Abstract
Gasifier system is one of the important components of coal gasification device. The technical characteristics of this system mainly lie in the following facts as huge technical scale and high complexity, and there is a dynamic correlation between the failure modes of gasification equipment. Traditional safety analysis methods such as fault tree and bow-tie diagram suffer from drawbacks as being static and ineffective in handling uncertainty, which hamper their application to risk analysis of process systems. This paper presents a newly developed model based on Dynamic Bow-Tie (DBT) and Dynamic Bayesian network (DBN) for quantitative dynamic risk assessment of gasifier system. In the meantime, in order to cope with the uncertainty of the failure data, fuzzy numbers and the defuzzification method are used to transform the experts' language into the failure rates. The results showed that dynamic risk assessment can solve the difficulties dealing with complex dynamic systems which have process variables and characteristics such as multiple, failure correlations, and noncoherence. And it also has important theoretical significance and application value for coal chemical industry to improve the scientificity of risk assessment.
Highlights
Coal gasification plays an important role in human history, and this technology has been widely developed throughout the world, especially after the oil crisis in the 1970s [1,2,3]
Reference [12] developed a comprehensive technique to control the major hazards of the GE coal gasification process. e technique consisted of process hazard identification based on critical events, barrier performance evaluation based on barrier diagrams, and quantification of risk influence factors based on Bayesian network
Dynamic Bayesian network (DBN) [22,23,24] has been introduced, and it is equipped with other techniques such as bow-tie model [25, 26]. is composite model has been applied to quantitative risk analysis of hydrogen generation unit leakage [27] and offshore drilling incidents [28]
Summary
Coal gasification plays an important role in human history, and this technology has been widely developed throughout the world, especially after the oil crisis in the 1970s [1,2,3]. Reference [17] proposed a complete set of dynamic risk assessment to predict the frequencies of abnormal events utilizing accident precursor data, helping to achieve inherently safer operations. Based on these methods, the failure probabilities of safety systems and end-states were estimated using copulas and Bayesian analysis to ensure better predictions. BN is time-independent, and it cannot reflect temporal evolution of system and give prevention measures effectively To this end, dynamic Bayesian network (DBN) [22,23,24] has been introduced, and it is equipped with other techniques such as bow-tie model [25, 26].
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