Application of Bayesian belief network for the analysis of accident data in the bioenergy manufacturing sector

This paper presents an analysis of the cause and effect relationships found in the safety analysis of bioenergy processes by employing a dynamic Bayesian Belief Network assessment over a period of 10 years in the United States. These networks are considered to be more descriptive to model the fundamental relationships in the safety analysis of process industries where diagnostics risk assessment tasks are being conducted. Combining Bayesian Belief Network assessment and statistical simulation provides a powerful tool for risk assessment. Agena Risk was used here as the software to execute/perform this risk assessment. The US accidental database in the bioenergy sector was constructed over a 10 years period for the input data. The analysis was divided between accidents that caused material damage and accidents resulting in injuries and fatalities. The real difference stands in the approach: here, the interdependence among different factors which interact and affect one another in building the accidental path. The Bayesian Belief Network method, supported by a customized tool, has been demonstrated to be flexible, transparent, and suitable for learning from the past and forecasting the future.