Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects

Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects

Khakzad, N. (TU Delft Safety and Security Science)
Landucci, G. (TU Delft Safety and Security Science; University of Pisa)
Reniers, G.L.L.M.E. (TU Delft Safety and Security Science; Katholieke Universiteit Leuven)

2017

The propagation of fire in chemical plants – also known as fire domino effects - largely depends on the performance of add-on passive and active protection systems such as sprinkler systems, water deluge systems, emergency shut down and emergency blow down systems, fireproofing, and emergency response. Although such safety barriers are widely employed to prevent or delay the initiation or escalation of fire domino effects, their inclusion in the modeling and risk assessment of fire domino effects has hardly been taken into account. In the present study, the dynamic evolution of fire protection systems has been investigated qualitatively using event tree analysis. To quantify the temporal changes and their impact on the escalation of fire domino effects, a dynamic Bayesian network methodology has been developed. The application of the methodology has been demonstrated using an illustrative case study, considering a variety of fire scenarios, target installations, and firefighting systems.

Dynamic Bayesian network
Event tree analysis
Fire domino effects
Fire protection systems
Quantitative risk assessment

http://resolver.tudelft.nl/uuid:fae414ca-2c7e-46ac-a9fb-d0ff236e4369

https://doi.org/10.1016/j.ress.2017.06.004

2019-06-28

0951-8320

Reliability Engineering & System Safety, 167, 232-247

Institutional Repository

journal article

© 2017 N. Khakzad, G. Landucci, G.L.L.M.E. Reniers