Risk Prediction of Gasifier System Based on Dynamic Cloud Bayesian Network

To address the current problem of insufficient risk level analysis of gasifier systems, an evaluation method combining the cloud model and dynamic Bayesian network is proposed, taking the gasifier system as the evaluation target, using the cloud model to discretize continuous data, and setting each risk factor in the evaluation system as a node in the dynamic Bayesian network to build a dynamic Bayesian network. The entropy weight method is used to calculate the weight of each risk indicator, the maximum likelihood estimation method is used to process the affiliation degree obtained from the cloud model, and the probability obtained by the affiliation-probability conversion method is input into the dynamic Bayesian network as evidence. Finally, the risk prediction assessment for the gasifier system is completed by using the features of forwarding and backward inference of the dynamic Bayesian network, combined with the comprehensive analysis of importance. The study shows that human maintenance efficiency, equipment integrity, gasifier pressure, and oxygen-coal ratio are the weak points that need to be focused on in the operation of the system.