Citation

Khakzad N, Reniers G. Reliab. Eng. Syst. Safety 2019; 191: e106114.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.ress.2018.03.030

PMID

unavailable

Abstract

Process plants can be potential targets to terrorist attacks with the aim of triggering domino effects. Compared to accidental domino effects where the possibility of having multiple primary events is very remote, man-made domino effects are likelier to be initiated from multiple units within the plant in order to increase the knock-on likelihood and thus causing maximum damage. In this regard, identification of critical units that - under attack - may lead to likelier and severer domino effects is crucial both to assess the vulnerability of process plants and subsequently to increase their robustness to such attacks. In the present work, we have applied graph theory and dynamic Bayesian network to identify critical units. Further, low-capacity utilization of process plants (e.g., by keeping some of the storage tanks empty) has been demonstrated as an effective strategy in the case of imminent terrorist attacks. As such, the robustness of the plant against intentional attacks can temporarily be increased while considering the cost incurred because of such a low-capacity utilization.

Language: en

Keywords

Domino effect; Dynamic Bayesian network; Graph theory; Process plants; Robustness; Terrorist attack