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.

Fires and explosions in chemical plants are still among the major accidents threatening human lives and causing huge asset losses. Although might not completely be eliminated, the risks of such accidents can be reduced by allocating safety measures, applying inherently safer design (ISD) methods, observing land use planning (LUP) regulations, and practicing emergency management. Compared to other risk reduction measures, applications of ISD and, in particular, LUP in chemical plants are new. In most of previous work, however, ISD and LUP have been considered as individual safety elements rather than parts of a coherent safety policy. This, to some extent, arises from contradictory guidelines inferred from the principles of ISD and the requirements of LUP. The present study aims to employ the principles of ISD and LUP, altogether, in risk-based design of chemical plants so that the levels of both on-site and off-site risks can be reduced as low as reasonably practicable. For this purpose, a Bayesian network (BN) methodology is employed to estimate both on-site and off-site risks posed by potential major accidents in chemical plants. The results of the BN modelling are then used as input data in Analytic Hierarchical Process (AHP), a multi-criteria decision analysis technique, to find an optimal layout for chemical plants of interest. While BN facilitates the incorporation of complicated interdependencies and conditional probabilities encountered in accident analysis and risk assessment, AHP allows considering incommensurate and conflicting decision parameters inevitable in most decision analyses. The outcome of the proposed methodology is an optimal layout for the chemical plant under consideration by taking ISD and LUP principles into account.

The complexity of domino scenarios represented a considerable obstacle to the quantitative assessment of risk posed by escalation events. In the present chapter, the state of the art concerning quantitative approaches proposed for the assessment of risk caused by domino accidents is summarized. A procedure for the quantitative risk assessment of domino accidents, suitable for the calculation of individual and societal risk indexes, is outlined. Alternative approaches proposed for the quantitative assessment of domino effects, based on Bayesian techniques and Monte Carlo simulations, are also described.