A Quantitative Analysis of Chemical Plant Safety Based on Bayesian Network

Abstract

Once a chemical production accident occurs in a chemical plant, it often causes serious economic losses, casualties, and environmental damage. Statistics show that many major accidents in the production and storage of chemicals are mainly caused by human factors. This article considers the influence of the human factor and proposes a quantitative analysis model of a chemical plant based on a Bayesian network. The model takes into account the main human factors in seven aspects: organization, information, job design, human system interface, task environment, workplace design, and operator characteristics. The Bayesian network modeling method and simulation were used to predict the safety quantitative value and safety level of the chemical plant. Using this model, we can quickly calculate the safe quantitative ratio of each factor in the chemical plant. Through the safety quantitative value, safety level, and sensitivity analysis, the safety hazards of chemical companies can be discovered. Immediate improvements of potential safety hazards in chemical plants are very effective in preventing major safety accidents. This model provides an effective method for chemical park managers to monitor and manage chemical plants based on quantitative safety data.