Statistic Models For Particle Movement And Char Combustion In Fluidized Beds

With the development of the computer science,the CFD based numerical simulation has become one of the most important methods for the study of gas-solid fluidized beds.After the decades of research,many popular numerical approaches based on the CFD have the ability to predict the detailed gas-solid flow,such as the CFD-DEM.However,the long computating time and the high computational load are always the bottlenecks of these kinds of methods for practical application.While the Markov chain method?MCM?based statistic model applied in granule processes has the advantages of simple theory,easy program and fast calculation.Thus,developing the MCM model of particle movement in fluidized beds is a potential method to break through the bottlenecks of the traditional numerical methods.In this work,the development of the MCM for fluidized beds and the oxy-fuel fluidized bed combustion which is a hot issue of CO₂ capture,are both taken into consideration.The deep study and exploration is made for the statistic model of the particle movement and char combustion in fluidized beds.The significant results are summarized as follows:?1?In order to overcome the deficiency of the MCM established by other researchers which can not provide the detailed particle movement,the statistic laws of particle movement are extracted from the CFD-DEM calculated results,and the fluidized bed is discretized into physical cells,which represent the Markov states of particles.The MCM models for the particle movement in both the 2D bubbling fluidized bed?FB?and the circulating fluidized bed?CFB?riser are established.The Markov property of the FB and the independence of the MCM parameters are discussed.Results show that the calculating speed of the MCM is faster than that of the CFD-DEM by about 2 orders of magnitude,and the MCM costs a low computation load.Besides,the MCM can accurately predict the macroscopic property of particle movement and track every single particle.?2?In order to improve the shortcomings of the MCM which can not simulate the bubble movement and the instantaneous property of particle movement in the FB,the statistic laws of bubble movement are extracted from the CFD-DEM calculated results through the graphic recognition method.A stochastic bubble developing model?SBDM?,which is coupled with the MCM,is established based on the statistic theory.The SBDM-MCM also has a high calculation speed and a low computation load,which successfully introduces the influence of bubbles on the particle movement into the MCM and has the ability to reproduce the instantaneous fluctuation of particle movement.?3?In order to improve the shortcomings of the MCM which can not simulate the particle cluster movement and the instantaneous property of particle movement in the CFB riser,the statistic laws of particle cluster movement are also extracted from the CFD-DEM calculated results through the graphic recognition method,and a stochastic cluster developing model?SCDM?coupled with the MCM is established based on the statistic theory.The SCDM-MCM calculates quickly too.Results show that the SCDM-MCM successfully predicts the instantaneous influence of the clusters on the particle movement.The SCDM-MCM results fit the sample date provided by the CFD-DEM much better than the MCM results of the CFB riser.?4?In order to solve the thorny problem that the SBDM-MCM can not afford the information of the gas phase,which makes it difficult to be coupled with the chemical reaction,the fluid sub-model and the mass transfer sub-model of the traditional fluidization two phase model along with the char combustion model are introduced into the SBDM-MCM.The calculation speed of the SBDM-MCM reaction model is almost 155 times faster than that of the CFD-DEM reaction model.The results show that the SBDM-MCM reaction model not only simulates the movement of bed material and char particles well,but also predicts the similar char reaction rate and gas component distribution to compare with the CFD-DEM reaction results.