Risk Analysis of Dust Explosion Scenarios Using Bayesian Networks

Abstract

In this study, a methodology has been proposed for risk analysis of dust explosion scenarios based on Bayesian network. Our methodology also benefits from a bow-tie diagram to better represent the logical relationships existing among contributing factors and consequences of dust explosions. In this study, the risks of dust explosion scenarios are evaluated, taking into account common cause failures and dependencies among root events and possible consequences. Using a diagnostic analysis, dust particle properties, oxygen concentration, and safety training of staff are identified as the most critical root events leading to dust explosions. The probability adaptation concept is also used for sequential updating and thus learning from past dust explosion accidents, which is of great importance in dynamic risk assessment and management. We also apply the proposed methodology to a case study to model dust explosion scenarios, to estimate the envisaged risks, and to identify the vulnerable parts of the system that need additional safety measures.

Quantitative risk analysis on leakage failure of submarine oil and gas pipelines using Bayesian network

“…Experience learning is an ability that present accident model can adapt the failure probabilities of safety barriers and occurrence probabilities of consequences using accident precursor data (Yuan et al 2015). In this paper, the casual relationship of leakage failure for pipeline has already been as depicted through Bayesian network, once the prior failure probabilities distribution and likelihood function of safety barriers are determined, the experience learning for failure probabilities of safety barriers can be implemented based on Bayesian updating mechanism shown in Eq.…”

“…Similar to bow-tie method, Bayesian network is also a graphical technology that describes the relationships between causes and consequences, which consists of nodes, arcs and condition probabilistic table (CPT) (Yuan et al 2015). The nodes represent the random variables, the arc represents dependency relationship between two linked nodes, and the CPT represents transition of mathematical logic from one random variable to others.…”

“…Bayesian network is a popular tool for conducting quantitative risk analysis in offshore oil and gas industry (Khakzad et al 2013). BN is a probabilistic inference technology for reasoning under uncertainty, and can relax the limitation of conventional methods, also consider conditional dependency, common cause failures and various states of the primary events in accident modeling process (Yuan et al 2015). The BN can also perform a probability updating and experience learning in risk analysis, the dynamic nature of leakage failure accident of submarine pipelines is considered through this way.…”

Dust explosions: A threat to the process industries

“…van dert Voort et al (2007) developed a quantitative risk assessment tool for dust explosions consisting of a series of sub-models. More recently, Yuan et al (2013Yuan et al ( , 2014 proposed a dust explosion risk analysis model based on the Bow-tie method and Bayesian network. In the aforementioned methodologies, the common step is the identification of hazards, which requires wide knowledge of both dust explosion mechanisms and examination of where the dust explosion takes place.…”

Tunnel collapse risk assessment based on multistate fuzzy Bayesian networks

“…Bayesian networks are widely used in quantitative risk analysis because of their capacity for both diagnostic and predictive analyses . Compared with other methods, one advantage of BNs is probability updating when new information becomes available over time . Conventional BNs, which are considered simplified BNs, have been widely used in risk evaluation .…”