Reliability Analysis Of Core System Of Pulverized Coal Gasifier

The process engineering of pulverized coal gasification technology includes: coal grinding and dry pulverized coal pressurization and initial water treatment of coal gasification,slag discharge,ash removal and wet washing of incoming coal,among which the coal gasification technology is the main link.The long time operation of large coal gasification unit is often accompanied by the occurrence of failure,which not only reduces the production efficiency of enterprises,but also has a great impact on personal safety.Therefore,the dynamic reliability analysis of the core gasifier system is of great significance to the coal gasification technology.Although some achievements have been made in the reliability analysis of the coal gasification system,there are few researches on the dynamic reliability of the coal gasification system without failure data.In order to solve the problem of dynamic failure of large complex system without failure data,a dynamic Bayesian network combined with Monte Carlo method was proposed to analyze the reliability of the core system of pulverized coal gasifier.The common failures of the core system of the pulverized coal gasifier and the logical relationship between the influencing factors were studied.On this basis,the reliability model of the system was built based on the dynamic Bayesian network to realize the structure learning.Monte Carlo simulation combined with Bayesian estimation of the end of time test to obtain a reliable sample data set,then use Bayesian estimation method to obtain the prior parameters of dynamic Bayesian network,effectively solve the problem of no failure data system,to achieve parameter learning.The Metropolis-Hastings algorithm discretize the prior distribution of dynamic nodes of dynamic Bayesian networks to obtain the target distribution,which can realize accurate inference of continuous dynamic Bayesian networks.The dynamic Bayesian network of the core system of the pulverized coal gasifier core system is used to carry out powerful bidirectional reasoning calculation.The predictive reasoning can accurately predict the reliability curve of the core system of the gasifier with time,and the reverse reasoning can quickly and effectively locate the weak links of the core system of the gasifier.The reliability analysis of the system considering time series and maintenance factors is completed.The Bayesian network toolbox is used to calculate the dynamic Bayesian network of the system,and the Bayesian network analysis software Genie2.1 is used to verify the feasibility and accuracy of the proposed method.The dynamic study on the reliability of pulverized gasifier core system in this paper can provide important theoretical basis for reliability analysis of large coal gasification unit.Therefore,the research can not only improve the coal chemical technology,but also ensure the stable operation of the gasification system and reduce the maintenance cost.