Using Bayesian Networks for Quantifying Domino Effect Probability in a Hydrocarbon Processing Area

Abstract

Accidents in process industries include fires, explosions, or toxic releases depending on the spilled material properties and ignition sources. One of the worst phenomena that may occur is the called domino effect. This triggers serious consequences on the people, the environment, and the economy. That is why the European Commission defined the domino effect prediction as a mandatory challenge for the years ahead. The quantification of the domino effect probability is a complex task due to the multiple and synergic effects among all accidents that should be included in the analysis. However, these techniques could be integrated with others in order to represent the domino effect occurrence reliably. In this matter, artificial intelligence plays a vital role. Bayesian networks, as one of the artificial intelligence nets, have been widely applied for domino effect likelihood determination. This research aims to provide a guide for quantifying domino effect probability using Bayesian networks in a hydrocarbon processing area. For this purpose, a four-step model is proposed integrating some classical risk analysis techniques with Bayesian networks. Moreover, this methodology is applied to an actual hydrocarbon storage and processing facility. After that, the joint probability can reach 9.37% for the process unit tank 703 which storages naphtha. Hence, safety management plans must be improved in this area for reducing this actual risk level. Finally, this research demonstrates how Artificial intelligence techniques should be integrated with classical ones in order to get more reliable results.