Bayesian Network-Based Risk Analysis of Chemical Plant Explosion Accidents

Abstract

The chemical industry has made great contributions to the national economy, but frequent chemical plant explosion accidents (CPEAs) have also caused heavy property losses and casualties, as the CPEA is the result of interaction of many related risk factors, leading to uncertainty in the evolution of the accident. To systematically excavate and analyze the underlying causes of accidents, this paper first integrates emergency elements in the frame of orbit intersection theory and proposes 14 nodes to represent the evolution path of the accident. Then, combined with historical data and expert experience, a Bayesian network (BN) model of CPEAs was established. Through scenario analysis and sensitivity analysis, the interaction between factors and the impact of the factors on accident consequences was evaluated. It is found that the direct factors have the most obvious influence on the accident consequences, and the unsafe conditions contribute more than the unsafe behaviors. Furthermore, considering the factor chain, the management factors, especially safety education and training, are the key link of the accident that affects unsafe behaviors and unsafe conditions. Moreover, effective government emergency response has played a more prominent role in controlling environmental pollution. In addition, the complex network relationship between elements is presented in a sensitivity index matrix, and we extracted three important risk transmission paths from it. The research provides support for enterprises to formulate comprehensive safety production management strategies and control key factors in the risk transmission path to reduce CPEA risks.

Devising a procedure for defining the general criteria of abnormal behavior of a computer system based on the improved criterion of uniformity of input data samples

“…The distribution of scattering power statistics in the criteria for belonging to the sample of a particular law converges to the central distribution of chi-squared at N = const, T→∝ [19,20].…”

A New Fuzzy Bayesian Inference Approach for Risk Assessments

“…The fuzzy probability of occurrence for leaf node T is defined using Equation (15) after determining the CM-IT1 FS-based a priori probabilities and conditional probabilities. The updated fuzzy a priori probability, called fuzzy posteriori probability, is defined using Equation (16). The fuzzy sensitivity performance measure (SPM), which denotes the sensitivity degree of root nodes, is defined using Equation (17).…”

“…BN inference has been explored as a potential valuable technique to conduct the risk assessment [9]. Recently, BN inference has been extensively employed for risk assessments in many complex systems that include nu-clear power plants (NPPs) [10][11][12], petrochemical plants [13][14][15][16], construction projects [17][18][19][20], hazardous material transportation [21][22][23][24], etc. However, BN inference uses crisp probabilities; thus, it is challenging to deal with the uncertain information from experts' knowledge [25,26].…”

Generation paths of major production safety accidents — A fuzzy-set qualitative comparative analysis based on Chinese data

“…Secondly, poor management is the key factor leading to accidents ( 21 ). On the one hand, the safety management ability of production enterprises ( 22 ) directly affects the severity of production safety accidents. The analysis results show that the explosion of dangerous goods at the port has complex causal factors, but the management factor plays a leading role in the causal structure of the whole accident ( 23 ).…”