Study On Flow Characteristics Of Mono-component/Binary Particles In Dense Zone Of Fluidized-bed Using Data Mining Methods

Heat treatment of biomass is one of the important means of its high value utilization.The mixing behavior of biomass particles in dense zone of the fluidized bed directly affects the efficiency of biomass heat treatment.However,the effect of the flow characteristics of mono-component particles in the dense zone on the mixing of binary particles is significant.Typically,biomass and bed materials have quite different properties,making it difficult to mix well.At present,research on particle mixing is mostly focuses on the single-scale level such as the reactor,and there is a lack of systematic research on the flow characteristics of mono-component particles transitioning to binary particles in the dense zone of the fluidized bed.The analysis method still needs to be improved.This study proposes a new method based on data mining for the study of flow characteristics in the dense zone of fluidized bed with mono-component particles transitioning to binary particles.The main works are as follows:(1)Based on the Hidden Markov Model(HMM),the flow characteristics of single-component particles in the dense zone of fluidized bed are studied;(2)According to Hilbert-Huang transform(HHT)method,the mixed flow characteristics of binary particle in the dense zone of the fluidized bed are investigated.Time series analysis methods such as time-domain,frequency-domain,and nonlinear analysis were used to extract the eigenvalues from the differential pressure fluctuation signals of mono-component particles in dense zone of the fluidized bed.The effect of superficial gas velocity on the eigenvalues was analyzed accordingly.Combined with digital images and differential pressure fluctuation signals analysis,the flow behavior of mono-component particles in the bubbling fluidization stage can be divided into local bubbling and complete bubbling stages with increasing superficial gas velocity.However,the relevant eigenvalues can not fully reflect the overall flow characteristics.Hidden Markov models(HMM)were established based on data mining of eigenvalues.Results showed that the flow behavior of mono-component particles in the local bubbling stage contains the characteristics of fixed bed and bubbling fluidization.Moreover,it contains the characteristics of bubbling fluidization and turbulent fluidization in the complete bubbling stage.Similarly,the characteristic probability of bubbling fluidization is also found in turbulent fluidization.The Hilbert-Huang transform was used to analyze the differential pressure fluctuation signal of binary particles in dense-phase zone of the fluidized bed,and the Intrinsic Mode Function(IMF)of the differential pressure fluctuation signal was mined to study the effect of superficial gas velocity,particle size and mass fraction on the mixing transition characteristics of binary particles(biomass and quartz sand).The results indicated that the mixing behavior of binary particles can be divided into three patterns: complete segregation,transitional mixing,and complete mixing with an increase in the superficial gas velocity.Under a higher superficial gas velocity,the energy of the IMF gradually shifts from the high-frequency band to the mediumfrequency band.When the mixing behavior of binary particles is in the stage of complete mixing,the energy in the middle frequency band of the IMF is the largest,and the percent of its value remains unchanged at 80%.The critical velocity of complete mixing can be determined based on the change in the IMF energy at different superficial gas velocities.Compared with the traditional pressure drop analysis method,the HHT method can easily determine the critical velocity of complete mixing.