitate investigation the digraph network was applied as the most flexible visual aid to describe a causal structure. Such structure consists of nodes and edges representing an event or condition in the accident scenario and a causal link respectively. Attributing the nodes and edges to the type of interaction, numbers of the same type can be counted, and so two metrics are developed: The P3 Interaction Contribution (PIC). This is the proportion of nodes and edges associated with an interaction between People, Plant and Procedures. The Average Edge Weight. This relates to the proportion of events in the scenario that are associated with the logical AND gate conjunction from its causes (incident nodes), where the event requires more than one simultaneous cause. The technique was tried on four CSB accident descriptions. Interesting differences are seen. Also, in view of a paper accepted to be published in Safety Science the approach seems quite helpful in process hazard analysis

Comparing capability of scenario hazard identification methods by the PIC (Plant-People-Procedure Interaction Contribution) network metric

Comparing the results of hazard identification (HAZID) methods is a complex task, but the question that drives this activity is vitally important: which HAZID method should be used to best identify an accident scenario? Despite many efforts to address this, effective metrics do not yet readily exist for clearly comparing HAZID results for a particular scenario. The complexity of socio-technical systems is often cited as a key factor that limits effective scenario identification, calling into question traditional HAZID efforts. Motivated by the observation that interactions between multiple component types, such as People, Plant and Procedures (P3), often significantly contribute to major process system accidents, being an expression of the complexity of the system, a novel, precise, network topology-based metric for calculating the contribution of P3 Interactions to accident scenarios is presented. This metric, called the P3 Interaction Contribution (PIC), is intended to be used for comparing the HAZID results. An illustrative example of using the PIC for HAZID comparison is included, whereby Failure Mode and Effects Analysis (FMEA), Blended Hazard Identification methodology (BLHAZID) and Systems Theoretic Process Analysis (STPA) were each applied to a heat exchanger start-up operation. The results show initial promise that the PIC can be effective as a HAZID comparison tool. The main outputs of this paper are the presentation of the PIC calculation process and the method for applying the PIC to HAZID results. The paper concludes with recommendations for further experimental work to explore the validation and assess the true value of the PIC